Google Earth Engine Based 30-Year LULC Change Detection and Driver Analysis in Central India
Researchers actively use Google Earth Engine to study land use and land cover changes in Central India. This powerful cloud platform processes massive satellite data over the past 30 years. As a result, they gain accurate insights into how landscapes have transformed.
The study focuses on regions including Madhya Pradesh, Chhattisgarh, and parts of Rajasthan. Scientists analyze Landsat satellite images from 1995 to 2025. Moreover, they classify land into categories such as forests, agriculture, urban areas, and barren land.
Advanced machine learning algorithms help detect major changes. Forests have significantly decreased in many districts. At the same time, agricultural land and built-up areas have expanded rapidly. Consequently, these shifts affect biodiversity and local climate patterns.
The research also examines the main drivers behind these changes. Population growth, agricultural expansion, infrastructure development, and mining activities play major roles. In addition, climate variability and government policies influence land conversion patterns.
Google Earth Engine makes this analysis faster and more efficient. Researchers apply temporal trend analysis and spatial statistics to identify hotspots of change. Furthermore, they create predictive models to forecast future land use scenarios under different conditions.
This study provides valuable information for policymakers and environmental planners. It helps them develop better strategies for sustainable land management and forest conservation. Moreover, the findings support climate change adaptation efforts in Central India.
Overall, Google Earth Engine enables detailed and large-scale LULC research. It reveals long-term patterns that traditional methods cannot capture easily. As a result, this approach strengthens evidence-based decision-making for regional development and environmental protection.