Description:
This project involves analyzing changes in land use and land cover over a 33-year period (1990–2023) using geospatial and remote sensing techniques. By leveraging satellite imagery from sources like Landsat and Sentinel-2, the study identifies, classifies, and quantifies shifts in land cover types, such as forests, urban areas, agricultural lands, and water bodies.
Objectives:
- Assess Changes: Detect and map significant changes in land cover types across the study area.
- Identify Trends: Analyze patterns of urbanization, deforestation, agricultural expansion, or other environmental changes.
- Support Decision-Making: Provide actionable insights to inform sustainable land management and planning policies.
Methodology:
- Data Collection: Utilize Landsat (1990–2013) and Sentinel-2 (2015–2023) imagery.
- Image Processing: Perform preprocessing tasks like atmospheric correction and cloud masking.
- Classification: Apply supervised classification (e.g., Maximum Likelihood Classification) to categorize land cover types.
- Change Detection: Compare classified images across time to assess the magnitude and nature of changes.
- Visualization: Use GIS tools like ArcGIS to create maps and charts that display the LULC dynamics.
Outcome:
This analysis highlights environmental and human-induced changes over time, providing valuable data for urban planning, resource management, and climate change adaptation strategies.