Abstract. Indian natural forest has a high ecological significance as it holds much biodiversity and is primarily affected due to deforestation. The present study exhibits the forest cover change on Global Forest Non-Forest (FNF) data for India and greenness trend using MOD15A2H LAI product, which is the best product available till date. JAXA uses of SAR datasets for forest classification based on FAO definitions. Later, Forest Survey of India (FSI) used different definitions for forest classification from FAO and was to compare with JAXA based forest cover. The global FNF study exhibited that total forest cover was reduced from 568249 Km2 to 534958 Km2 during 2007–17 in India. The significant loss of forest cover (33291.59 Km2; by −5.85% change) was primarily evident in Eastern Himalayas followed by Western Himalayas. Whereas forest cover increase was observed in Eastern and the Western Ghats from 2007 to 2017. The state of forest report by FSI states an increase in the forest cover from 690889 Km2 to 708273 Km2 during 2007–17 by 2.51%. The difference in forest cover as estimated by JAXA global FNF datasets and FSI report is attributed to differences in forest cover mapping definitions by both the agencies and use of varied datasets (SAR datasets by JAXA and optical datasets by FSI). It is to note that SAR is highly sensitive to forest cover and vegetation’s as compare to optical datasets. Recent satellite-based (2000–2018) LAI product reveals the increase in leaf area of vegetation during 2000–18. It may be attributed to proper human land use management and implications of green revolutions in the region. The greening in India is most evident from the croplands with insignificant contribution from forest cover.

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The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

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