High_Level_Doc

Introduction :

High-Level System Overview

The system comprises several JavaScript modules, each with distinct primary objectives, designed to handle various aspects of data extraction, processing, and analysis. Below is a brief overview of each module and its purpose:

extraction.js
- Primary Objectives: This module is responsible for extracting relevant data from various sources. It serves as the initial step in the data pipeline, ensuring that raw data is collected and prepared for further processing.
index.js
- Primary Objectives: Acting as the main entry point of the application, this module orchestrates the overall workflow. It integrates different modules, manages dependencies, and ensures smooth execution of the program.
landsat.js
- Primary Objectives: This module handles data specific to the Landsat satellite. It processes and prepares Landsat imagery for analysis, ensuring that the data is in a usable format.
landsatToa.js
- Primary Objectives: Focused on Top of Atmosphere (TOA) corrections for Landsat data, this module applies necessary adjustments to the satellite imagery to account for atmospheric interference, enhancing data accuracy.
sentinel.js
- Primary Objectives: Similar to landsat.js, this module deals with data from the Sentinel satellite. It processes Sentinel imagery, making it ready for subsequent analysis.
statistics.js
- Primary Objectives: This module performs statistical analysis on the processed data. It calculates various metrics and provides insights that are crucial for decision-making and further research.
transects.js
- Primary Objectives: This module is designed to handle transect data, which involves analyzing linear features across the landscape. It extracts and processes transect information, facilitating detailed spatial analysis.

Purpose

The primary purpose of this system is to streamline the extraction, processing, and analysis of satellite imagery and related data. By modularizing the workflow, the system ensures that each step is handled efficiently and accurately, providing high-quality data for research, environmental monitoring, and decision-making processes.

Full System Overview

Module Overview

Module: extraction.js

Comprehensive Analysis of `extraction.js`

Module Name

extraction.js

Primary Objectives

The primary purpose of this module is to process satellite imagery to identify and extract shorelines. It involves various geospatial operations such as image partitioning, noise removal, and smoothing to derive accurate shorelines from raster data.

Critical Functions

identifyWaterFeature
- Partitions the raster image using a water index and a threshold function, then reduces the output raster to a vector corresponding to the water body.
removeShorelineNoise
- Removes noise from the shoreline by keeping only the polygons that have the most intersections with transects.
gaussianKernel
- Creates a Gaussian kernel controlled by size, mean, and sigma.
linearGaussianFilter
- Convolves over the list of coordinates producing local averages weighted by the Gaussian kernel to smooth the shoreline.
thresholdingAlgorithm
- Finds the appropriate thresholds for a given histogram and count of classes.
selectThreshold
- Selects the threshold function according to the input threshold.
extractShoreline
- Extracts the raw shoreline of the given image by identifying the water feature and converting it into linestrings.
deriveShoreline
- Fully derives the shoreline using the satellite image, producing a single smoothed linestring without noisy features.
deriveShorelines
- Acquires and extracts shorelines given an array of dates.
expandCoastline
- Expands the given coordinates by a specified distance.
rectifyJunctions
- Normalizes the junctions of segments.

Key Variables

internalBandName: Used to store the name of the water index band.
threshold: Stores the threshold value for water feature identification.
firstImage: Used to store the first image for registration purposes in deriveShoreline.

Interdependencies

Earth Engine (ee): The module heavily relies on Google Earth Engine (GEE) for geospatial operations.
Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module.
Metadata Module: Used for handling metadata.
Utils Module: Provides utility functions like getDate.
Material-UI Icons: Imported but not used in the provided code.

Core vs. Auxiliary Operations

Core Operations:
- identifyWaterFeature
- removeShorelineNoise
- extractShoreline
- deriveShoreline
- deriveShorelines
Auxiliary Operations:
- gaussianKernel
- linearGaussianFilter
- thresholdingAlgorithm
- selectThreshold
- expandCoastline
- rectifyJunctions

Operational Sequence

Image Acquisition: Images are acquired based on dates and satellite information.
Water Feature Identification: The water feature is identified using the identifyWaterFeature function.
Shoreline Extraction: The raw shoreline is extracted using extractShoreline.
Noise Removal: Noise is removed from the shoreline using removeShorelineNoise.
Smoothing: The shoreline is smoothed using linearGaussianFilter.
Shoreline Derivation: The final shoreline is derived using deriveShoreline and deriveShorelines.

Performance Aspects

Scalability: The module uses Earth Engine, which is designed to handle large-scale geospatial data efficiently.
Efficiency: Functions like reduceToVectors and reduceRegion are computationally intensive and should be optimized for performance.

Reusability

The module is highly reusable for any geospatial analysis involving shoreline extraction from satellite imagery. Functions are modular and can be adapted for different types of geospatial data and thresholds.

Usage

The module is used to process satellite images to extract and analyze shorelines. It can be integrated into larger geospatial analysis pipelines for environmental monitoring, coastal management, and other applications.

Assumptions

Image Quality: Assumes that the input satellite images are of sufficient quality for analysis.
Threshold Functions: Assumes that the provided threshold functions are appropriate for the given images.
Earth Engine Availability: Assumes that Google Earth Engine services are available and accessible.
Geospatial Accuracy: Assumes that the geospatial data (coordinates, geometries) are accurate and correctly formatted.

This analysis provides a detailed overview of the extraction.js module, highlighting its primary objectives, critical functions, and operational flow. The module is designed for efficient and accurate shoreline extraction using satellite imagery, leveraging the capabilities of Google Earth Engine.

Flow Diagram [via mermaid]

Module: extraction.js

Comprehensive Analysis of `extraction.js`

Module Name

extraction.js

Primary Objectives

The primary purpose of this module is to process satellite imagery to identify and extract shoreline features. It involves various geospatial operations such as image partitioning, noise removal, and smoothing to derive accurate shorelines from raster data.

Critical Functions

identifyWaterFeature
- Partitions the raster image using a water index and a threshold function, then reduces the output raster to a vector corresponding to the water body.
removeShorelineNoise
- Removes noise from the shoreline by keeping only the polygons that have the most intersections with transects.
gaussianKernel
- Creates a Gaussian kernel controlled by size, mean, and sigma.
linearGaussianFilter
- Convolves over the list of coordinates producing local averages weighted by the Gaussian kernel, resulting in a smoothed list.
thresholdingAlgorithm
- Finds the appropriate thresholds for a given histogram and count of classes.
selectThreshold
- Selects the threshold function according to the input threshold.
extractShoreline
- Extracts the raw shoreline of the given image by identifying the water feature and converting it into linestrings.
deriveShoreline
- Fully derives the shoreline using the satellite image, resulting in a single smoothed linestring without noisy features.
deriveShorelines
- Acquires and extracts shorelines given an array of dates.
expandCoastline
- Expands the given coordinates by a specified distance.
rectifyJunctions
- Normalizes the junctions of segments.

Key Variables

internalBandName: Used to store the name of the water index band.
threshold: Stores the threshold value for water feature identification.
firstImage: Used to store the first image for registration purposes.
maxPixels: Maximum number of pixels to process in certain operations.

Interdependencies

Earth Engine (ee): The module heavily relies on Google Earth Engine for geospatial operations.
Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module.
Metadata Module: Used for handling metadata.
Utils Module: Provides utility functions like getDate.
Material-UI Icons: Used for UI components, though not directly related to core functionality.

Core vs. Auxiliary Operations

Core Operations:
- identifyWaterFeature
- removeShorelineNoise
- extractShoreline
- deriveShoreline
- deriveShorelines
Auxiliary Operations:
- gaussianKernel
- linearGaussianFilter
- thresholdingAlgorithm
- selectThreshold
- expandCoastline
- rectifyJunctions

Operational Sequence

Image Acquisition: Images are acquired based on dates and satellite information.
Water Feature Identification: The water feature is identified using the identifyWaterFeature function.
Shoreline Extraction: The raw shoreline is extracted using extractShoreline.
Noise Removal: Noise is removed from the shoreline using removeShorelineNoise.
Smoothing: The shoreline is smoothed using linearGaussianFilter.
Final Derivation: The final shoreline is derived and returned.

Performance Aspects

Scale and Resolution: The scale and optical resolution are critical for accurate shoreline extraction.
Thresholding: The choice of thresholding algorithm can significantly impact performance and accuracy.
Max Pixels: Operations are constrained by the maximum number of pixels to ensure performance.

Reusability

The module is highly reusable for any geospatial analysis involving shoreline extraction from satellite imagery. Functions like gaussianKernel and linearGaussianFilter can be reused in other contexts requiring smoothing and noise reduction.

Usage

The module is used to process satellite images to derive shorelines. It can be integrated into larger geospatial analysis systems or used as a standalone tool for shoreline extraction.

Assumptions

Image Quality: Assumes that the input satellite images are of sufficient quality and resolution.
Threshold Function: Assumes that the provided threshold function is appropriate for the given images.
Earth Engine Availability: Assumes that Google Earth Engine services are available and accessible.
Geospatial Accuracy: Assumes that the geospatial data (coordinates, geometry) provided is accurate.

This analysis provides a detailed overview of the extraction.js module, highlighting its purpose, key functions, and operational flow.

Flow Diagram [via mermaid]

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]

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:

toDegrees(lng, lat):
- Converts longitude and latitude from radians to degrees.
- Ensures the longitude is within the range of -180 to 180 degrees.
toRadians(value):
- Converts a numerical value from degrees to radians.
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.
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:

Conversion to Radians:
- Input values (longitude, latitude, bearing, distance) are converted to radians.
Trigonometric Calculations:
- Perform necessary trigonometric operations to compute new coordinates or bearing.
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]

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

sliceByRevisit(collection, startingDate, days):
- Role: Slices a given image collection based on a starting date and a specified number of days.
acquireFromDate(date, collection, geometry):
- Role: Acquires a mosaic image from a specific date, filters it by geometry, and scores it for cloud coverage.
processCollection(satellite, geometry):
- Role: Processes a collection of satellite images to compute valid dates for image acquisition based on the satellite's revisit cycle.
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

Image Acquisition:
- Use acquireFromDate to get a mosaic image for a specific date.
Collection Processing:
- Use processCollection to determine valid dates for image acquisition.
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]

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

addGridPosition(satellite)
- Adds grid position metadata to an image based on the satellite's computed grid position.
getDate(image)
- Retrieves the date from the image metadata.
mergeFootprints(collection)
- Merges the geometries of a collection of images into a single footprint.
retrieveExtremes(collection)
- Retrieves the earliest and latest dates from a collection of images.
mergeProperties(collection)
- Merges properties of a collection of images, including the earliest date and footprint.
combineReducers(...reducers)
- Combines multiple reducers into a single reducer.
serializeList(subject)
- Serializes a list into a string format.
generateVisualizationParams(mission)
- Generates visualization parameters for a mission based on its bands.
evaluate(query)
- Binds the evaluate method to a query.
applyExpression(image, expressionOrLabel, bands)
- Applies an expression to an image using specified bands.
imageToKey(image)
- Converts an image to a key by picking specific properties.
getSatelliteCollection(satellite)
- Retrieves the image collection associated with a satellite.
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

Image Processing: Functions like addGridPosition, applyExpression, and simplify are used to process individual images.
Collection Operations: Functions like mergeFootprints, retrieveExtremes, and mergeProperties operate on collections of images.
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]

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

landParsing(landcover, sidesCoords, centerCoord, shoreline)
- Parses land cover data using Earth Engine.
landParsingTurf(landcover, sidesCoords, centerCoord, shoreline)
- Parses land cover data using Turf.js.
extractShoreLine(baseLineCoords, waterGeometry)
- Extracts the shoreline using Earth Engine.
extractShoreLineTurf(baseLine, waterGeometry)
- Extracts the shoreline using Turf.js.
landCoversIntersections(transect, landcoversdata, shoreLine, year)
- Computes intersections of land covers using Earth Engine.
landCoversIntersectionsTurf(transect, landcoversdata, shoreLine, year)
- Computes intersections of land covers using Turf.js.
filterElevation(image, elevation, type = null, satellite = "LANDSAT")
- Filters images based on elevation.
applyScaleFactors(image)
- Applies scale factors to satellite images.
getMapBiomasClassificationsList()
- Retrieves a list of MapBiomas classifications.
getMapBiomasClassificationYear(AOI, year, elevation = 10)
- Retrieves MapBiomas classification for a specific year.
getLandCoverLabelMapBiomas(value)
- Maps land cover values to labels.
maskCloudLandsat(image, qa_band)
- Masks clouds in Landsat images.
maskCloudSentinel(image)
- Masks clouds in Sentinel images.
getImageCSqueezeInfos(missionName)
- Retrieves metadata for a given satellite mission.
applySaviBand(image, bands)
- Applies the SAVI band to an image.
applyEviBand(image, bands)
- Applies the EVI band to an image.
applyNdviBand(image, bands)
- Applies the NDVI band to an image.
applyMndwiBand(image, bands)
- Applies the MNDWI band to an image.
applyUiBand(image, bands)
- Applies the UI band to an image.
trainAndClassify(image, classificationAreas, bands, AOICoords)
- Trains a classifier and classifies an image.
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

Initialization: Import necessary libraries and define constants.
Land Cover Parsing: Use landParsing or landParsingTurf to parse land cover data.
Shoreline Extraction: Use extractShoreLine or extractShoreLineTurf to extract shorelines.
Land Cover Intersections: Use landCoversIntersections or landCoversIntersectionsTurf to compute intersections.
Elevation Filtering: Use filterElevation to filter images based on elevation.
Image Processing: Apply scale factors and mask clouds using respective functions.
Classification: Train and classify images using trainAndClassify.
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]

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]

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]

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

scoreCloudRatio:
- Role: Calculates the ratio of cloud coverage in a given region by analyzing a masked image.
- Parameters: masked, bandName, geometry, scale, maxPixels.
scoreClouds:
- Role: Scores the cloud coverage in an image based on a quality assessment (QA) band.
- Parameters: image, geometry, qa.
generateLayer:
- Role: Generates a visualization layer for a given image.
- Parameters: image, mission, name, params.
createThumbnail:
- Role: Creates a thumbnail image for a given satellite image and geometry.
- Parameters: image, geometry, params, callback.
createThumbnailCSqueeze:
- Role: Similar to createThumbnail, but includes additional scaling factors for Landsat Collection 2.
- Parameters: image, geometry, params, callback.
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

Cloud Scoring:
- Use scoreCloudRatio or scoreClouds to analyze cloud coverage.
Layer Generation:
- Use generateLayer to create a visualization layer for the image.
Thumbnail Creation:
- Use createThumbnail or createThumbnailCSqueeze to generate a thumbnail image.
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]

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:

Import the default export from the satellite module.
Import all exports from the shoreline, acquisition, and utils modules.
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]

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:

toDegrees(lng, lat):
- Converts longitude and latitude from radians to degrees.
- Ensures the longitude is within the range of -180 to 180 degrees.
toRadians(value):
- Converts a numerical value from degrees to radians.
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).
computeBearing(lng1, lat1, lng2, lat2):
- Computes the bearing (direction) from one geographical coordinate (lng1, lat1) to another (lng2, lat2).

Key Variables:

EARTHS_RADIUS:
- Represents the Earth's radius in meters (6371000 meters).
delta:
- Represents the angular distance traveled on the Earth's surface.
latLeft, latRight, newLat:
- Intermediate variables used in the calculation of the new latitude.
lngY, lngX, newLng:
- Intermediate variables used in the calculation of the new longitude.
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:

Conversion to Radians:
- Input values (longitude, latitude, theta, distance) are converted to radians.
Displacement Calculation:
- Intermediate trigonometric calculations are performed to determine the new latitude and longitude.
Conversion to Degrees:
- The resulting coordinates are converted back to degrees.
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]

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

sliceByRevisit(collection, startingDate, days):
- Role: Slices a collection of images based on a starting date and a specified number of days (revisit period).
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.
processCollection(satellite, geometry):
- Role: Processes a collection of satellite images to compute valid dates for image acquisition based on the satellite's revisit cycle.
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

Image Acquisition:
- Use acquireFromDate to get a mosaic image for a specific date.
Collection Processing:
- Use processCollection to determine valid dates for image acquisition.
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]

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:

addGridPosition(satellite):
- Adds grid position metadata to an image based on the satellite's computed grid position.
getDate(image):
- Retrieves the date from the image metadata.
mergeFootprints(collection):
- Merges the geometries of a collection of images into a single geometry.
retrieveExtremes(collection):
- Retrieves the earliest and latest dates from a collection of images.
mergeProperties(collection):
- Merges properties of a collection of images, including the earliest date and footprint.
combineReducers(...reducers):
- Combines multiple reducers into a single reducer.
serializeList(subject):
- Serializes a list into a string format.
generateVisualizationParams(mission):
- Generates visualization parameters for a mission based on its bands.
evaluate(query):
- Binds the evaluate method to a query.
applyExpression(image, expressionOrLabel, bands):
- Applies an expression to an image using specified bands.
imageToKey(image):
- Converts an image to a key by picking specific properties.
getSatelliteCollection(satellite):
- Retrieves the image collection associated with a satellite.
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]

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

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

Initialization: Import necessary modules and define constants.
Data Parsing: Use landParsing or landParsingTurf to parse land cover data.
Shoreline Extraction: Use extractShoreLine or extractShoreLineTurf to extract shorelines.
Intersection Calculation: Use landCoversIntersections or landCoversIntersectionsTurf to calculate land cover intersections.
Elevation Filtering: Apply filterElevation to filter images based on elevation.
Cloud Masking: Use maskCloudLandsat or maskCloudSentinel to mask clouds in images.
Vegetation Index Calculation: Apply various vegetation indices using corresponding functions.
Classification: Train and classify images using trainAndClassify.
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.

Flow Diagram [via mermaid]

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]

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]

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

scoreCloudRatio
- Purpose: Calculates the ratio of cloud coverage in a given region.
- Parameters: masked, bandName, geometry, scale, maxPixels
- Returns: Cloud ratio as a number.
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.
generateLayer
- Purpose: Generates a visualization layer for a given image.
- Parameters: image, mission, name, params
- Returns: A new Layer object.
createThumbnail
- Purpose: Creates a thumbnail image for a given region.
- Parameters: image, geometry, params, callback
- Returns: URL of the generated thumbnail.
createThumbnailCSqueeze
- Purpose: Creates a thumbnail image with specific scaling factors applied.
- Parameters: image, geometry, params, callback
- Returns: URL of the generated thumbnail.
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

Cloud Scoring:
- scoreCloudRatio and scoreClouds are used to analyze cloud coverage in satellite images.
Layer Generation:
- generateLayer creates visualization layers for the processed images.
Thumbnail Creation:
- createThumbnail and createThumbnailCSqueeze generate thumbnail images for quick visualization.
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]

Module: landsat.js

Comprehensive Analysis of the `landsat.js` Module

Module Name:

landsat.js

Primary Objectives:

The primary purpose of this module is to interact with the Google Earth Engine (GEE) to process and analyze Landsat satellite imagery. It focuses on acquiring images based on specific dates, filtering images by cloud cover, and processing collections of images to extract useful metadata.

Critical Functions:

addGridPosition(element):
- Role: Adds a grid position to an image based on its path and row metadata.
sliceByRevisit(collection, startingDate, days):
- Role: Filters an image collection by a date range starting from a given date and spanning a specified number of days.
acquireFromDate(date, mission, geometry):
- Role: Acquires a mosaic image from a specific date, mission, and geometry, and scores it for cloud cover.
processCollection(mission, geometry):
- Role: Processes an image collection to compute valid dates for image acquisition based on the mission's revisit cycle and geometry.
generateCloudMap(dates, mission, geometry):
- Role: Generates a list of cloud cover percentages for a series of dates.
queryAvailable(mission):
- Role: Queries available images for a mission and geometry, returning a dictionary of dates and their corresponding cloud cover.
getAvailable(mission):
- Role: Placeholder function for getting available images for a mission and geometry.
acquire(mission):
- Role: Acquires an image for a specific date and geometry.
format(properties):
- Role: Formats image properties into a readable string.

Key Variables:

Metadata.GRID_POSITION: Used to store the computed grid position of an image.
mission.cycle: Represents the revisit cycle of the satellite mission.
geometry: The geographical area of interest for image acquisition and processing.

Interdependencies:

Google Earth Engine (GEE): The module heavily relies on GEE for image collection, filtering, and processing.
Utility Functions: Functions like mergeProperties, retrieveExtremes, and getDate from the utils module are used for various processing tasks.
Imagery Functions: The scoreClouds function from the imagery module is used to evaluate cloud cover in images.
Metadata Module: Common metadata operations are handled by the metadata module.

Core vs. Auxiliary Operations:

Core Operations:
- Image acquisition (acquireFromDate, acquire)
- Image collection processing (processCollection)
- Cloud cover evaluation (generateCloudMap)
Auxiliary Operations:
- Adding grid positions (addGridPosition)
- Formatting properties (format)
- Placeholder functions (getAvailable)

Operational Sequence:

Image Acquisition:
- Use acquireFromDate to get images for a specific date and geometry.
Collection Processing:
- Use processCollection to filter and process image collections based on mission parameters.
Cloud Map Generation:
- Use generateCloudMap to evaluate cloud cover for a series of dates.
Querying Available Images:
- Use queryAvailable to get a dictionary of available dates and their cloud cover.

Performance Aspects:

Efficiency: The module uses GEE's efficient filtering and processing capabilities to handle large datasets.
Scalability: Designed to work with large image collections and extensive geographical areas.
Cloud Cover Scoring: The scoreClouds function is critical for performance as it determines the usability of images based on cloud cover.

Reusability:

Modular Design: Functions are designed to be reusable across different missions and geometries.
Parameterization: Functions accept parameters like mission, date, and geometry, making them adaptable for various use cases.

Usage:

Image Acquisition: Used to acquire and process Landsat images for specific dates and regions.
Cloud Cover Analysis: Evaluates and filters images based on cloud cover, ensuring high-quality data for analysis.
Metadata Extraction: Processes image collections to extract and format useful metadata.

Assumptions:

Valid Mission Data: Assumes that the mission object contains valid data such as name and cycle.
Geometry Validity: Assumes that the provided geometry is valid and correctly formatted.
GEE Availability: Assumes that the Google Earth Engine service is available and accessible.
Utility Functions: Assumes that utility functions like mergeProperties, retrieveExtremes, and getDate are correctly implemented and available.

This comprehensive analysis provides a detailed understanding of the landsat.js module, its functions, and its role within the larger system.

Flow Diagram [via mermaid]

Module: landsat.js

Comprehensive Analysis of the `landsat.js` Module

Module Name:

landsat.js

Primary Objectives:

The primary purpose of this module is to interact with the Google Earth Engine (GEE) to process and retrieve Landsat satellite imagery data. It focuses on acquiring images based on specific dates, filtering images by cloud cover, and processing collections of images to identify valid dates for data retrieval.

Critical Functions:

addGridPosition(element):
- Role: Adds a grid position to an image based on its path and row metadata.
sliceByRevisit(collection, startingDate, days):
- Role: Filters an image collection by a date range starting from a given date and spanning a specified number of days.
acquireFromDate(date, mission, geometry):
- Role: Acquires a mosaic of images from a specific date, mission, and geometry, and scores them based on cloud cover.
processCollection(mission, geometry):
- Role: Processes an image collection to compute valid dates for data retrieval based on the mission's revisit cycle and the geometry of the area of interest.
generateCloudMap(dates, mission, geometry):
- Role: Generates a list of cloud cover scores for a series of dates.
queryAvailable(mission):
- Role: Queries available dates and cloud cover information for a given mission and geometry.
getAvailable(mission):
- Role: Placeholder function for getting available data.
acquire(mission):
- Role: Acquires imagery for a specific date and geometry.
format(properties):
- Role: Formats image properties into a readable string.

Key Variables:

Metadata.GRID_POSITION: Used to store the computed grid position of an image.
mission.name: The name of the satellite mission (e.g., Landsat 8).
mission.cycle: The revisit cycle of the satellite mission.
mission.bands.qa: Quality assessment bands used for scoring clouds.

Interdependencies:

Google Earth Engine (GEE): The module heavily relies on GEE for image processing and data retrieval.
Utility Functions: Functions like mergeProperties, retrieveExtremes, and getDate from the utils module are used for various processing tasks.
Metadata Module: Used for handling metadata properties.

Core vs. Auxiliary Operations:

Core Operations:
- acquireFromDate
- processCollection
- generateCloudMap
- queryAvailable
- acquire
Auxiliary Operations:
- addGridPosition
- sliceByRevisit
- format

Operational Sequence:

Data Query: Use queryAvailable to get available dates and cloud cover information.
Data Acquisition: Use acquire to retrieve imagery for a specific date and geometry.
Data Processing: Use processCollection to process image collections and identify valid dates.
Cloud Scoring: Use generateCloudMap to score images based on cloud cover.

Performance Aspects:

Efficiency: The module uses GEE's efficient image processing capabilities, but performance can be affected by the size of the image collections and the complexity of the operations.
Scalability: Designed to handle large datasets typical of satellite imagery.

Reusability:

Adaptability: The module is adaptable for reuse with different satellite missions by changing the mission parameters.
Modularity: Functions are modular and can be reused in different contexts within the GEE framework.

Usage:

Typical Use Case: The module is used to retrieve and process Landsat imagery for specific dates and regions, particularly for applications requiring cloud-free images.
Integration: Can be integrated into larger systems for environmental monitoring, land use analysis, and other geospatial applications.

Assumptions:

GEE Availability: Assumes that the Google Earth Engine service is available and accessible.
Mission Parameters: Assumes that the mission parameters (name, cycle, bands) are correctly defined.
Geometry Validity: Assumes that the provided geometry for image filtering and clipping is valid and correctly formatted.

This analysis provides a detailed overview of the landsat.js module, highlighting its structure, functionality, and usage within the context of satellite imagery processing.

Flow Diagram [via mermaid]

Module: landsatToa.js

Comprehensive Analysis of `landsatToa.js`

Module Name

Module Name: landsatToa.js

Primary Objectives

Purpose: This module is designed to process Landsat Top of Atmosphere (TOA) imagery using Google Earth Engine (GEE). It focuses on cloud masking, image acquisition based on specific dates, and generating cloud maps for given geometries.

Critical Functions

addGridPosition(element): Adds grid position metadata to an image.
sliceByRevisit(collection, startingDate, days): Filters an image collection by a specific date range.
maskTOAClouds(image, bandName): Masks clouds in a TOA image using bitwise operations on quality assessment bands.
acquireFromDate(date, mission, geometry): Acquires a mosaicked image for a given date, mission, and geometry, and calculates the cloud ratio.
processCollection(mission, geometry): Processes an image collection to find valid dates for image acquisition based on the mission's revisit cycle.
generateCloudMap(dates, mission, geometry): Generates a list of cloud ratios for a series of dates.
queryAvailable(mission): Queries available dates and cloud ratios for a given mission and geometry.
getAvailable(mission): Placeholder function for getting available data.
acquire(mission): Acquires an image for a specific date and geometry.
format(properties): Formats image properties for display.

Key Variables

Metadata.GRID_POSITION: Key for storing grid position metadata.
cloudMask, cloudConf, shadowConf, cirrusConf: Bitwise masks for different cloud and shadow conditions.
global, enhanced, northeasternPosition, filtered, northeastern, difference, remainder, step, earliestDate, completeCycles, additions, carriers, valid: Variables used in processCollection to manage image collection processing and filtering.

Interdependencies

Dependencies on Other Modules:
- ee from ../../services/earth-engine: Google Earth Engine API.
- mergeProperties, retrieveExtremes, getDate from ../utils: Utility functions for merging properties, retrieving date extremes, and formatting dates.
- scoreCloudRatio from ../imagery: Function to score cloud ratios.
- Metadata from ../../common/metadata: Metadata constants.

Core vs. Auxiliary Operations

Core Operations:
- acquireFromDate
- processCollection
- generateCloudMap
- queryAvailable
- acquire
Auxiliary Operations:
- addGridPosition
- sliceByRevisit
- maskTOAClouds
- format

Operational Sequence

Image Acquisition:
- Use acquireFromDate to get a mosaicked image for a specific date.
Cloud Masking:
- Apply maskTOAClouds to mask clouds in the image.
Collection Processing:
- Use processCollection to find valid dates for image acquisition.
Cloud Map Generation:
- Generate cloud maps using generateCloudMap.
Querying:
- Use queryAvailable to get available dates and cloud ratios.

Performance Aspects

Performance Considerations:
- Efficient filtering and processing of large image collections.
- Use of bitwise operations for cloud masking to enhance performance.
- Leveraging GEE's parallel processing capabilities.

Reusability

Adaptability for Reuse:
- Functions are modular and can be reused for different missions and geometries.
- Utility functions (mergeProperties, retrieveExtremes, getDate) enhance reusability.
- The module can be extended to support additional missions or different types of imagery.

Usage

How It Is Used:
- Import the module and call its functions to process Landsat TOA imagery.
- Use queryAvailable to get available dates and cloud ratios for a specific mission and geometry.
- Use acquire to acquire images for specific dates.
- Format image properties using format.

Assumptions

Assumptions Made:
- The input geometry is valid and within the bounds of the mission's coverage.
- The mission object contains necessary metadata such as name, cycle, and bands.
- The GEE environment is properly set up and accessible.
- The utility functions (mergeProperties, retrieveExtremes, getDate) and metadata constants are correctly defined and imported.

This analysis provides a detailed overview of the landsatToa.js module, covering its primary objectives, critical functions, key variables, interdependencies, and more.

Flow Diagram [via mermaid]

Module: landsatToa.js

Comprehensive Analysis of `landsatToa.js`

Module Name

Module Name: landsatToa.js

Primary Objectives

Purpose: This module is designed to process Landsat Top of Atmosphere (TOA) imagery using Google Earth Engine (GEE). It focuses on cloud masking, image acquisition based on specific dates, and generating cloud maps for given geometries.

Critical Functions

addGridPosition(element): Adds grid position metadata to an image.
sliceByRevisit(collection, startingDate, days): Filters an image collection by a date range.
maskTOAClouds(image, bandName): Masks clouds in a TOA image based on specific bitwise operations.
acquireFromDate(date, mission, geometry): Acquires a mosaicked image for a given date, mission, and geometry, and calculates the cloud ratio.
processCollection(mission, geometry): Processes an image collection to find valid dates for image acquisition.
generateCloudMap(dates, mission, geometry): Generates a list of cloud ratios for given dates.
queryAvailable(mission): Queries available images and their cloud ratios for a given mission and geometry.
getAvailable(mission): Placeholder function for getting available images.
acquire(mission): Acquires an image for a given date and geometry.
format(properties): Formats image properties for display.

Key Variables

Metadata.GRID_POSITION: Key for storing grid position metadata.
cloudMask, cloudConf, shadowConf, cirrusConf: Bitwise masks for cloud, cloud confidence, shadow confidence, and cirrus confidence.

Interdependencies

External Modules:
- ee from ../../services/earth-engine: Google Earth Engine API.
- Utility functions from ../utils: mergeProperties, retrieveExtremes, getDate.
- scoreCloudRatio from ../imagery: Function to score cloud ratio.
- Metadata from ../../common/metadata: Common metadata keys.

Core vs. Auxiliary Operations

Core Operations:
- acquireFromDate
- processCollection
- generateCloudMap
- queryAvailable
- acquire
Auxiliary Operations:
- addGridPosition
- sliceByRevisit
- maskTOAClouds
- format

Operational Sequence

Image Acquisition:
- Use processCollection to find valid dates.
- Use acquireFromDate to acquire images for those dates.
Cloud Masking:
- Apply maskTOAClouds to mask clouds in the images.
Cloud Map Generation:
- Use generateCloudMap to create a list of cloud ratios for the images.
Querying:
- Use queryAvailable to get available images and their cloud ratios.

Performance Aspects

Efficiency: The module uses GEE's efficient filtering and mapping functions to handle large datasets.
Scalability: Designed to work with large image collections and extensive geographical areas.

Reusability

Adaptability: Functions are modular and can be reused for different missions and geometries. The use of GEE makes it adaptable to various remote sensing tasks.

Usage

How it is used:
- Import the module and call its functions to process Landsat TOA imagery.
- Use queryAvailable to get available images and cloud ratios.
- Use acquire to acquire specific images.
- Use format to format image properties for display.

Assumptions

Assumptions Made:
- The input geometries are valid and within the bounds of the image collection.
- The mission object contains necessary metadata like name, cycle, and bands.
- The GEE environment is properly set up and authenticated.

This analysis provides a detailed overview of the landsatToa.js module, covering its primary objectives, critical functions, key variables, interdependencies, and more.

Flow Diagram [via mermaid]

Module: sentinel.js

Comprehensive Analysis of `sentinel.js`

Module Name

Module Name: sentinel.js

Primary Objectives

Purpose: This module is designed to interact with satellite imagery data, specifically Sentinel and Landsat missions, to process and filter images based on cloud coverage and other criteria. It provides functionalities to acquire images from specific dates, process collections of images, and generate cloud maps.

Critical Functions

addGridPosition(element): Adds grid position metadata to an image.
sliceByRevisit(collection, startingDate, days): Filters a collection of images by a date range.
getImageDate(image): Extracts the date from an image's ID.
maskS2Clouds(img, geometry, bandName, scale, imgDateParam): Masks clouds in Sentinel-2 images and calculates the cloud coverage ratio.
acquireFromDate(date, mission, geometry): Acquires an image from a specific date and mission, applying cloud masking.
processCollection(mission, geometry): Processes a collection of images to find valid dates for image acquisition.
generateCloudMap(dates, mission, geometry): Generates a list of cloud coverage values for a series of dates.
queryAvailable(mission): Returns a function that queries available images for a mission and geometry.
getAvailable(mission): Similar to queryAvailable, but returns the results directly.
acquire(mission): Returns a function to acquire an image for a specific date and geometry.
format(properties): Formats image properties for display.

Key Variables

Metadata.GRID_POSITION: Metadata key for grid position.
bandName: Name of the band used for cloud masking.
scale: Scale used for cloud masking.
geometry: Geometric area of interest.
mission: Object containing mission details like name and cycle.

Interdependencies

External Services:
- ee from ../../services/earth-engine: Earth Engine API for image processing.
Utility Functions:
- mergeProperties, retrieveExtremes, getDate from ../utils.
Algorithms:
- acquireFromDateLandSat from ../../algorithms/satellite/landsat.
Common Metadata:
- Metadata from ../../common/metadata.

Core vs. Auxiliary Operations

Core Operations:
- acquireFromDate
- processCollection
- generateCloudMap
- maskS2Clouds
Auxiliary Operations:
- addGridPosition
- sliceByRevisit
- getImageDate
- queryAvailable
- getAvailable
- acquire
- format

Operational Sequence

Image Acquisition: acquireFromDate is used to acquire images from a specific date and mission.
Cloud Masking: maskS2Clouds is applied to filter out cloud-covered areas.
Collection Processing: processCollection processes image collections to find valid dates.
Cloud Map Generation: generateCloudMap generates cloud coverage data for a series of dates.
Querying: queryAvailable and getAvailable are used to query available images and their cloud coverage.

Performance Aspects

Efficiency: The module uses Earth Engine's efficient image processing capabilities.
Scalability: Designed to handle large collections of satellite images.
Optimization: Cloud masking and filtering are optimized for performance.

Reusability

Adaptability: Functions are modular and can be reused for different satellite missions and geometries.
Extensibility: New missions or additional processing steps can be easily integrated.

Usage

Image Processing: Used to process and filter satellite images based on cloud coverage.
Data Analysis: Generates cloud maps and valid image acquisition dates for further analysis.
Integration: Can be integrated into larger systems for environmental monitoring, agriculture, and other applications.

Assumptions

Image Metadata: Assumes images have specific metadata like system:id and CLOUDY_PIXEL_PERCENTAGE.
Date Format: Assumes image IDs contain date information in a specific format.
Geometry: Assumes valid geometric areas are provided for image processing.
Mission Details: Assumes mission objects contain necessary details like name and cycle.

This analysis provides a comprehensive overview of the sentinel.js module, detailing its structure, functionality, and usage within a larger system.

Flow Diagram [via mermaid]

Module: sentinel.js

Comprehensive Analysis of `sentinel.js`

Module Name

Module Name: sentinel.js

Primary Objectives

Purpose: The module is designed to interact with satellite imagery data, specifically Sentinel and Landsat missions, to process and filter images based on cloud coverage and other criteria. It provides functionalities to acquire images from specific dates, process collections of images, and generate cloud maps.

Critical Functions

addGridPosition(element): Adds grid position metadata to an image.
sliceByRevisit(collection, startingDate, days): Filters a collection of images by a date range.
getImageDate(image): Extracts the date from an image's ID.
maskS2Clouds(img, geometry, bandName, scale, imgDateParam): Masks clouds in Sentinel-2 images and calculates the cloud coverage ratio.
acquireFromDate(date, mission, geometry): Acquires an image from a specific date and mission, applying cloud masking.
processCollection(mission, geometry): Processes a collection of images to find valid dates for image acquisition.
generateCloudMap(dates, mission, geometry): Generates a list of cloud coverage values for a series of dates.
queryAvailable(mission): Returns a function that queries available images for a mission and geometry.
getAvailable(mission): Returns a function that gets available images for a mission and geometry.
acquire(mission): Returns a function that acquires an image for a specific date and geometry.
format(properties): Formats image properties for display.

Key Variables

Metadata.GRID_POSITION: Metadata key for grid position.
bandName: Name of the band used for cloud masking.
scale: Scale for cloud masking operations.
geometry: Geometric area of interest for image processing.
mission: Object containing mission details like name and cycle.

Interdependencies

Dependencies on Other Modules:
- ../../services/earth-engine: Provides Earth Engine services.
- ../utils: Utility functions like mergeProperties, retrieveExtremes, and getDate.
- ../../algorithms/satellite/landsat: Specific functions for Landsat satellite data.
- ../imagery: Functions related to imagery processing.
- ../../common/metadata: Common metadata definitions.

Core vs. Auxiliary Operations

Core Operations:
- acquireFromDate
- processCollection
- generateCloudMap
- maskS2Clouds
Auxiliary Operations:
- addGridPosition
- sliceByRevisit
- getImageDate
- queryAvailable
- getAvailable
- acquire
- format

Operational Sequence

Image Acquisition: acquireFromDate is used to acquire images from a specific date and mission.
Cloud Masking: maskS2Clouds is applied to filter out cloud-covered areas.
Collection Processing: processCollection processes a collection of images to find valid dates.
Cloud Map Generation: generateCloudMap generates a list of cloud coverage values for a series of dates.
Querying and Formatting: queryAvailable, getAvailable, and format are used for querying and formatting image data.

Performance Aspects

Performance Considerations:
- Efficient filtering and masking of large image collections.
- Handling of large datasets with Earth Engine's reduceRegion and filterDate methods.
- Use of ee.Algorithms.If and ee.Number for conditional operations and calculations.

Reusability

Adaptability for Reuse:
- The module is designed with reusable functions that can be adapted for different satellite missions and geometries.
- Functions like maskS2Clouds and acquireFromDate can be reused with minor modifications for other satellite data.

Usage

How It Is Used:
- The module is used to acquire and process satellite images, specifically for cloud masking and date-based filtering.
- It can be integrated into larger systems for environmental monitoring, agricultural analysis, and other geospatial applications.

Assumptions

Assumptions Made:
- The input images have specific metadata fields like system:id and CLOUDY_PIXEL_PERCENTAGE.
- The Earth Engine service is available and accessible.
- The geometry provided for image processing is valid and correctly formatted.
- The mission object contains necessary details like name and cycle.

This comprehensive analysis provides a detailed understanding of the sentinel.js module, its functionalities, and its role within a larger system.

Flow Diagram [via mermaid]

Module: statistics.js

Comprehensive Analysis of `statistics.js`

Module Name

Module Name: statistics.js

Primary Objectives

Purpose: This module is designed to calculate and analyze various statistics related to shorelines and transects. It includes functions to compute distances, classify changes, and generate comprehensive statistical summaries.

Critical Functions

calculateDistances:
- Role: Calculates the distance of each shoreline to a point of interest on a transect.
- Parameters: transect, baseline, shorelines
- Returns: ee.Dictionary containing distances.
estevesLabelling:
- Role: Classifies the transect change rate using intervals proposed by Esteves and Finkl (1998).
- Parameters: lrr (linear regression rate)
- Returns: ee.String with the classified label.
calculateStatistics:
- Role: Computes general DSAS statistics (SCE, NSM, EPR, LRR) and Esteves classification for a transect.
- Parameters: measurement (dictionary of distances)
- Returns: ee.Dictionary with the calculated statistics.
complementaryProperties:
- Role: Appends useful transect data to its properties.
- Parameters: transect, measurement, keepProps
- Returns: ee.Dictionary with useful and kept properties.
generateTransectsStatistics:
- Role: Adds DSAS statistics and Esteves classification to transects.
- Parameters: transects, baseline, shorelines, keepProps
- Returns: ee.List<ee.Feature> with added statistics.
summaryShorelineStatistics:
- Role: Adds statistics about the distances observed in transects to shorelines.
- Parameters: transects, shorelines
- Returns: ee.FeatureCollection with added statistics.

Key Variables

distanceKey: Key for distance in the dictionary.
idKey: Key for ID in the dictionary.
poi: Point of interest on the transect.
distanceTo: Function to calculate distance to a coordinate.
classification: Result of Esteves classification.
distanceList: List of distances from the measurement dictionary.
initialState: Initial state for reducing distances.
stats: Reduced statistics dictionary.
regression: List of regression points.
trend: Result of linear regression and correlation.
summary: Summary dictionary for complementary properties.
shorelineList: List of shorelines.
shorelineTable: Dictionary mapping shoreline IDs to features.
distances: Flattened list of distances.
transformed: Dictionary of reduced distances per shoreline.

Interdependencies

Interactions with Other Components:
- earth-engine: Utilizes Earth Engine services for geospatial computations.
- utils: Uses utility functions like combineReducers and serializeList.
- metadata: Accesses common metadata constants like TIME_START, INTERNALS, and ESTEVES_LABELS.
- common/utils: Uses common utility constants like EPOCH.

Core vs. Auxiliary Operations

Core Operations:
- Distance calculations (calculateDistances)
- Statistical computations (calculateStatistics)
- Classification (estevesLabelling)
- Generating transect statistics (generateTransectsStatistics)
- Summarizing shoreline statistics (summaryShorelineStatistics)
Auxiliary Operations:
- Appending complementary properties (complementaryProperties)

Operational Sequence

Distance Calculation: calculateDistances computes distances from shorelines to transects.
Classification: estevesLabelling classifies the linear regression rate.
Statistical Computation: calculateStatistics computes various statistics for the transects.
Property Appending: complementaryProperties appends additional properties to transects.
Transect Statistics Generation: generateTransectsStatistics integrates all computations and classifications into transects.
Shoreline Statistics Summary: summaryShorelineStatistics summarizes the statistics for shorelines.

Performance Aspects

Performance Considerations:
- Efficient use of Earth Engine's parallel processing capabilities.
- Iterative reduction operations for large datasets.
- Conditional checks to handle edge cases and empty collections.

Reusability

Adaptability for Reuse:
- Functions are modular and can be reused in different contexts involving geospatial analysis.
- Parameterized functions allow for flexibility in input data.
- Utility functions like combineReducers and serializeList enhance reusability.

Usage

How It Is Used:
- Integrated into a larger geospatial analysis system.
- Called by other modules or scripts to compute and append statistical data to geospatial features.
- Utilized in workflows involving shoreline and transect analysis.

Assumptions

Assumptions Made:
- Input data (transects, shorelines, baseline) are correctly formatted and valid.
- Earth Engine services are available and functioning.
- Metadata constants (TIME_START, INTERNALS, ESTEVES_LABELS) are correctly defined.
- Utility functions (combineReducers, serializeList) are available and correctly implemented.

This comprehensive analysis provides a detailed understanding of the statistics.js module, its functions, and its role within a larger geospatial analysis system.

Flow Diagram [via mermaid]

Module: statistics.js

Comprehensive Analysis of `statistics.js`

Module Name

Module Name: statistics.js

Primary Objectives

Purpose: This module is designed to calculate and analyze various statistics related to shorelines and transects. It includes functions to compute distances, classify changes, and generate comprehensive statistical summaries.

Critical Functions

calculateDistances:
- Role: Computes the distance of each shoreline to a point of interest on a transect.
- Parameters: transect, baseline, shorelines
- Returns: ee.Dictionary containing distances.
estevesLabelling:
- Role: Classifies the transect change rate using intervals proposed by Esteves and Finkl (1998).
- Parameters: lrr (linear regression rate)
- Returns: ee.String representing the classification.
calculateStatistics:
- Role: Calculates general DSAS statistics (SCE, NSM, EPR, LRR) and Esteves classification for a transect.
- Parameters: measurement (dictionary of distances)
- Returns: ee.Dictionary containing the statistics.
complementaryProperties:
- Role: Appends useful transect data to its properties.
- Parameters: transect, measurement, keepProps
- Returns: ee.Dictionary with useful and kept properties.
generateTransectsStatistics:
- Role: Adds DSAS statistics and Esteves classification to transects.
- Parameters: transects, baseline, shorelines, keepProps
- Returns: ee.List<ee.Feature> with added statistics.
summaryShorelineStatistics:
- Role: Adds statistics about the distances observed in transects to shorelines.
- Parameters: transects, shorelines
- Returns: ee.FeatureCollection with added statistics.

Key Variables

distanceKey: Key for distance in the dictionary.
idKey: Key for ID in the dictionary.
poi: Point of interest on the transect.
distanceTo: Function to calculate distance to a coordinate.
classification: Result of Esteves classification.
distanceList: List of distances from the measurement dictionary.
initialState: Initial state for reducing distances.
stats: Reduced statistics dictionary.
regression: List of regression points.
trend: Result of linear regression and correlation.
summary: Summary dictionary for complementary properties.

Interdependencies

Interactions with Other Components:
- Earth Engine (ee): Utilizes Google Earth Engine for geospatial computations.
- Common Metadata: Imports constants like TIME_START, INTERNALS, ESTEVES_LABELS.
- Utilities: Uses utility functions like combineReducers, serializeList.

Core vs. Auxiliary Operations

Core Operations:
- Distance calculation (calculateDistances)
- Statistical analysis (calculateStatistics)
- Classification (estevesLabelling)
- Generating transect statistics (generateTransectsStatistics)
- Summarizing shoreline statistics (summaryShorelineStatistics)
Auxiliary Operations:
- Appending properties (complementaryProperties)
- Utility functions for combining reducers and serializing lists.

Operational Sequence

Distance Calculation: calculateDistances computes distances from shorelines to transects.
Statistical Analysis: calculateStatistics processes these distances to generate DSAS statistics.
Classification: estevesLabelling classifies the transect change rate.
Property Appending: complementaryProperties adds useful properties to transects.
Transect Statistics Generation: generateTransectsStatistics integrates all previous steps to enrich transects with statistics.
Shoreline Statistics Summary: summaryShorelineStatistics aggregates and summarizes statistics for shorelines.

Performance Aspects

Considerations:
- Efficiency: Uses Earth Engine's efficient geospatial computation capabilities.
- Scalability: Designed to handle large datasets typical in geospatial analysis.
- Optimization: Utilizes reducers and efficient data structures to minimize computation time.

Reusability

Adaptability:
- Functions are modular and can be reused in different contexts involving geospatial analysis.
- Utility functions like combineReducers and serializeList enhance reusability.

Usage

How It Is Used:
- Integrated into larger geospatial analysis workflows.
- Called by other modules or scripts to compute and analyze shoreline and transect statistics.
- Utilized in environmental monitoring, coastal management, and research projects.

Assumptions

Assumptions Made:
- Input data (transects, shorelines, baseline) are correctly formatted and valid.
- Earth Engine environment is properly set up and accessible.
- Constants and utility functions are correctly imported and available.
- Dates and distances are meaningful and within expected ranges for the analysis.

This comprehensive analysis provides a detailed understanding of the statistics.js module, its functions, and its role within a larger geospatial analysis system.

Flow Diagram [via mermaid]

Module: transects.js

Comprehensive Analysis of `transects.js`

Module Name

transects.js

Primary Objectives

The primary purpose of this module is to generate and manipulate transects (lines orthogonal to a given polygon) using geospatial data. It provides functionality to create these transects and expand them horizontally.

Critical Functions

offsetMapper(extent, origin, theta)
- Creates a function that maps offsets to transects based on the given extent, origin, and angle (theta).
transectAccumulator(step, extent)
- Accumulates transects by iterating over a list of coordinates, computing the necessary transects at each step.
generateOrthogonalTransects(coordinates, step, extent)
- Generates transects orthogonal to a specified polygon based on the provided coordinates, step size, and extent.
expandHorizontally(transects, amount)
- Expands the generated transects horizontally by a specified amount.

Key Variables

extent: The width of the transect.
origin: The starting point for computing displacements.
theta: The angle used for computing the direction of the transect.
step: The distance between consecutive transects.
coordinates: The list of coordinates defining the polygon.
amount: The amount by which to expand the transects horizontally.

Interdependencies

Earth Engine (ee): The module heavily relies on Google Earth Engine (GEE) for geospatial computations and data structures.
Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module to perform geospatial calculations.
Metadata Module: Uses INTERNALS from the metadata module to store additional information in the generated features.

Core vs. Auxiliary Operations

Core Operations:
- generateOrthogonalTransects: Main function to generate transects.
- transectAccumulator: Core logic for accumulating transects.
Auxiliary Operations:
- offsetMapper: Helper function for mapping offsets to transects.
- expandHorizontally: Function to expand transects horizontally.

Operational Sequence

Initialization: Import necessary modules and define helper functions.
Transect Generation:
- Use generateOrthogonalTransects to create transects based on input coordinates.
- Internally, transectAccumulator and offsetMapper are used to compute and accumulate transects.
Expansion:
- Optionally, use expandHorizontally to expand the generated transects.

Performance Aspects

Efficiency: The use of Earth Engine's efficient data structures and algorithms ensures that the module can handle large geospatial datasets.
Scalability: Designed to work with GEE, which is optimized for large-scale geospatial analysis.

Reusability

Modular Design: Functions are designed to be reusable and can be easily integrated into other geospatial analysis workflows.
Parameterization: Functions accept parameters like coordinates, step, and extent, making them adaptable to different use cases.

Usage

Geospatial Analysis: Used in applications requiring the generation of transects for environmental monitoring, land use analysis, and other geospatial studies.
Integration: Can be integrated into larger geospatial analysis pipelines within the GEE environment.

Assumptions

Coordinate System: Assumes that the input coordinates are in a format compatible with GEE.
Earth Engine Availability: Assumes that the Google Earth Engine service is available and accessible.
Geodesy Functions: Assumes that computeBearing and computeDisplacement functions are correctly implemented and available.

This analysis provides a detailed overview of the transects.js module, highlighting its purpose, key components, and usage within geospatial analysis workflows.

Flow Diagram [via mermaid]

Module: transects.js

Comprehensive Analysis of `transects.js`

Module Name

transects.js

Primary Objectives

The primary purpose of this module is to generate and manipulate transects (lines orthogonal to a given polygon) using geospatial data. It provides functionality to create these transects and expand them horizontally.

Critical Functions

offsetMapper(extent, origin, theta)
- Role: Creates a function that maps offsets to transects based on the given extent, origin, and angle (theta).
transectAccumulator(step, extent)
- Role: Accumulates transects along a path defined by a series of coordinates, using a specified step size and extent.
generateOrthogonalTransects(coordinates, step, extent)
- Role: Generates transects orthogonal to a specified polygon based on given coordinates, step size, and extent.
expandHorizontally(transects, amount)
- Role: Expands the generated transects horizontally by a specified amount.

Key Variables

extent: Represents the extent of the transect.
origin: The starting point for displacement calculations.
theta: The angle used for calculating bearings and displacements.
step: The step size for generating transects.
coordinates: List of coordinates defining the polygon.
amount: The amount by which to expand the transects horizontally.

Interdependencies

Earth Engine (ee): The module heavily relies on Google Earth Engine (GEE) for geospatial computations and data structures.
Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module for geospatial calculations.
Metadata Module: Uses INTERNALS from the metadata module to store additional information in features.

Core vs. Auxiliary Operations

Core Operations:
- generateOrthogonalTransects: Main function for generating transects.
- transectAccumulator: Core logic for accumulating transects.
Auxiliary Operations:
- offsetMapper: Helper function for mapping offsets to transects.
- expandHorizontally: Utility function for expanding transects.

Operational Sequence

Initialization: Import necessary modules and define helper functions.
Transect Generation:
- Use generateOrthogonalTransects to create transects based on input coordinates.
- Internally, transectAccumulator and offsetMapper are used to compute and accumulate transects.
Expansion:
- Optionally, use expandHorizontally to expand the generated transects.

Performance Aspects

Efficiency: The use of Earth Engine's efficient data structures and algorithms ensures that the module can handle large geospatial datasets.
Scalability: Designed to work with large-scale geospatial data, leveraging GEE's cloud-based processing capabilities.

Reusability

Modular Design: Functions are designed to be reusable and can be easily integrated into other geospatial analysis workflows.
Parameterization: Functions accept parameters like coordinates, step, and extent, making them adaptable to different use cases.

Usage

Geospatial Analysis: Used in applications requiring the generation of transects for environmental monitoring, land use analysis, and other geospatial studies.
Integration: Can be integrated into larger geospatial data processing pipelines using Google Earth Engine.

Assumptions

Coordinate System: Assumes input coordinates are in a format compatible with Earth Engine.
Earth Engine Availability: Assumes that the Google Earth Engine service is available and accessible.
Geodesy Functions: Assumes that computeBearing and computeDisplacement functions are correctly implemented and available.

This analysis provides a detailed overview of the transects.js module, highlighting its purpose, key components, and usage within geospatial data processing workflows.

High_Level_Doc

Introduction :

High-Level System Overview

Purpose

Full System Overview

Module Overview

Module: extraction.js

Comprehensive Analysis of extraction.js

Module Name

Primary Objectives

Critical Functions

Key Variables

Interdependencies

Core vs. Auxiliary Operations

Operational Sequence

Performance Aspects

Reusability

Usage

Assumptions

Flow Diagram [via mermaid]

Module: extraction.js

Comprehensive Analysis of extraction.js

Module Name

Primary Objectives

Critical Functions

Key Variables

Interdependencies

Core vs. Auxiliary Operations

Operational Sequence

Performance Aspects

Reusability

Usage

Assumptions

Flow Diagram [via mermaid]

Module: index.js

Module Name

Primary Objectives

Critical Functions

Key Variables

Interdependencies

Core vs. Auxiliary Operations

Operational Sequence

Performance Aspects

Reusability

Usage

Assumptions

Flow Diagram [via mermaid]

Module: index.js

Comprehensive Analysis of the Module

Module Name:

Primary Objectives:

Critical Functions:

Key Variables:

Interdependencies:

Core vs. Auxiliary Operations:

Operational Sequence:

Performance Aspects:

Reusability:

Usage:

Assumptions:

Flow Diagram [via mermaid]

Module: index.js

Comprehensive Analysis of index.js

Module Name

Primary Objectives

Critical Functions

Key Variables

Interdependencies

Core vs. Auxiliary Operations

Operational Sequence

Performance Aspects

Reusability

Usage

Assumptions

Flow Diagram [via mermaid]

Module: index.js

Comprehensive Analysis of the Module

Module Name

Primary Objectives

Critical Functions

Comprehensive Analysis of `extraction.js`

Comprehensive Analysis of `extraction.js`

Comprehensive Analysis of `index.js`