We have compiled an inventory of 1004 rock glaciers for Uttarakhand State, India, using high‐resolution satellite data freely available on Google Earth. The inventory is used to analyze the origin, spatial distribution, geometry and dynamics of rock glaciers using a combination of optical remote sensing techniques with a geographic information system (GIS). Results show that development of rock glaciers in this region depends strongly on high elevation (> 4000 m a.s.l.) and slope aspect. Rock glaciers are more dominant towards the southern quadrant (S, SE, SW) than the northern quadrant (N, NE, NW). A large number (n = 608) of small (<0.5 km2) rock glaciers originating from glacial moraine indicates glacial retreat in this region as one of the major causes for the formation of such a large number of rock glaciers. Median elevation of intact rock glaciers indicates that climatic conditions above 4600 m a.s.l. are suitable for the existence of permafrost in this region and that the lower limit of discontinuous permafrost gradually increases from west to east. Despite mean annual air temperatures below 0°C, increasing mean temperatures during warmest quarter of the year could be a strong controlling factor for permafrost thawing in the region. Logistic regression modeling using WorldClim version 2 climate data sets and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data show that these models can produce fairly reliable estimates of permafrost probability in the studied area. MODIS LST climate data sets can be crucial for mapping and monitoring permafrost in the region.

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

We examine spatial and temporal variability in normalized difference vegetation index (NDVI), snow cover and land surface temperature (LST) in Himachal Pradesh between 2001 and 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Mann-Kendall trend tests and Sen's slope estimates indicate increasing NDVI trends during the postmonsoon period. Increasing snow cover trend is observed during winter and premonsoon whereas decreasing annual LST trends are observed for Himachal Pradesh. Pearson's correlation coefficient (PCC) indicate a strong positive correlation between NDVI and LST (PCC = .808) and strong negative correlation between LST and snow cover (PCC = −.809) and NDVI and snow cover (PCC = −.838). Coefficient of determination greater than .90, between MODIS LST and snow cover observations and weather station records, indicate fair representation of ground conditions using the MODIS dataset. Low (2.4°C/1,000 m) and 2 of 26 | Natural Resource Modeling HAQ ET AL.

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