首页|Abandoned land identification in karst mountain area based on time series SAR characteristics at geo-parcels scale

Abandoned land identification in karst mountain area based on time series SAR characteristics at geo-parcels scale

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Mapping abandoned land is very important for accurate agricultural management.However,in karst mountainous areas,continuous high-resolution optical images are difficult to obtain in rainy weather,and the land is fragmented,which poses a great challenge for remote sensing monitoring of agriculture activities.In this study,a new method for identifying abandoned land is proposed:firstly,a few Google Earth images are used to transform arable land into accurate vectorized geo-parcels;secondly,a time-series data set was constructed using Sentinel-1A Alpha parameters for 2020 on each farmland geo-parcel;thirdly,the semi-variation function(SVF)was used to analyze the spatial-temporal characteristics,then identify abandoned land.The results show:(1)On the basis of accurate spatial information and boundary of farmland land,the SAR time-series dataset reflects the structure and time-series response.The method eventually extracted abandoned land with an accuracy of 80.25%.The problem of remote sensing monitoring in rainy regions and complex surface areas is well-resolved.(2)The spatial heterogeneity of abandoned land is more obvious than that of cultivated land within geo-parcels.The step size for significant changes in the SVF of abandoned land is shorter than that of cultivated land.(3)The SVF time sequence curve presented a strong peak feature when farmland was abandoned.This reveals that the internal spatial structure of abandoned land is more disordered and complex.It showed that time-series variations of spatial structure within cultivated land have broader applications in remote sensing monitoring of agriculture in complex imaging environments.

Sentinel-1 SARAbandoned farmlandSemi variogram functionFarmland geo parcelTime series characteristicsTexture featureKarst mountainous area

ZHOU Zhong-fa、WANG Ling-yu、CHEN Quan、LUO Jian-cheng、ZHAO Xin、ZHANG Shu、ZHANG Wen-hui、LIAO Juan、LYU Zhi-jun

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School of Karst Science,Guizhou Normal University,Guiyang 550001,China

Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China,Ministry of Natural Resources,Guangzhou 510670,China

Department of Information Engineering,Guizhou Institute of Light Industry,Guiyang 550001,China

Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China

Hengyang Normal University,Hengyang 421000,China

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Guizhou Provincial Science and Technology FoundationNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaKey Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China Ministry of Natural ResourcesExcellent Youth Project of Hunan Provincial Education Department

Qiankehe ZK[2022]-3024166108841631179420713162017YFB05036002022NRM000422B0725

2023

10.1007/s11629-021-7281-6

山地科学学报(英文版)
中国科学院水利部成都山地灾害与环境研究所

山地科学学报(英文版)

CSTPCDCSCD
影响因子：0.228
ISSN：1672-6316
年,卷(期)：2023.20(3)
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