Mapping of township agricultural land use based on Sentinel-2A images

Y. Jiang, Zhongyou Liu, Xiuchun Dong, Guoye Ren, Zongnan Li
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Abstract

Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.
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基于Sentinel-2A影像的乡镇农业用地制图
目的:实现乡镇规模农业用地和作物分布的准确、快速制图。方法:本研究基于时序遥感指数阈值分类、最大似然等具体方法,对各土地利用类型进行分类,提取作物空间信息,在Sentinel-2A遥感影像的指导下,开展乡镇尺度农业用地制图。并与基于WorldVieW-2融合图像的0.5 m分辨率农业空间信息地图进行对比验证。结果:(1)粮油作物用地、蔬菜用地、农业设施用地和园林用地的面积精度较好,分别为92.93%、98.98%、95.71%和95.14%,与实际面积偏差在8%以内;(2)将OSM道路数据与历史高分辨率影像产生的地块边界、农田路网、运河网空间信息与Sentinel-2A多光谱影像分类结果叠加成图,提高了10 m分辨率影像分类结果地块边界信息的精度。结论:利用多源信息融合方法,利用Sentinel-2A光学影像产生的农业土地利用和作物分布空间大数据,可有效提高乡镇尺度土地利用制图的准确性和时效性,为应用遥感大数据开展乡镇尺度农业景观分析、农业结构优化调整等提供技术参考。
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