Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region.

Mohd Anul Haq,Prashant Baral,Shivaprakash Yaragal,Biswajeet Pradhan

doi:10.3390/s21217416

Abstract

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.

Highlights

A good understanding of changes related to temperature, areas covered with snow and vegetation is a preliminary step towards developing meaningful conclusions associated with the variations in these environmental components [1,2]
This study aims to provide information about the degree of agreement of Moderate Resolution Imaging Spectroradiometer (MODIS) climate data with the global climate data set available from different sources
Results related to changes in NDVI from the present investigation closely correspond to outcomes obtained from past studies that document analogous negative trends related to NDVI parameters for the Uttarakhand state through analysis of MODIS grids [46]

Summary

Introduction

A good understanding of changes related to temperature, areas covered with snow and vegetation is a preliminary step towards developing meaningful conclusions associated with the variations in these environmental components [1,2]. There are examples of studies where remote sensing observations along with machine learning algorithms have been used to analyse environmental components over wide-ranging areas [6,7]. Some previous studies have mentioned the changes in environmental components in the Uttarakhand state of the north-western Himalayan region [8,9]. Most of the studies in this state have mentioned some local changes or considered only one or two environmental parameters [10,11]. Examples of studies where remotely sensed data in combination with machine learning algorithms are applied to study environmental components in the north-western Himalayan region of Uttarakhand are not pronounced [12]

Objectives

Results

Discussion

Conclusion