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

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Autores principales:	Mohd Anul Haq, Prashant Baral, Shivaprakash Yaragal, Biswajeet Pradhan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning remote sensing global climate data MODIS Uttarakhand Himalaya Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/49408d3b2f8045c39fc7b9c90a19ad6a
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id	oai:doaj.org-article:49408d3b2f8045c39fc7b9c90a19ad6a
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spelling	oai:doaj.org-article:49408d3b2f8045c39fc7b9c90a19ad6a2021-11-11T19:20:02ZBulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region10.3390/s212174161424-8220https://doaj.org/article/49408d3b2f8045c39fc7b9c90a19ad6a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7416https://doaj.org/toc/1424-8220Studies 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.Mohd Anul HaqPrashant BaralShivaprakash YaragalBiswajeet PradhanMDPI AGarticlemachine learningremote sensingglobal climate dataMODISUttarakhandHimalayaChemical technologyTP1-1185ENSensors, Vol 21, Iss 7416, p 7416 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning remote sensing global climate data MODIS Uttarakhand Himalaya Chemical technology TP1-1185
spellingShingle	machine learning remote sensing global climate data MODIS Uttarakhand Himalaya Chemical technology TP1-1185 Mohd Anul Haq Prashant Baral Shivaprakash Yaragal Biswajeet Pradhan Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
description	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.
format	article
author	Mohd Anul Haq Prashant Baral Shivaprakash Yaragal Biswajeet Pradhan
author_facet	Mohd Anul Haq Prashant Baral Shivaprakash Yaragal Biswajeet Pradhan
author_sort	Mohd Anul Haq
title	Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
title_short	Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
title_full	Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
title_fullStr	Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
title_full_unstemmed	Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region
title_sort	bulk processing of multi-temporal modis data, statistical analyses and machine learning algorithms to understand climate variables in the indian himalayan region
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/49408d3b2f8045c39fc7b9c90a19ad6a
work_keys_str_mv	AT mohdanulhaq bulkprocessingofmultitemporalmodisdatastatisticalanalysesandmachinelearningalgorithmstounderstandclimatevariablesintheindianhimalayanregion AT prashantbaral bulkprocessingofmultitemporalmodisdatastatisticalanalysesandmachinelearningalgorithmstounderstandclimatevariablesintheindianhimalayanregion AT shivaprakashyaragal bulkprocessingofmultitemporalmodisdatastatisticalanalysesandmachinelearningalgorithmstounderstandclimatevariablesintheindianhimalayanregion AT biswajeetpradhan bulkprocessingofmultitemporalmodisdatastatisticalanalysesandmachinelearningalgorithmstounderstandclimatevariablesintheindianhimalayanregion
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Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

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