Detection of Black Soil Change in the Jiguan District Based on Sentinel-2 Satellite Remote Sensing Images

DOI: https://doi.org/10.62517/jbdc.202401403

Author(s)

Yuejiao Han, Ping Zhang*

Affiliation(s)

Faculty of Environmental Arts and Architectural Engineering, Heilongjiang University of Technology, Jixi, Heilongjiang, China *Corresponding Author.

Abstract

In this paper, the sentinel-2 satellite remote sensing images are mainly used to detect the changes of black soil in Jiguan District, Jixi City. Satellite remote sensing images in the same month of 2021 and 2024 were selected to complete the land use classification based on the maximum likelihood method, and the dynamic change trend of black soil land use in the study area in the past four years was discussed by combining the transfer matrix and spatial distribution. The experimental results show that the overall classification accuracy of time series images is better than 99%, and the Kappa coefficient is more than 98%. From 2021 to 2024, the area of black soil meadow will decrease significantly. There are transfers of black soil and transfer-out, among them, the transfer is manifested as the conversion of black soil woodland to black soil and the conversion of black soil meadow to black soil, and most of the transfer-out is black soil to black soil woodland, and the overall transfer out is greater than the transfer-in.

Keywords

Land Use; Change Detection; Jiguan District; Black Soil

References

[1] Dong Yanqin, Ren Jintong, Zhang Tao. Research on land use change detection of GF-1 image based on IR-MAD algorithm. Wireless Internet Technology, 2023, 20(03): 109-113. [2] Li Yibo, Jian Ji. Detection of land use change in Chengdu from 2015 to 2020 based on random forest. Science and Technology Innovation and Application, 2023, 13(03): 95-97+102. [3] Li Congyi, Kong Xiangbing, Yang Na, et al. A high-resolution image land use/cover change detection method based on U-Net. Bulletin of Soil and Water Conservation, 2021, 41(04): 133-138+144. [4] Li Dan, Zhou Jia, Zhan Daqing. Spatiotemporal variation and driving factors of cultivated land in Heilongjiang Province. Scientia Geographica Sinica, 2021, 41(7): 1266-1275. [5] Man Weidong, Wang Zongming, Liu Mingyue, et al. Remote sensing analysis of spatiotemporal changes of cultivated land in Northeast China from 1990 to 2013. Transactions of the CSAE, 2016, 32(7): 1-10. [6] Yao Dongheng, Cao Ying, Cheng Jingwen, et al. Spatiotemporal evolution and driving factors of black soil under the framework of the earth's critical zone. Resources Science, 2023, 45(09): 1856-1868. [7] Xiang Chaosheng, Deng Liang, Chen Jun, et al. Land use and change detection of high-resolution remote sensing images. Beijing Surveying and Mapping, 2024, 38(02): 124-130. [8] Ouyang Jianting, Xu Mingyu, Chen Ping. 2013-2022year G city land use/cover change analysis. Anhui Agricultural Science Bulletin, 2024, 30(21): 66-70. [9] Aynegarr Yasen, Bumariyyam Maimaiti. Analysis of spatio-temporal changes of agricultural land use in Aksu City from 2000 to 2020.Modern Agricultural Science and Technology, 2024, (20): 99-103. [10] Luo Xufu, Dong Zhengliang. Spatiotemporal change and driving force analysis of land use pattern in Zigong City from 2000 to 2020.Sichuan Forestry Science and Technology, 2024, 45(05): 58-67.