Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets. (16th March 2020)

• Record Type: Journal Article
• Authors: Haq, Mohd Anul; Baral, Prashant; Yaragal, Shivaprakash; Rahaman, Gazi
• Is Part Of: Natural resource modelling. Volume 33:Number 2(2020:May)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-16
• Subjects: Himachal Pradesh -- land surface temperature -- MODIS -- NDVI -- snow cover; Conservation of natural resources -- Mathematical models -- Periodicals; Ecology -- Mathematical models -- Periodicals; 371.397
• DOI: 10.1111/nrm.12262
• Languages: English
• ISSNs: 0890-8575

Abstract

Abstract: 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 steep (7.1°C/1, 000 m) temperature lapse rate is observed during monsoon and winter, respectively. Recommendations for Resource Managers: Environmental variables such as snow cover, land surface vegetation distribution, and land surface temperature can be studied over large areas in Himalayan regions using remote sensing datasets to overcome the limitations imposed due to harsh climatic conditions and remote terrains. Elevation based analysis of snow cover; land surface vegetation distribution and land surface temperature in the Himalayan region can provide interesting results regarding the interrelationship between the three variables. Statistical analyses such as correlation and regression analyses, forecasting models such as ARIMA and machine learning models such as Support vector regression can be beneficial for analyzing environmental parameters, their interrelationship and their changes over time. Additional studies covering larger areas and datasets related to different components of the mountain environment can bring out interesting results regarding the interrelationship between the environmental variables and how these variables change over time.