🛰️ Remote Sensing Analysis using Google Earth Engine

This repository contains remote sensing workflows developed using Google Earth Engine (GEE) for analyzing satellite imagery from multiple Earth observation missions.
The scripts demonstrate satellite data preprocessing, vegetation analysis, and land use/land cover (LULC) classification.

The study area used in the workflows is Coimbatore, Tamil Nadu, India.

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📌 Table of Contents

• Overview
• Sensors & Data Sources
• Repository Structure
• Methodology
• LULC Classification
• Example Outputs
• Tools & Technologies
• Requirements
• Author

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Overview

This repository documents practical remote sensing analysis workflows implemented in Google Earth Engine using multiple satellite datasets.

The work focuses on:

• Satellite image preprocessing
• Cloud masking of optical imagery
• Vegetation analysis using NDVI
• Land Use Land Cover (LULC) classification
• Change detection using multi-temporal imagery

The scripts are written primarily in Google Earth Engine JavaScript API, with optional Python-based analysis.

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Sensors & Data Sources

Sensor	Satellite	Spatial Resolution	Purpose
ETM+	Landsat 7	30 m (15 m pan)	LULC classification and change detection
OLI/TIRS	Landsat 8	30 m	Cloud masking and preprocessing
OLI-2/TIRS-2	Landsat 9	30 m	LULC classification (2023–2024)
MSI	Sentinel-2	10 m / 20 m	NDVI vegetation analysis

All imagery was accessed through the Google Earth Engine Data Catalog.

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Repository Structure

remote-sensing-analysis/
│
├── GEE/
│   │
│   ├── Landsat7/
│   │   └── ChangeDetection_Cbe.js
│   │
│   ├── Landsat8/
│   │   └── CloudMasking_Cbe.js
│   │
│   ├── Landsat9/
│   │   └── LULC_Classification_Cbe_2023_2024.js
│   │
│   └── Sentinel2/
│       └── NDVI_Analysis_Cbe.js
│
├── README.md
└── LICENSE

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Methodology

1. Data Filtering

Satellite image collections were filtered using:

• Area of Interest (Coimbatore region)
• Date range selection
• Cloud cover threshold

Example:

var dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
              .filterBounds(aoi)
              .filterDate('2023-01-01','2024-01-01')
              .filterMetadata('CLOUD_COVER','less_than',10);

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2. Preprocessing

Landsat-8 Cloud Masking

Cloud and cloud shadow pixels were removed using the QA_PIXEL band.

Steps:

• Extract QA band
• Identify cloud pixels
• Mask unwanted pixels
• Generate clean imagery for analysis

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Sentinel-2 Preprocessing

Sentinel-2 imagery was used to compute vegetation indices such as NDVI.

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3. Vegetation Analysis

NDVI was computed using Sentinel-2 spectral bands:

NDVI = (NIR - Red) / (NIR + Red)

Where:

Band	Description
NIR	Near Infrared Band
Red	Red Band

NDVI helps identify vegetation health and density across the study area.

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4. LULC Classification

Supervised classification was implemented using Support Vector Machine (SVM).

Training samples were collected from representative land cover types.

Typical classes used in the classification include:

Class	Description
Water Bodies	Rivers, lakes and reservoirs
Vegetation	Forest and dense vegetation
Agricultural Land	Crop fields and plantations
Built-up	Urban areas and infrastructure
Barren Land	Exposed soil and rocky terrain

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Example Outputs

Outputs generated from the scripts include:

• NDVI vegetation maps
• Cloud-masked Landsat imagery
• LULC classification maps
• Land cover change maps for Coimbatore region

Example visual outputs may include:

• NDVI distribution maps
• Classified LULC maps
• Satellite imagery composites

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Tools & Technologies

The following tools were used in the workflows:

• Google Earth Engine (GEE)
• JavaScript API
• Satellite imagery from Landsat and Sentinel missions
• Python (optional analysis)

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Requirements

To run the scripts:

1. Create a Google Earth Engine account
   https://earthengine.google.com/signup/

2. Use the Google Earth Engine Code Editor

https://code.earthengine.google.com/

3. Copy any .js script from this repository and run it inside the editor.

Optional Python packages:

pip install earthengine-api geemap numpy pandas matplotlib

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Author

Harshidha M
M.Tech Geoinformatics
National Institute of Technology Warangal

Email: hm25cem5r04@student.nitw.ac.in

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This repository contains academic exercises and experiments in remote sensing and geospatial analysis using satellite imagery.