Non-agricultural monitoring and spatio-temporal analysis study of cultivated land based on deep learning method:a case study of Kaiyang county

doi:10.13474/j.cnki.11-2246.2024.0303

Abstract

Abstract: How to quickly detect illegal cultivated land non-agriculturalization and understand its spatial distribution and change process is a central issue of fundamentally reducing cultivated land non-agriculturalization.Based on multi-temporal remote sensing image,a monitoring index system for land non-agriculturalization and a sample database with local topographic features are established.A remote sensing change detection model is established using deep learning technology,and applied to the temporal monitoring of land non-agriculturalization in Kaiyang county.On this basis,the spatial and temporal distribution characteristics of regional land non-agriculturalization are discussed using kernel density estimation spatial analysis method.The results show that the combination of satellite remote sensing and deep learning technology can achieve rapid and dynamic monitoring of land non-agriculturalization in a large range.The overall trend of new illegal land non-agriculturalization activities monitored in Kaiyang county from April 2021 to December 2022 is decreasing,but there are local aggregation areas and the number of illegal activities shows relatively obvious seasonal characteristics.

Key words: remote sensing images, cultivated land, non-agriculturalization, deep leaming, kernel density estimation

CLC Number:

P237

ZHANG Lanlan, WANG Honglei. Non-agricultural monitoring and spatio-temporal analysis study of cultivated land based on deep learning method:a case study of Kaiyang county[J]. Bulletin of Surveying and Mapping, 2024, 0(3): 13-18.

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