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Spatiotemporal Variation in Land Use Land Cover in the Response to Local Climate Change Using Multispectral Remote Sensing Data

Author

Listed:
  • Sajjad Hussain

    (Department of Environmental Sciences, COMSATS University Islamabad, Vehari Campus, Islamabad 61100, Pakistan
    These authors contributed equally to this work.)

  • Linlin Lu

    (Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    These authors contributed equally to this work.)

  • Muhammad Mubeen

    (Department of Environmental Sciences, COMSATS University Islamabad, Vehari Campus, Islamabad 61100, Pakistan)

  • Wajid Nasim

    (Department of Agronomy, University College of Agriculture and Environmental Sciences, The Islamia University of Bahawalpur (IUB), Bahawalpur 63100, Pakistan)

  • Shankar Karuppannan

    (Department of Applied Geology, School of Applied Natural Science, Adama Science & Technology University, Adama P.O. Box 1888, Ethiopia)

  • Shah Fahad

    (Department of Agronomy, The University of Haripur, Haripur 22620, Pakistan)

  • Aqil Tariq

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • B. G. Mousa

    (Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt)

  • Faisal Mumtaz

    (State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences (UCAS), Beijing 101408, China)

  • Muhammad Aslam

    (School of Computing Engineering and Physical Sciences, University of West of Scotland, Paisley G72 0LH, UK)

Abstract

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R 2 ) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R 2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level.

Suggested Citation

  • Sajjad Hussain & Linlin Lu & Muhammad Mubeen & Wajid Nasim & Shankar Karuppannan & Shah Fahad & Aqil Tariq & B. G. Mousa & Faisal Mumtaz & Muhammad Aslam, 2022. "Spatiotemporal Variation in Land Use Land Cover in the Response to Local Climate Change Using Multispectral Remote Sensing Data," Land, MDPI, vol. 11(5), pages 1-19, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:595-:d:796832
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    References listed on IDEAS

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    Cited by:

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    2. Sajjad Hussain & Muhammad Mubeen & Wajid Nasim & Shah Fahad & Musaddiq Ali & Muhammad Azhar Ehsan & Ali Raza, 2023. "Investigation of Irrigation Water Requirement and Evapotranspiration for Water Resource Management in Southern Punjab, Pakistan," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. Seema Mehra Parihar & Vijendra Kumar Pandey & Anshu & Karuna Shree & Khusro Moin & Mohammed Baber Ali & Kanchana Narasimhan & Jeetesh Rai & Azka Kamil, 2022. "Land Use Dynamics and Impact on Regional Climate Post-Tehri Dam in the Bhilangana Basin, Garhwal Himalaya," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    4. Amba Shalishe & Anirudh Bhowmick & Kumneger Elias, 2023. "Agricultural drought analysis and its association among land surface temperature, soil moisture and precipitation in Gamo Zone, Southern Ethiopia: a remote sensing approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 57-70, May.
    5. Tiberiu Iancu & Valentina Constanta Tudor & Eduard Alexandru Dumitru & Cristina Maria Sterie & Marius Mihai Micu & Dragos Smedescu & Liviu Marcuta & Elena Tonea & Paula Stoicea & Catalin Vintu & Andy , 2022. "A Scientometric Analysis of Climate Change Adaptation Studies," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    6. Sanghun Son & Seong-Hyeok Lee & Jaegu Bae & Minji Ryu & Doi Lee & So-Ryeon Park & Dongju Seo & Jinsoo Kim, 2022. "Land-Cover-Change Detection with Aerial Orthoimagery Using SegNet-Based Semantic Segmentation in Namyangju City, South Korea," Sustainability, MDPI, vol. 14(19), pages 1-13, September.

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