Assessing the Influence of Land Use/Land Cover Changes on Land Surface Temperature by Satellite Data Imagery and Supervised Classification Algorithm

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

2 Department of Soil Science, University of Zanjan

Abstract

This research aims to evaluate the abilities of four non-parametric machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Tree (CART), and Minimum Distance (MD), to produce LULC maps. Utilizing multi-temporal data from Sentinel-2 and Landsat-8 sensors, the investigation was conducted within the Google Earth Engine (GEE) framework. The outcomes underscore the superior reliability of Sentinel-2 data compared to Landsat-8 data across all classifiers. The SVM classifier, with an overall accuracy of 92.9% and 92.2% for Sentinel-2 and Landsat-8 images, respectively, provided the best performance compared to other classifiers. The results pertaining to the identification of LULC alterations during the study duration, employing the optimal classifier (SVM), revealed an expansion in the expanse of olive groves, rice paddies, and built-up areas, alongside a contraction in water bodies and barren lands. The evaluation of the implications of LULC variations on Land Surface Temperature (LST) manifested that augmenting vegetation cover corresponded with diminished LST values within the study area. This shift led to LST values ranging from 36.48 to 21.8 Celsius in 2019, which evolved to 33.84 and 19.67 Celsius in 2023. The research concludes that the combination of high-spatial-resolution satellite data and the SVM algorithm presents an accurate and efficient approach for generating LULC maps and assessing environmental transformations.

Keywords

Main Subjects

EXTENDED ABSTRACT

 

Introduction

The significance of comprehensive Land Use and Land Cover (LULC) mapping is indispensable in environmental monitoring, urban planning, and management of natural resources. The integration of cutting-edge satellite imagery and sophisticated machine-learning algorithms has substantially enhanced the precision and practicality of LULC mapping.

Purpose

In this study, advanced image processing and satellite data analysis methods are used to produce high-precision LULC maps, enabling a more comprehensive investigation of the influence of LULC changes on LST. The principal aims of this study encompass the following:

  1. Identify optimal satellite imagery sources and machine learning algorithms for LULC map development.
  2. Assessment of temporal LULC changes within the study duration.
  3. Examination of the impact of LULC changes on LST.

Research method

To produce image collections and process time-series data for this investigation, the GEE cloud computing platform was utilized. All atmospherically corrected surface reflectance products from Landsat-8 and Sentinel-2 in the research area were employed as the main input for spectral-temporal feature extraction and classification. Then, necessary filters and corrections, such as cloud cover removal, were applied.

Then we evaluated the abilities of four non-parametric machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Tree (CART), and Minimum Distance (MD), to produce LULC maps.

Results

The outcomes underscore the superior reliability of Sentinel-2 data compared to Landsat-8 data across all classifiers. The SVM classifier, with an overall accuracy of 92.9% and 92.2% for Sentinel-2 and Landsat-8 images, respectively, provided the best performance compared to other classifiers. The results of the identification of LULC alterations during the study duration, employing the optimal classifier (SVM), revealed an expansion in the expanse of olive groves, rice paddies, and built-up areas, alongside a contraction in water bodies and barren lands. The evaluation of the implications of LULC variations on Land Surface Temperature (LST) manifested that augmenting vegetation cover corresponded with diminished LST values within the study area. This shift led to LST values ranging from 36.48 to 21.8 Celsius in 2019, which evolved to 33.84 and 19.67 Celsius in 2023.

Conclusion

The research concludes that classifying land use and land cover (LULC) using high-resolution imagery and the SVM algorithm in the Google Earth Engine platform is an accurate and efficient method for assessing changes in land use and land cover over different periods. Therefore, this method can be used as a useful tool in natural and urban resource planning and management, such as land use planning and water and soil resource management. The ability to analyze changes in land use and land cover over time provides additional management capabilities, and this information can contribute to better and optimal decision-making in natural resource management and regional planning. Using this method, it is possible to monitor and analyze changes in various land use types accurately and comprehensively across different periods.

Author Contributions

Conceptualization, K.M. and A.G.; Methodology, K.M.; software, S.H.; validation, S.H., M.S.A. and K.M.; Formal Analysis, S.H.; Investigation, S.H.; Resources, S.H.; Data Curation, A.G.; Writing—Original Draft Preparation, S.H.; Writing—Review and Editing, A.G.; Visualization, S.H.; Supervision, K.M.; Project Administration, A.G.; Funding Acquisition, S.H. All authors have read and agreed to the published version of the manuscript.”

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

Data available on request from the authors.

 

Acknowledgements

The authors would like to thank all participants of the present study.

Ethical considerations

The study was approved by the Ethics Committee of the University of Zanjan (Ethical code: 14359/S. S, 2022/9/12). The authors avoided data fabrication, falsification, plagiarism, and misconduct

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Iranian Journal of Soil and Water Research
Volume 55, Issue 12
March 2025
Pages 2443-2466

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