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 fragm...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 geoparcel;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.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 geoparcels.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.展开更多
基金supported by the Guizhou Provincial Science and Technology Foundation(Qiankehe ZK[2022]-302)the National Natural Science Foundation of China,(Grant NO.41661088,41631179 and 42071316)+2 种基金the National Key Research and Development Program of China(Grant NO.2017YFB0503600)the Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China,Ministry of Natural Resources(No.2022NRM0004)Excellent Youth Project of Hunan Provincial Education Department(22B0725)。
文摘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 geoparcel;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.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 geoparcels.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.
文摘为准确快速获取喀斯特石漠化地区植被和植被覆盖度信息,本研究利用四旋翼无人机采集喀斯特石漠化区域的可见光影像,选择过绿指数(EXG,excess green)、可见光波段差异植被指数(VDVI,visible-band difference vegetation indx)、红绿蓝植被指数(RGBRI,red green and blue vegetation index)、过绿减过红指数(ExG-ExR,excess green-excess red),利用植被指数时序图交点法和样本统计法思想,运用阈值分割法进行植被和植被覆盖度的信息提取,并以监督分类得到的植被和植被覆盖度信息为真实值,进行精度验证。结果表明,在贵州省关岭贞丰花江喀斯特石漠化地区,对于植被信息提取,ExG-ExR的精度最高,总体精度为95.56%,Kappa系数为0.919;ExG-ExR得到的植被覆盖度精度最好,为99.174%,均方根误差RMSE为0.097,R2为0.977。由此可见,在喀斯特地区利用植被指数时序图交点法和样本统计法思想,适合该地区的植被信息提取和植被覆盖度的提取,具有较高的精度。
