Coupling Benefit of Land Use, Land Cover Change and Soil Erosion Under Algorithmic Optimization Model
DOI:
https://doi.org/10.4108/eetsis.7351Keywords:
improved interpolation, land cover, soil erosionAbstract
Technology realizes the quantitative and positioning acquisition of soil erosion and land use information, grasps the relationship between the two from space, and provides theoretical reference and scientific basis for local ecological environment construction and soil and water conservation work. This paper uses remote sensing images in my country in 2020 and 2021 as the data source and obtains land use data in four periods respectively. The experimental results show that the land use structure in my country has undergone great changes in 2020, and the land use type has gradually changed from a structure dominated by cultivated land, grassland, and unused land to grassland, forest land, and cultivated land. The other four types of land use area have increased to varying degrees, the unused land has decreased significantly, and the grassland and forest land have increased significantly; there are differences in the changes in the degree of land use in each study period, and the overall level of land use has developed phase by phase toward higher levels. In addition, this paper also studies the clustering algorithm in machine learning and proposes an improved interpolation algorithm for the completion of the original rainfall data. This algorithm can also be applied to the calculation process of rainfall erosion factors, which realizes the automatic calculation system of soil erosion model factors, realizes the real-time calculation and monitoring of soil erosion in the form of calculation tasks, and solves the problem that manual calculation consumes manpower and material resources.
References
[1] Liu J, Sleeter B, Selmants PC, Diao J, Moritsch M. Modeling watershed carbon dynamics as affected by land cover change and soil erosion. Ecol Model. 2021;459(2):109724.
[2] Kilic OM. Effects of land use and land cover changes on soil erosion in semi-arid regions of Turkey: A case study in Almus Lake watershed. Carpathian J Earth Environ Sci. 2021;16(1):129-138.
[3] Jaiswal MK, Amin N. Impact of land-use land cover dynamics on runoff in Panchnoi river basin, north east India. GeoScape. 2021;15(1):19-29.
[4] Zhang L, Lv J. Land-use change from cropland to plantations affects the abundance of nitrogen cycle-related microorganisms and genes in the Loess Plateau of China. Appl Soil Ecol. 2021;161(12):103873.
[5] Pasaribu S, Nasution D. Land use changes simulation for erosion control in the Asahan Hulu sub-watershed. IOP Conf Ser: Earth Environ Sci. 2021;782(2):022070 (8pp).
[6] Zhang X, Lark TJ, Clark CM, Yuan Y, Leduc SD. Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016. Environ Res Lett. 2021;16(5):054018 (13pp).
[7] Chen P. Research on Business English approaches from the perspective of cross-cultural communication competence. Int J Hous Sci Appl. 2024;45(2):13-22.
[8] Wang W. ESG performance on the financing cost of A-share listed companies and an empirical study. Int J Hous Sci Appl. 2024;45(2):1-7.
[9] Ejegu MA, Yegizaw ES. Modelling soil erosion susceptibility and LULC dynamics for land degradation management using geoinformation technology in Debre Tabor district, Northwestern Highlands of Ethiopia. J Degrad Min Lands Manag. 2021;8(2):2623-2633.
[10] Maina VM, Boitt MK. Hungarian association of agricultural informatics European federation for information technology in agriculture. J Agric Inform. 2021;11(2):12-21.
[11] Yuan J, Ouyang Z, Zheng H, Su Y. Ecosystem carbon storage following different approaches to grassland restoration in Southeastern Horqin sandy land, Northern China. Glob Ecol Conserv. 2021;26(73)
[12] Novianti YS, Saismana U, Yuhanes Y, Fikri HN. Mining disposal erosion evaluation: A case study. IOP Conf Ser: Earth Environ Sci. 2021;882(1):012050 (10pp).
[13] Sreekar P. Cost-effective Cloud-Based Big Data Mining with K-means Clustering: An Analysis of Gaussian Data. International Journal of Engineering & Science Research. 2020; 10(1): 229-249.
[14] Rajya L G. IoT-based Weighted K-means Clustering with Decision Tree for Sedentary Behavior Analysis in Smart Healthcare Industry. 2024 Second International Conference on Data Science and Information Systems (ICDSIS). 2024.
[15] Wu Y. Exploration of the integration and application of the modern new Chinese style interior design. Int J Hous Sci Appl. 2024;45(2):28-36.
[16] Guo Z, Yu K, Kumar N, Wei W, Mumtaz S, Guizani M. Deep-distributed-learning-based POI recommendation under mobile-edge networks. IEEE Internet Things J. 2023;10(1):303-317.
[17] Dong Q, Liu X. Optimization practice of university innovation and entrepreneurship education based on the perspective of OBE. J Comb Math Comb Comput. 2021;118:181-189.
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