GOOGLE EARTH ENGINE BASED TEMPORAL ANALYSIS OF INDICES USED FOR FOREST FIRE STUDY IN MIZORAM, INDIA

9 citations as recorded by crossref.

1. Integrating geospatial tools in mapping forest fire severity and burned areas in the Western Usambara Mountain Forests, Lushoto, Tanzania B. Mkiwa et al. 10.1371/journal.pone.0311865
2. Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India M. Sarkar et al. 10.1016/j.ecoinf.2024.102598
3. Biennial analysis of probable permafrost distribution for Kullu district, North‐west Himalaya using Landsat 8 satellite data I. Pradhan & D. Shukla 10.1002/ldr.4921
4. Rock glaciers as proxy for machine learning based debris‐covered glacier mapping of Kinnaur District, Himachal Pradesh I. Pradhan et al. 10.1002/esp.5888
5. Assessing the environmental impacts of the Capilla del Monte wildfire in Punilla Valley of Argentina using Landsat-9 and Sentinel-5P F. Yilgan et al. 10.1007/s10661-025-14374-y
6. Answer to “Comments on Machine learning-based forest fire susceptibility mapping of Southern Mizoram, a part of Indo-Burma Biodiversity Hotspot” P. Gupta et al. 10.1007/s11356-025-36831-4
7. Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm P. Gupta & D. Shukla 10.1007/s11356-024-33094-3
8. Multisensor Data Fusion for Quantifying Agricultural Fire Impacts on Air Quality and Environmental Degradation P. Bu et al. 10.1109/JSTARS.2025.3578637
9. Implications of Russia–Ukraine war on land surface temperature and air quality: long-term and short-term analysis P. Gupta & D. Shukla 10.1007/s11356-024-32800-5