High-resolution landfill characterization using SAR remote sensing and cloud-based processing

Area of Science:

• Environmental Science
• Geospatial Analysis
• Remote Sensing

Background:

• Developing countries like India struggle with solid waste management due to poor monitoring and landfill data.
• Existing methods lack reliable reporting mechanisms for landfill statistics, hindering effective planning.

Purpose of the Study:

• To develop and validate a remote sensing methodology for quantifying landfill characteristics in India.
• To establish a scalable and cost-effective framework for nationwide landfill monitoring.

Main Methods:

• Integration of Python programming with Sentinel Application Platform (SNAP) to generate Digital Elevation Models (DEMs) from Sentinel-1 SAR imagery.
• Utilizing Google Earth Engine (GEE) for large-scale analysis of waste height and volumetric estimates from 80 landfill sites across India.
• Field validation using drone surveys and Differential Global Positioning System (DGPS) at two representative landfill sites.

Main Results:

• The study presents the first nationwide assessment of landfill volume using a uniform remote sensing framework.
• Field validation demonstrated high accuracy, with volume deviations as low as 0.8% for specific sites.
• The proposed SAR-based DEM approach proves reliable for estimating landfill height, area, and volume.

Conclusions:

• Remote sensing using SAR-based DEMs provides a cost-effective, scalable solution for systematic, near real-time landfill monitoring.
• This framework supports crucial capacity planning, environmental assessments, and policy formulation for waste management.
• The methodology offers a pathway for developing countries to adopt data-driven waste management strategies amidst urbanization and increasing waste generation.