Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia

Abstract

The present study investigates the spatiotemporal pattern of Land Use land cover (LULC) and land surface temperature (LST) in Abha for the years 1990, 2000, and 2018. This research also forecasts the future LULC and LST for the year 2028. The support vector machine (SVM) was utilised to classify the LULC for the periods 1990-. The LST for the same period was derived using the mono window algorithm. The artificial neural network-cellular automata model (ANN-CA) was employed to forecast LULC and LST for the year 2028. The results indicated that urban areas rose by 434.6% between 1990 and 2018, while the LST soared to 50 °C in 2018, covering half of the study area. The built-up area, as well as the high LST zone, will be expanded in 2028. As a result, sustainable management strategies should be implemented to limit uncontrolled urban sprawl and LST.

Keywords:

• Machine learning
• support vector machine
• CA-ANN
• projected LULC
• land surface temperature

Acknowledgements

The authors extend their appreciation to the deputyship for research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 669.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [author initials], upon reasonable request.

Additional information

Funding

The authors extend their appreciation to the deputyship for research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 669.