IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p11873-d920772.html
   My bibliography  Save this article

Developing a Spatiotemporal Model to Forecast Land Surface Temperature: A Way Forward for Better Town Planning

Author

Listed:
  • Umer Khalil

    (ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat, 997514 AE Enschede, The Netherlands)

  • Umar Azam

    (Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Islamabad 47040, Pakistan)

  • Bilal Aslam

    (School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA)

  • Israr Ullah

    (Division of Earth Sciences and Geography, RWTH Aachen University, 52062 Aachen, Germany)

  • Aqil Tariq

    (Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, 775 Stone Boulevard, Starkville, MS 39762, USA
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China)

  • Qingting Li

    (Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Linlin Lu

    (Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

The change in the local climate is attributed primarily to rapid urbanization, and this change has a strong influence on the adjacent areas. Lahore is one of the fast-growing metropolises in Pakistan, representing a swiftly urbanizing cluster. Anthropogenic materials sweep the usual land surfaces owing to the rapid urbanization, which adversely influences the environment causing the Surface Urban Heat Island (SUHI) effect. For the analysis of the SUHI effect, the parameter of utmost importance is the Land Surface Temperature (LST). The current research aimed to develop a model to forecast the LST to evaluate the SUHI effect on the surface of the Lahore district. For LST prediction, remote sensing data from Advanced Spaceborne Thermal Emission and the Reflection Radiometer Global Digital Elevation Model and Moderate-Resolution Imaging Spectroradiometer sensor are exploited. Different parameters are used to develop the Long Short-Term Memory (LSTM) model. In the present investigation, for the prediction of LST, the input parameters to the model included 10 years of LST data (2009 to 2019) and the Enhanced Vegetation Index (EVI), road density, and elevation. Data for the year 2020 are used to validate the outcomes of the LSTM model. An assessment of the measured and model-forecasted LST specified that the extent of mean absolute error is 0.27 K for both periods. In contrast, the mean absolute percentage error fluctuated from 0.12 to 0.14%. The functioning of the model is also assessed through the number of pixels of the research area, classified based on the error in the forecasting of LST. The LSTM model is contrasted with the Artificial Neural Network (ANN) model to evaluate the skill score factor of the LSTM model in relation to the ANN model. The skill scores computed for both periods expressed absolute values, which distinctly illustrated the efficiency of the LSTM model for better LST prediction compared to the ANN model. Thus, the LST prediction for evaluating the SUHI effect by the LSTM model is practically acceptable.

Suggested Citation

  • Umer Khalil & Umar Azam & Bilal Aslam & Israr Ullah & Aqil Tariq & Qingting Li & Linlin Lu, 2022. "Developing a Spatiotemporal Model to Forecast Land Surface Temperature: A Way Forward for Better Town Planning," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11873-:d:920772
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/11873/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/11873/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuan Yu & Suixiang Shi & Lingyu Xu & Yaya Liu & Qingsheng Miao & Miao Sun, 2020. "A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, May.
    2. Dipankar Bera & Nilanjana Das Chatterjee & Faisal Mumtaz & Santanu Dinda & Subrata Ghosh & Na Zhao & Sudip Bera & Aqil Tariq, 2022. "Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India," Land, MDPI, vol. 11(9), pages 1-28, September.
    3. Salma Hamza & Imran Khan & Linlin Lu & Hua Liu & Farkhunda Burke & Syed Nawaz-ul-Huda & Muhammad Fahad Baqa & Aqil Tariq, 2021. "The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    4. Xiaoqing Zhao & Junwei Pu & Xingyou Wang & Junxu Chen & Liang Emlyn Yang & Zexian Gu, 2018. "Land-Use Spatio-Temporal Change and Its Driving Factors in an Artificial Forest Area in Southwest China," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    5. Stefan Tsokov & Milena Lazarova & Adelina Aleksieva-Petrova, 2022. "A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction," Sustainability, MDPI, vol. 14(9), pages 1-38, April.
    6. Muhammad Fahad Baqa & Fang Chen & Linlin Lu & Salman Qureshi & Aqil Tariq & Siyuan Wang & Linhai Jing & Salma Hamza & Qingting Li, 2021. "Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan," Land, MDPI, vol. 10(7), pages 1-17, July.
    7. Wheatcroft, Edward, 2019. "Interpreting the skill score form of forecast performance metrics," International Journal of Forecasting, Elsevier, vol. 35(2), pages 573-579.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ge Wang & Ziqi Zhou & Jianguo Xia & Dinghua Ou & Jianbo Fei & Shunya Gong & Yuxiao Xiang, 2023. "Optimal Allocation of Territorial Space in the Minjiang River Basin Based on a Double Optimization Simulation Model," Land, MDPI, vol. 12(11), pages 1-26, October.
    2. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    3. Oznur Isinkaralar & Kaan Isinkaralar & Dilara Yilmaz, 2023. "Climate-related spatial reduction risk of agricultural lands on the Mediterranean coast in Türkiye and scenario-based modelling of urban growth," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 13199-13217, November.
    4. 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.
    5. Hao Zhou & Tao Wang & Hongchao Zhao & Zicheng Wang, 2022. "Updated Prediction of Air Quality Based on Kalman-Attention-LSTM Network," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    6. Muhammad Fahad Baqa & Fang Chen & Linlin Lu & Salman Qureshi & Aqil Tariq & Siyuan Wang & Linhai Jing & Salma Hamza & Qingting Li, 2021. "Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan," Land, MDPI, vol. 10(7), pages 1-17, July.
    7. Thi-Thu Vu & Yuan Shen, 2021. "Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces," Land, MDPI, vol. 10(8), pages 1-13, July.
    8. Jing Liu & Chunchun Hu & Xionghua Kang & Fei Chen, 2023. "A Loosely Coupled Model for Simulating and Predicting Land Use Changes," Land, MDPI, vol. 12(1), pages 1-19, January.
    9. Hoyong Kim & Donghyun Kim, 2022. "Changes in Urban Growth Patterns in Busan Metropolitan City, Korea: Population and Urbanized Areas," Land, MDPI, vol. 11(8), pages 1-18, August.
    10. Sandeep Kumar & Fulena Rajak, 2023. "Assessment of Urban Green Open Spaces of Micro- and Meso-Level Zones, Based on the Growth Pattern: Case of Patna City," Sustainability, MDPI, vol. 15(2), pages 1-29, January.
    11. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    12. Wheatcroft Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 273-287, December.
    13. Wheatcroft, Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," LSE Research Online Documents on Economics 111494, London School of Economics and Political Science, LSE Library.
    14. Yanan Li & Linghua Duo & Ming Zhang & Zhenhua Wu & Yanjun Guan, 2021. "Assessment and Estimation of the Spatial and Temporal Evolution of Landscape Patterns and Their Impact on Habitat Quality in Nanchang, China," Land, MDPI, vol. 10(10), pages 1-19, October.
    15. Muhammad Majeed & Aqil Tariq & Muhammad Mushahid Anwar & Arshad Mahmood Khan & Fahim Arshad & Faisal Mumtaz & Muhammad Farhan & Lili Zhang & Aroosa Zafar & Marjan Aziz & Sanaullah Abbasi & Ghani Rahma, 2021. "Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data," Land, MDPI, vol. 10(10), pages 1-17, September.
    16. Mirza Waleed & Muhammad Sajjad & Anthony Owusu Acheampong & Md. Tauhidul Alam, 2023. "Towards Sustainable and Livable Cities: Leveraging Remote Sensing, Machine Learning, and Geo-Information Modelling to Explore and Predict Thermal Field Variance in Response to Urban Growth," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    17. Seyd Teymoor Seydi & Reza Shah-Hosseini & Meisam Amani, 2022. "A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
    18. Kassouri, Yacouba & Alola, Andrew Adewale, 2022. "Towards unlocking sustainable land consumption in sub-Saharan Africa: Analysing spatio-temporal variation of built-up land footprint and its determinants," Land Use Policy, Elsevier, vol. 120(C).
    19. Deddy Romulo Siagian & Rajendra P. Shrestha & Sangam Shrestha & John K. M. Kuwornu, 2019. "Factors Driving Rice Land Change 1989–2018 in the Deli Serdang Regency, Indonesia," Agriculture, MDPI, vol. 9(9), pages 1-22, August.
    20. Uisso, Amani Michael & Tanrıvermiş, Harun, 2021. "Driving factors and assessment of changes in the use of arable land in Tanzania," Land Use Policy, Elsevier, vol. 104(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11873-:d:920772. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.