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Module: index.js

Sure, let's break down the provided code module index.js and analyze it comprehensively:

Module Name

  • index.js

Primary Objectives

  • The primary purpose of this module is to serve as an entry point that consolidates and re-exports components from different sub-modules (satellite, shoreline, acquisition, and utils). This allows for easier and more organized imports in other parts of the application.

Critical Functions

  • This module does not define any functions itself. Instead, it re-exports components from other modules.

Key Variables

  • There are no explicit variables defined in this module. The key elements are the imported and re-exported modules.

Interdependencies

  • Satellite: Imported from ./satellite and re-exported as the default export.
  • Shoreline: Imported from ./shoreline and re-exported as a namespace.
  • Acquisition: Imported from ./acquisition and re-exported as a namespace.
  • Utils: Imported from ./utils and re-exported as a namespace.

Core vs. Auxiliary Operations

  • Core Operations: The re-exporting of modules to provide a unified interface.
  • Auxiliary Operations: There are no auxiliary operations in this module.

Operational Sequence

  • The module imports components from specified paths and then re-exports them. The sequence is straightforward:
    1. Import Satellite from ./satellite.
    2. Import everything from ./shoreline as Shoreline.
    3. Import everything from ./acquisition as Acquisition.
    4. Import everything from ./utils as Utils.
    5. Re-export these imports.

Performance Aspects

  • The performance impact of this module is minimal as it primarily deals with re-exporting. However, the performance of the overall application can be influenced by the size and complexity of the imported modules.

Reusability

  • This module is highly reusable as it provides a centralized way to import and re-export various components. Other parts of the application can import from this module instead of importing each sub-module individually.

Usage

  • This module is used to simplify and organize imports in other parts of the application. Instead of importing Satellite, Shoreline, Acquisition, and Utils separately, other modules can import them from index.js.

Assumptions

  • The paths ./satellite, ./shoreline, ./acquisition, and ./utils are assumed to be correct and the modules they point to are assumed to exist.
  • It is assumed that the imported modules are correctly structured and exported in a way that allows for re-exporting.

In summary, index.js acts as a central hub for re-exporting various components, making the import process more streamlined and organized for other parts of the application.

Flow Diagram [via mermaid]

graph TD
    A[index.js] --> B[Satellite]
    A --> C[Shoreline]
    A --> D[Acquisition]
    A --> E[Utils]

Module: index.js

Comprehensive Analysis of the Module

Module Name:

  • index.js

Primary Objectives:

  • The primary purpose of this module is to perform geospatial calculations, specifically to compute displacement and bearing between geographical coordinates using trigonometric functions and Earth Engine (EE) services.

Critical Functions:

  1. toDegrees(lng, lat):

    • Converts longitude and latitude from radians to degrees.
    • Ensures the longitude is within the range of -180 to 180 degrees.

  2. toRadians(value):

    • Converts a numerical value from degrees to radians.

  3. computeDisplacement(lng, lat, theta, distance):

    • Calculates the new geographical coordinates after moving a certain distance in a specified direction (bearing) from an initial point.
    • Uses trigonometric functions to compute the new latitude and longitude.

  4. computeBearing(lng1, lat1, lng2, lat2):

    • Computes the bearing (direction) from one geographical coordinate to another.
    • Uses trigonometric functions to determine the angle between the two points.

Key Variables:

  • EARTHS_RADIUS:
    • Represents the Earth's radius in meters (6371000 meters).
  • ee:
    • Imported from the Earth Engine services, used for numerical operations and trigonometric functions.

Interdependencies:

  • The module relies on the Earth Engine (ee) services for numerical and trigonometric operations.
  • It does not explicitly interact with other system components but can be integrated into larger geospatial analysis systems.

Core vs. Auxiliary Operations:

  • Core Operations:
    • computeDisplacement: Main function for calculating new coordinates based on distance and bearing.
    • computeBearing: Main function for calculating the bearing between two coordinates.
  • Auxiliary Operations:
    • toDegrees: Helper function for converting radians to degrees.
    • toRadians: Helper function for converting degrees to radians.

Operational Sequence:

  1. Conversion to Radians:
    • Input values (longitude, latitude, bearing, distance) are converted to radians.
  2. Trigonometric Calculations:
    • Perform necessary trigonometric operations to compute new coordinates or bearing.
  3. Conversion to Degrees:
    • Convert the results back to degrees for the final output.

Performance Aspects:

  • The performance is largely dependent on the efficiency of the Earth Engine (ee) services.
  • Trigonometric calculations are computationally intensive but necessary for accurate geospatial computations.

Reusability:

  • The functions are designed to be reusable for any geospatial application requiring displacement or bearing calculations.
  • The modular design allows easy integration into other systems or projects.

Usage:

  • computeDisplacement(lng, lat, theta, distance):
    • Used to find the new position after moving a certain distance in a given direction from an initial point.
  • computeBearing(lng1, lat1, lng2, lat2):
    • Used to determine the direction from one point to another on the Earth's surface.

Assumptions:

  • The input coordinates are in degrees.
  • The Earth is assumed to be a perfect sphere with a radius of 6371000 meters.
  • The Earth Engine (ee) services are available and properly configured for numerical operations.

This analysis provides a detailed overview of the index.js module, highlighting its purpose, key functions, and how it fits into a larger geospatial analysis context.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Input Coordinates| B[computeDisplacement]
    A[User] -->|Input Coordinates| C[computeBearing]
    B[computeDisplacement] -->|Calls| D[toRadians]
    B[computeDisplacement] -->|Calls| E[toDegrees]
    C[computeBearing] -->|Calls| D[toRadians]
    D[toRadians] -->|Uses| F[Earth Engine Service]
    E[toDegrees] -->|Uses| F[Earth Engine Service]
    B[computeDisplacement] -->|Returns New Coordinates| G[Output]
    C[computeBearing] -->|Returns Bearing| G[Output]

Module: index.js

Comprehensive Analysis of index.js

Module Name

  • Module Name: index.js

Primary Objectives

  • Purpose: This module is designed to process satellite imagery data using Google Earth Engine (GEE). It focuses on acquiring images from specific dates, processing collections of satellite images, and generating cloud maps based on the processed data.

Critical Functions

  1. sliceByRevisit(collection, startingDate, days):

    • Role: Slices a given image collection based on a starting date and a specified number of days.

  2. acquireFromDate(date, collection, geometry):

    • Role: Acquires a mosaic image from a specific date, filters it by geometry, and scores it for cloud coverage.

  3. processCollection(satellite, geometry):

    • Role: Processes a collection of satellite images to compute valid dates for image acquisition based on the satellite's revisit cycle.

  4. generateCloudMap(dates, collection, geometry):

    • Role: Generates a list of cloud scores for a series of dates within a specified geometry.

Key Variables

  • REVISIT_DAYS: A constant set to 16, representing the revisit period of the satellite.
  • qaBand: A variable that determines the quality assessment band based on the selected satellite.

Interdependencies

  • Interactions with Other System Components:
    • ../../services/earth-engine: Provides the ee object for interacting with Google Earth Engine.
    • ../utils: Provides utility functions such as mergeProperties, addGridPosition, retrieveExtremes, and getDate.
    • ../imagery: Provides the scoreClouds function for cloud scoring.
    • ../../common/metadata: Provides metadata constants used in the module.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
  • Auxiliary Operations:
    • sliceByRevisit
    • Utility functions imported from ../utils and ../imagery

Operational Sequence

  1. Image Acquisition:
    • Use acquireFromDate to get a mosaic image for a specific date.
  2. Collection Processing:
    • Use processCollection to determine valid dates for image acquisition.
  3. Cloud Map Generation:
    • Use generateCloudMap to create a list of cloud scores for the specified dates.

Performance Aspects

  • Performance Considerations:
    • Efficient slicing of image collections using sliceByRevisit.
    • Filtering and processing of large image collections in processCollection.
    • Cloud scoring in acquireFromDate which may be computationally intensive.

Reusability

  • Adaptability for Reuse:
    • The module is highly reusable for different satellite imagery processing tasks.
    • Functions like acquireFromDate and processCollection can be adapted for different satellites and geometries.

Usage

  • How it is Used:
    • The module is used to process satellite imagery data, particularly for acquiring images from specific dates, processing collections to find valid acquisition dates, and generating cloud maps.

Assumptions

  • Assumptions Made:
    • The satellite has a fixed revisit period of 16 days (REVISIT_DAYS).
    • The selectedSatellite is stored in window.sessionStorage.
    • The utility functions and metadata constants are correctly implemented and imported.
    • The Google Earth Engine (ee) is properly configured and accessible.

This analysis provides a detailed overview of the index.js module, covering its primary objectives, critical functions, key variables, interdependencies, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|requests| B[processCollection]
    B -->|calls| C[getSatelliteCollection]
    B -->|calls| D[retrieveExtremes]
    B -->|calls| E[addGridPosition]
    B -->|calls| F[ee.Filter.eq]
    B -->|calls| G[ee.Number]
    B -->|calls| H[ee.List.sequence]
    B -->|calls| I[ee.Algorithms.IsEqual]
    B -->|calls| J[ee.Algorithms.If]
    B -->|calls| K[ee.ImageCollection.fromImages]
    B -->|calls| L[getDate]
    B -->|returns| M[Valid Dates]

    A -->|requests| N[acquireFromDate]
    N -->|calls| O[sliceByRevisit]
    N -->|calls| P[ee.ImageCollection]
    N -->|calls| Q[mergeProperties]
    N -->|calls| R[scoreClouds]
    N -->|returns| S[Processed Image]

    A -->|requests| T[generateCloudMap]
    T -->|calls| U[acquireFromDate]
    T -->|returns| V[Cloud Map]

    subgraph Utils
        C
        D
        E
        L
    end

    subgraph Imagery
        R
    end

    subgraph EarthEngine
        F
        G
        H
        I
        J
        K
        P
        Q
    end

Module: index.js

Comprehensive Analysis of the Module

Module Name

  • index.js

Primary Objectives

  • The module primarily deals with operations related to satellite imagery and Earth Engine (EE) functionalities. It provides utility functions for manipulating and analyzing satellite image collections, including grid positioning, date retrieval, footprint merging, and property merging.

Critical Functions

  1. addGridPosition(satellite)

    • Adds grid position metadata to an image based on the satellite's computed grid position.

  2. getDate(image)

    • Retrieves the date from the image metadata.

  3. mergeFootprints(collection)

    • Merges the geometries of a collection of images into a single footprint.

  4. retrieveExtremes(collection)

    • Retrieves the earliest and latest dates from a collection of images.

  5. mergeProperties(collection)

    • Merges properties of a collection of images, including the earliest date and footprint.

  6. combineReducers(...reducers)

    • Combines multiple reducers into a single reducer.

  7. serializeList(subject)

    • Serializes a list into a string format.

  8. generateVisualizationParams(mission)

    • Generates visualization parameters for a mission based on its bands.

  9. evaluate(query)

    • Binds the evaluate method to a query.

  10. applyExpression(image, expressionOrLabel, bands)

    • Applies an expression to an image using specified bands.

  11. imageToKey(image)

    • Converts an image to a key by picking specific properties.

  12. getSatelliteCollection(satellite)

    • Retrieves the image collection associated with a satellite.

  13. simplify(amount)

    • Simplifies a feature by a specified amount.

Key Variables

  • Metadata.GRID_POSITION: Used to store grid position metadata.
  • Metadata.TIME_START: Used to store the start time of an image.
  • Metadata.NAME: Used to store the name of an image.
  • Metadata.FOOTPRINT: Used to store the footprint of an image.
  • sensorKey: Key for the sensor ID property.

Interdependencies

  • lodash/pick: Used for picking specific properties from an object.
  • ../../services/earth-engine: Provides Earth Engine functionalities.
  • ../../common/metadata: Provides metadata constants.
  • ../../common/indices: Provides indices for image expressions.

Core vs. Auxiliary Operations

  • Core Operations:

    • addGridPosition
    • getDate
    • mergeFootprints
    • retrieveExtremes
    • mergeProperties
    • applyExpression
    • getSatelliteCollection

  • Auxiliary Operations:

    • combineReducers
    • serializeList
    • generateVisualizationParams
    • evaluate
    • imageToKey
    • simplify

Operational Sequence

  1. Image Processing: Functions like addGridPosition, applyExpression, and simplify are used to process individual images.
  2. Collection Operations: Functions like mergeFootprints, retrieveExtremes, and mergeProperties operate on collections of images.
  3. Utility Functions: Functions like combineReducers, serializeList, and generateVisualizationParams provide additional utility operations.

Performance Aspects

  • Efficiency: The module uses Earth Engine's efficient image and collection operations.
  • Scalability: Designed to handle large collections of satellite images.
  • Optimization: Functions like simplify and applyExpression are optimized for performance.

Reusability

  • Modular Design: Functions are designed to be reusable across different parts of the application.
  • Parameterization: Functions accept parameters that make them adaptable to different use cases.

Usage

  • Satellite Image Analysis: Used for analyzing and processing satellite images.
  • Earth Engine Integration: Integrates with Google Earth Engine for geospatial data processing.
  • Metadata Management: Manages and manipulates metadata associated with satellite images.

Assumptions

  • Earth Engine Availability: Assumes that Google Earth Engine is available and properly configured.
  • Valid Metadata: Assumes that images and collections have valid metadata.
  • Correct Band Names: Assumes that the band names provided in applyExpression are correct and available in the images.

This analysis provides a detailed overview of the index.js module, highlighting its primary objectives, critical functions, key variables, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests| B[addGridPosition]
    A[User] -->|Requests| C[getDate]
    A[User] -->|Requests| D[mergeFootprints]
    A[User] -->|Requests| E[retrieveExtremes]
    A[User] -->|Requests| F[mergeProperties]
    A[User] -->|Requests| G[combineReducers]
    A[User] -->|Requests| H[serializeList]
    A[User] -->|Requests| I[generateVisualizationParams]
    A[User] -->|Requests| J[evaluate]
    A[User] -->|Requests| K[applyExpression]
    A[User] -->|Requests| L[imageToKey]
    A[User] -->|Requests| M[getSatelliteCollection]
    A[User] -->|Requests| N[simplify]

    B -->|Uses| O[ee]
    C -->|Uses| O[ee]
    D -->|Uses| O[ee]
    E -->|Uses| O[ee]
    F -->|Uses| O[ee]
    G -->|Uses| O[ee]
    H -->|Uses| O[ee]
    I -->|Uses| O[ee]
    J -->|Uses| O[ee]
    K -->|Uses| O[ee]
    L -->|Uses| O[ee]
    M -->|Uses| O[ee]
    N -->|Uses| O[ee]

    B -->|Uses| P[Metadata]
    C -->|Uses| P[Metadata]
    D -->|Uses| P[Metadata]
    E -->|Uses| P[Metadata]
    F -->|Uses| P[Metadata]
    G -->|Uses| P[Metadata]
    H -->|Uses| P[Metadata]
    I -->|Uses| P[Metadata]
    J -->|Uses| P[Metadata]
    K -->|Uses| P[Metadata]
    L -->|Uses| P[Metadata]
    M -->|Uses| P[Metadata]
    N -->|Uses| P[Metadata]

    K -->|Uses| Q[Indices]
    L -->|Uses| R[lodash/pick]

Module: index.js

Comprehensive Analysis of the Module

Module Name

  • index.js

Primary Objectives

  • The primary purpose of this module is to perform various geospatial analyses and operations using Earth Engine (EE) and Turf.js libraries. It focuses on land cover parsing, shoreline extraction, elevation filtering, and satellite image processing.

Critical Functions

  1. landParsing(landcover, sidesCoords, centerCoord, shoreline)
    • Parses land cover data using Earth Engine.
  2. landParsingTurf(landcover, sidesCoords, centerCoord, shoreline)
    • Parses land cover data using Turf.js.
  3. extractShoreLine(baseLineCoords, waterGeometry)
    • Extracts the shoreline using Earth Engine.
  4. extractShoreLineTurf(baseLine, waterGeometry)
    • Extracts the shoreline using Turf.js.
  5. landCoversIntersections(transect, landcoversdata, shoreLine, year)
    • Computes intersections of land covers using Earth Engine.
  6. landCoversIntersectionsTurf(transect, landcoversdata, shoreLine, year)
    • Computes intersections of land covers using Turf.js.
  7. filterElevation(image, elevation, type = null, satellite = "LANDSAT")
    • Filters images based on elevation.
  8. applyScaleFactors(image)
    • Applies scale factors to satellite images.
  9. getMapBiomasClassificationsList()
    • Retrieves a list of MapBiomas classifications.
  10. getMapBiomasClassificationYear(AOI, year, elevation = 10)
    • Retrieves MapBiomas classification for a specific year.
  11. getLandCoverLabelMapBiomas(value)
    • Maps land cover values to labels.
  12. maskCloudLandsat(image, qa_band)
    • Masks clouds in Landsat images.
  13. maskCloudSentinel(image)
    • Masks clouds in Sentinel images.
  14. getImageCSqueezeInfos(missionName)
    • Retrieves metadata for a given satellite mission.
  15. applySaviBand(image, bands)
    • Applies the SAVI band to an image.
  16. applyEviBand(image, bands)
    • Applies the EVI band to an image.
  17. applyNdviBand(image, bands)
    • Applies the NDVI band to an image.
  18. applyMndwiBand(image, bands)
    • Applies the MNDWI band to an image.
  19. applyUiBand(image, bands)
    • Applies the UI band to an image.
  20. trainAndClassify(image, classificationAreas, bands, AOICoords)
    • Trains a classifier and classifies an image.
  21. getMangroves()
    • Retrieves mangrove data from the ESA WorldCover dataset.

Key Variables

  • options: Configuration for Turf.js operations.
  • landcovers: Array of land cover types.
  • lengthSide, distancesFromBaseLine, distancesFromShoreLine, landCoverIntersectPoint, landCoverBiggestSide, waterSide, waterIntersectPoint: Variables used in land cover parsing functions.

Interdependencies

  • Earth Engine (ee): Used for geospatial operations and data manipulation.
  • Turf.js (turf): Used for geospatial operations and data manipulation.
  • Satellite Data (standard as satellite): Used for satellite image processing.

Core vs. Auxiliary Operations

  • Core Operations:
    • Land cover parsing (landParsing, landParsingTurf)
    • Shoreline extraction (extractShoreLine, extractShoreLineTurf)
    • Land cover intersections (landCoversIntersections, landCoversIntersectionsTurf)
    • Elevation filtering (filterElevation)
    • Image processing (applyScaleFactors, maskCloudLandsat, maskCloudSentinel)
    • Classification (trainAndClassify)
  • Auxiliary Operations:
    • Utility functions (getMapBiomasClassificationsList, getMapBiomasClassificationYear, getLandCoverLabelMapBiomas, getImageCSqueezeInfos, getMangroves)

Operational Sequence

  1. Initialization: Import necessary libraries and define constants.
  2. Land Cover Parsing: Use landParsing or landParsingTurf to parse land cover data.
  3. Shoreline Extraction: Use extractShoreLine or extractShoreLineTurf to extract shorelines.
  4. Land Cover Intersections: Use landCoversIntersections or landCoversIntersectionsTurf to compute intersections.
  5. Elevation Filtering: Use filterElevation to filter images based on elevation.
  6. Image Processing: Apply scale factors and mask clouds using respective functions.
  7. Classification: Train and classify images using trainAndClassify.
  8. Utility Operations: Retrieve metadata, classifications, and other auxiliary data.

Performance Aspects

  • Efficiency: The module uses efficient geospatial libraries (EE and Turf.js) for large-scale data processing.
  • Scalability: Designed to handle large datasets and complex geospatial operations.

Reusability

  • Modular Design: Functions are designed to be reusable and can be easily integrated into other projects.
  • Parameterization: Functions accept parameters that allow for flexible usage in different contexts.

Usage

  • Geospatial Analysis: Used for analyzing land cover, shorelines, and elevation data.
  • Satellite Image Processing: Used for processing and classifying satellite images.
  • Environmental Monitoring: Useful for monitoring environmental changes and land cover dynamics.

Assumptions

  • Data Availability: Assumes availability of required geospatial data and satellite images.
  • Library Support: Assumes that Earth Engine and Turf.js libraries are available and properly configured.
  • Coordinate Systems: Assumes that input coordinates are in a compatible format for the operations performed.

This comprehensive analysis provides a detailed overview of the index.js module, highlighting its primary objectives, critical functions, key variables, interdependencies, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests Data| B[Main Module: index.js]
    B -->|Imports| C[Earth Engine Service]
    B -->|Imports| D[Satellites Common Module]
    B -->|Imports| E[Turf Library]
    B -->|Calls| F[landParsing Function]
    B -->|Calls| G[landParsingTurf Function]
    B -->|Calls| H[extractShoreLine Function]
    B -->|Calls| I[extractShoreLineTurf Function]
    B -->|Calls| J[landCoversIntersections Function]
    B -->|Calls| K[landCoversIntersectionsTurf Function]
    B -->|Calls| L[filterElevation Function]
    B -->|Calls| M[applyScaleFactors Function]
    B -->|Calls| N[getMapBiomasClassificationsList Function]
    B -->|Calls| O[getMapBiomasClassificationYear Function]
    B -->|Calls| P[getLandCoverLabelMapBiomas Function]
    B -->|Calls| Q[maskCloudLandsat Function]
    B -->|Calls| R[maskCloudSentinel Function]
    B -->|Calls| S[getImageCSqueezeInfos Function]
    B -->|Calls| T[applySaviBand Function]
    B -->|Calls| U[applyEviBand Function]
    B -->|Calls| V[applyNdviBand Function]
    B -->|Calls| W[applyMndwiBand Function]
    B -->|Calls| X[applyUiBand Function]
    B -->|Calls| Y[trainAndClassify Function]
    B -->|Calls| Z[getMangroves Function]

Module: index.js

Certainly! Here is a comprehensive analysis of the provided code module:

  • Module Name:

    • index.js

  • Primary Objectives:

    • The purpose of this module is to serve as an entry point that consolidates and exports functionalities from three different modules: Landsat, LandsatTOA, and Sentinel.

  • Critical Functions:

    • This module does not define any functions itself. Instead, it imports and re-exports functionalities from other modules. The critical functions would be those defined in the Landsat, LandsatTOA, and Sentinel modules.

  • Key Variables:

    • The key variables in this module are the imported modules themselves:
      • Landsat
      • LandsatTOA
      • Sentinel

  • Interdependencies:

    • This module depends on three other modules:
      • ./landsat
      • ./landsatToa
      • ./sentinel
    • It acts as a bridge to make these modules available for import in other parts of the system.

  • Core vs. Auxiliary Operations:

    • Core Operations:
      • Importing the Landsat, LandsatTOA, and Sentinel modules.
      • Exporting these modules as a single object.
    • Auxiliary Operations:
      • There are no auxiliary operations in this module as it is primarily focused on re-exporting.

  • Operational Sequence:

    • The operational sequence is straightforward:
      1. Import the Landsat, LandsatTOA, and Sentinel modules.
      2. Export these modules as properties of a default export object.

  • Performance Aspects:

    • Performance considerations for this module are minimal since it only involves importing and exporting modules. The performance impact would be more relevant in the imported modules themselves.

  • Reusability:

    • This module is highly reusable as it provides a centralized way to access multiple related modules. Any part of the system that needs to use Landsat, LandsatTOA, or Sentinel can simply import this module.

  • Usage:

    • This module is used to simplify the import process in other parts of the application. Instead of importing Landsat, LandsatTOA, and Sentinel separately, a developer can import them all at once from this module: 
      import SatelliteModules from './index';
const { Landsat, LandsatTOA, Sentinel } = SatelliteModules;

  • Assumptions:

    • It is assumed that the Landsat, LandsatTOA, and Sentinel modules are correctly implemented and available in the specified paths.
    • It is also assumed that these modules are compatible with being re-exported in this manner.

This analysis provides a detailed overview of the index.js module, its purpose, and its role within the larger system.

Flow Diagram [via mermaid]

graph TD
    A[index.js] --> B[Landsat]
    A[index.js] --> C[LandsatTOA]
    A[index.js] --> D[Sentinel]

Module: index.js

Certainly! Let's break down the provided code module based on the given criteria:

Comprehensive Analysis

Module Name

  • Module Name: index.js

Primary Objectives

  • Purpose: The primary objective of this module is to re-export functionalities from three other modules: extraction, statistics, and transects. This allows for a consolidated import interface, making it easier to manage and use these functionalities in other parts of the application.

Critical Functions

  • Main Methods/Functions:
    • The index.js file itself does not define any functions. Instead, it re-exports all the exports from the following modules:
      • extraction
      • statistics
      • transects

Key Variables

  • Essential Variables:
    • There are no variables defined in the index.js file itself. It serves purely as a re-exporting module.

Interdependencies

  • Interactions with Other System Components:
    • This module interacts with the extraction, statistics, and transects modules by re-exporting their exports. Any changes in these modules will directly affect what is available through index.js.

Core vs. Auxiliary Operations

  • Main Operations:
    • The core operation of this module is to re-export functionalities from other modules.
  • Auxiliary Operations:
    • There are no auxiliary operations in this module.

Operational Sequence

  • Distinct Flow:
    • The operational sequence is straightforward:
      1. The module imports all exports from extraction, statistics, and transects.
      2. It then re-exports them, making them available for import in other parts of the application.

Performance Aspects

  • Performance Considerations:
    • The performance impact of this module is minimal since it only re-exports functionalities. However, the performance of the overall application can be influenced by the performance of the extraction, statistics, and transects modules.

Reusability

  • Adaptability for Reuse:
    • This module is highly reusable as it provides a single point of access for multiple functionalities. Any module that needs to use functionalities from extraction, statistics, or transects can simply import from index.js.

Usage

  • How it is Used:
    • Other parts of the application can import functionalities from index.js to access the combined exports of extraction, statistics, and transects. For example: 
      import { someFunction } from './index';

Assumptions

  • Assumptions Made:
    • It is assumed that the extraction, statistics, and transects modules are correctly implemented and exported.
    • It is assumed that the consumer of this module is aware of the functionalities provided by the re-exported modules.

This analysis provides a comprehensive overview of the index.js module, detailing its purpose, structure, and usage within a larger application.

Flow Diagram [via mermaid]

graph TD
    A[index.js] --> B[extraction]
    A[index.js] --> C[statistics]
    A[index.js] --> D[transects]

Module: index.js

Comprehensive Analysis of the Module

Module Name

  • Module Name: index.js

Primary Objectives

  • Purpose: This module is designed to handle various operations related to satellite imagery, particularly focusing on cloud detection, visualization, and thumbnail generation. It leverages Google Earth Engine (GEE) for processing and analyzing geospatial data.

Critical Functions

  1. scoreCloudRatio:

    • Role: Calculates the ratio of cloud coverage in a given region by analyzing a masked image.
    • Parameters: masked, bandName, geometry, scale, maxPixels.

  2. scoreClouds:

    • Role: Scores the cloud coverage in an image based on a quality assessment (QA) band.
    • Parameters: image, geometry, qa.

  3. generateLayer:

    • Role: Generates a visualization layer for a given image.
    • Parameters: image, mission, name, params.

  4. createThumbnail:

    • Role: Creates a thumbnail image for a given satellite image and geometry.
    • Parameters: image, geometry, params, callback.

  5. createThumbnailCSqueeze:

    • Role: Similar to createThumbnail, but includes additional scaling factors for Landsat Collection 2.
    • Parameters: image, geometry, params, callback.

  6. cumulativeWindow:

    • Role: Calculates the cumulative window of a histogram by removing a specified percentile of pixels from each side.
    • Parameters: fullHistogram, percentile.

Key Variables

  • cloudyArea: Represents the area covered by clouds.
  • cloudClassSum: Sum of cloud class values in the specified region.
  • imageArea: Area of the image being analyzed.
  • res: Result of the region reduction operation.
  • visualization: Parameters for visualizing the image.
  • generationParams: Parameters for generating thumbnails.

Interdependencies

  • Interactions with Other System Components:
    • ../../services/earth-engine: Provides the ee object for interacting with Google Earth Engine.
    • ../../common/classes: Provides the Layer class for creating visualization layers.
    • ../utils: Provides utility functions like generateVisualizationParams.

Core vs. Auxiliary Operations

  • Core Operations:
    • scoreCloudRatio
    • scoreClouds
    • generateLayer
    • createThumbnail
    • createThumbnailCSqueeze
  • Auxiliary Operations:
    • cumulativeWindow

Operational Sequence

  1. Cloud Scoring:
    • Use scoreCloudRatio or scoreClouds to analyze cloud coverage.
  2. Layer Generation:
    • Use generateLayer to create a visualization layer for the image.
  3. Thumbnail Creation:
    • Use createThumbnail or createThumbnailCSqueeze to generate a thumbnail image.
  4. Histogram Analysis:
    • Use cumulativeWindow to analyze the histogram of pixel values.

Performance Aspects

  • Performance Considerations:
    • The functions involve operations on large geospatial datasets, which can be computationally intensive.
    • Parameters like scale and maxPixels are used to control the resolution and size of the data being processed, impacting performance.

Reusability

  • Adaptability for Reuse:
    • The functions are designed to be modular and can be reused in different contexts involving geospatial data analysis.
    • The use of parameters allows for flexibility in different use cases.

Usage

  • How It Is Used:
    • The module is used for processing satellite imagery, particularly for cloud detection and visualization.
    • It can be integrated into larger systems that require geospatial data analysis, such as environmental monitoring or agricultural applications.

Assumptions

  • Assumptions Made:
    • The input images are compatible with Google Earth Engine.
    • The geometry parameter accurately defines the region of interest.
    • The bandName and qa parameters correspond to valid bands in the input images.
    • The user has appropriate access to Google Earth Engine services.

This analysis provides a detailed overview of the index.js module, covering its primary objectives, critical functions, key variables, interdependencies, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests Cloud Ratio| B[scoreCloudRatio]
    A[User] -->|Requests Cloud Score| C[scoreClouds]
    A[User] -->|Requests Layer Generation| D[generateLayer]
    A[User] -->|Requests Thumbnail Creation| E[createThumbnail]
    A[User] -->|Requests CSqueeze Thumbnail Creation| F[createThumbnailCSqueeze]
    A[User] -->|Requests Cumulative Window Calculation| G[cumulativeWindow]

    B -->|Uses| H[ee]
    B -->|Uses| I[geometry]
    B -->|Uses| J[masked]
    B -->|Uses| K[bandName]

    C -->|Uses| H[ee]
    C -->|Uses| I[geometry]
    C -->|Uses| L[image]
    C -->|Uses| M[qa]

    D -->|Uses| L[image]
    D -->|Uses| N[mission]
    D -->|Uses| O[name]
    D -->|Uses| P[params]
    D -->|Uses| Q[generateVisualizationParams]

    E -->|Uses| L[image]
    E -->|Uses| I[geometry]
    E -->|Uses| P[params]
    E -->|Uses| R[callback]

    F -->|Uses| L[image]
    F -->|Uses| I[geometry]
    F -->|Uses| P[params]
    F -->|Uses| R[callback]

    G -->|Uses| S[fullHistogram]
    G -->|Uses| T[percentile]

Module: index.js

Sure, let's break down the provided code module in a comprehensive analysis:

Module Name

index.js

Primary Objectives

The primary purpose of this module is to serve as an entry point that consolidates and re-exports components from various other modules. This allows for easier and more organized imports in other parts of the application.

Critical Functions

The module itself does not define any functions. Instead, it re-exports components from other modules:

  • Satellite: Default export from the satellite module.
  • Shoreline: All exports from the shoreline module.
  • Acquisition: All exports from the acquisition module.
  • Utils: All exports from the utils module.

Key Variables

There are no variables defined in this module. It only handles exports.

Interdependencies

This module interacts with the following components:

  • satellite: Provides the default export.
  • shoreline: Provides all exports.
  • acquisition: Provides all exports.
  • utils: Provides all exports.

Core vs. Auxiliary Operations

  • Core Operations: The re-exporting of components from other modules.
  • Auxiliary Operations: There are no auxiliary operations in this module.

Operational Sequence

The operational sequence is straightforward:

  1. Import the default export from the satellite module.
  2. Import all exports from the shoreline, acquisition, and utils modules.
  3. Re-export these imports for use in other parts of the application.

Performance Aspects

  • Efficiency: The module is efficient as it only handles re-exports and does not perform any computational tasks.
  • Scalability: The module can easily scale by adding more re-exports as needed.

Reusability

  • Adaptability: This module is highly reusable as it serves as a centralized point for importing and re-exporting components. It can be easily adapted to include additional modules or remove existing ones.

Usage

  • Importing: Other parts of the application can import components from this module instead of importing from individual modules. This simplifies the import statements and improves code organization.

Assumptions

  • Module Existence: It assumes that the satellite, shoreline, acquisition, and utils modules exist and are correctly implemented.
  • Export Consistency: It assumes that the modules being imported have consistent and expected exports.

In summary, the index.js module is a centralized re-exporter that simplifies the import process for other parts of the application. It does not perform any operations itself but serves as an organizational tool to streamline code management.

Flow Diagram [via mermaid]

graph TD
    A[index.js] -->|export default| B[Satellite]
    A[index.js] -->|export *| C[Shoreline]
    A[index.js] -->|export *| D[Acquisition]
    A[index.js] -->|export *| E[Utils]

Module: index.js

Comprehensive Analysis of the Module

Module Name:

index.js

Primary Objectives:

The primary purpose of this module is to perform geospatial calculations, specifically to compute the displacement and bearing between geographical coordinates using trigonometric functions and Earth Engine services.

Critical Functions:

  1. toDegrees(lng, lat):

    • Converts longitude and latitude from radians to degrees.
    • Ensures the longitude is within the range of -180 to 180 degrees.

  2. toRadians(value):

    • Converts a numerical value from degrees to radians.

  3. computeDisplacement(lng, lat, theta, distance):

    • Calculates the new geographical coordinates after moving a certain distance in a specified direction (theta) from a starting point (lng, lat).

  4. computeBearing(lng1, lat1, lng2, lat2):

    • Computes the bearing (direction) from one geographical coordinate (lng1, lat1) to another (lng2, lat2).

Key Variables:

  1. EARTHS_RADIUS:

    • Represents the Earth's radius in meters (6371000 meters).

  2. delta:

    • Represents the angular distance traveled on the Earth's surface.

  3. latLeft, latRight, newLat:

    • Intermediate variables used in the calculation of the new latitude.

  4. lngY, lngX, newLng:

    • Intermediate variables used in the calculation of the new longitude.

  5. deltaLng, y, rightTerm, x:

    • Intermediate variables used in the calculation of the bearing.

Interdependencies:

  • The module imports the ee object from the Earth Engine service (../../services/earth-engine), which is used for numerical operations and trigonometric functions.

Core vs. Auxiliary Operations:

  • Core Operations:

    • computeDisplacement: Main function to calculate new coordinates based on displacement.
    • computeBearing: Main function to calculate the bearing between two points.

  • Auxiliary Operations:

    • toDegrees: Helper function to convert radians to degrees.
    • toRadians: Helper function to convert degrees to radians.

Operational Sequence:

  1. Conversion to Radians:
    • Input values (longitude, latitude, theta, distance) are converted to radians.
  2. Displacement Calculation:
    • Intermediate trigonometric calculations are performed to determine the new latitude and longitude.
  3. Conversion to Degrees:
    • The resulting coordinates are converted back to degrees.
  4. Bearing Calculation:
    • Trigonometric calculations are performed to determine the bearing between two points.

Performance Aspects:

  • The module relies on efficient trigonometric operations provided by the Earth Engine service, which should be performant for typical use cases involving geographical calculations.
  • Performance considerations might include the precision of floating-point operations and the efficiency of the Earth Engine service.

Reusability:

  • The functions are designed to be reusable for any application requiring geospatial calculations.
  • The modular design allows for easy integration into other systems or projects that use Earth Engine services.

Usage:

  • The module can be used in applications that require geospatial analysis, such as mapping software, navigation systems, or geographic information systems (GIS).
  • Example usage: 
    import { computeDisplacement, computeBearing } from './index.js';

const newCoords = computeDisplacement(-73.935242, 40.730610, 45, 1000);
const bearing = computeBearing(-73.935242, 40.730610, -74.935242, 41.730610);

Assumptions:

  • The input coordinates are provided in degrees.
  • The Earth is assumed to be a perfect sphere with a radius of 6371000 meters.
  • The Earth Engine service (ee) is available and correctly imported.
  • The trigonometric functions provided by the Earth Engine service are accurate and performant.

This analysis provides a detailed overview of the index.js module, highlighting its purpose, key components, and usage scenarios.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Input Coordinates| B[computeDisplacement]
    A[User] -->|Input Coordinates| C[computeBearing]
    B[computeDisplacement] -->|Calls| D[toRadians]
    B[computeDisplacement] -->|Calls| E[toDegrees]
    C[computeBearing] -->|Calls| D[toRadians]
    D[toRadians] -->|Uses| F[Earth Engine Service]
    E[toDegrees] -->|Uses| F[Earth Engine Service]
    C[computeBearing] -->|Uses| F[Earth Engine Service]

Module: index.js

Comprehensive Analysis of index.js

Module Name

  • Module Name: index.js

Primary Objectives

  • Purpose: This module is designed to process satellite imagery data using Google Earth Engine (GEE). It focuses on acquiring images from specific dates, processing collections of satellite images, and generating cloud maps based on the processed data.

Critical Functions

  1. sliceByRevisit(collection, startingDate, days):

    • Role: Slices a collection of images based on a starting date and a specified number of days (revisit period).

  2. acquireFromDate(date, collection, geometry):

    • Role: Acquires a mosaic image from a specific date, clips it to the given geometry, and scores it for cloud cover.

  3. processCollection(satellite, geometry):

    • Role: Processes a collection of satellite images to compute valid dates for image acquisition based on the satellite's revisit cycle.

  4. generateCloudMap(dates, collection, geometry):

    • Role: Generates a list of cloud cover scores for a series of dates.

Key Variables

  • REVISIT_DAYS: Constant representing the revisit period of the satellite (16 days).
  • qaBand: Variable to store the quality assurance band name based on the selected satellite.

Interdependencies

  • Interactions with Other System Components:
    • ../../services/earth-engine: Provides the ee object for interacting with Google Earth Engine.
    • ../utils: Provides utility functions like mergeProperties, addGridPosition, retrieveExtremes, and getDate.
    • ../imagery: Provides the scoreClouds function for cloud scoring.
    • ../../common/metadata: Provides metadata constants like GRID_POSITION and FOOTPRINT.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
  • Auxiliary Operations:
    • sliceByRevisit

Operational Sequence

  1. Image Acquisition:
    • Use acquireFromDate to get a mosaic image for a specific date.
  2. Collection Processing:
    • Use processCollection to determine valid dates for image acquisition.
  3. Cloud Map Generation:
    • Use generateCloudMap to create a list of cloud cover scores for the valid dates.

Performance Aspects

  • Performance Considerations:
    • Efficient filtering and slicing of large image collections.
    • Use of GEE's parallel processing capabilities to handle large datasets.
    • Minimizing the number of API calls to GEE by using batch operations.

Reusability

  • Adaptability for Reuse:
    • The module is highly reusable for different satellite datasets by changing the satellite parameter.
    • Utility functions and metadata constants are modular and can be reused in other parts of the application.

Usage

  • How it is Used:
    • The module is used to process satellite imagery data for specific regions and dates.
    • It can be integrated into larger applications that require satellite image analysis, such as environmental monitoring or agricultural assessment.

Assumptions

  • Assumptions Made:
    • The satellite data follows a regular revisit cycle (e.g., 16 days).
    • The selectedSatellite is stored in window.sessionStorage.
    • The utility functions and metadata constants are correctly implemented and available.
    • The geometry provided is valid and correctly formatted for GEE operations.

This analysis provides a detailed overview of the index.js module, highlighting its purpose, key functions, and how it interacts with other components. It also discusses performance considerations and the module's reusability in different contexts.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|requests data| B[processCollection]
    B -->|retrieves satellite collection| C[getSatelliteCollection]
    B -->|retrieves extremes| D[retrieveExtremes]
    B -->|adds grid position| E[addGridPosition]
    B -->|filters by grid position| F[ee.Filter.eq]
    B -->|computes difference| G[ee.Number.difference]
    B -->|generates slices| H[ee.List.sequence]
    B -->|transforms slices| I[ee.Algorithms.IsEqual]
    B -->|filters valid images| J[ee.Filter.contains]
    B -->|maps dates| K[getDate]
    A -->|requests cloud map| L[generateCloudMap]
    L -->|maps dates to clouds| M[ee.List.map]
    M -->|acquires image from date| N[acquireFromDate]
    N -->|slices by revisit| O[sliceByRevisit]
    N -->|scores clouds| P[scoreClouds]
    N -->|merges properties| Q[mergeProperties]
    N -->|gets satellite collection| R[getSatelliteCollection]

Module: index.js

Comprehensive Analysis of the Module

Module Name:

  • index.js

Primary Objectives:

  • The module primarily deals with operations related to satellite imagery using Google Earth Engine (GEE). It includes functions for manipulating, analyzing, and retrieving metadata from satellite images.

Critical Functions:

  1. addGridPosition(satellite):

    • Adds grid position metadata to an image based on the satellite's computed grid position.

  2. getDate(image):

    • Retrieves the date from the image metadata.

  3. mergeFootprints(collection):

    • Merges the geometries of a collection of images into a single geometry.

  4. retrieveExtremes(collection):

    • Retrieves the earliest and latest dates from a collection of images.

  5. mergeProperties(collection):

    • Merges properties of a collection of images, including the earliest date and footprint.

  6. combineReducers(...reducers):

    • Combines multiple reducers into a single reducer.

  7. serializeList(subject):

    • Serializes a list into a string format.

  8. generateVisualizationParams(mission):

    • Generates visualization parameters for a mission based on its bands.

  9. evaluate(query):

    • Binds the evaluate method to a query.

  10. applyExpression(image, expressionOrLabel, bands):

    • Applies an expression to an image using specified bands.

  11. imageToKey(image):

    • Converts an image to a key by picking specific properties.

  12. getSatelliteCollection(satellite):

    • Retrieves the image collection associated with a satellite.

  13. simplify(amount):

    • Simplifies a feature by a specified amount.

Key Variables:

  • Metadata: Contains metadata constants.
  • Indices: Contains indices and their expressions.
  • ee: Google Earth Engine object for image and feature operations.

Interdependencies:

  • lodash/pick: Used for picking specific properties from an object.
  • ../../services/earth-engine: Provides the ee object for GEE operations.
  • ../../common/metadata: Provides metadata constants.
  • ../../common/indices: Provides indices and their expressions.

Core vs. Auxiliary Operations:

  • Core Operations:

    • addGridPosition
    • getDate
    • mergeFootprints
    • retrieveExtremes
    • mergeProperties
    • applyExpression
    • getSatelliteCollection

  • Auxiliary Operations:

    • combineReducers
    • serializeList
    • generateVisualizationParams
    • evaluate
    • imageToKey
    • simplify

Operational Sequence:

  • The module does not have a strict operational sequence but provides utility functions that can be used in various sequences depending on the specific use case. For example, one might first retrieve a satellite collection, then apply expressions, and finally merge properties.

Performance Aspects:

  • Performance considerations would primarily revolve around the efficiency of GEE operations, such as image collection retrieval, sorting, and geometry merging. The use of ee functions ensures that operations are optimized for large-scale geospatial data.

Reusability:

  • The functions are designed to be highly reusable. They are generic and can be applied to various satellite imagery tasks. The modular nature allows for easy integration into different parts of a larger system.

Usage:

  • The module is used for processing and analyzing satellite imagery within the context of Google Earth Engine. It can be used in scripts that require manipulation of image collections, extraction of metadata, and application of indices.

Assumptions:

  • The module assumes that the user has access to Google Earth Engine and understands its API.
  • It assumes that the input data (e.g., satellite images and collections) conforms to expected formats and contains necessary metadata.
  • The presence of specific metadata keys (e.g., Metadata.TIME_START) is assumed for certain operations.

This analysis provides a detailed overview of the index.js module, highlighting its purpose, key components, and usage within a larger system.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests| B[Main Module: index.js]
    B -->|Calls| C[addGridPosition]
    B -->|Calls| D[getDate]
    B -->|Calls| E[mergeFootprints]
    B -->|Calls| F[retrieveExtremes]
    B -->|Calls| G[mergeProperties]
    B -->|Calls| H[combineReducers]
    B -->|Calls| I[serializeList]
    B -->|Calls| J[generateVisualizationParams]
    B -->|Calls| K[evaluate]
    B -->|Calls| L[applyExpression]
    B -->|Calls| M[imageToKey]
    B -->|Calls| N[getSatelliteCollection]
    B -->|Calls| O[simplify]

Module: index.js

Comprehensive Analysis of the Module

Module Name

  • index.js

Primary Objectives

  • The primary purpose of this module is to perform various geospatial analyses and operations using Earth Engine (EE) and Turf.js libraries. It includes functions for land cover parsing, shoreline extraction, elevation filtering, cloud masking, and vegetation index calculations.

Critical Functions

  1. landParsing(landcover, sidesCoords, centerCoord, shoreline)
    • Parses land cover data using Earth Engine.
  2. landParsingTurf(landcover, sidesCoords, centerCoord, shoreline)
    • Parses land cover data using Turf.js.
  3. extractShoreLine(baseLineCoords, waterGeometry)
    • Extracts the shoreline using Earth Engine.
  4. extractShoreLineTurf(baseLine, waterGeometry)
    • Extracts the shoreline using Turf.js.
  5. landCoversIntersections(transect, landcoversdata, shoreLine, year)
    • Computes intersections of land covers using Earth Engine.
  6. landCoversIntersectionsTurf(transect, landcoversdata, shoreLine, year)
    • Computes intersections of land covers using Turf.js.
  7. filterElevation(image, elevation, type = null, satellite = "LANDSAT")
    • Filters an image based on elevation.
  8. applyScaleFactors(image)
    • Applies scale factors to an image.
  9. getMapBiomasClassificationsList()
    • Retrieves a list of MapBiomas classifications.
  10. getMapBiomasClassificationYear(AOI, year, elevation = 10)
    • Retrieves MapBiomas classification for a specific year.
  11. getLandCoverLabelMapBiomas(value)
    • Maps a value to a land cover label.
  12. maskCloudLandsat(image, qa_band)
    • Masks clouds in Landsat images.
  13. maskCloudSentinel(image)
    • Masks clouds in Sentinel images.
  14. getImageCSqueezeInfos(missionName)
    • Retrieves metadata for a satellite mission.
  15. applySaviBand(image, bands)
    • Applies the SAVI band to an image.
  16. applyEviBand(image, bands)
    • Applies the EVI band to an image.
  17. applyNdviBand(image, bands)
    • Applies the NDVI band to an image.
  18. applyMndwiBand(image, bands)
    • Applies the MNDWI band to an image.
  19. applyUiBand(image, bands)
    • Applies the UI band to an image.
  20. trainAndClassify(image, classificationAreas, bands, AOICoords)
    • Trains a classifier and classifies an image.
  21. getMangroves()
    • Retrieves mangrove data.

Key Variables

  • options: Configuration for Turf.js operations.
  • landcovers: Array of land cover types.
  • lengthSide, distancesFromBaseLine, distancesFromShoreLine, landCoverIntersectPoint, landCoverBiggestSide, waterSide, waterIntersectPoint: Variables used in land cover parsing functions.

Interdependencies

  • Earth Engine (ee): Used for geospatial operations and data manipulation.
  • Turf.js (turf): Used for geospatial operations and data manipulation.
  • satellite: Imported from a common module for satellite data.

Core vs. Auxiliary Operations

  • Core Operations:
    • Land cover parsing (landParsing, landParsingTurf)
    • Shoreline extraction (extractShoreLine, extractShoreLineTurf)
    • Land cover intersections (landCoversIntersections, landCoversIntersectionsTurf)
    • Elevation filtering (filterElevation)
    • Cloud masking (maskCloudLandsat, maskCloudSentinel)
    • Vegetation index calculations (applySaviBand, applyEviBand, applyNdviBand, applyMndwiBand, applyUiBand)
    • Classification (trainAndClassify)
  • Auxiliary Operations:
    • Metadata retrieval (getImageCSqueezeInfos)
    • MapBiomas classifications (getMapBiomasClassificationsList, getMapBiomasClassificationYear)
    • Land cover label mapping (getLandCoverLabelMapBiomas)
    • Mangrove data retrieval (getMangroves)

Operational Sequence

  1. Initialization: Import necessary modules and define constants.
  2. Data Parsing: Use landParsing or landParsingTurf to parse land cover data.
  3. Shoreline Extraction: Use extractShoreLine or extractShoreLineTurf to extract shorelines.
  4. Intersection Calculation: Use landCoversIntersections or landCoversIntersectionsTurf to calculate land cover intersections.
  5. Elevation Filtering: Apply filterElevation to filter images based on elevation.
  6. Cloud Masking: Use maskCloudLandsat or maskCloudSentinel to mask clouds in images.
  7. Vegetation Index Calculation: Apply various vegetation indices using corresponding functions.
  8. Classification: Train and classify images using trainAndClassify.
  9. Metadata and Classification Retrieval: Use auxiliary functions for metadata and classification retrieval.

Performance Aspects

  • Efficiency: The module uses efficient geospatial libraries (Earth Engine and Turf.js) for heavy computations.
  • Scalability: Designed to handle large geospatial datasets.
  • Accuracy: Relies on well-established algorithms and datasets for accurate results.

Reusability

  • Modular Design: Functions are designed to be reusable and can be easily integrated into other projects.
  • Parameterization: Functions accept parameters that allow them to be adapted for different use cases.

Usage

  • Geospatial Analysis: Used for analyzing land cover, shorelines, and vegetation indices.
  • Environmental Monitoring: Useful for monitoring environmental changes over time.
  • Remote Sensing: Applicable in remote sensing projects for data processing and analysis.

Assumptions

  • Data Availability: Assumes availability of required geospatial data (e.g., satellite images, land cover data).
  • Correctness of Imported Modules: Assumes that imported modules (Earth Engine, Turf.js) are correctly implemented and functional.
  • Coordinate Systems: Assumes that input coordinates are in the correct format and projection.

This comprehensive analysis provides a detailed overview of the index.js module, highlighting its primary objectives, critical functions, key variables, interdependencies, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests| B[Main Module: index.js]
    B -->|Uses| C[Earth Engine Service]
    B -->|Uses| D[Satellites Common Module]
    B -->|Uses| E[Turf Library]
    B -->|Calls| F[landParsing Function]
    B -->|Calls| G[landParsingTurf Function]
    B -->|Calls| H[extractShoreLine Function]
    B -->|Calls| I[extractShoreLineTurf Function]
    B -->|Calls| J[landCoversIntersections Function]
    B -->|Calls| K[landCoversIntersectionsTurf Function]
    B -->|Calls| L[filterElevation Function]
    B -->|Calls| M[applyScaleFactors Function]
    B -->|Calls| N[getMapBiomasClassificationsList Function]
    B -->|Calls| O[getMapBiomasClassificationYear Function]
    B -->|Calls| P[getLandCoverLabelMapBiomas Function]
    B -->|Calls| Q[maskCloudLandsat Function]
    B -->|Calls| R[maskCloudSentinel Function]
    B -->|Calls| S[getImageCSqueezeInfos Function]
    B -->|Calls| T[applySaviBand Function]
    B -->|Calls| U[applyEviBand Function]
    B -->|Calls| V[applyNdviBand Function]
    B -->|Calls| W[applyMndwiBand Function]
    B -->|Calls| X[applyUiBand Function]
    B -->|Calls| Y[trainAndClassify Function]
    B -->|Calls| Z[getMangroves Function]

Module: index.js

Certainly! Here is a comprehensive analysis of the provided code module:

Module Name

  • index.js

Primary Objectives

  • The primary purpose of this module is to serve as an entry point that consolidates and exports functionalities from three different modules: Landsat, LandsatTOA, and Sentinel.

Critical Functions

  • The module itself does not define any functions. Instead, it imports and re-exports functionalities from other modules.

Key Variables

  • Landsat: Imported from ./landsat.
  • LandsatTOA: Imported from ./landsatToa.
  • Sentinel: Imported from ./sentinel.

Interdependencies

  • This module depends on three other modules:
    • ./landsat
    • ./landsatToa
    • ./sentinel
  • It acts as a central hub to aggregate and re-export these dependencies.

Core vs. Auxiliary Operations

  • Core Operations: The core operation of this module is the import and export of the Landsat, LandsatTOA, and Sentinel modules.
  • Auxiliary Operations: There are no auxiliary operations in this module.

Operational Sequence

  • The operational sequence is straightforward:
    1. Import the Landsat, LandsatTOA, and Sentinel modules.
    2. Export these modules as a single default export object.

Performance Aspects

  • Performance considerations are minimal for this module since it only involves importing and exporting other modules. The performance impact would primarily depend on the imported modules themselves.

Reusability

  • This module is highly reusable as it provides a centralized way to access the functionalities of the Landsat, LandsatTOA, and Sentinel modules. Any other part of the application can import this module to gain access to these functionalities.

Usage

  • This module is used to simplify the import process in other parts of the application. Instead of importing Landsat, LandsatTOA, and Sentinel separately, other modules can import this single module to access all three.

Assumptions

  • The module assumes that the paths ./landsat, ./landsatToa, and ./sentinel are correct and that these modules exist and are accessible.
  • It also assumes that the imported modules (Landsat, LandsatTOA, and Sentinel) are correctly implemented and do not have any import/export issues.

This analysis should provide a comprehensive understanding of the index.js module and its role within the larger system.

Flow Diagram [via mermaid]

graph TD
    A[index.js] --> B[Landsat]
    A[index.js] --> C[LandsatTOA]
    A[index.js] --> D[Sentinel]

Module: index.js

Certainly! Let's break down the provided code module based on the given criteria:

Comprehensive Analysis

Module Name:

  • index.js

Primary Objectives:

  • The primary purpose of this module is to re-export functionalities from three other modules: extraction, statistics, and transects. This allows for a consolidated import interface, making it easier to manage and use these functionalities in other parts of the application.

Critical Functions:

  • Since index.js is only re-exporting, it does not contain any functions of its own. However, it makes the functions from the following modules available:
    • extraction: Functions related to data extraction.
    • statistics: Functions for statistical analysis.
    • transects: Functions dealing with transect data (likely related to geographical or environmental data).

Key Variables:

  • There are no variables defined in index.js itself. The key variables would be within the extraction, statistics, and transects modules.

Interdependencies:

  • index.js depends on the extraction, statistics, and transects modules. It acts as a bridge to make their functionalities accessible from a single entry point.
  • Other system components that need functionalities from extraction, statistics, or transects will depend on index.js for a simplified import process.

Core vs. Auxiliary Operations:

  • Core Operations: The core operation of index.js is to re-export the modules.
  • Auxiliary Operations: There are no auxiliary operations in index.js itself.

Operational Sequence:

  • The operational sequence is straightforward:
    1. index.js imports everything from extraction, statistics, and transects.
    2. It then re-exports these imports, making them available for other modules to use.

Performance Aspects:

  • index.js itself does not introduce any significant performance considerations since it only re-exports modules.
  • Performance considerations would be more relevant in the extraction, statistics, and transects modules, depending on their implementations.

Reusability:

  • index.js enhances reusability by providing a single point of access to multiple modules. This makes it easier to manage dependencies and import statements in other parts of the application.
  • The reusability of the functionalities from extraction, statistics, and transects is also improved as they can be imported collectively or individually as needed.

Usage:

  • index.js is used to simplify the import process in other parts of the application. Instead of importing from extraction, statistics, and transects separately, a developer can import everything they need from index.js. 
    // Instead of this:
import { funcA } from './extraction';
import { funcB } from './statistics';
import { funcC } from './transects';

// You can do this:
import { funcA, funcB, funcC } from './index';

Assumptions:

  • It is assumed that the extraction, statistics, and transects modules are correctly implemented and exported.
  • It is assumed that the consumer of index.js is aware of the functionalities provided by the re-exported modules.
  • It is assumed that there are no naming conflicts between the exports of extraction, statistics, and transects.

This analysis provides a comprehensive overview of the index.js module and its role within the larger system.

Flow Diagram [via mermaid]

graph TD
    A[index.js] --> B[extraction]
    A[index.js] --> C[statistics]
    A[index.js] --> D[transects]

Module: index.js

Comprehensive Analysis of the Module

Module Name

  • index.js

Primary Objectives

  • The module is designed to handle various operations related to satellite imagery, including cloud scoring, layer generation, and thumbnail creation. It leverages Google Earth Engine (GEE) for processing and analyzing geospatial data.

Critical Functions

  1. scoreCloudRatio

    • Purpose: Calculates the ratio of cloud coverage in a given region.
    • Parameters: masked, bandName, geometry, scale, maxPixels
    • Returns: Cloud ratio as a number.

  2. scoreClouds

    • Purpose: Scores clouds in an image based on quality assessment (QA) bands.
    • Parameters: image, geometry, qa
    • Returns: Image with cloud ratio set as a property.

  3. generateLayer

    • Purpose: Generates a visualization layer for a given image.
    • Parameters: image, mission, name, params
    • Returns: A new Layer object.

  4. createThumbnail

    • Purpose: Creates a thumbnail image for a given region.
    • Parameters: image, geometry, params, callback
    • Returns: URL of the generated thumbnail.

  5. createThumbnailCSqueeze

    • Purpose: Creates a thumbnail image with specific scaling factors applied.
    • Parameters: image, geometry, params, callback
    • Returns: URL of the generated thumbnail.

  6. cumulativeWindow

    • Purpose: Calculates the cumulative window of a histogram by removing a specified percentile of pixels.
    • Parameters: fullHistogram, percentile
    • Returns: List containing the cumulative window bounds (min/max).

Key Variables

  • cloudyArea: Represents the area covered by clouds.
  • cloudClassSum: Sum of cloud class pixel values.
  • imageArea: Area of the image being processed.
  • res: Result of the region reduction operation.
  • visualization: Visualization parameters for image rendering.
  • generationParams: Parameters for thumbnail generation.

Interdependencies

  • Google Earth Engine (GEE): The module heavily relies on GEE for image processing and analysis.
  • Layer Class: Used for creating visualization layers.
  • generateVisualizationParams: Utility function for generating visualization parameters.

Core vs. Auxiliary Operations

  • Core Operations:
    • scoreCloudRatio
    • scoreClouds
    • generateLayer
    • createThumbnail
    • createThumbnailCSqueeze
  • Auxiliary Operations:
    • cumulativeWindow

Operational Sequence

  1. Cloud Scoring:
    • scoreCloudRatio and scoreClouds are used to analyze cloud coverage in satellite images.
  2. Layer Generation:
    • generateLayer creates visualization layers for the processed images.
  3. Thumbnail Creation:
    • createThumbnail and createThumbnailCSqueeze generate thumbnail images for quick visualization.
  4. Histogram Analysis:
    • cumulativeWindow calculates the cumulative window for histogram data.

Performance Aspects

  • Scale and MaxPixels: Parameters like scale and maxPixels are crucial for performance, especially when dealing with large datasets.
  • Efficient Use of GEE: The module leverages GEE's efficient data processing capabilities to handle large-scale geospatial data.

Reusability

  • Modular Functions: The functions are designed to be modular and can be reused in different contexts involving satellite image processing.
  • Parameterization: Functions accept various parameters, making them adaptable for different missions and datasets.

Usage

  • Cloud Analysis: Used for scoring and analyzing cloud coverage in satellite images.
  • Visualization: Generates layers and thumbnails for visualizing satellite data.
  • Histogram Analysis: Provides tools for analyzing histograms of pixel values.

Assumptions

  • GEE Availability: Assumes that Google Earth Engine is available and properly configured.
  • Input Data: Assumes that the input images and geometries are valid and correctly formatted.
  • Session Storage: Assumes that the selected satellite information is stored in window.sessionStorage.

This analysis provides a detailed overview of the index.js module, highlighting its primary objectives, critical functions, key variables, and other important aspects.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests Cloud Ratio| B[scoreCloudRatio]
    A -->|Requests Cloud Score| C[scoreClouds]
    A -->|Requests Layer Generation| D[generateLayer]
    A -->|Requests Thumbnail| E[createThumbnail]
    A -->|Requests CSqueeze Thumbnail| F[createThumbnailCSqueeze]
    A -->|Requests Cumulative Window| G[cumulativeWindow]

    B -->|Uses| H[ee]
    B -->|Uses| I[geometry]
    B -->|Uses| J[masked]
    B -->|Uses| K[bandName]

    C -->|Uses| H[ee]
    C -->|Uses| I[geometry]
    C -->|Uses| L[image]
    C -->|Uses| M[qa]

    D -->|Uses| L[image]
    D -->|Uses| N[mission]
    D -->|Uses| O[name]
    D -->|Uses| P[params]
    D -->|Uses| Q[generateVisualizationParams]

    E -->|Uses| L[image]
    E -->|Uses| I[geometry]
    E -->|Uses| P[params]
    E -->|Uses| R[callback]

    F -->|Uses| L[image]
    F -->|Uses| I[geometry]
    F -->|Uses| P[params]
    F -->|Uses| R[callback]

    G -->|Uses| S[fullHistogram]
    G -->|Uses| T[percentile]