Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine

A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

Bibliographic Data

ID22032401
AuthorsImene Selka (Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf), A M Mokhtari (0000-0002-9725-5995, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf), Abderahemane Mokhtari (Laboratory Materials, Soil and Thermal (LMST), Faculty of Architecture and Civil Engineering, Mohamed BOUDIAF Sciences and Technology University, U.S.T.O-MB, Bp 1505 El Menaouer, Oran 31000, Algeria), Kheira Anissa Tabet Aoul (0000-0002-5750-8353, United Arab Emirates University, corresponding author), Djamal Bengusmia (0000-0003-4480-8637, Senior Study Framework, National Bureau of Studies for Rural Development (BNEDER) Bouchaoui, Chéraga 16084, Algeria), Malika Kacemi (0000-0002-0151-5335, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf), Khadidja Djebbar (Department of Architecture, Faculty of Technology, University of Abou Bekr Belkaid of Tlemcen, Tlemcen 13000, Algeria), Khadidja El-Bahdja Djebbar (0000-0002-4940-6357, University of Abou Bekr Belkaïd)
Year2024
Volume13
Issue7
Pages237
Publication date2024-07-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi13070237
OpenAlexW4400229360
LanguageEN
Citations received2
References cited47

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels

Climate change · Climatology · Geography · Land cover · Land use · Meteorology · Normalized Difference Vegetation Index · Physical geography · Remote sensing · Urban heat island · Urbanization · Computer Science · Environmental Science · Land Use and Ecosystem Services · Urban Green Space and Health · Urban Heat Island Mitigation · Ecology · Geology

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