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Aqui
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<details>
<summary>
# Summary
</summary>
[[_TOC_]]
</details>
# Algotithms
<hr>
## Acquisition
<hr>
The given code is a JavaScript module that contains several functions
related to acquiring and processing satellite imagery data.
1. The \`sliceByRevisit\` function takes a collection of satellite
images, a starting date, and a number of days as input. It filters
the collection by the specified date and duration, and returns the
filtered collection.
2. The \`acquireFromDate\` function takes a specific date, a
satellite image collection, and a geometry (region of interest) as
input. It uses the \`sliceByRevisit\` function to filter the
collection based on the specified date and a fixed number of days
(16 days in this case). It then applies a mosaic operation to the
filtered collection and clips the resulting mosaic to the specified
geometry. The function sets additional properties to the mosaic
image using the \`mergeProperties\` function. It retrieves the
selected satellite type from the session storage and determines the
appropriate QA band name. Finally, it calls the \`scoreClouds\`
function with the mosaic image, geometry, and QA band name to
calculate a cloud score for the image.
3. The \`processCollection\` function takes a satellite type and a
geometry as input. It retrieves the satellite image collection for
the specified type using the \`getSatelliteCollection\` function.
It then retrieves the earliest and latest dates from the collection
using the \`retrieveExtremes\` function. The function adds grid
positions to each image in the collection and sorts them in
ascending order. It retrieves the northeasternmost grid position
within the specified geometry. It filters the images in the
collection based on the northeastern position. It again retrieves
the earliest and latest dates from the filtered collection. The
function calculates the difference in days between the earliest
image in the northeastern position and the globally earliest image.
It computes the remainder of the difference divided by the fixed
number of days (16 days). It then calculates the step (number of
days to go back) by subtracting the remainder from the fixed number
of days. The function computes the date of the earliest possible
image in the northeastern position by going back in time using the
calculated step. It calculates the number of complete orbital cycles
between the earliest and latest possible dates of the images in the
northeastern position. It generates slices of 16 days for each
complete cycle. For each slice, it creates an empty image and sets
metadata related to the corresponding orbital cycle. It keeps only
the images whose combined geometries contain the region of interest.
Finally, it returns a list of dates from the valid images.
4. The \`generateCloudMap\` function takes a list of dates, a
satellite image collection, and a geometry as input. It maps over
the list of dates and calls the \`acquireFromDate\` function to
acquire the cloud score image for each date. It retrieves the cloud
score from each image and returns a list of cloud scores.
<hr>
### **sliceByRevisit**
The \`sliceByRevisit\` function filters a collection of satellite images
based on a starting date and a number of days.
``` javascript
const sliceByRevisit = (collection, startingDate, days) => {
const start = ee.Date(startingDate).update(null, null, null, 0, 0, 0);
const end = start.advance(days, "day");
return collection.filterDate(start, end);
};
```
##### **Inputs:**
\- collection: A collection of satellite images. - startingDate: The
starting date for filtering the collection. - days: The number of days
for filtering the collection.
##### **Output:**
\- Returns a filtered collection of satellite images.
#### **Use**
To use the \`sliceByRevisit\` function, follow these steps: - Call the
function \`sliceByRevisit\` and provide the collection of satellite
images, starting date, and number of days as inputs. - The function will
filter the collection based on the specified starting date and number of
days. - The filtered collection will be returned as the output.
``` javascript
const filteredCollection = sliceByRevisit(satelliteImages, "2021-01-01", 16);
```
<hr>
### **acquireFromDate**
The \`acquireFromDate\` function retrieves satellite images from a given
date onwards, filters them based on a geometry, and calculates cloud
scores for the filtered images.
``` javascript
export const acquireFromDate = (date, collection, geometry) => {
const slice = sliceByRevisit(
ee.ImageCollection(collection).filterBounds(geometry),
date,
REVISIT_DAYS
);
const mosaicked = slice.mosaic().clip(geometry);
const image = mosaicked.set(mergeProperties(slice));
let satellite = window.sessionStorage.getItem("selectedSatellite");
let qaBand = satellite= "Landsat" ? "QA_PIXEL" : "QA60";
console.log("QA BAND AQUI", qaBand)
return scoreClouds(image, geometry, qaBand);
};
```
##### **Inputs:**
\- date: The starting date to retrieve satellite images from. -
collection: The collection of satellite images to retrieve from. -
geometry: The geometry to filter the satellite images by.
##### **Output:**
\- Returns a cloud score for the filtered satellite image.
##### **Use**
To use the \`acquireFromDate\` function, follow these steps: - Call the
function \`acquireFromDate\` and provide the starting date, collection
of satellite images, and geometry as inputs. - The function will filter
the satellite images based on the provided geometry and starting date. -
It will calculate the cloud score for the filtered image. - The cloud
score will be returned as the output.
``` javascript
const cloudScore = acquireFromDate("2021-01-01", satelliteImages, geometry);
```
<hr>
### **acquireFromDate**
The \`acquireFromDate\` function retrieves satellite images from a given
date onwards, filters them based on a geometry, and calculates cloud
scores for the filtered images.
``` javascript
export const acquireFromDate = (date, collection, geometry) => {
const slice = sliceByRevisit(
ee.ImageCollection(collection).filterBounds(geometry),
date,
REVISIT_DAYS
);
const mosaicked = slice.mosaic().clip(geometry);
const image = mosaicked.set(mergeProperties(slice));
let satellite = window.sessionStorage.getItem("selectedSatellite");
let qaBand = satellite= "Landsat" ? "QA_PIXEL" : "QA60";
console.log("QA BAND AQUI", qaBand)
return scoreClouds(image, geometry, qaBand);
};
```
##### **Inputs:**
\- date: The starting date to retrieve satellite images from. -
collection: The collection of satellite images to retrieve from. -
geometry: The geometry to filter the satellite images by.
##### **Output:**
\- Returns a cloud score for the filtered satellite image.
##### **Use**
To use the \`acquireFromDate\` function, follow these steps: - Call the
function \`acquireFromDate\` and provide the starting date, collection
of satellite images, and geometry as inputs. - The function will filter
the satellite images based on the provided geometry and starting date. -
It will calculate the cloud score for the filtered image. - The cloud
score will be returned as the output.
``` javascript
const cloudScore = acquireFromDate("2021-01-01", satelliteImages, geometry);
```
<hr>
### **processCollection**
The \`processCollection\` function performs a series of operations on a
satellite image collection based on the selected satellite and a
specified geometry.
``` javascript
export const processCollection = (satellite, geometry) => {
const query = getSatelliteCollection(satellite).filterBounds(geometry);
const global = retrieveExtremes(query);
const enhanced = query
.map(addGridPosition(satellite))
.sort(Metadata.GRID_POSITION);
const northeasternPosition = ee
.Image(enhanced.toList(1).get(0))
.get(Metadata.GRID_POSITION);
const filtered = enhanced.filter(
ee.Filter.eq(Metadata.GRID_POSITION, northeasternPosition)
);
const northeastern = retrieveExtremes(filtered);
const difference = northeastern.earliest
.difference(global.earliest, "day")
.abs();
const remainder = ee.Number(difference).mod(REVISIT_DAYS);
const step = ee.Number(REVISIT_DAYS).subtract(remainder);
const earliestDate = global.earliest.advance(step.multiply(-1), "day");
const completeCycles = ee
.Number(earliestDate.difference(global.latest, "day").abs())
.divide(REVISIT_DAYS)
.ceil();
const additions = ee.List.sequence(0, null, REVISIT_DAYS, completeCycles);
const carriers = additions.map((increment) => {
const startingDate = earliestDate.advance(increment, "day");
const collection = sliceByRevisit(query, startingDate, REVISIT_DAYS);
const empty = ee.Algorithms.IsEqual(collection.size(), 0);
const properties = ee.Algorithms.If(empty, {}, mergeProperties(collection));
return ee.Image(0).set(properties);
});
const valid = ee.ImageCollection.fromImages(carriers).filter(
ee.Filter.contains(Metadata.FOOTPRINT, geometry)
);
return ee.List(valid.toList(valid.size()).map(getDate));
};
```
##### **Inputs:**
\- satellite: The selected satellite for processing the image
collection. - geometry: The geometry to filter the image collection by.
##### **Output:**
\- Returns a list of dates corresponding to the processed images in the
collection.
##### **Use**
To use the \`processCollection\` function, follow these steps: - Call
the function \`processCollection\` and provide the satellite and
geometry as inputs. - The function will filter the image collection
based on the provided satellite and geometry. - It will perform various
operations on the filtered collection, including retrieving extremes,
sorting by grid position, and filtering by northeastern position. - The
function will generate slices of images based on complete orbital cycles
and add metadata to the slices. - It will filter the slices based on the
geometry and return a list of dates corresponding to the valid images.
``` javascript
const dates = processCollection("Landsat", geometry);
```
<hr>
### **generateCloudMap**
The \`generateCloudMap\` function takes a list of dates, an image
collection, and a geometry as inputs. It generates a list of cloud
values corresponding to the provided dates and collection.
``` javascript
export const generateCloudMap = (dates, collection, geometry) => {
const cloudList = ee.List(dates).map((date) => {
const image = ee.Image(acquireFromDate(date, collection, geometry));
return image.get("CLOUDS");
});
return cloudList;
};
```
##### **Inputs:**
\- dates: A list of dates for which to generate the cloud values. -
collection: The image collection from which to acquire images. -
geometry: The geometry to filter the image collection by.
##### **Output:**
\- Returns a list of cloud values corresponding to the provided dates
and collection.
##### **Use**
To use the \`generateCloudMap\` function, follow these steps: - Call the
function \`generateCloudMap\` and provide the dates, collection, and
geometry as inputs. - The function will iterate over the provided dates
and acquire the corresponding image from the collection based on the
date and geometry. - It will extract the cloud value from each image and
create a list of these values. - The function will return the generated
list of cloud values.
``` javascript
const cloudValues = generateCloudMap(dates, imageCollection, geometry);
```
<hr>
## CSqueeze
The given code is used for geospatial analysis. It primarily analyses
land cover and shorelines using Google Earth Engine (imported as 'ee')
and Turf.js (imported as 'turf'), two powerful libraries for geospatial
analysis. The code contains the functions bellow:
1. `landParsing` : This function takes in four parameters: landcover,
sidesCoords, centerCoord, and shoreline. It checks if the given
landcover is water, calculates the intersection of the sides of the
landcover with its geometry, and then calculates the length and
coordinates of these intersections. It also calculates the distance
of the intersections from the center and shoreline. It then
determines which side of the landcover is larger and if the
landcover is water, it notes the side and intersection point.
Finally, it returns an object containing details about the
landcover, including its name, class, length of sides, biggest side,
intersection points, distances from the baseline and shoreline, and
information related to water.
2. `landParsingTurf` : This function is similar to `landParsing` but
uses the Turf.js library for geospatial operations instead of Google
Earth Engine. It takes the same parameters and performs similar
operations but uses Turf.js methods like `turf.point` ,
`turf.lineString` , `turf.buffer` , `turf.feature` ,
`turf.intersect` , `turf.length` , `turf.getCoords` ,
`turf.distance` , and `turf.nearestPointOnLine` . 3.
`extractShoreLine` : This function takes in two parameters:
baseLineCoords and waterGeometry. It creates a geometry for the
baseline and water body, then calculates the intersection of the
baseline with the water body. This intersection represents the
shoreline. The code also imports and uses a list of landcovers and a
'satellite' object from other modules. The landcovers list is used
to assign names and classes to the landcover based on its id.
3. `extractShoreLineTurf(baseLine, waterGeometry)` : This function
takes a base line and a water geometry as inputs. It converts the
water geometry into a turf feature, intersects it with the base
line, and then converts the intersection into a line, which
represents the shoreline.
4. `landCoversIntersections(transect, landcoversdata, shoreLine, year)`
: This function takes a transect, land cover data, a shoreline, and
a year as inputs. It then calculates the intersections of the
transect with the land cover data. The function also determines
which side of the transect intersects with the water body.
5. `landCoversIntersectionsTurf(transect, landcoversdata, shoreLine, year)`
: This function is similar to the previous one, but it uses Turf.js
functions for the calculations.
6. `filterElevation(image, elevation, type, satellite)` : This function
takes an image, an elevation value, a type, and a satellite as
inputs. It filters the image based on the elevation value, using
different methods depending on the type and satellite provided. It
returns the filtered image. 5. `applyScaleFactors(image)` : This
function takes an image as input and applies scale factors to the
optical and thermal bands of the image. It returns the image with
the adjusted bands. 6. `getMapBiomasClassificationsList()` : This
function retrieves a list of band IDs from a specific image in the
MapBiomas project on Google Earth Engine. It returns this list.
Overall, these functions are used for processing and analyzing
geographical data, likely as part of a larger application for
environmental or geographical studies.
7. `getMapBiomasClassificationYear(AOI, year, elevation = 10)` : This
function retrieves a classification image from the MapBiomas
database for a specific year and area of interest (AOI). The image
is then clipped to the AOI and filtered by elevation.
8. `getLandCoverLabelMapBiomas(value)` : This function returns a label
for a given land cover value. It maps the input value to one of four
categories: mangrove, vegetation, water, or human.
9. `maskCloudLandsat(image, qa_band)` and `maskCloudSentinel(image)` :
These two functions are used to mask cloud cover from Landsat and
Sentinel satellite images, respectively. They both use bitwise
operations to filter out pixels that are flagged as clouds or cloud
shadows.
10. `getImageCSqueezeInfos(missionName)` : This function retrieves
metadata for a given satellite mission name.
11. `applySaviBand(image, bands)` , `applyEviBand(image, bands)` ,
`applyNdviBand(image, bands)` , `applyMndwiBand(image, bands)` ,
`applyUiBand(image, bands)` : These functions calculate various
vegetation indices (SAVI, EVI, NDVI, MNDWI, UI) from an image using
specific band combinations.
12. `trainAndClassify(image, classificationAreas, bands, AOICoords)` :
This function trains a random forest classifier using the provided
image, classification areas, and bands. The trained classifier is
then used to classify the image.
13. `getMangroves()` : This function retrieves a global mangrove map
from the ESA WorldCover database, masks out all non-mangrove areas,
calculates the slope of the mangrove areas, and returns an overlay
of the slope map.
<hr>
### **landParsing**
*This function takes several inputs and performs calculations to
determine various properties related to land cover and water bodies.*
``` javascript
function landParsing(landcover, sidesCoords, centerCoord, shoreline) {
var isWater = landcover.id === 2;
var center = ee.Geometry.Point(centerCoord);
var intersectionSideOne = ee.Geometry.LineString(sidesCoords[0]).intersection(landcover.geometry, 1);
var intersectionSideTwo = ee.Geometry.LineString(sidesCoords[1]).intersection(landcover.geometry, 1);
var intersectionData = ee.Dictionary({
intersectionSideOneLength: intersectionSideOne.length(),
intersectionSideTwoLength: intersectionSideTwo.length(),
intersectionSideOneCoordinatesFlatten: intersectionSideOne.coordinates().flatten(),
intersectionSideTwoCoordinatesFlatten: intersectionSideTwo.coordinates().flatten()
}).getInfo()
var lengthSide = [0, 0]
var distancesFromBaseLine = [0, 0];
var distancesFromShoreLine = [0, 0];
var landCoverIntersectPoint = [null, null];
var landCoverBiggestSide = -1;
var waterSide = -1;
var waterIntersectPoint = [0, 0];
var oneCoordsFlatten = intersectionData.intersectionSideOneCoordinatesFlatten.reverse()
var twoCoordsFlatten = intersectionData.intersectionSideTwoCoordinatesFlatten.reverse()
if (oneCoordsFlatten.length > 0 && twoCoordsFlatten.length > 0) {
lengthSide[0] = intersectionData.intersectionSideOneLength;
lengthSide[1] = intersectionData.intersectionSideTwoLength;
landCoverIntersectPoint[0] = [oneCoordsFlatten[1], oneCoordsFlatten[0]];
landCoverIntersectPoint[1] = [twoCoordsFlatten[1], twoCoordsFlatten[0]];
distancesFromBaseLine[0] = landCoverIntersectPoint[0] !== null ? ee.Geometry.Point(landCoverIntersectPoint[0]).distance(center, 1).getInfo() : null;
distancesFromBaseLine[1] = landCoverIntersectPoint[1] !== null ? ee.Geometry.Point(landCoverIntersectPoint[1]).distance(center, 1).getInfo() : null;
distancesFromShoreLine[0] = landCoverIntersectPoint[0] !== null ? ee.Geometry.Point(landCoverIntersectPoint[0]).distance(shoreline, 1).getInfo() : null;
distancesFromShoreLine[1] = landCoverIntersectPoint[1] !== null ? ee.Geometry.Point(landCoverIntersectPoint[1]).distance(shoreline, 1).getInfo() : null;
if (lengthSide[0] > lengthSide[1]) landCoverBiggestSide = 0;
else landCoverBiggestSide = 1;
}
else if (twoCoordsFlatten.length > 0) {
lengthSide[1] = intersectionData.intersectionSideTwoLength;
landCoverIntersectPoint[1] = [twoCoordsFlatten[1], twoCoordsFlatten[0]];
distancesFromBaseLine[1] = landCoverIntersectPoint[1] !== null ? ee.Geometry.Point(landCoverIntersectPoint[1]).distance(center, 1).getInfo() : null;
distancesFromShoreLine[1] = landCoverIntersectPoint[1] !== null ? ee.Geometry.Point(landCoverIntersectPoint[1]).distance(shoreline, 1).getInfo() : null;
landCoverBiggestSide = 1;
}
else if (oneCoordsFlatten.length > 0) {
lengthSide[0] = intersectionData.intersectionSideOneLength;
landCoverIntersectPoint[0] = [oneCoordsFlatten[1], oneCoordsFlatten[0]];
distancesFromBaseLine[0] = landCoverIntersectPoint[0] !== null ? ee.Geometry.Point(landCoverIntersectPoint[0]).distance(center, 1).getInfo() : null;
distancesFromShoreLine[0] = landCoverIntersectPoint[0] !== null ? ee.Geometry.Point(landCoverIntersectPoint[0]).distance(shoreline, 1).getInfo() : null;
landCoverBiggestSide = 0;
}
if (isWater) {
waterSide = landCoverBiggestSide;
waterIntersectPoint = landCoverIntersectPoint[landCoverBiggestSide];
}
var name = landcovers[landcover.id];
var classe = landcovers[landcover.id];
return {
label: landcover.id,
name: name,
classe: classe,
lengthSide: lengthSide,
biggestSide: landCoverBiggestSide,
intersectPoint: landCoverIntersectPoint,
baseLineDistance: distancesFromBaseLine,
shoreLineDistance: distancesFromShoreLine,
waterSide: waterSide,
waterIntersectPoint: waterIntersectPoint
};
}
```
#### 'Inputs:'
\- landcover: The land cover object. - sidesCoords: An array containing
the coordinates of the two sides. - centerCoord: The coordinate of the
center point. - shoreline: The shoreline geometry.
#### 'Output:'
\- label: The ID of the land cover. - name: The name of the land
cover. - classe: The class of the land cover. - lengthSide: An array
containing the lengths of the two sides. - biggestSide: The index of the
biggest side. - intersectPoint: An array containing the intersection
points of the land cover with the sides. - baseLineDistance: An array
containing the distances from the base line. - shoreLineDistance: An
array containing the distances from the shoreline. - waterSide: The
index of the water side. - waterIntersectPoint: The intersection point
of the water side.
#### 'Use'
\- Create an instance of the land cover object. - Define the coordinates
of the two sides. - Specify the center coordinate. - Provide the
shoreline geometry. - Call the function landParsing with the inputs
mentioned above. - Retrieve the desired properties from the output.
``` javascript
var landcover = ...; // create the land cover object
var sidesCoords = [...]; // define the coordinates of the sides
var centerCoord = ...; // specify the center coordinate
var shoreline = ...; // provide the shoreline geometry
var result = landParsing(landcover, sidesCoords, centerCoord, shoreline);
```
<hr>
### **landParsingTurf**
This function parses a given landcover to determine its attributes and
the relationships to other geographical features. It takes in four
parameters: landcover, sidesCoords, centerCoord, and shoreline. The
function uses the Turf.js library to perform various geographical
operations, including creating points and lines, buffering lines,
intersecting shapes, and calculating distances. It checks if the
landcover is water, calculates the intersection of the landcover with
two sides, and determines the length and coordinates of the
intersections. It then calculates the distances from the baseline and
shoreline for each side, determines the biggest side, and if the
landcover is water, it assigns the water side and intersection point.
Finally, it returns an object with all the calculated attributes and
measurements.
``` javascript
function landParsingTurf(landcover, sidesCoords, centerCoord, shoreline) {
var isWater = landcover.id === 2;
var center = turf.point(centerCoord);
var sideOneTurf = turf.lineString(sidesCoords[0])
var sideOne = turf.buffer(sideOneTurf, 1, options);
var sideTwoTurf = turf.lineString(sidesCoords[1])
var sideTwo = turf.buffer(sideTwoTurf, 1, options);
var landcoverTurf = turf.feature(landcover.geometry)
// if (landcover.geometry.type === "MultiPolygon") {
// landcoverTurf = turf.multiPolygon(landcover.geometry.coordinates)
// } else {
// landcoverTurf = turf.polygon(landcover.geometry.coordinates)
// }
var intersectionSideOne = turf.intersect(sideOne, landcoverTurf);
var intersectionSideTwo = turf.intersect(sideTwo, landcoverTurf);
var intersectionData = {
intersectionSideOneLength: intersectionSideOne ? turf.length(intersectionSideOne, options) : 0,
intersectionSideTwoLength: intersectionSideTwo ? turf.length(intersectionSideTwo, options) : 0,
intersectionSideOneCoordinatesFlatten: intersectionSideOne ? turf.getCoords(intersectionSideOne).flat(Infinity) : [],
intersectionSideTwoCoordinatesFlatten: intersectionSideTwo ? turf.getCoords(intersectionSideTwo).flat(Infinity) : []
};
var lengthSide = [0, 0]
var distancesFromBaseLine = [0, 0];
var distancesFromShoreLine = [0, 0];
var landCoverIntersectPoint = [null, null];
var landCoverBiggestSide = -1;
var waterSide = -1;
var waterIntersectPoint = [0, 0];
var oneCoordsFlatten = intersectionData.intersectionSideOneCoordinatesFlatten.reverse()
var twoCoordsFlatten = intersectionData.intersectionSideTwoCoordinatesFlatten.reverse()
if (oneCoordsFlatten.length > 0 && twoCoordsFlatten.length > 0) {
lengthSide[0] = intersectionData.intersectionSideOneLength;
lengthSide[1] = intersectionData.intersectionSideTwoLength;
landCoverIntersectPoint[0] = [oneCoordsFlatten[1], oneCoordsFlatten[0]];
landCoverIntersectPoint[1] = [twoCoordsFlatten[1], twoCoordsFlatten[0]];
distancesFromBaseLine[0] = landCoverIntersectPoint[0] !== null ? turf.distance(turf.point(landCoverIntersectPoint[0]), center, options) : null;
distancesFromBaseLine[1] = landCoverIntersectPoint[1] !== null ? turf.distance(turf.point(landCoverIntersectPoint[1]), center, options) : null;
var pontoShoreLineProximo0 = turf.nearestPointOnLine(shoreline, turf.point(landCoverIntersectPoint[0]), options);
distancesFromShoreLine[0] = landCoverIntersectPoint[0] !== null ? turf.distance(turf.point(landCoverIntersectPoint[0]), pontoShoreLineProximo0, options) : null;
var pontoShoreLineProximo1 = turf.nearestPointOnLine(shoreline, turf.point(landCoverIntersectPoint[1]), options);
distancesFromShoreLine[1] = landCoverIntersectPoint[1] !== null ? turf.distance(turf.point(landCoverIntersectPoint[1]), pontoShoreLineProximo1, options) : null;
if (lengthSide[0] > lengthSide[1]) landCoverBiggestSide = 0;
else landCoverBiggestSide = 1;
}
else if (twoCoordsFlatten.length > 0) {
lengthSide[1] = intersectionData.intersectionSideTwoLength;
landCoverIntersectPoint[1] = [twoCoordsFlatten[1], twoCoordsFlatten[0]];
distancesFromBaseLine[1] = landCoverIntersectPoint[1] !== null ? turf.distance(turf.point(landCoverIntersectPoint[1]), center, options) : null;
var pontoShoreLineProximo1 = turf.nearestPointOnLine(shoreline, turf.point(landCoverIntersectPoint[1]), options);
distancesFromShoreLine[1] = landCoverIntersectPoint[1] !== null ? turf.distance(turf.point(landCoverIntersectPoint[1]), pontoShoreLineProximo1, options) : null;
landCoverBiggestSide = 1;
}
else if (oneCoordsFlatten.length > 0) {
lengthSide[0] = intersectionData.intersectionSideOneLength;
landCoverIntersectPoint[0] = [oneCoordsFlatten[1], oneCoordsFlatten[0]];
distancesFromBaseLine[0] = landCoverIntersectPoint[0] !== null ? turf.distance(turf.point(landCoverIntersectPoint[0]), center, options) : null;
var pontoShoreLineProximo0 = turf.nearestPointOnLine(shoreline, turf.point(landCoverIntersectPoint[0]), options);
distancesFromShoreLine[0] = landCoverIntersectPoint[0] !== null ? turf.distance(turf.point(landCoverIntersectPoint[0]), pontoShoreLineProximo0, options) : null;
landCoverBiggestSide = 0;
}
if (isWater) {
waterSide = landCoverBiggestSide;
waterIntersectPoint = landCoverIntersectPoint[landCoverBiggestSide];
}
var name = landcovers[landcover.id];
var classe = landcovers[landcover.id];
return {
label: landcover.id,
name: name,
classe: classe,
lengthSide: lengthSide,
biggestSide: landCoverBiggestSide,
intersectPoint: landCoverIntersectPoint,
baseLineDistance: distancesFromBaseLine,
shoreLineDistance: distancesFromShoreLine,
waterSide: waterSide,
waterIntersectPoint: waterIntersectPoint
};
}
function landParsingTurf(landcover, sidesCoords, centerCoord, shoreline) {...}
```
#### **Inputs:**
\- landcover: The landcover object to be parsed. - sidesCoords: An array
containing the coordinates of the two sides. - centerCoord: The
coordinates of the center point. - shoreline: The shoreline object.
#### **Output:**
An object containing the following properties: - label: The ID of the
landcover. - name: The name of the landcover. - classe: The class of the
landcover. - lengthSide: The lengths of the two sides. - biggestSide:
The biggest side. - intersectPoint: The intersection points of the
landcover with the two sides. - baseLineDistance: The distances from the
baseline for each side. - shoreLineDistance: The distances from the
shoreline for each side. - waterSide: The water side, if the landcover
is water. - waterIntersectPoint: The water intersection point, if the
landcover is water.
#### **Use**
\- Call the function landParsingTurf with the required parameters:
landcover, sidesCoords, centerCoord, and shoreline. - The function will
perform various geographical operations and calculations using the
Turf.js library. - It will return an object with the calculated
attributes and measurements.
``` javascript
landParsingTurf(landcover, sidesCoords, centerCoord, shoreline);
```
<hr>
### **extractShoreLine**
This function takes in the baseLineCoords and waterGeometry as input
parameters and returns the intersection of the base line geometry and
the water geometry lines.
``` javascript
export const extractShoreLine = (baseLineCoords, waterGeometry) => {
var baseLineGeometry = ee.Geometry(baseLineCoords.geometry);
var waterGeometryLines = ee.FeatureCollection(waterGeometry.coordinates.map(function (l) {
return ee.Feature(ee.Geometry.MultiLineString(l))
})).geometry();
return baseLineGeometry.intersection(waterGeometryLines, ee.ErrorMargin(1));
}
```
#### **Inputs:**
\- baseLineCoords: The coordinates of the base line geometry. This
should be in a format that can be converted to an ee.Geometry object. -
waterGeometry: The geometry of the water. This should be in a format
that can be converted to an ee.FeatureCollection object.
#### **Output:**
\- Intersection: The intersection of the base line geometry and the
water geometry lines.
#### **Use**
\- Create a base line geometry using the desired coordinates. - Create a
water geometry using the desired coordinates. - Call the
extractShoreLine function, passing in the base line geometry and water
geometry as input parameters. - The function will return the
intersection of the base line geometry and the water geometry lines.
``` javascript
extractShoreLine(baseLineCoords, waterGeometry);
```
<hr>
### **extractShoreLineTurf**
This function takes in the baseLine and waterGeometry as input
parameters and returns the shoreline as a line geometry.
``` javascript
export const extractShoreLineTurf = (baseLine, waterGeometry) => {
var waterTurf = turf.feature(waterGeometry);
var intersection = turf.intersect(baseLine, waterTurf);
var shoreLine = turf.polygonToLine(intersection);
return shoreLine;
}
```
#### **Inputs:**
\- baseLine: The base line geometry. This should be in a format that can
be converted to a turf feature object. - waterGeometry: The geometry of
the water. This should be in a format that can be converted to a turf
feature object.
#### **Output:**
\- shoreLine: The shoreline as a line geometry.
#### **Use**
\- Create a base line geometry using the desired coordinates. - Create a
water geometry using the desired coordinates. - Call the
extractShoreLineTurf function, passing in the base line geometry and
water geometry as input parameters. - The function will return the
shoreline as a line geometry.
``` javascript
extractShoreLineTurf(baseLine, waterGeometry);
```
<hr>
### **landCoversIntersections**
This function takes in the transect, landcoversdata, shoreLine, and year
as input parameters and returns an object containing various outputs
related to land cover intersections.
``` javascript
export const landCoversIntersections = (transect, landcoversdata, shoreLine, year) => {
var sideOneCoords = transect.sides[0].coords.map(arr => Object.values(arr));
var sideTwoCoords = transect.sides[1].coords.map(arr => Object.values(arr));
var sideOne = sideOneCoords;
var sideTwo = sideTwoCoords;
var completeLine = [sideOne, sideTwo];
var lineCenter = transect.center;
// // var landcoversCodes = ee.Algorithms.If(filter, filter, landcovers.distinct('label').aggregate_array('label'))
var waterSide = -1;
var waterIntersectPoint = [0, 0];
var landcoversOutput = [];
var waters = landcoversdata.filter(function (l) { return l.id === 2 })
var outputwater = waters.map(function (water) {
return landParsing(water, [sideOne, sideTwo], lineCenter, shoreLine)
})[0]
waterSide = outputwater.waterSide;
waterIntersectPoint = outputwater.waterIntersectPoint;
if (waterSide > -1) {
if (waterSide === 0) {
sideOne = sideTwoCoords
sideTwo = [lineCenter, waterIntersectPoint]
completeLine = [sideOne, sideTwo]
}
if (waterSide === 1) {
sideOne = sideOneCoords
sideTwo = [lineCenter, waterIntersectPoint]
completeLine = [sideOne, sideTwo]
}
}
landcoversOutput = landcoversdata.map(landcover => {
return landParsing(landcover, [sideOne, sideTwo], lineCenter, shoreLine)
});
var saida = {
id: transect.transect,
year,
center: lineCenter,
completeLine,
sides: transect.sides,
waterSide: waterSide,
waterIntersectPoint: waterIntersectPoint,
landCoversIntersections: landcoversOutput
};
return saida;
}
```
#### **Inputs:**
\- transect: The transect object containing information about the
transect line. - landcoversdata: The land cover data used for
intersection analysis. This should be an array of land cover objects. -
shoreLine: The shoreline as a line geometry. This should be in a format
that can be used for intersection analysis. - year: The year associated
with the analysis.
#### **Output:**
\- saida: An object containing various outputs related to land cover
intersections. The properties of saida include:
` - id: The transect ID.`
` - year: The year associated with the analysis.`
` - center: The center point of the transect line.`
` - completeLine: The complete line geometry of the transect.`
` - sides: An array containing the two sides of the transect line.`
` - waterSide: The side of the transect line where water is intersected (-1 if no water intersection).`
` - waterIntersectPoint: The coordinates of the point where the transect line intersects with water.`
` - landCoversIntersections: An array containing the intersection results for each land cover object in the landcoversdata array.`
#### **Use**
\- Create a transect object containing information about the transect
line. - Prepare the land cover data for intersection analysis. - Create
the shoreline as a line geometry. - Specify the year associated with the
analysis. - Call the landCoversIntersections function, passing in the
transect, landcoversdata, shoreLine, and year as input parameters. - The
function will return an object containing various outputs related to
land cover intersections.
``` javascript
landCoversIntersections(transect, landcoversdata, shoreLine, year);
```
<hr>
### **landCoversIntersectionsTurf**
This function takes in the transect, landcoversdata, shoreLine, and year
as input parameters and returns an object containing various outputs
related to land cover intersections using the Turf library.
``` javascript
export const landCoversIntersectionsTurf = (transect, landcoversdata, shoreLine, year) => {
var sideOneCoords = transect.sides[0].coords.map(arr => Object.values(arr));
var sideTwoCoords = transect.sides[1].coords.map(arr => Object.values(arr));
var sideOne = sideOneCoords;
var sideTwo = sideTwoCoords;
var completeLine = [sideOne, sideTwo];
var lineCenter = transect.center;
// // var landcoversCodes = ee.Algorithms.If(filter, filter, landcovers.distinct('label').aggregate_array('label'))
var waterSide = -1;
var waterIntersectPoint = [0, 0];
var landcoversOutput = [];
var waters = landcoversdata.filter(function (l) { return l.id === 2 })
var outputwater = waters.map(function (water) {
return landParsingTurf(water, [sideOne, sideTwo], lineCenter, shoreLine)
})[0]
waterSide = outputwater.waterSide;
waterIntersectPoint = outputwater.waterIntersectPoint;
if (waterSide > -1) {
if (waterSide === 0) {
sideOne = sideTwoCoords
sideTwo = [lineCenter, waterIntersectPoint]
completeLine = [sideOne, sideTwo]
}
if (waterSide === 1) {
sideOne = sideOneCoords
sideTwo = [lineCenter, waterIntersectPoint]
completeLine = [sideOne, sideTwo]
}
}
landcoversOutput = landcoversdata.map(landcover => {
return landParsingTurf(landcover, [sideOne, sideTwo], lineCenter, shoreLine)
});
var saida = {
id: transect.transect,
year,
center: lineCenter,
completeLine,
sides: transect.sides,
waterSide: waterSide,
waterIntersectPoint: waterIntersectPoint,
landCoversIntersections: landcoversOutput
};
return saida;
}
```
#### **Inputs:**
\- transect: The transect object containing information about the
transect line. - landcoversdata: The land cover data used for
intersection analysis. This should be an array of land cover objects. -
shoreLine: The shoreline as a line geometry. This should be in a format
that can be used for intersection analysis. - year: The year associated
with the analysis.
#### **Output:**
\- saida: An object containing various outputs related to land cover
intersections. The properties of saida include:
` - id: The transect ID.`
` - year: The year associated with the analysis.`
` - center: The center point of the transect line.`
` - completeLine: The complete line geometry of the transect.`
` - sides: An array containing the two sides of the transect line.`
` - waterSide: The side of the transect line where water is intersected (-1 if no water intersection).`
` - waterIntersectPoint: The coordinates of the point where the transect line intersects with water.`
` - landCoversIntersections: An array containing the intersection results for each land cover object in the landcoversdata array.`
#### **Use**
\- Create a transect object containing information about the transect
line. - Prepare the land cover data for intersection analysis. - Create
the shoreline as a line geometry. - Specify the year associated with the
analysis. - Call the landCoversIntersectionsTurf function, passing in
the transect, landcoversdata, shoreLine, and year as input parameters. -
The function will return an object containing various outputs related to
land cover intersections using the Turf library.
``` javascript
landCoversIntersectionsTurf(transect, landcoversdata, shoreLine, year);
```
<hr>
### **filterElevation**
This function takes in the image, elevation, type, and satellite as
input parameters and returns an image with an elevation mask applied.
``` javascript
export const filterElevation = (image, elevation, type = null, satellite = "LANDSAT") => {
var elevationMask;
// Digital Elevation Model (DEM)
// var dem = ee.Image("USGS/SRTMGL1_003").lte(elevation);
// ALOS DSM: Global 30m v3.2
// var dem = ee.ImageCollection('JAXA/ALOS/AW3D30/V3_2').select('DSM').mosaic().lte(elevation);
var dataset = ee.ImageCollection('JAXA/ALOS/AW3D30/V3_2').select(0).mosaic().lte(elevation);
// var dem = ee.Image("users/nlang/ETH_GlobalCanopyHeightSD_2020_10m_v1").lte(elevation)
// NADA ELEVATION
// var nasa = ee.Image('NASA/NASADEM_HGT/001').select(0).lte(elevation);
if (type === "mapbiomas") {
// Reprojeta e redimensiona a imagem DSM para a mesma projeção e resolução espacial da imagem classification
var dataset_reproj = dataset.reproject({
crs: image.projection(),
scale: image.projection().nominalScale(),
});
// Subtrai a imagem DSM da imagem classification
var classification_minus_dataset = image.subtract(dataset_reproj);
// Define a máscara para excluir a área do DSM
var mask = dataset_reproj.eq(0).not();
// Aplica a máscara à imagem classification_minus_dsm
elevationMask = classification_minus_dataset.updateMask(mask);
return image.updateMask(elevationMask)
} else {
if (satellite === "S2") {
dataset = ee.Image('USGS/SRTMGL1_003');
elevationMask = dataset.lte(elevation);
} else {
elevationMask = dataset.subtract(image.select(0));
}
}
return image.updateMask(elevationMask);
}
```
#### **Inputs:**
\- image: The image to be filtered based on elevation. - elevation: The
elevation threshold used for filtering. - type: The type of filter to
apply ("mapbiomas" or null). - satellite: The satellite used for the
filter ("LANDSAT" or "S2").
#### **Output:**
\- image: The filtered image with an elevation mask applied.
#### **Use**
\- Prepare the image to be filtered based on elevation. - Specify the
elevation threshold for filtering. - Optional: Specify the type of
filter to apply ("mapbiomas" or null). - Optional: Specify the satellite
used for the filter ("LANDSAT" or "S2"). - Call the filterElevation
function, passing in the image, elevation, type, and satellite as input
parameters. - The function will return the filtered image with an
elevation mask applied.
``` javascript
filterElevation(image, elevation, type, satellite);
```
<hr>
### **applyScaleFactors**
This function takes in an image as input and applies scale factors to
the optical and thermal bands. It then adds the scaled bands to the
input image and returns the modified image.
``` javascript
export const applyScaleFactors = (image) => {
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBand = image.select('ST_B.*').multiply(0.00341802).add(149.0);
return image.addBands(opticalBands, null, true)
.addBands(thermalBand, null, true);
}
```
#### **Inputs:**
\- image: The input image to which scale factors will be applied.
#### **Output:**
\- image: The modified image with scaled bands added.
#### **Use**
\- Prepare the image to which scale factors will be applied. - Call the
applyScaleFactors function, passing in the image as an input
parameter. - The function will apply scale factors to the optical and
thermal bands of the image and add the scaled bands to the input
image. - The modified image with scaled bands added will be returned.
``` javascript
applyScaleFactors(image);
```
<hr>
### **getMapBiomasClassificationsList**
This function retrieves the list of classifications from the MapBiomas
dataset. It fetches the MapBiomas image and extracts the band IDs to
create a list of classifications.
``` javascript
export const getMapBiomasClassificationsList = () => {
var mapbiomas = ee.Image("projects/mapbiomas-workspace/public/collection7/mapbiomas_collection70_integration_v2").getInfo();
var list = mapbiomas.bands.map((m) => m.id);
return list;
}
```
#### **Inputs:**
No input parameters for this function.
#### **Output:**
\- list: The list of classifications from the MapBiomas dataset.
#### **Use**
\- Call the getMapBiomasClassificationsList function. - The function
will retrieve the list of classifications from the MapBiomas dataset. -
The list of classifications will be returned.
``` javascript
getMapBiomasClassificationsList();
```
<hr>
### **getMapBiomasClassificationYear**
This function retrieves the MapBiomas classification image for a
specific year within a given Area of Interest (AOI). It selects the
classification band corresponding to the specified year and clips the
image to the AOI. The function also applies an optional elevation filter
before returning the classified image.
``` javascript
export const getMapBiomasClassificationYear = (AOI, year, elevation = 10) => {
let classified = ee
.Image(
"projects/mapbiomas-workspace/public/collection7/mapbiomas_collection70_integration_v2"
)
.select("classification_" + year)
.clip(ee.Geometry.Polygon(AOI));
classified = filterElevation(classified, elevation, "mapbiomas")
return classified;
}
```
#### **Inputs:**
\- AOI: The Area of Interest (AOI) for which the MapBiomas
classification image will be retrieved. - year: The specific year for
which the MapBiomas classification image will be retrieved. - elevation
(optional): The elevation threshold for filtering the classification
image. Default value is 10.
#### **Output:**
\- classified: The MapBiomas classification image for the specified year
and AOI.
#### **Use**
\- Define the Area of Interest (AOI) and the specific year for which the
MapBiomas classification image is needed. - Call the
getMapBiomasClassificationYear function, passing in the AOI and year as
input parameters. - The function will retrieve the MapBiomas
classification image for the specified year and AOI. - If an elevation
threshold is provided, the function will apply the elevation filter to
the classification image. - The classified image will be returned.
``` javascript
getMapBiomasClassificationYear(AOI, year, elevation);
```
<hr>
### **getLandCoverLabelMapBiomas**
This function returns the label for a given land cover value based on
the MapBiomas land cover types. It takes a land cover value as input and
checks if it belongs to any of the predefined land cover types. If a
match is found, the corresponding label is returned. If no match is
found, a default label of 3 is returned.
``` javascript
export function getLandCoverLabelMapBiomas(value) {
let landcoverstypes = {
mangrove: {
label: 0,
types: [5]
},
vegetation: {
label: 1,
types: [1, 3, 4, 5, 49, 10, 11, 12, 32, 29, 50, 13]
},
water: {
label: 2,
types: [26, 33]
},
human: {
label: 3,
types: [14, 15, 18, 19, 39, 20, 40, 62, 41, 36, 46, 47, 48, 9, 21, 22, 23, 24, 30, 25, 31, 27]
},
}
for (const type of Object.entries(landcoverstypes)) {
if (type[1].types.includes(value)) {
return type[1].label;
}
}
return 3;
}
```
#### **Inputs:**
\- value: The land cover value for which the label will be determined.
#### **Output:**
\- label: The label corresponding to the given land cover value. If no
match is found, a default label of 3 is returned.
#### **Use**
\- Define the land cover value for which the label is needed. - Call the
getLandCoverLabelMapBiomas function, passing in the land cover value as
an input parameter. - The function will determine the label based on the
land cover value. - The label will be returned.
``` javascript
getLandCoverLabelMapBiomas(value);
```
<hr>
### **maskCloudLandsat**
This function masks cloud and cloud shadow pixels in a Landsat image
using the quality assessment (QA) band. It takes an input image and the
QA band name as parameters. The function applies bitwise operations to
create a mask that identifies cloud and cloud shadow pixels based on
specific bit masks. The image is then updated with the mask, effectively
masking out the cloud and cloud shadow pixels.
``` javascript
export function maskCloudLandsat(image, qa_band) {
var cloudShadowBitMask = (1 << 3);
var cloudsBitMask = (1 << 5);
var qa = image.select(qa_band);
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0).and(qa.bitwiseAnd(cloudsBitMask).eq(0));
return image.updateMask(mask);
}
```
#### **Inputs:**
\- image: The Landsat image to be masked. - qa_band: The name of the
quality assessment (QA) band in the Landsat image.
#### **Output:**
\- image: The input Landsat image with cloud and cloud shadow pixels
masked out.
#### **Use**
\- Provide an input Landsat image and the name of the quality assessment
(QA) band. - Call the maskCloudLandsat function, passing in the image
and qa_band as input parameters. - The function will create a mask based
on the QA band to identify cloud and cloud shadow pixels. - The input
image will be updated with the mask, masking out the cloud and cloud
shadow pixels. - The updated image will be returned.
``` javascript
maskCloudLandsat(image, qa_band);
```
<hr>
### **maskCloudSentinel**
This function masks cloud and cirrus pixels in a Sentinel-2 image using
the quality assessment (QA) band. It takes an input image as a
parameter. The function applies bitwise operations to create a mask that
identifies cloud and cirrus pixels based on specific bit masks. The
image is then updated with the mask, effectively masking out the cloud
and cirrus pixels and dividing the image by 10000.
``` javascript
export function maskCloudSentinel(image) {
var qa = image.select('QA60');
// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
.and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask).divide(10000);
}
```
#### **Inputs:**
\- image: The Sentinel-2 image to be masked.
#### **Output:**
\- image: The input Sentinel-2 image with cloud and cirrus pixels masked
out and divided by 10000.
#### **Use**
\- Provide an input Sentinel-2 image. - Call the maskCloudSentinel
function, passing in the image as an input parameter. - The function
will create a mask based on the QA band to identify cloud and cirrus
pixels. - The input image will be updated with the mask, masking out the
cloud and cirrus pixels and dividing the image by 10000. - The updated
image will be returned.
``` javascript
maskCloudSentinel(image);
```
<hr>
### **getImageCSqueezeInfos**
This function retrieves metadata information for a given mission name.
It takes a missionName parameter as input. The function checks if the
missionName includes the string "LANDSAT". If true, it retrieves the
metadata from the second satellite object in the satellite array.
Otherwise, it retrieves the metadata from the first satellite object.
The function returns an object containing the metadata.
``` javascript
export const getImageCSqueezeInfos = (missionName) => {
let metadata;
if (missionName.includes("LANDSAT")) {
metadata = satellite[1].get(missionName);
} else {
metadata = satellite[0].get(missionName);
}
return { metadata }
}
```
#### **Inputs:**
\- missionName: The name of the mission for which metadata information
is to be retrieved.
#### **Output:**
\- metadata: The metadata information for the given mission name.
#### **Use**
\- Provide a mission name as input. - Call the getImageCSqueezeInfos
function, passing in the missionName as an input parameter. - The
function will check if the missionName includes the string "LANDSAT". -
If true, it will retrieve the metadata from the second satellite object
in the satellite array. - If false, it will retrieve the metadata from
the first satellite object. - The function will return an object
containing the metadata information.
``` javascript
getImageCSqueezeInfos(missionName);
```
<hr>
### **applySaviBand**
This function applies the Soil-Adjusted Vegetation Index (SAVI) to an
input image using the specified bands. It takes two parameters: image
and bands. The function calculates the SAVI using the formula: 1.5 \*
(NIR - RED) / (NIR + RED + 5000), where NIR represents the Near-Infrared
band and RED represents the Red band. The result is then renamed as
"SAVI".
``` javascript
export const applySaviBand = (image, bands) => image
.expression("1.5 * (NIR - RED) / (NIR + RED + 5000)", {
NIR: image.select(bands.nir),
RED: image.select(bands.red),
})
.rename("SAVI");
```
#### **Inputs:**
\- image: The input image to which SAVI will be applied. - bands: An
object containing the names of the NIR and RED bands used in the SAVI
calculation.
#### **Output:**
\- image: The input image with the SAVI band applied and renamed as
"SAVI".
#### **Use**
\- Provide an input image and a bands object with the names of the NIR
and RED bands. - Call the applySaviBand function, passing in the image
and bands as input parameters. - The function will calculate the SAVI
using the NIR and RED bands from the input image. - The result will be a
new image with the SAVI band applied and renamed as "SAVI".
``` javascript
applySaviBand(image, bands);
```
<hr>
### **applyEviBand**
This function applies the Enhanced Vegetation Index (EVI) to an input
image using the specified bands. It takes two parameters: image and
bands. The function calculates the EVI using the formula: (2.5 \*
((NIR - RED)) / (NIR + 6 \* RED - 7.5 \* BLUE + 1)), where NIR
represents the Near-Infrared band, RED represents the Red band, and BLUE
represents the Blue band. The result is then renamed as "EVI".
``` javascript
export const applyEviBand = (image, bands) => image
.expression(
"(2.5 * ((NIR - RED)) / (NIR + 6 * RED - 7.5 * BLUE + 1))",
{
NIR: image.select(bands.nir),
RED: image.select(bands.red),
BLUE: image.select(bands.blue),
}
)
.rename("EVI");
```
#### **Inputs:**
\- image: The input image to which EVI will be applied. - bands: An
object containing the names of the NIR, RED, and BLUE bands used in the
EVI calculation.
#### **Output:**
\- image: The input image with the EVI band applied and renamed as
"EVI".
#### **Use**
\- Provide an input image and a bands object with the names of the NIR,
RED, and BLUE bands. - Call the applyEviBand function, passing in the
image and bands as input parameters. - The function will calculate the
EVI using the NIR, RED, and BLUE bands from the input image. - The
result will be a new image with the EVI band applied and renamed as
"EVI".
``` javascript
applyEviBand(image, bands);
```
<hr>
### **applyNdviBand**
This function applies the Normalized Difference Vegetation Index (NDVI)
to an input image using the specified bands. It takes two parameters:
image and bands. The function calculates the NDVI using the formula:
(NIR - RED) / (NIR + RED), where NIR represents the Near-Infrared band
and RED represents the Red band. The result is then renamed as "NDVI".
``` javascript
export const applyNdviBand = (image, bands) =>
image.expression('(NIR - RED) / (NIR + RED)', {
'NIR': image.select(bands.nir),
'RED': image.select(bands.red),
}).rename('NDVI')
```
#### **Inputs:**
\- image: The input image to which NDVI will be applied. - bands: An
object containing the names of the NIR and RED bands used in the NDVI
calculation.
#### **Output:**
\- image: The input image with the NDVI band applied and renamed as
"NDVI".
#### **Use**
\- Provide an input image and a bands object with the names of the NIR
and RED bands. - Call the applyNdviBand function, passing in the image
and bands as input parameters. - The function will calculate the NDVI
using the NIR and RED bands from the input image. - The result will be a
new image with the NDVI band applied and renamed as "NDVI".
``` javascript
applyNdviBand(image, bands);
```
<hr>
### **applyMndwiBand**
This function applies the Modified Normalized Difference Water Index
(MNDWI) to an input image using the specified bands. It takes two
parameters: image and bands. The function calculates the MNDWI using the
formula: (GREEN - SWIR) / (GREEN + SWIR), where GREEN represents the
Green band and SWIR represents the Shortwave Infrared band. The result
is then renamed as "MNDWI".
``` javascript
export const applyMndwiBand = (image, bands) =>
image.expression('(GREEN - SWIR) / (GREEN + SWIR)', {
'GREEN': image.select(bands.green),
'SWIR': image.select(bands.swir),
}).rename('MNDWI')
```
#### **Inputs:**
\- image: The input image to which MNDWI will be applied. - bands: An
object containing the names of the Green and Shortwave Infrared (SWIR)
bands used in the MNDWI calculation.
#### **Output:**
\- image: The input image with the MNDWI band applied and renamed as
"MNDWI".
#### **Use**
\- Provide an input image and a bands object with the names of the Green
and SWIR bands. - Call the applyMndwiBand function, passing in the image
and bands as input parameters. - The function will calculate the MNDWI
using the Green and SWIR bands from the input image. - The result will
be a new image with the MNDWI band applied and renamed as "MNDWI".
``` javascript
applyMndwiBand(image, bands);
```
<hr>
### **trainAndClassify**
This function trains a classifier using the provided image and
classification areas, and then classifies the image based on the trained
classifier. It takes four parameters: image, classificationAreas, bands,
and AOICoords. The function performs the following steps:
`1. Selects the specified bands from the input image.`
2\. Samples regions within the classification areas using the selected
bands and the "LandCover" property. 3. Trains a random forest classifier
using the sampled training data, with the "LandCover" property as the
class property and the selected bands as the input properties. 4.
Classifies the input image using the trained classifier. 5. Returns the
classified image.
``` javascript
export const trainAndClassify = (image, classificationAreas, bands, AOICoords) => {
let trainingData = image
.select(bands)
.sampleRegions({
collection: classificationAreas,
properties: ["LandCover"],
scale: 30,
});
let classifier = ee.Classifier.smileRandomForest(30).train({
features: trainingData,
classProperty: "LandCover",
inputProperties: bands,
});
let classified = image
.select(bands)
.classify(classifier);
return classified;
}
```
#### **Inputs:**
\- image: The input image to be classified. - classificationAreas: The
collection of areas used for training the classifier. - bands: The names
of the bands to be used for training and classification. - AOICoords:
The coordinates of the area of interest (AOI).
#### **Output:**
\- classified: The classified image.
#### **Use**
\- Provide an input image, a collection of classification areas, the
names of the bands, and the coordinates of the AOI. - Call the
trainAndClassify function, passing in the required parameters. - The
function will train a classifier using the image and classification
areas. - It will then classify the image based on the trained
classifier. - The result will be the classified image.
``` javascript
trainAndClassify(image, classificationAreas, bands, AOICoords);
```
<hr>
### **getMangroves**
This function retrieves the mangrove areas from the
"ESA/WorldCover/v200" dataset. It performs the following steps: 1.
Retrieves the first image from the "ESA/WorldCover/v200" dataset. 2.
Creates a mask to keep only the mangrove areas (value 95 represents
mangroves). 3. Applies the mask to the original image, resulting in an
image with only the mangrove areas. 4. Sets the visualization parameters
for the mangrove areas, including the minimum and maximum values and the
color palette. 5. Calculates the slope of the mangrove areas using the
ee.Terrain.slope function. 6. Retrieves the map ID and creates an Earth
Engine TileSource using the visualization parameters. 7. Creates an
ImageOverlay using the TileSource. 8. Returns the ImageOverlay.
``` javascript
export const getMangroves = function () {
var dataset = ee.ImageCollection("ESA/WorldCover/v200").first();
var mangroveMask = dataset.select('Map').eq(95);
var mangroves = dataset.updateMask(mangroveMask);
var visualization = {
min: 95,
max: 95,
palette: ['000000', 'red']
};
var slope = ee.Terrain.slope(mangroves);
var mapId = slope.getMap(visualization);
var tileSource = new ee.layers.EarthEngineTileSource(mapId);
var overlay = new ee.layers.ImageOverlay(tileSource);
return overlay;
}
```
#### **Inputs:**
No input parameters for this function.
#### **Output:**
\- overlay: An ImageOverlay containing the mangrove areas.
#### **Use**
\- Call the getMangroves function. - The function will retrieve the
mangrove areas from the "ESA/WorldCover/v200" dataset and create an
ImageOverlay. - The ImageOverlay can be used to display the mangrove
areas on a map.
``` javascript
getMangroves();
```
<hr>
## Geodesy
This code is a module that provides functions for computing displacement
and bearing between two geographic coordinates using Earth Engine.
1. The code begins by importing the "ee" module from the
"../../services/earth-engine" directory. Next, the constant variable
"EARTHS_RADIUS" is defined with a value of 6371000, which represents
the radius of the Earth in meters.
2. The code then defines two helper functions: "toDegrees" and
"toRadians". The "toDegrees" function takes longitude and latitude
values as input and converts them from radians to degrees. It
multiplies the latitude and longitude values by 180 divided by
Math.PI to convert them to degrees. It then returns an array
containing the converted longitude and latitude values.
3. The "toRadians" function takes a value in degrees as input and
converts it to radians. It multiplies the input value by Math.PI
divided by 180 and returns the result as an ee.Number object. Next,
the code exports two functions: "computeDisplacement" and
"computeBearing".
4. The "computeDisplacement" function takes longitude, latitude, theta
(bearing angle), and distance as input. It first converts the
latitude and longitude values to radians using the "toRadians"
function. The theta and distance values are converted to ee.Number
objects.
5. The function then calculates the delta value by dividing the
distance by the Earth's radius.
6. It then calculates the latitude components of the new coordinates
using trigonometric functions. The latitude left component is
obtained by multiplying the sine of latitude by the cosine of delta.
The latitude right component is obtained by multiplying the cosine
of latitude by the sine of delta and the cosine of theta. The
latitude components are added together and passed through the
arcsine function to obtain the new latitude value.
7. Next, the function calculates the longitude components of the new
coordinates. The longitude Y component is obtained by multiplying
the sine of theta by the sine of delta and the cosine of latitude.
The longitude X component is obtained by subtracting the sine of
latitude multiplied by the sine of the new latitude from the cosine
of delta. The longitude components are then used to calculate the
new longitude value using the atan2 function.
8. Finally, the function calls the "toDegrees" function to convert the
new longitude and latitude values from radians to degrees and
returns the result.
9. The "computeBearing" function takes four arguments: the longitude
and latitude values of two points. It first converts the latitude
and longitude values to radians using the "toRadians" function.
10. The function then calculates the delta longitude by subtracting the
first longitude from the second longitude.
11. Next, the function calculates the y component of the bearing vector
by multiplying the sine of the delta longitude by the cosine of the
second latitude.
12. The function then calculates the x component of the bearing vector
using trigonometric functions. It subtracts the product of the sine
of the first latitude and the cosine of the second latitude
multiplied by the cosine of the delta longitude from the product of
the cosine of the first latitude and the sine of the second
latitude.
13. Finally, the function uses the atan2 function to calculate the
bearing angle from the x and y components and returns the result.
<hr>
#### **computeDisplacement**
This function calculates the displacement between two geographic
coordinates using Earth Engine.
``` javascript
const computeDisplacement = (lng, lat, theta, distance) => {
lat = toRadians(lat);
lng = toRadians(lng);
theta = ee.Number(theta);
distance = ee.Number(distance);
const delta = distance.divide(EARTHS_RADIUS);
const latLeft = lat.sin().multiply(delta.cos());
const latRight = lat.cos().multiply(delta.sin()).multiply(theta.cos());
const newLat = latLeft.add(latRight).asin();
const lngY = theta.sin().multiply(delta.sin()).multiply(lat.cos());
const lngX = delta.cos().subtract(lat.sin().multiply(newLat.sin()));
const newLng = lng.add(lngX.atan2(lngY));
return toDegrees(newLng, newLat);
};
```
##### **Inputs:**
\- lng: The longitude of the starting point. - lat: The latitude of the
starting point. - theta: The bearing angle in degrees. - distance: The
distance to travel in meters.
##### **Output:**
\- An array containing the longitude and latitude of the new location.
##### **Use**
\- Call the function with the starting longitude, latitude, bearing
angle, and distance as inputs. - The function will return an array with
the longitude and latitude of the new location.
``` javascript
computeDisplacement(10, 20, 45, 1000);
```
<hr>
#### **computeBearing**
This function calculates the bearing angle between two geographic
coordinates using Earth Engine.
``` javascript
const computeBearing = (lng1, lat1, lng2, lat2) => {
lat1 = toRadians(lat1);
lat2 = toRadians(lat2);
lng1 = toRadians(lng1);
lng2 = toRadians(lng2);
const deltaLng = lng2.subtract(lng1);
const y = deltaLng.sin().multiply(lat2.cos());
const rightTerm = lat1.sin().multiply(lat2.cos()).multiply(deltaLng.cos());
const x = lat1.cos().multiply(lat2.sin()).subtract(rightTerm);
return x.atan2(y);
};
```
##### **Inputs:**
\- lng1: The longitude of the first point. - lat1: The latitude of the
first point. - lng2: The longitude of the second point. - lat2: The
latitude of the second point.
##### **Output:**
\- The bearing angle in radians.
##### **Use**
\- Call the function with the longitude and latitude values of the two
points as inputs. - The function will return the bearing angle between
the two points.
``` javascript
computeBearing(10, 20, 30, 40);
```
<hr>
## Imagery
The code is written in JavaScript and appears to be part of a larger
application related to geospatial analysis, specifically for analyzing
satellite imagery. It uses Google Earth Engine (GEE) for processing and
analyzing geospatial data. Here is a step-by-step explanation of the
code:
1. The \`scoreCloudRatio\` function calculates the ratio of cloud
coverage in a given region. It takes a masked image with pixel
values indicating cloud presence (1 for cloud, 0 for no cloud), a
band name, and a geometry representing the region of interest. It
also takes optional parameters for scale and maximum pixels. It
first multiplies the masked image by the pixel area to get the
cloudy area, then it reduces the region by adding pixel values and
divides it by the pixel count to get the cloud ratio.
2. The \`scoreClouds\` function calculates the ratio of cloud
coverage in an image. It takes an image, a geometry, and a quality
assurance (qa) band. It creates expressions for cloud and shadow
based on bit shifting operations, applies these expressions to the
image, and calculates the ratio of cloudy area to total area. The
function then sets this ratio as a property "CLOUDS" on the image
and returns the image.
3. The \`generateLayer\` function creates a new layer from a given
image, mission, name, and optional parameters. If parameters are
undefined, it generates visualization parameters based on the
mission.
4. The \`createThumbnail\` and \`createThumbnailCSqueeze\`
functions generate a thumbnail for the given image. The functions
take an image, a geometry, parameters, and a callback function as
arguments. They apply different scaling factors and visualization
parameters based on the satellite type (Landsat or Sentinel-2)
stored in the session storage. The functions then return the
thumbnail URL.
5. The \`cumulativeWindow\` function calculates the cumulative window
of a histogram by removing up to a certain percentile of pixels in
each side of the histogram's buckets. It takes a full histogram and
a percentile as arguments and returns the bounds (min/max) of the
cumulative window.
Overall, the code is designed to process and analyze satellite imagery,
specifically for cloud coverage and creating thumbnails of the images.
<hr>
### **scoreCloudRatio**
This function calculates the ratio of the sum of cloud pixels to the
total area of a given geometry.
``` javascript
export const scoreCloudRatio = (
masked,
bandName,
geometry,
scale = 30,
maxPixels = 1e12
) => {
const cloudyArea = masked.multiply(ee.Image.pixelArea());
const cloudClassSum = cloudyArea.reduceRegion({
reducer: ee.Reducer.sum(),
scale: scale,
maxPixels: maxPixels,
geometry: geometry,
});
return ee.Number(cloudClassSum.get(bandName)).divide(geometry.area());
};
```
##### **Inputs:**
\- masked: The input image with cloud masking applied. - bandName: The
name of the band to calculate the cloud sum for. - geometry: The
geometry to calculate the cloud sum within. - scale (optional): The
scale at which to reduce the image. Default is 30. - maxPixels
(optional): The maximum number of pixels to reduce. Default is 1e12.
##### **Output:**
\- A number representing the ratio of the sum of cloud pixels to the
total area of the geometry.
##### **Use**
To use the scoreCloudRatio function, follow these steps: - Provide the
required input parameters: masked, bandName, and geometry. - Optionally,
specify the scale and maxPixels parameters if needed. - Call the
function by passing the input parameters. - The function will return the
calculated cloud ratio.
``` javascript
scoreCloudRatio(masked, bandName, geometry);
```
<hr>
### **scoreClouds**
This function calculates the ratio of cloudy pixels to the total area of
a given geometry in an input image. It takes three parameters: image,
geometry, and qa. The function uses the specified quality assessment
(QA) band to filter out cloudy and shadow pixels using the C2 algorithm.
It then calculates the area of the remaining cloudy pixels and divides
it by the area of the input geometry to obtain the cloudy pixel ratio.
The function returns the input image with the cloudy pixel ratio added
as a property named "CLOUDS".
``` javascript
export const scoreClouds = (image, geometry, qa) => {
const cloud = "((b(0) >> 3) & 1)"; //C2
const shadow = "((b(0) >> 4) & 1)"; //C2
const expr = `(${cloud} || ${shadow})` ;
const filtered = image.select(qa).expression(expr);
const imageArea = filtered.multiply(ee.Image.pixelArea());
const res = imageArea.reduceRegion({
reducer: ee.Reducer.sum(),
scale: 1000,
maxPixels: 1e9,
geometry: geometry,
});
const cloudyArea = res.get(qa);
const ratio = ee.Number(cloudyArea).divide(geometry.area(1));
return image.set("CLOUDS", ratio);
};
```
##### **Inputs:**
\- image: The input image to calculate the cloudy pixel ratio for. -
geometry: The geometry to calculate the cloudy pixel ratio within. - qa:
The name of the quality assessment (QA) band to use for filtering cloudy
and shadow pixels.
##### **Output:**
\- An image with the cloudy pixel ratio added as a property named
"CLOUDS".
##### **Use**
To use the scoreClouds function, follow these steps: - Provide the
required input parameters: image, geometry, and qa. - Call the function
by passing the input parameters. - The function will calculate the
cloudy pixel ratio using the C2 algorithm and the specified QA band. -
The result will be an image with the cloudy pixel ratio added as a
property named "CLOUDS".
``` javascript
scoreClouds(image, geometry, qa);
```
<hr>
### **generateLayer**
This function generates a layer using an input image, mission, name, and
parameters. If the params parameter is undefined, the function calls the
generateVisualizationParams function to generate default visualization
parameters based on the mission. The function then creates a new Layer
object using the input image, name, and params, and returns it.
``` javascript
export const generateLayer = (image, mission, name, params) => {
if (params=== undefined) {
console.log("params is undefined", params);
params = generateVisualizationParams(mission);
}
return new Layer(image, name, params);
};
```
#### **Inputs:**
\- image: The input image to generate the layer from. - mission: The
mission associated with the layer. - name: The name of the layer. -
params: Optional visualization parameters for the layer. If not
provided, default parameters will be generated based on the mission.
#### **Output:**
\- layer: A new Layer object generated using the input image, name, and
params.
#### **Use**
To use the generateLayer function, follow these steps: - Provide the
required input parameters: image, mission, and name. - Optionally
provide the params parameter for custom visualization parameters. - Call
the function by passing the input parameters. - If the params parameter
is not provided, the function will generate default visualization
parameters based on the mission. - The function will create a new Layer
object using the input image, name, and params (or default
parameters). - The result will be the generated layer.
``` javascript
generateLayer(image, mission, name, params);
```
<hr>
### **createThumbnail**
This function is used to create a thumbnail image based on the input
image, geometry, parameters, and callback function. The function first
checks the selected satellite from the session storage. If the satellite
is "Landsat", it applies scaling factors to the image using the
applyScaleFactors function. It then defines a visualization object with
the specified bands, minimum value, and maximum value. The function
visualizes the image using the visualization parameters and returns the
thumbnail URL. If the selected satellite is not "Landsat", the function
generates a thumbnail using generic code for LANDSAT COL1 and SENTINEL
satellites. The generation parameters are defined based on the input
geometry, format, dimensions, and additional parameters. The function
checks the date of the image and applies specific generation parameters
if the image belongs to the Sentinel-2 satellite and the date is after
February 1, 2022. The function then returns the thumbnail URL.
``` javascript
export const createThumbnail = (image, geometry, params, callback) => {
//code for LANDSAT COLLECTION 2
let satellite = window.sessionStorage.getItem("selectedSatellite");
if (satellite === "Landsat") {
//if(image.getInfo().bands[0].id.indexOf("SR") != -1){
// Applies scaling factors.
function applyScaleFactors(image) {
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
return image.addBands(opticalBands, null, true).addBands(thermalBands, null, true);
}
// image = applyScaleFactors(image);
console.log("bands thumbnail", params.bands, "image", image, image.getInfo());
var visualization = {
bands: params.bands,//['SR_B4', 'SR_B3', 'SR_B2']
min: 0.0,
max: 0.4, //0.3
};
console.log("IMG::", "param", params, "bands", params.bands, image.getInfo(), "Id()", image.id(), "Id().getInfo()", image.id, image.id().getInfo(), "properties", image.propertyNames().getInfo());
//LANDSAT- 5 (3,2,1)
var outImg = image.visualize(visualization);
//console.log("createThumbnail", "visualization", outImg.getThumbURL({region: geometry,dimensions: 512, format: 'png'}))
return outImg.getThumbURL({ region: geometry, dimensions: 512, format: 'png' }, callback);
} else {
//generic code (LANDSAT COL1, SENTINEL)
const generationParams = {
region: geometry,
format: "jpg",
dimensions: 512,
...params,
};
if (satellite === "Sentinel-2") {
let dt = new Date(image.getInfo().properties["system:time_start"].value);
let dtThreshold = new Date(2022, 1, 1);// 01.02.2022
if (dt > dtThreshold) {
generationParams.max = 4000;
generationParams.min = 1000;
return image.getThumbURL(generationParams, callback);
}
}
return image.getThumbURL(generationParams, callback);
}
};
};
```
#### **Inputs:**
\- image: The input image to create a thumbnail from. - geometry: The
geometry to define the region of interest for the thumbnail. - params:
Additional parameters for thumbnail generation. - callback: The callback
function to handle the thumbnail URL.
#### **Output:**
\- thumbnailURL: The URL of the generated thumbnail image.
#### **Use**
To use the createThumbnail function, follow these steps: - Provide the
required input parameters: image, geometry, params, and callback. -
Optionally provide additional parameters for customizing the thumbnail
generation. - Call the function by passing the input parameters. - The
function will generate a thumbnail image based on the input image,
geometry, and parameters. - The result will be the URL of the generated
thumbnail image.
``` javascript
createThumbnail(image, geometry, params, callback);
```
<hr>
### **createThumbnailCSqueeze**
*This function is used to create a thumbnail image based on the given
parameters.*
``` javascript
export const createThumbnailCSqueeze = (image, geometry, params, callback) => {
console.log("CREATING CSQUEEZE THUMBNAIL", params, geometry)
//code for LANDSAT COLLECTION 2
let satellite = window.sessionStorage.getItem("selectedSatellite");
if (satellite === "Landsat") {
//if(image.getInfo().bands[0].id.indexOf("SR") != -1){
// Applies scaling factors.
function applyScaleFactors(image) {
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
return image.addBands(opticalBands, null, true).addBands(thermalBands, null, true);
}
image = applyScaleFactors(image);
console.log("bands thumbnail", params.bands, "image", image, image.getInfo());
var visualization = {
bands: params.bands,//['SR_B4', 'SR_B3', 'SR_B2']
min: 0.0,
max: 0.3, //0.3
};
// console.log("IMG::", "param", params, "bands", params.bands, image.getInfo(), "Id()", image.id(), "Id().getInfo()", image.id, image.id().getInfo(), "properties", image.propertyNames().getInfo());
//LANDSAT- 5 (3,2,1)
var outImg = image.visualize(visualization);
//console.log("createThumbnail", "visualization", outImg.getThumbURL({region: geometry,dimensions: 512, format: 'png'}))
return outImg.getThumbURL({ region: geometry, dimensions: 512, format: 'png' }, callback);
} else {
//generic code (LANDSAT COL1, SENTINEL)
const generationParams = {
region: geometry,
format: "jpg",
dimensions: 512,
...params,
};
// if (satellite === "Sentinel-2") {
// let dt = new Date(image.getInfo().properties["system:time_start"].value);
// let dtThreshold = new Date(2022, 1, 1);// 01.02.2022
// if (dt > dtThreshold) {
// generationParams.max = 4000;
// generationParams.min = 1000;
// return image.getThumbURL(generationParams, callback);
// }
// }
return image.getThumbURL(generationParams, callback);
}
};
```
##### **Inputs:**
\- image: The image to create a thumbnail from. - geometry: The geometry
of the thumbnail. - params: Additional parameters for creating the
thumbnail. - callback: The callback function to be executed after the
thumbnail is created.
##### **Output:**
\- The URL of the created thumbnail image.
##### **Use**
\- Call the function with the required inputs to create a thumbnail
image.
``` javascript
createThumbnailCSqueeze(image, geometry, params, callback);
```
<hr>
### **cumulativeWindow**
*Calculates the cumulative window of the \*fullHistogram\* by removing
up to \*percentile\* pixels in each side of the histogram's buckets.*
``` javascript
export const cumulativeWindow = (fullHistogram, percentile) => {
/* Cast to dictionary and retrieve histogram and buckets */
const cast = ee.Dictionary(fullHistogram);
const histogram = ee.List(cast.get("histogram"));
const buckets = ee.List(cast.get("bucketMeans"));
const pixelCount = ee.Number(histogram.reduce(ee.Reducer.sum()));
const cumulativeCut = pixelCount.multiply(percentile);
/*
* lowerBound returns the min level where cumulative sum
* would have exceeded the percentile threshold
*/
const lowerBound = (histogram, buckets) => {
const cumulativeMin = ee.List.sequence(
0,
buckets.size().subtract(1)
).iterate(
(idx, acc) => {
const ctx = ee.Dictionary(acc);
const sum = ctx.getNumber("sum").add(histogram.getNumber(idx));
const min = ee.Algorithms.If(
sum.gt(cumulativeCut),
ctx.getNumber("min"),
buckets.getNumber(idx)
);
return ee.Dictionary({ min, sum });
},
{ min: buckets.getNumber(0), sum: 0 }
);
return ee.Dictionary(cumulativeMin).getNumber("min");
};
/* Calculate the bound of each side */
const lower = lowerBound(histogram, buckets);
const upper = lowerBound(histogram.reverse(), buckets.reverse());
return ee.List([lower, upper]);
};
```
##### **Inputs:**
\- fullHistogram: An ee.Dictionary representing the full histogram. -
percentile: A number or ee.Number representing the percentile to remove
from each side of the histogram.
##### **Output:**
\- An ee.List representing the cumulative window bounds (min/max).
##### **Use**
\- Call the function with the required inputs to calculate the
cumulative window.
``` javascript
cumulativeWindow(fullHistogram, percentile);
```
<hr>
## Satellite-Landsat
The code provided is a collection of functions related to acquiring and
processing satellite imagery data. Here is a step-by-step explanation of
the code:
1. The code imports necessary modules and functions from other files.
2. The function \`addGridPosition\` takes an image element as input and
adds a property called "GRID_POSITION" to the image. The
"GRID_POSITION" is computed based on the "WRS_PATH" and "WRS_ROW"
properties of the image.
3. The function \`sliceByRevisit\` takes a collection, a starting date,
and a number of days as input. It filters the collection based on
the date range specified by the starting date and the number of
days.
4. The function \`acquireFromDate\` takes a date, a mission, and a
geometry as input. It uses the \`sliceByRevisit\` function to filter
the image collection based on the mission and geometry. It then
creates a mosaic of the filtered images and clips it to the
specified geometry. The resulting image is then passed to the
\`scoreClouds\` function along with the geometry and the mission's
QA bands to compute a cloud score for the image.
5. The function \`processCollection\` takes a mission and a geometry as
input. It retrieves the image collection for the mission and filters
it based on the geometry. It then performs several operations on the
filtered collection, including retrieving the globally extreme
dates, computing the grid position of each image, filtering images
based on the northeasternmost grid position, computing the
difference in days between the earliest image in the northeastern
position and the globally earliest image, and generating slices of
images based on complete orbital cycles. Finally, it returns a list
of valid dates within the region.
6. The function \`generateCloudMap\` takes a list of dates, a mission,
and a geometry as input. It iterates over the list of dates and
calls the \`acquireFromDate\` function to acquire the corresponding
image for each date. It then retrieves the "CLOUDS" property of each
image and returns a list of cloud values.
7. The function \`queryAvailable\` takes a mission as input and returns
a function that takes a geometry as input. It calls the
\`processCollection\` function to retrieve the image collection for
the mission and filters it based on the geometry. It then formats
the dates as ISO standard formatting and UTC and generates a cloud
map using the \`generateCloudMap\` function. Finally, it returns a
dictionary with the dates as keys and the corresponding cloud values
as values.
8. The functions \`getAvailable\` and \`acquire\` are placeholders and
currently do not have any implementation.
9. The function \`format\` takes a dictionary of properties as input
and returns a formatted string that includes the "system:time_start"
and "cloud" properties.
10. The code exports an object with several functions as properties,
including \`queryAvailable\` , \`getAvailable\` , \`acquire\` , and
\`format\` .
<hr>
### **addGridPosition**
*This function adds the grid position to an element. It takes an element
as input and returns an image with the grid position added.*
``` javascript
const addGridPosition = (element) => {
const image = ee.Image(element);
const rawPosition = {
path: image.get("WRS_PATH"),
row: image.get("WRS_ROW"),
};
const position = ee
.Number(rawPosition.path)
.multiply(100)
.add(ee.Number(rawPosition.row));
return image.set({ [Metadata.GRID_POSITION]: position });
};
```
#### **Inputs:**
*- element: The element to which the grid position will be added.*
#### **Output:**
*- image: The image with the grid position added.*
#### **Use**
*To use this function, follow these steps:* - Call the addGridPosition
function and pass the element as an argument. - The function will create
an image from the element. - It will retrieve the WRS_PATH and WRS_ROW
properties from the image. - The function will calculate the grid
position by multiplying the path by 100 and adding the row. - Finally,
it will set the grid position as a property on the image and return the
updated image.
``` javascript
addGridPosition(element);
```
<hr>
### **sliceByRevisit**
*This function slices a collection of images based on a starting date
and a number of days. It takes three parameters: collection,
startingDate, and days. The function filters the collection to include
only the images within the specified date range.*
``` javascript
const sliceByRevisit = (collection, startingDate, days) => {
const start = ee.Date(startingDate).update(null, null, null, 0, 0, 0);
const end = start.advance(days, "day");
return collection.filterDate(start, end);
};
```
#### **Inputs:**
\- collection: The collection of images to be sliced. - startingDate:
The starting date of the slice. - days: The number of days to include in
the slice.
#### **Output:**
\- collection: The sliced collection of images.
#### **Use**
\- Provide a collection of images, a starting date, and the number of
days. - Call the sliceByRevisit function, passing in the collection,
startingDate, and days as input parameters. - The function will convert
the startingDate to an ee.Date object and set the time to 00:00:00. - It
will then calculate the end date by advancing the startingDate by the
specified number of days. - The function will filter the collection to
include only the images within the specified date range. - The sliced
collection will be returned as the output.
``` javascript
sliceByRevisit(collection, startingDate, days);
```
<hr>
### **acquireFromDate**
*This function acquires an image from a specific date and mission,
within a given geometry. It takes three parameters: date, mission, and
geometry. The function first slices the image collection based on the
provided date and mission using the sliceByRevisit function. It then
mosaics the sliced images and clips the result to the specified
geometry. The function sets the properties of the mosaicked image by
merging the properties of the sliced images. Finally, it applies the
scoreClouds function to the image using the provided geometry and
mission bands.qa, and returns the resulting image.*
``` javascript
export const acquireFromDate = (date, mission, geometry) => {
const slice = sliceByRevisit(
ee.ImageCollection(mission.name).filterBounds(geometry),
date,
mission.cycle
);
const mosaicked = slice.mosaic().clip(geometry);
const image = mosaicked.set(mergeProperties(slice));
return scoreClouds(image, geometry, mission.bands.qa);
};
```
#### **Inputs:**
\- date: The specific date from which to acquire the image. - mission:
An object containing information about the mission, including its name,
cycle, and bands.qa. - geometry: The geometry within which to acquire
the image.
#### **Output:**
\- image: The acquired image with clouds scored, clipped to the
specified geometry, and with merged properties from the sliced images.
#### **Use**
\- Provide a specific date, a mission object, and a geometry. - Call the
acquireFromDate function, passing in the date, mission, and geometry as
input parameters. - The function will filter the image collection based
on the mission name and the provided geometry. - It will then slice the
collection using the sliceByRevisit function, with the date and mission
cycle as parameters. - The function will mosaic the sliced images and
clip the result to the specified geometry. - It will set the properties
of the mosaicked image by merging the properties of the sliced images. -
The function will apply the scoreClouds function to the image using the
provided geometry and mission bands.qa. - The resulting image, with
clouds scored and clipped to the geometry, will be returned as the
output.
``` javascript
acquireFromDate(date, mission, geometry);
```
<hr>
### **processCollection**
*This function processes an image collection for a specific mission and
geometry. It takes two parameters: mission and geometry. The function
first filters the image collection based on the provided mission name
and geometry using the ee.ImageCollection().filterBounds() method. It
then defines a process function that performs several operations on the
filtered image collection. The process function retrieves the globally
extreme dates (earliest and latest) using the retrieveExtremes()
function. It computes the grid position of each image in the collection
and sorts them in ascending order. The function retrieves the
northeasternmost grid position within the specified bounds. It keeps the
images in the slice where the satellite passed first (northeast) using
the filter() method. The function retrieves the extremes in the
northeastern position using the retrieveExtremes() function. It computes
the difference in days between the earliest image in the northeastern
position and the globally earliest image. The function computes the
amount of complete orbital cycles between the earliest and latest
possible dates of the images in the northeastern position. It generates
slices of 16, one for each complete cycle. The function transforms each
slice into an empty image and adds metadata related to the corresponding
orbital cycle. It keeps only the images whose combined geometries
contain the AOI using the filter() method. Finally, the function returns
a list of the dates of the valid images.*
``` javascript
export const processCollection = (mission, geometry) => {
const query = ee.ImageCollection(mission.name).filterBounds(geometry);
const process = (available) => {
const global = retrieveExtremes(available);
const enhanced = available
.map(addGridPosition)
.sort(Metadata.GRID_POSITION);
const northeasternPosition = ee
.Image(enhanced.toList(1).get(0))
.get(Metadata.GRID_POSITION);
const filtered = enhanced.filter(
ee.Filter.eq(Metadata.GRID_POSITION, northeasternPosition)
);
const northeastern = retrieveExtremes(filtered);
const difference = northeastern.earliest
.difference(global.earliest, "day")
.abs();
const remainder = ee.Number(difference).mod(mission.cycle);
const step = ee.Number(mission.cycle).subtract(remainder);
const earliestDate = global.earliest.advance(step.multiply(-1), "day");
const completeCycles = ee
.Number(earliestDate.difference(global.latest, "day").abs())
.divide(mission.cycle)
.ceil();
const additions = ee.List.sequence(0, null, mission.cycle, completeCycles);
const carriers = additions.map((increment) => {
const startingDate = earliestDate.advance(increment, "day");
const collection = sliceByRevisit(available, startingDate, mission.cycle);
const empty = ee.Algorithms.IsEqual(collection.size(), 0);
const properties = ee.Algorithms.If(
empty,
{},
mergeProperties(collection)
);
return ee.Image(0).set(properties);
});
const valid = ee.ImageCollection.fromImages(carriers).filter(
ee.Filter.contains(Metadata.FOOTPRINT, geometry)
);
return ee.List(valid.toList(valid.size()).map(getDate));
};
return ee.List(
ee.Algorithms.If(query.size().gt(0), process(query), ee.List([]))
);
};
```
#### **Inputs:**
\- mission: An object containing information about the mission,
including its name. - geometry: The geometry within which to process the
image collection.
#### **Output:**
\- A list of dates corresponding to the valid images in the processed
image collection.
#### **Use**
\- Provide a mission object and a geometry. - Call the processCollection
function, passing in the mission and geometry as input parameters. - The
function will filter the image collection based on the mission name and
the provided geometry. - It will then perform several operations on the
filtered image collection, including retrieving extreme dates, computing
grid positions, and filtering images based on the northeastern
position. - The function will generate slices and transform them into
empty images with metadata related to the orbital cycle. - It will keep
only the images whose combined geometries contain the AOI. - The
function will return a list of dates corresponding to the valid images
in the processed image collection.
``` javascript
processCollection(mission, geometry);
```
<hr>
### **generateCloudMap**
This function generates a cloud map based on the given dates, mission,
and geometry.
``` javascript
export const generateCloudMap = (dates, mission, geometry) => {
const cloudList = ee.List(dates).map((date) => {
const image = ee.Image(acquireFromDate(date, mission, geometry));
return image.get("CLOUDS");
});
return cloudList;
};
```
#### **Inputs:**
\- dates: An array of dates. - mission: The mission name. - geometry:
The geometry of the area.
#### **Output:**
\- cloudList: A list of cloud values.
#### **Use**
\- Call the function with the desired inputs to generate a cloud map.
``` javascript
generateCloudMap(dates, mission, geometry);
```
<hr>
### **queryAvailable**
This function queries the availability of data for a given mission and
geometry. It takes the mission as a parameter and returns a function
that takes the geometry as a parameter. The returned function processes
the collection based on the mission and geometry, and then generates a
dictionary with dates and corresponding cloud values.
``` javascript
const queryAvailable = (mission) => (geometry) => {
const query = processCollection(mission, geometry);
const process = (available) => {
const datesQuery = available.map((date) =>
// Format Data as ISO standart formating and and UTC
ee.Date(date).format(null, 'UTC')
);
const cloudQuery = generateCloudMap(datesQuery, mission, geometry);
return ee.Dictionary.fromLists(datesQuery, cloudQuery);
};
return ee.Dictionary(
ee.Algorithms.If(query.size().gt(0), process(query), ee.Dictionary({}))
);
};
```
#### **Inputs:**
\- mission: The name of the mission. - geometry: The geometry of the
area.
#### **Output:**
\- A dictionary containing the dates and corresponding cloud values.
#### **Use**
\- Call the queryAvailable function with the desired mission as the
input. - The function will return another function that takes the
geometry as the input. - Call the returned function with the desired
geometry. - The function will query the availability of data for the
given mission and geometry. - It will generate a dictionary with dates
formatted in ISO standard and UTC, and corresponding cloud values.
``` javascript
queryAvailable(mission)(geometry);
```
<hr>
### **acquire**
This function is used to acquire data for a specific mission, date, and
geometry. It takes the mission as a parameter and returns a function
that takes the date and geometry as parameters. The returned function
calls the acquireFromDate function to acquire data for the specified
date, mission, and geometry.
``` javascript
const acquire = (mission) => (date, geometry) => {
return acquireFromDate(date, mission, geometry);
};
```
#### **Inputs:**
\- mission: The name of the mission. - date: The specific date for which
data needs to be acquired. - geometry: The geometry of the area.
#### **Output:**
\- Data acquired for the specified mission, date, and geometry.
#### **Use**
\- Call the acquire function with the desired mission as the input. -
The function will return another function that takes the date and
geometry as inputs. - Call the returned function with the desired date
and geometry. - The function will acquire data for the specified
mission, date, and geometry using the acquireFromDate function. - The
acquired data will be returned as the output.
``` javascript
acquire(mission)(date, geometry);
```
<hr>
### **format**
This function takes a properties object as input and returns a formatted
string. The formatted string is created by concatenating the values of
the "system:time_start" and "cloud" properties of the input object,
separated by " -- ".
``` javascript
const format = (properties) => {
return properties["system:time_start"] + " -- " + properties["cloud"];
};
```
#### **Inputs:**
\- properties: An object containing the properties of the data.
#### **Output:**
\- A formatted string created by concatenating the values of the
"system:time_start" and "cloud" properties, separated by " -- ".
#### **Use**
\- Provide an object with the desired properties. - Call the format
function, passing in the properties object as an input parameter. - The
function will return a formatted string that combines the values of the
"system:time_start" and "cloud" properties.
``` javascript
format(properties);
```
<hr>
## Satellite-Sentinel
This code is a collection of functions that are used to process
satellite imagery data from Google Earth Engine. It performs various
operations such as masking clouds, acquiring images from a specific
date, and retrieving a list of valid dates for image retrieval.
`Here is a step-by-step explanation:`
`1. The code starts by importing necessary dependencies and utility functions from different modules.`
`` 2. `addGridPosition` function is defined, which adds the grid position as metadata to an image. ``
`` 3. `sliceByRevisit` function is defined, which filters the image collection based on the given date and the number of days. ``
`` 4. `maskS2Clouds` function is defined, which creates a mask for clouds in the image. ``
`` 5. `acquireFromDate` function is defined, which retrieves an image from a specific date, calculates the cloud ratio, and sets it as metadata. ``
`` 6. `processCollection` function is defined, which computes a list of valid dates for image retrieval. It retrieves the earliest and latest dates, filters images based on grid position, calculates the difference in days between the earliest images, and generates slices for each complete cycle. ``
`` 7. `generateCloudMap` function is defined, which maps the list of dates to their respective cloud ratio. ``
`` 8. `queryAvailable` and `getAvailable` functions are defined, which process the image collection and create a dictionary mapping the dates to their respective cloud ratio. ``
`` 9. `acquire` function is defined, which acquires an image from a specific date if the current URL does not contain "bathymetry". ``
`` 10. `format` function is defined, which formats the properties of an image. ``
`11. Finally, the functions are exported in an object at the end.`
<hr>
### **addGridPosition**
This function takes an element as input and returns an image with a grid
position property set. The grid position property is obtained from the
"MGRS_TILE" property of the input image.
``` javascript
const addGridPosition = (element) => {
const image = ee.Image(element);
return image.set({ [Metadata.GRID_POSITION]: image.get("MGRS_TILE") });
};
```
#### **Inputs:**
\- element: The element to be processed.
#### **Output:**
\- image: The input image with the grid position property set.
#### **Use**
To use this function, follow these steps: - Call the function with an
element as input. - The function will return an image with the grid
position property set.
``` javascript
addGridPosition(element);
```
### **sliceByRevisit**
This function filters a collection of images based on a starting date
and a number of days. It returns the filtered collection.
``` javascript
const sliceByRevisit = (collection, startingDate, days) => {
const start = ee.Date(startingDate).update(null, null, null, 0, 0, 0);
const end = start.advance(days, "day");
return collection.filterDate(start, end);
};
```
#### **Inputs:**
\- collection: The collection of images to be filtered. - startingDate:
The starting date for filtering. - days: The number of days to include
in the filtered collection.
#### **Output:**
\- collection: The filtered collection of images.
#### **Use**
To use this function, follow these steps: - Call the function with the
collection, starting date, and number of days as inputs. - The function
will return the filtered collection.
``` javascript
sliceByRevisit(collection, startingDate, days);
```
### **maskS2Clouds**
This function masks out clouds in a Sentinel-2 image using the QA60
band.
#### **Inputs:**
\- image: The Sentinel-2 image to be processed.
#### **Output:**
\- image: The input image with clouds masked out.
#### **Use**
To use this function, follow these steps: - Call the function with the
Sentinel-2 image as input. - The function will return the input image
with clouds masked out.
``` javascript
maskS2Clouds(image);
```
### **acquireFromDate**
This function acquires an image from a specific date, mission, and
geometry. It filters an image collection based on the mission name and
geometry, selects the image with the closest revisit date to the
specified date, and clips it to the geometry. It also calculates the
cloud ratio and adds it as a property to the image.
#### **Inputs:**
\- date: The date for acquiring the image. - mission: The mission object
containing the mission name and cycle. - geometry: The geometry to clip
the acquired image.
#### **Output:**
\- image: The acquired image with cloud ratio property added.
#### **Use**
To use this function, follow these steps: - Call the function with the
date, mission, and geometry as inputs. - The function will return the
acquired image with the cloud ratio property added.
``` javascript
acquireFromDate(date, mission, geometry);
```
### **processCollection**
This function computes a list of valid dates in the region to be
retrieved with acquireFromDate.
**`Inputs:`**
\- mission: The mission object containing the name of the image
collection to be processed. - geometry: The geometry object representing
the region of interest.
**`Output:`**
\- A list of valid dates in the specified region.
**`Use:`**
1\. Call the function by providing the mission object and the geometry
object as inputs. 2. The function filters the image collection based on
the specified region. 3. It retrieves the globally extreme dates
(earliest and latest) from the filtered collection. 4. The function
computes the grid position of each image and sorts them in ascending
order. 5. It retrieves the northeasternmost grid position within the
specified bounds. 6. The function keeps the images in the slice where
the satellite passed first (northeast). 7. It retrieves the extremes in
the northeastern position. 8. The function computes the difference in
days between the earliest image in the northeastern position and the
globally earliest image. 9. It computes the remainder of the difference
divided by the mission cycle. 10. The amount of days to go back is given
by subtracting the remainder from the mission cycle. 11. The function
computes the date of the earliest possible image in the northeastern
position by going back in time. 12. It computes the amount of complete
orbital cycles between the earliest and latest possible dates of the
images in the northeastern position. 13. The function generates slices
of 5, one for each complete cycle. 14. It transforms each slice into an
empty image and adds metadata related to the correspondent orbital
cycle. 15. The function keeps only the images whose combined geometries
contain the region of interest. 16. It returns a list of valid dates.
**`Use`` ``example:`**
const mission = { name: 'exampleMission' }; const geometry =
ee.Geometry.Point(\[-122.084, 37.42\]);
`const validDates = processCollection(mission, geometry);`
print(validDates);
<hr>
### **generateCloudMap**
This function generates a cloud map for a given list of dates, mission,
and geometry.
**`Inputs:`**
\- dates: A list of dates for which the cloud map needs to be
generated. - mission: The mission object containing the name of the
image collection to be processed. - geometry: The geometry object
representing the region of interest.
**`Output:`**
\- A list of cloud maps corresponding to the input dates.
**`Use:`**
1\. Call the function by providing the dates, mission, and geometry as
inputs. 2. The function creates a list of cloud maps by acquiring the
cloud information for each date using the acquireFromDate function. 3.
It returns the list of cloud maps.
**`Use`` ``example:`**
const dates = \['2021-01-01', '2021-02-01', '2021-03-01'\]; const
mission = { name: 'exampleMission' }; const geometry =
ee.Geometry.Point(\[-122.084, 37.42\]);
`const cloudMaps = generateCloudMap(dates, mission, geometry);`
print(cloudMaps);
<hr>
### **queryAvailable**
This function queries the availability of data for a given mission and
geometry.
**`Inputs:`**
\- mission: The mission object containing the name of the image
collection to be processed.
**`Output:`**
\- A dictionary with dates as keys and corresponding cloud maps as
values.
**`Use:`**
1\. Call the function by providing the mission object. 2. The function
returns a function that takes the geometry object as an input. 3. Call
the returned function by providing the geometry object. 4. The function
processes the collection for the given mission and geometry. 5. It
formats the data as ISO standard formatting and UTC. 6. The function
generates the cloud maps using the generateCloudMap function. 7. It
returns a dictionary with dates as keys and corresponding cloud maps as
values.
**`Use`` ``example:`**
const mission = { name: 'exampleMission' }; const geometry =
ee.Geometry.Point(\[-122.084, 37.42\]);
`const availability = queryAvailable(mission)(geometry);`
print(availability);
<hr>
### **getAvailable**
This function returns a dictionary of available dates and cloud cover
values for a given mission and geometry.
``` javascript
const getAvailable = (mission) => (geometry) => {
const query = processCollection(mission, geometry);
// Format Data as ISO standart formating and and UTC
const datesQuery = query.map((date) => ee.Date(date).format(null, 'UTC'));
const cloudQuery = generateCloudMap(datesQuery, mission, geometry);
return ee.Dictionary.fromLists(datesQuery, cloudQuery);
};
```
#### **Inputs:**
\- mission: The mission for which the available dates and cloud cover
values are requested. - geometry: The geometry object representing the
area of interest.
#### **Output:**
\- A dictionary where the keys are ISO-formatted dates and the values
are the corresponding cloud cover values.
#### **Use**
\- Call the function with the desired mission and geometry to retrieve
the available dates and cloud cover values.
``` javascript
getAvailable('mission')(geometry);
```
<hr>
### **acquire**
This function acquires data for a specific date, mission, and geometry.
``` javascript
const acquire = (mission) => (date, geometry) => {
if(window.location.href.indexOf("bathymetry") !== -1) return false;
console.log("PARAMS", date, mission, geometry);
return acquireFromDate(date, mission, geometry);
};
```
#### **Inputs:**
\- mission: The mission for which the data is requested. - date: The
specific date for which the data is requested. - geometry: The geometry
object representing the area of interest.
#### **Output:**
\- The acquired data for the specified date, mission, and geometry.
#### **Use**
1\. Check if the current URL contains "bathymetry". If it does, return
false. 2. Print the parameters (date, mission, geometry) to the console.
3. Call the acquireFromDate function with the specified date, mission,
and geometry to acquire the data.
``` javascript
acquire('mission')('date', geometry);
```
<hr>
### **format**
This function formats the properties of a data object.
``` javascript
const format = (properties) => {
return properties["system:time_start"] + " -- " + properties["cloud"];
};
```
#### **Inputs:**
\- properties: The properties object of a data object.
#### **Output:**
\- A formatted string containing the "system:time_start" property value
followed by a dash and the "cloud" property value.
#### **Use**
\- Call the function with the properties object to format the
properties.
``` javascript
format(properties);
```
<hr>
## Satellite-LandsatTOA
This code is written in JavaScript and uses the Google Earth Engine (EE)
library to analyze satellite imagery. It provides a series of functions
to process and analyze satellite images based on their metadata, such as
their geographical location, date, and cloud coverage.
`Here's a step-by-step explanation:`
`` 1. `addGridPosition` : This function adds a grid position to an image based on its 'WRS_PATH' and 'WRS_ROW' properties. It multiplies the path by 100 and adds the row to it, setting the result as the 'GRID_POSITION' metadata. ``
`` 2. `sliceByRevisit` : This function filters the image collection by date, returning only the images captured within a specified number of days starting from a specific date. ``
`` 3. `maskTOAClouds` : This function masks the clouds in an image. It uses bitwise operations to create a mask that identifies areas in the image where there are clouds, cloud shadows, or cirrus clouds. ``
`` 4. `acquireFromDate` : This function retrieves an image from a specified date, mission, and geometry. It mosaics the images, clips them to the specified geometry, and calculates a cloud ratio score. ``
`` 5. `processCollection` : This function processes the images in a specified mission and geometry. It retrieves the extreme dates (earliest and latest), sorts the images by their grid position, filters the images by their grid position, calculates the difference in days, and generates slices of images for each complete cycle. ``
`` 6. `generateCloudMap` : This function generates a list of cloud ratios for a list of dates. ``
`` 7. `queryAvailable` : This function queries the available images for a specified mission and geometry. It processes the collection, formats the dates, and generates a cloud map. It returns a dictionary with dates as keys and cloud ratios as values. ``
`` 8. `getAvailable` and `acquire` : These are placeholders for functions that should return available images and acquire an image from a specific date and geometry, respectively. ``
`` 9. `format` : This function formats the properties of an image. ``
`` 10. The `export default` statement exports the `queryAvailable` , `getAvailable` , `acquire` , and `format` functions so they can be used in other modules. ``
### **addGridPosition**
The function 'addGridPosition' is used to add a grid position to an
image object. It retrieves the path and row properties from the image
metadata, multiplies the path by 100 and adds the row number to create a
unique position identifier.
``` javascript
const addGridPosition = (element) => {
const image = ee.Image(element);
const rawPosition = {
path: image.get("WRS_PATH"),
row: image.get("WRS_ROW"),
};
const position = ee
.Number(rawPosition.path)
.multiply(100)
.add(ee.Number(rawPosition.row));
return image.set({ [Metadata.GRID_POSITION]: position });
};
```
#### **Inputs:**
- element: The image element that needs a grid position added.
#### **Output:**
- It returns the image with the added grid position metadata.
#### **Use**
This function is used to add a grid position to an image object. This
grid position is calculated by multiplying the path by 100 and adding
the row number. This unique position identifier is then added to the
image metadata.
``` javascript
addGridPosition(image);
```
<hr>
### **sliceByRevisit**
The function 'sliceByRevisit' filters an image collection by a given
date range. It takes in a collection of images, a starting date and a
number of days, and returns a subset of the collection that falls within
this date range.
``` javascript
const sliceByRevisit = (collection, startingDate, days) => {
const start = ee.Date(startingDate).update(null, null, null, 0, 0, 0);
const end = start.advance(days, "day");
return collection.filterDate(start, end);
};
```
#### **Inputs:**
- collection: The collection of images to be filtered.
- startingDate: The start date for the date range.
- days: The number of days for the date range.
#### **Output:**
- It returns a subset of the image collection that falls within the
specified date range.
#### **Use**
This function is used to filter an image collection by a given date
range. The starting date and the number of days form the date range. The
function returns a subset of the image collection that falls within this
date range.
``` javascript
sliceByRevisit(collection, '2022-01-01', 30);
```
<hr>
### **maskTOAClouds**
This function masks out clouds in an image based on the specified band.
``` javascript
export const maskTOAClouds = (image, bandName) => {
const band = image.select(bandName);
const cloudMask = 1 << 4,
cloudConf = 3 << 5,
shadowConf = 3 << 7,
cirrusConf = 3 << 11;
return band
.bitwiseAnd(cloudMask)
.and(
band
.bitwiseAnd(cloudConf)
.gt(1)
.or(band.bitwiseAnd(shadowConf).gt(1))
.or(band.bitwiseAnd(cirrusConf).gt(1))
);
};
```
#### **Inputs:**
\- image: The image to be processed. - bandName: The name of the band to
be used for masking.
#### **Output:**
\- A masked image with clouds removed.
#### **Use**
\- Select an image and specify the band name to be used for masking. -
Call the function with the image and band name as inputs. - The function
will return a masked image with clouds removed.
``` javascript
maskTOAClouds(image, "qa");
```
<hr>
### **acquireFromDate**
This function acquires an image from a specific date, mission, and
geometry.
``` javascript
export const acquireFromDate = (date, mission, geometry) => {
const slice = sliceByRevisit(
ee.ImageCollection(mission.name).filterBounds(geometry),
date,
mission.cycle
);
const mosaicked = slice.mosaic().clip(geometry);
const image = mosaicked.set(mergeProperties(slice));
const ratio = scoreCloudRatio(
maskTOAClouds(image, mission.bands.qa),
mission.bands.qa,
geometry
);
return image.set("CLOUDS", ratio);
};
```
#### **Inputs:**
\- date: The date of the image to be acquired. - mission: The mission
object containing the name, cycle, and bands information. - geometry:
The geometry to filter the image collection.
#### **Output:**
\- An acquired image with clouds masked and additional metadata.
#### **Use**
\- Specify the date, mission, and geometry for acquiring the image. -
Call the function with the specified inputs. - The function will return
an acquired image with clouds masked and additional metadata.
``` javascript
acquireFromDate("2022-01-01", missionObject, geometryObject);
```
<hr>
### **processCollection**
This function processes a collection of satellite images based on the
given mission and geometry.
``` javascript
export const processCollection = (mission, geometry) => {
const query = ee.ImageCollection(mission.name).filterBounds(geometry);
// Retrieve the globally extreme dates (earliest and latest)
const global = retrieveExtremes(query);
// Compute the grid position of each image and sort in ascending order
const enhanced = query.map(addGridPosition).sort(Metadata.GRID_POSITION);
// Retrieve the northeasternmost grid position within the specified bounds
const northeasternPosition = ee
.Image(enhanced.toList(1).get(0))
.get(Metadata.GRID_POSITION);
// Keep images in the slice where the satellite passed first (northeast)
const filtered = enhanced.filter(
ee.Filter.eq(Metadata.GRID_POSITION, northeasternPosition)
);
// Retrieve the extremes in the northeastern position
const northeastern = retrieveExtremes(filtered);
// Compute the difference in days between the earliest image in the
// northeastern position and the globally earliest image
const difference = northeastern.earliest
.difference(global.earliest, "day")
.abs();
const remainder = ee.Number(difference).mod(mission.cycle);
// The amount of days we need to go back is given by the reverse of the
// difference, in terms of the duration of an orbit
const step = ee.Number(mission.cycle).subtract(remainder);
// Compute the date of the earliest possible image in the northeastern
// position (whether or not it exists) by going back in time
const earliestDate = global.earliest.advance(step.multiply(-1), "day"); // 1.21 gigawatts!
// Compute the amount of complete orbital cycles between the earliest and
// latest possible dates of the images in the northeastern position (whether
// or not they exist)
const completeCycles = ee
.Number(earliestDate.difference(global.latest, "day").abs())
.divide(mission.cycle)
.ceil();
// Generate slices of 16 (0, 16, 32, 48, ...), one for each complete cycle
const additions = ee.List.sequence(0, null, mission.cycle, completeCycles);
// Transform each slice into an empty image. If the slice contains at least
// one image, we add metadata related to the correspondent orbital cycle,
// to allow for filtering later
const carriers = additions.map((increment) => {
const startingDate = earliestDate.advance(increment, "day");
const collection = sliceByRevisit(query, startingDate, mission.cycle);
const empty = ee.Algorithms.IsEqual(collection.size(), 0);
const properties = ee.Algorithms.If(empty, {}, mergeProperties(collection));
return ee.Image(0).set(properties);
});
// Keep only the images whose combined geometries contain the AOI
const valid = ee.ImageCollection.fromImages(carriers).filter(
ee.Filter.contains(Metadata.FOOTPRINT, geometry)
);
if(valid.size()===0){
console.log("Valid size is 0");
return null;
}
// For now only the date of the slice is important.
return ee.List(valid.toList(valid.size()).map(getDate));
};
```
#### **Inputs:**
\- mission: The mission object containing the name of the satellite
mission. - geometry: The geometry object defining the area of interest.
#### **Output:**
\- A list of dates representing the slices of satellite images within
the specified bounds.
#### **Use**
\- Create a mission object specifying the name of the satellite
mission. - Create a geometry object defining the area of interest. -
Call the processCollection function with the mission and geometry as
inputs. - The function will process the collection of satellite images
based on the given mission and geometry. - The function will return a
list of dates representing the slices of satellite images within the
specified bounds.
``` javascript
processCollection(mission, geometry);
```
<hr>
### **generateCloudMap**
This function generates a cloud map based on the provided dates,
mission, and geometry.
``` javascript
export const generateCloudMap = (dates, mission, geometry) => {
const cloudList = ee.List(dates).map((date) => {
const image = ee.Image(acquireFromDate(date, mission, geometry));
return image.get("CLOUDS");
});
return cloudList;
};
```
#### **Inputs:**
\- dates: A list of dates. - mission: The mission name. - geometry: The
geometry for the cloud map.
#### **Output:**
\- cloudList: A list of cloud maps.
#### **Use**
\- Call the function with the desired inputs to generate a cloud map.
``` javascript
generateCloudMap(dates, mission, geometry);
```
<hr>
### **queryAvailable**
This function queries the availability of data for a given mission and
geometry. It takes two parameters: mission and geometry. The function
first processes the collection using the processCollection function. If
the query is null, it logs a message and returns null. Otherwise, it
logs the query and its information, formats the dates in ISO standard
format and UTC, generates a cloud map using the generateCloudMap
function, and returns a dictionary with the dates and corresponding
cloud maps.
``` javascript
const queryAvailable = (mission) => (geometry) => {
const query = processCollection(mission, geometry);
if(query===null){
console.log("query is null");
return null;
}
console.log("query", query, query.getInfo());
const datesQuery = query.map((date) => ee.Date(date).format(null, 'UTC')); // Format Data as ISO standart formating and and UTC
console.log("datesQuery", datesQuery,datesQuery.getInfo());
const cloudQuery = generateCloudMap(datesQuery, mission, geometry);
return ee.Dictionary.fromLists(datesQuery, cloudQuery);
};
```
#### **Inputs:**
\- mission: The mission name. - geometry: The geometry for the query.
#### **Output:**
\- A dictionary containing the dates as keys and the corresponding cloud
maps as values.
#### **Use**
\- Provide a mission name and a geometry for the query. - Call the
queryAvailable function, passing in the mission and geometry as input
parameters. - The function will process the collection, log the query
and its information, format the dates in ISO standard format and UTC,
generate a cloud map, and return a dictionary with the dates and
corresponding cloud maps.
``` javascript
queryAvailable(mission)(geometry);
```
<hr>
### **getAvailable**
This function retrieves the available data for a given mission and
geometry. It takes two parameters: mission and geometry. The function is
currently empty and does not have any functionality implemented.
``` javascript
const getAvailable = (mission) => (geometry) => {};
```
#### **Inputs:**
\- mission: The mission name. - geometry: The geometry for the query.
#### **Output:**
\- No output parameters for this function.
#### **Use**
\- Provide a mission name and a geometry for the query. - Call the
getAvailable function, passing in the mission and geometry as input
parameters. - Currently, the function does not have any functionality
implemented.
``` javascript
getAvailable(mission)(geometry);
```
<hr>
### **acquire**
This function acquires data for a given mission, date, and geometry. It
takes three parameters: mission, date, and geometry. The function calls
the acquireFromDate function, passing in the date, mission, and
geometry, and returns the result.
``` javascript
const acquire = (mission) => (date, geometry) => {
return acquireFromDate(date, mission, geometry);
};
```
#### **Inputs:**
\- mission: The mission name. - date: The date for which to acquire the
data. - geometry: The geometry for the acquisition.
#### **Output:**
\- The result of the acquireFromDate function.
#### **Use**
\- Provide a mission name, a date, and a geometry for the acquisition. -
Call the acquire function, passing in the mission, date, and geometry as
input parameters. - The function will call the acquireFromDate function
with the provided parameters and return the result.
``` javascript
acquire(mission)(date, geometry);
```
<hr>
### **format**
This function takes a properties object as input and returns a formatted
string. The formatted string consists of the value of the
"system:time_start" property concatenated with the value of the "cloud"
property, separated by "--".
``` javascript
const format = (properties) => {
return properties["system:time_start"] + " -- " + properties["cloud"];
};
```
#### **Inputs:**
\- properties: The properties object to be formatted.
#### **Output:**
\- A formatted string.
#### **Use**
\- Provide a properties object as input. - Call the format function,
passing in the properties object. - The function will return a formatted
string, concatenating the values of the "system:time_start" and "cloud"
properties with "--" in between.
``` javascript
format(properties);
```
<hr>
### **queryAvailable**
This function is exported as part of the default export object. It is
not defined in the given code snippet.
``` javascript
export default {
queryAvailable,
getAvailable,
acquire,
format,
};
```
#### **Inputs:**
\- No input parameters for this function.
#### **Output:**
\- No output parameters for this function.
#### **Use**
\- This function is part of the default export object. - It can be
accessed by importing the default export and using the "queryAvailable"
property. - The function is not defined in the given code snippet, so
its usage is not provided.
``` javascript
import myFunctions from 'myFunctions.js';
| code = myFunctions.queryAvailable();
```
<hr>
### **getAvailable**
This function is exported as part of the default export object. It is
not defined in the given code snippet.
``` javascript
export default {
queryAvailable,
getAvailable,
acquire,
format,
};
```
#### **Inputs:**
\- No input parameters for this function.
#### **Output:**
\- No output parameters for this function.
#### **Use**
\- This function is part of the default export object. - It can be
accessed by importing the default export and using the "getAvailable"
property. - The function is not defined in the given code snippet, so
its usage is not provided.
``` javascript
import myFunctions from 'myFunctions.js';
| code = myFunctions.getAvailable();
```
<hr>
### **acquire**
This function is exported as part of the default export object. It is
not defined in the given code snippet.
``` javascript
export default {
queryAvailable,
getAvailable,
acquire,
format,
};
```
#### **Inputs:**
\- No input parameters for this function.
#### **Output:**
\- No output parameters for this function.
#### **Use**
\- This function is part of the default export object. - It can be
accessed by importing the default export and using the "acquire"
property. - The function is not defined in the given code snippet, so
its usage is not provided.
``` javascript
import myFunctions from 'myFunctions.js';
| code = myFunctions.acquire();
```
<hr>
## Shoreline
This JavaScript code is part of a larger application that uses Google
Earth Engine to analyze satellite imagery. Specifically, this script is
designed to identify, analyze, and extract shorelines from the images.
Here's a step-by-step breakdown:
1. The code starts by importing various modules and functions from
other parts of the application. These include Earth Engine service,
geodesy computations, metadata handling, utility functions, and
Material UI icons.
2. The \`identifyWaterFeature\` function is defined. This function
partitions a raster image using a water index and a threshold
function, then reduces the output raster to a vector, which
corresponds to the water body in the image.
3. The \`removeShorelineNoise\` function is defined. This function
removes noises from the shoreline by keeping only the polygons that
have the most intersections with the transects.
4. The \`gaussianKernel\` function is defined. This function creates a
Gaussian kernel controlled by size, mean, and sigma.
5. The \`linearGaussianFilter\` function is defined. This function
applies a linear Gaussian filter to a list of coordinates, producing
local averages weighted by a Gaussian kernel.
6. The \`thresholdingAlgorithm\` function is defined.
7. The function \`expandCoastline\` takes in the \`coordinates\` and
\`distance\` parameters.
8. The \`coordinates\` parameter is converted to an \`ee.List\` object.
9. The \`neighbors\` variable is created by extracting all elements
from the \`coordinates\` list except the first one.
10. The \`displacedSegments\` variable is created by mapping over the
\`coordinates\` and \`neighbors\` lists. For each pair of
coordinates, the function \`computeBearing\` is called to calculate
the bearing between the current and neighbor coordinates. Then, the
function \`computeDisplacement\` is called twice to calculate the
new displaced points ( \`p1\` and \`p2\` ) based on the bearing and
distance.
11. The \`rectifyJunctions\` function is called with the
\`displacedSegments\` and \`coordinates\` as parameters. This
function normalizes the junctions of the segments by calculating the
angles and lengths between adjacent segments.
12. The \`combined\` variable is created by reversing the
\`coordinates\` list, splicing the \`displacedSegments\` at the
beginning, and creating a new list.
13. Finally, the function returns an \`ee.Feature\` object with a
\`Polygon\` geometry created from the \`combined\` list.
`` The `rectifyJunctions` function performs the following steps: ``
1. The function takes in the \`segments\` and \`originalCoordinates\`
parameters.
2. The \`segments\` parameter is converted to an \`ee.List\` object.
3. The \`originalCoordinates\` parameter is sliced to remove the first
element.
4. The \`neighbors\` variable is created by extracting all elements
from the \`segments\` list except the first one.
5. The \`processed\` variable is created by mapping over the indices of
the \`neighbors\` list. For each index, the function calculates the
angles and lengths between the current segment, neighbor segment,
and original coordinates. The centroid of the displacement is then
calculated using the \`computeDisplacement\` function.
6. The first and last coordinates of the segments are extracted and
added to the \`processed\` list.
7. The \`processed\` list is returned as the result.
<hr>
### **identifyWaterFeature**
This function identifies water features in an image by performing a
thresholding operation. It returns a binary image with water features
identified.
#### **Inputs:**
No input parameters for this function.
#### **Output:**
- Binary image with water features identified.
#### **Use**
- Load an image
- Call the identifyWaterFeature() function passing the image as
argument.
``` javascript
var image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318');
var water = identifyWaterFeature(image);
```
### **removeShorelineNoise**
This function removes noise from a shoreline geometry by performing
morphological closing and simplification.
#### **Inputs:**
- Shoreline geometry
- Kernel size for morphological closing
- Tolerance for geometry simplification
#### **Output:**
- Shoreline geometry with noise removed
#### **Use**
- Obtain a shoreline geometry
- Call the removeShorelineNoise() function passing the geometry and
kernel size and tolerance as arguments.
``` javascript
var shoreline = /* Obtain shoreline geometry */;
var cleaned = removeShorelineNoise(shoreline, 3, 10);
```
### **gaussianKernel**
This function creates a Gaussian kernel.
#### **Inputs:**
- Kernel size
- Standard deviation
- Kernel type (square or circle)
#### **Output:**
- Gaussian kernel
#### **Use**
- Call the gaussianKernel() function passing kernel size, standard
deviation and kernel type as arguments.
``` javascript
var kernel = gaussianKernel(5, 2, 'square');
```
### **linearGaussianFilter**
This function performs Gaussian filtering on a line geometry.
#### **Inputs:**
- Line geometry
- Kernel size
- Standard deviation
- Kernel type (square or circle)
#### **Output:**
- Smoothed line geometry
#### **Use**
- Obtain a line geometry
- Call the linearGaussianFilter() function passing the geometry,
kernel size, standard deviation and kernel type as arguments.
``` javascript
var line = /* Obtain line geometry */;
var smoothed = linearGaussianFilter(line, 5, 2, 'square');
```
### **thresholdingAlgorithm**
This function performs thresholding on an image to extract shorelines.
It has options for single thresholding, multi-Otsu thresholding and
interactive thresholding.
#### **Inputs:**
- Image
- Thresholding method (single, multiOtsu or interactive)
- Threshold value (for single thresholding)
#### **Output:**
- Binary image with shorelines extracted
#### **Use**
- Load an image
- Call the thresholdingAlgorithm() function passing the image,
thresholding method and threshold value (if single thresholding) as
arguments.
``` javascript
var image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318');
var binary = thresholdingAlgorithm(image, 'single', 0.2);
```
### **selectThreshold**
This function allows the user to interactively select a threshold value
from an image histogram.
#### **Inputs:**
- Image
#### **Output:**
- Selected threshold value
#### **Use**
- Load an image
- Call the selectThreshold() function passing the image as argument.
- The function will display the image histogram and allow the user to
select a threshold.
``` javascript
var image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318');
var threshold = selectThreshold(image);
```
### **extractShoreline**
This function extracts the shoreline from an image using the selected
thresholding method.
#### **Inputs:**
- Image
- Thresholding method (single, multiOtsu or interactive)
- Threshold value (for single thresholding)
- Buffer distance
- Simplification tolerance
#### **Output:**
- Shoreline geometry
#### **Use**
- Load an image
- Select a threshold value (using selectThreshold() for interactive
method)
- Call the extractShoreline() function passing the image, thresholding
method, threshold value, buffer distance and simplification
tolerance as arguments.
``` javascript
var image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318');
var threshold = 0.2; // Selected using selectThreshold()
var shoreline = extractShoreline(image, 'single', threshold, 10, 5);
```
### **deriveShoreline**
This function derives the shoreline for a single image.
#### **Inputs:**
- Image
- Satellite (Landsat or Sentinel-2)
- Region of interest
- Kernel size for morphological closing
- Simplification tolerance
#### **Output:**
- Shoreline feature
#### **Use**
- Load an image
- Define a region of interest
- Call the deriveShoreline() function passing the image, satellite,
region of interest, kernel size and simplification tolerance as
arguments.
``` javascript
var image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318');
var roi = /* Define region of interest */;
var shoreline = deriveShoreline(image, 'Landsat', roi, 3, 10);
```
### **deriveShorelines**
This function derives shorelines for multiple images.
#### **Inputs:**
- List of images
- Satellite (Landsat or Sentinel-2)
- Region of interest
- Kernel size for morphological closing
- Simplification tolerance
#### **Output:**
- FeatureCollection containing shorelines for all images
#### **Use**
- Load a list of images
- Define a region of interest
- Call the deriveShorelines() function passing the list of images,
satellite, region of interest, kernel size and simplification
tolerance as arguments.
``` javascript
var images = [/* List of images */];
var roi = /* Define region of interest */;
var shorelines = deriveShorelines(images, 'Landsat', roi, 3, 10);
```
### **expandCoastline**
This function expands a shoreline by a specified distance.
#### **Inputs:**
- Shoreline geometry
- Expansion distance
#### **Output:**
- Expanded shoreline geometry
#### **Use**
- Obtain a shoreline geometry
- Call the expandCoastline() function passing the shoreline geometry
and expansion distance as arguments.
``` javascript
var shoreline = /* Obtain shoreline geometry */;
var expanded = expandCoastline(shoreline, 10);
```
### **rectifyJunctions**
This function rectifies junctions in a MultiLineString geometry.
#### **Inputs:**
- MultiLineString geometry
#### **Output:**
- MultiLineString geometry with junctions rectified
#### **Use**
- Obtain a MultiLineString geometry
- Call the rectifyJunctions() function passing the geometry as
argument.
``` javascript
var mls = /* Obtain MultiLineString geometry */;
var rectified = rectifyJunctions(mls);
```
## Utils
This code contains several utility functions that perform various tasks
related to image processing and data manipulation. Here is a
step-by-step explanation of each function:
1\. \`addGridPosition\`: This function takes a satellite object and
returns a function that adds a grid position property to an image. It
computes the grid position using the \`computeGridPosition\` method of
the satellite object and sets it as a property of the image.
2\. \`getDate\`: This function takes an image and returns the date of
the image using the \`TIME_START\` metadata property.
3\. \`mergeFootprints\`: This function takes a collection of images and
merges their footprints into a single geometry using the \`geometry\`
method of the \`ee.FeatureCollection\` class.
4\. \`retrieveExtremes\`: This function takes a collection of images and
retrieves the earliest and latest images in the collection based on
their start time. It sorts the collection by start time, converts it to
a list, and returns the dates of the earliest and latest images.
5\. \`mergeProperties\`: This function takes a collection of images and
merges their properties into a single object. It retrieves the date of
the earliest image in the collection, merges the footprints of all
images, and sets the sensor ID property.
6\. \`combineReducers\`: This function takes multiple reducers and
combines them into a single reducer using the \`combine\` method. It
iterates over the reducers and combines them one by one.
7\. \`serializeList\`: This function takes a subject (typically a list)
and serializes it into a string representation.
8\. \`generateVisualizationParams\`: This function takes a mission
object and generates visualization parameters for the mission. It
extracts the red, green, and blue bands from the mission's bands
property and combines them with the mission's visualization parameters.
9\. \`evaluate\`: This function takes a query object and returns a
function that can be used to evaluate the query.
10\. \`applyExpression\`: This function applies an expression or label
to an image. It searches for the corresponding index in the list of
available indices and applies the expression to the image using the
specified bands.
11\. \`imageToKey\`: This function converts an image object into a
string key by picking the "id" and "version" properties and serializing
them as a JSON string.
12\. \`getSatelliteCollection\`: This function takes a satellite object
and returns the corresponding image collection. If the collection set
property is an array, it selects the first element as the image
collection.
13\. \`simplify\`: This function takes an amount parameter and returns a
function that simplifies a feature geometry by reducing the number of
vertices.
These utility functions can be used in various image processing and data
manipulation tasks to simplify and streamline the code.
### **addGridPosition**
This function adds a grid position property to each image in a
collection.
#### **Inputs:**
\- satellite: The satellite object used to compute the grid position.
#### **Output:**
\- Returns a function that takes an element (image) and sets the grid
position property on the image.
#### **Use**
1\. Define the satellite object. 2. Create the addGridPosition function
by calling the addGridPosition function and passing the satellite
object. 3. Apply the addGridPosition function to a collection of images
using the map function.
``` javascript
const satellite = ...
const addGridPositionFunc = addGridPosition(satellite);
const collectionWithGridPosition = imageCollection.map(addGridPositionFunc);
```
<hr>
### **getDate**
This function retrieves the date of an image.
#### **Inputs:**
\- image: The image for which to retrieve the date.
#### **Output:**
\- Returns the date of the image.
#### **Use**
1\. Pass an image to the getDate function. 2. The function will return
the date of the image.
``` javascript
const image = ...
const date = getDate(image);
```
<hr>
### **mergeFootprints**
This function merges the footprints of a collection of features into a
single geometry.
#### **Inputs:**
\- collection: The collection of features whose footprints to merge.
#### **Output:**
\- Returns a single geometry representing the merged footprints.
#### **Use**
1\. Pass a collection of features to the mergeFootprints function. 2.
The function will return a single geometry representing the merged
footprints.
``` javascript
const collection = ...
const mergedFootprints = mergeFootprints(collection);
```
<hr>
### **retrieveExtremes**
This function retrieves the earliest and latest dates from a collection
of images.
#### **Inputs:**
\- collection: The collection of images from which to retrieve the
extremes.
#### **Output:**
\- Returns an object with the earliest and latest dates as properties.
#### **Use**
1\. Pass a collection of images to the retrieveExtremes function. 2. The
function will return an object with the earliest and latest dates.
``` javascript
const collection = ...
const extremes = retrieveExtremes(collection);
console.log(extremes.earliest, extremes.latest);
```
<hr>
### **mergeProperties**
This function merges properties from the first image in a collection to
create a new object.
#### **Inputs:**
\- collection: The collection of images from which to merge properties.
#### **Output:**
\- Returns a new object with merged properties.
#### **Use**
1\. Pass a collection of images to the mergeProperties function. 2. The
function will merge properties from the first image and return a new
object.
``` javascript
const collection = ...
const mergedProperties = mergeProperties(collection);
console.log(mergedProperties);
```
<hr>
### **combineReducers**
This function combines multiple reducers into a single reducer.
#### **Inputs:**
\- reducers: The reducers to combine.
#### **Output:**
\- Returns a combined reducer.
#### **Use**
1\. Pass multiple reducers to the combineReducers function. 2. The
function will return a combined reducer.
``` javascript
const reducer1 = ...
const reducer2 = ...
const combinedReducer = combineReducers(reducer1, reducer2);
```
<hr>
### **serializeList**
This function serializes a list into a string.
#### **Inputs:**
\- subject: The list to serialize.
#### **Output:**
\- Returns a serialized string representation of the list.
#### **Use**
1\. Pass a list to the serializeList function. 2. The function will
return a serialized string representation of the list.
``` javascript
const list = ...
const serialized = serializeList(list);
console.log(serialized);
```
<hr>
### **generateVisualizationParams**
This function generates visualization parameters for a mission.
#### **Inputs:**
\- mission: The mission object containing band information.
#### **Output:**
\- Returns an object with generated visualization parameters.
#### **Use**
1\. Pass a mission object to the generateVisualizationParams function.
2. The function will return an object with generated visualization
parameters.
``` javascript
const mission = ...
const visualizationParams = generateVisualizationParams(mission);
console.log(visualizationParams);
```
<hr>
### **evaluate**
This function returns a function that can be used to evaluate a query.
#### **Inputs:**
\- query: The query to evaluate.
#### **Output:**
\- Returns a function that can be used to evaluate the query.
#### **Use**
1\. Pass a query to the evaluate function. 2. The function will return a
function that can be used to evaluate the query.
``` javascript
const query = ...
const evaluateFunc = evaluate(query);
console.log(evaluateFunc());
```
<hr>
### **applyExpression**
This function applies an expression or label to an image.
#### **Inputs:**
\- image: The image to apply the expression or label to. -
expressionOrLabel: The expression or label to apply. - bands: An object
containing band information.
#### **Output:**
\- Returns a new image with the applied expression or label.
#### **Use**
1\. Pass an image, expression or label, and bands object to the
applyExpression function. 2. The function will return a new image with
the applied expression or label.
``` javascript
const image = ...
const expressionOrLabel = ...
const bands = ...
const result = applyExpression(image, expressionOrLabel, bands);
console.log(result);
```
<hr>
### **imageToKey**
This function converts an image to a key string.
#### **Inputs:**
\- image: The image to convert.
#### **Output:**
\- Returns a key string representing the image.
#### **Use**
1\. Pass an image to the imageToKey function. 2. The function will
return a key string representing the image.
``` javascript
const image = ...
const key = imageToKey(image);
console.log(key);
```
<hr>
### **getSatelliteCollection**
This function retrieves the satellite collection from a satellite
object.
#### **Inputs:**
\- satellite: The satellite object.
#### **Output:**
\- Returns the satellite collection as an ImageCollection.
#### **Use**
1\. Pass a satellite object to the getSatelliteCollection function. 2.
The function will return the satellite collection as an ImageCollection.
``` javascript
const satellite = ...
const collection = getSatelliteCollection(satellite);
console.log(collection);
```
<hr>
### **simplify**
This function simplifies a feature by reducing the number of vertices.
#### **Inputs:**
\- amount: The amount of simplification to apply.
#### **Output:**
\- Returns a function that takes a feature and simplifies it.
#### **Use**
1\. Define the amount of simplification. 2. Create the simplify function
by calling the simplify function and passing the amount. 3. Apply the
simplify function to a feature using the map function.
``` javascript
const amount = ...
const simplifyFunc = simplify(amount);
const simplifiedFeature = featureCollection.map(simplifyFunc);
```
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