Study on dynamic change of land usage in Hongta District based on Sentinel-2

Qihong Ren; Tao Wang; Jiankai Hu; Ran Shi; Wenqiu Zhao

doi:10.1117/12.2667380

10 February 2023 Study on dynamic change of land usage in Hongta District based on Sentinel-2

Qihong Ren, Tao Wang, Jiankai Hu, Ran Shi, Wenqiu Zhao

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125520H (2023) https://doi.org/10.1117/12.2667380
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land; the extent of something like the changing land usage; land use transfer; and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly.

Citation Download Citation

Qihong Ren, Tao Wang, Jiankai Hu, Ran Shi, and Wenqiu Zhao "Study on dynamic change of land usage in Hongta District based on Sentinel-2", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125520H (10 February 2023); https://doi.org/10.1117/12.2667380

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KEYWORDS

Remote sensing

Analytical research

Education and training

Agriculture

Image classification

Land cover

Photography

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