Assessing the Influence of Land Use/Land Cover Alteration on Climate Variability: An Analysis in the Aurangabad District of Maharashtra State, India

Md Masroor, Ram Avtar, Haroon Sajjad, Pandurang Choudhari, Luc Cimusa Kulimushi, Khaled Mohamed Khedher, Akinola Adesuji Komolafe, Ali P. Yunus and Netrananda Sahu
Additional contact information
Md Masroor: Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
Ram Avtar: Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan
Haroon Sajjad: Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
Pandurang Choudhari: Department o f Geography, University of Mumbai, Mumbai 400098, India
Luc Cimusa Kulimushi: Department of Environmental Studies, University of Lay Adventists of Kigali, Kigali P.O. Box 6392, Rwanda
Khaled Mohamed Khedher: Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
Akinola Adesuji Komolafe: Department of Remote Sensing and GIS, Federal University of Technology, Akure P.M.B. 704, Nigeria
Ali P. Yunus: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Netrananda Sahu: Department of Geography, Delhi School of Economics, University of Delhi, New Delhi 110007, India

Sustainability, 2022, vol. 14, issue 2, 1-28

Abstract: Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

Keywords: climate variability; google earth engine; machine learning algorithm; random forest; support vector machine; multiple linear regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/2/642/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/2/642/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:2:p:642-:d:719595

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().