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<p>The Hindu Kush Himalayan (HKH) region in northern India, encompassing Uttarakhand, Himachal Pradesh, Jammu and Kashmir, and Ladakh, is facing significant deforestation caused by natural and anthropogenic factors. The present study aimed to assess forest cover dynamics in the HKH region from 2016 to 2022, utilizing geospatial techniques and machine learning-based algorithms. The forest cover maps were generated from Sentinel-2 data in Google Earth Engine using a Random Forest (RF) classifier, Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI) for the years 2016, 2019, and 2022. The study area was divided into 5&#xa0;km × 5&#xa0;km grids to assess the forest cover at the micro-level. The region experienced substantial deforestation, with an increase in built-up areas and scrubland. The integration of the NDVI and SAVI indices provided a balanced measure of forest cover. Transition maps indicate some stability and persistence of forest cover in certain areas. The highest deforestation rate was observed from 2019 to 2022, indicating an accelerated forest loss. The overall trend showed a slight decrease in the deforestation rate compared to that in 2019–2022. The findings highlight the need for effective conservation strategies, robust policy frameworks, and community engagement to ensure the long-term sustainability of forest ecosystems in this ecologically vital region.</p>

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Assessment of forest cover dynamics for the detection of deforestation in the Hindu Kush Himalayan region using geospatial and machine learning approaches

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Laxmi Gupta,
  • Jagabandhu Dixit,
  • Prem Chandra Pandey,
  • Manish Pandey

摘要

The Hindu Kush Himalayan (HKH) region in northern India, encompassing Uttarakhand, Himachal Pradesh, Jammu and Kashmir, and Ladakh, is facing significant deforestation caused by natural and anthropogenic factors. The present study aimed to assess forest cover dynamics in the HKH region from 2016 to 2022, utilizing geospatial techniques and machine learning-based algorithms. The forest cover maps were generated from Sentinel-2 data in Google Earth Engine using a Random Forest (RF) classifier, Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI) for the years 2016, 2019, and 2022. The study area was divided into 5 km × 5 km grids to assess the forest cover at the micro-level. The region experienced substantial deforestation, with an increase in built-up areas and scrubland. The integration of the NDVI and SAVI indices provided a balanced measure of forest cover. Transition maps indicate some stability and persistence of forest cover in certain areas. The highest deforestation rate was observed from 2019 to 2022, indicating an accelerated forest loss. The overall trend showed a slight decrease in the deforestation rate compared to that in 2019–2022. The findings highlight the need for effective conservation strategies, robust policy frameworks, and community engagement to ensure the long-term sustainability of forest ecosystems in this ecologically vital region.