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

10 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. Forest fire susceptibility zonation of Mizoram using GIS: A comparative analysis of bivariate models J. Barman et al. 10.1016/j.rines.2025.100126
9. Multisensor Data Fusion for Quantifying Agricultural Fire Impacts on Air Quality and Environmental Degradation P. Bu et al. 10.1109/JSTARS.2025.3578637
10. 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