In the heart of India, the Haiderpur Wetlands have long been a vital ecological hub, but their transformation over the decades has remained largely undocumented—until now. A groundbreaking study led by A. Galodha from the School of Interdisciplinary Research (SIRe) at the Indian Institute of Technology (IIT) Delhi has unveiled a comprehensive, longitudinal land use and land cover (LULC) dataset spanning from 1990 to 2023. This research, published in the *ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences* (Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences), leverages advanced satellite imagery and machine learning to offer unprecedented insights into the region’s ecological and urban evolution.
Using Google Earth Engine (GEE) and multi-spectral imagery from Landsat 5, 7, and 8, as well as Sentinel-2, Galodha and their team employed a Random Forest classifier to create a high-resolution (30 m) dataset. This dataset captures key land cover categories, including waterbodies, built-up areas, agriculture, bare soil, swamp vegetation, and forest. The study also incorporated spatial and temporal analyses using indices such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI) to assess vegetation dynamics and water features over time.
“The absence of a comprehensive LULC dataset has been a significant barrier to understanding the environmental transformations and urban heat island (UHI) effects in the Haiderpur region,” Galodha explained. “Our study aims to bridge this gap by providing a detailed, high-resolution dataset that can support sustainable urban planning and conservation efforts.”
The research not only documents past changes but also extends predictions into the future, projecting LULC changes for the years 2025 and 2030. Additionally, the study evaluated land surface temperature (LST) variations, highlighting significant thermal changes corresponding with LULC transformations. The dataset achieved an overall accuracy of 82%, underscoring its reliability for environmental monitoring.
One of the most striking findings is the increase in built-up areas, which has had a corresponding impact on thermal dynamics. Meanwhile, total wetland and forest areas exhibited more stability, indicating a complex interplay of natural and anthropogenic factors. “Our findings confirm that anthropogenic influence is a primary driver of change in the region,” Galodha noted. “This research provides a robust tool for researchers and policymakers to support sustainable urban planning, conservation efforts, and climate change adaptation.”
The implications of this research extend beyond environmental monitoring. For the energy sector, understanding the ecological dynamics of regions like Haiderpur can inform the development of sustainable energy projects, such as hydropower and solar farms, which rely on stable water and land resources. By providing a detailed, high-resolution dataset, this study offers a valuable resource for energy companies looking to minimize their environmental footprint and maximize the efficiency of their projects.
Moreover, the study’s alignment with the United Nations Sustainable Development Goals (SDGs), particularly Goal 6 (Clean Water and Sanitation), Goal 13 (Climate Action), and Goal 15 (Life on Land), underscores its broader relevance. As the world grapples with the impacts of climate change, research like this is crucial for developing strategies that balance economic growth with environmental sustainability.
In the coming years, this research could shape future developments in the field of environmental monitoring and urban planning. By providing a comprehensive, longitudinal dataset, it offers a foundation for further studies and practical applications. As Galodha and their team continue to refine their methods and expand their dataset, the potential for this research to drive meaningful change in the Haiderpur region and beyond is immense.