| 基于Sentinel-2影像的西南山区不同生长期水稻识别 |
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| 引用本文： | 陈安旭,李月臣.基于Sentinel-2影像的西南山区不同生长期水稻识别[J].农业工程学报,2020,36(7):192-199. |
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| 作者姓名： | 陈安旭 李月臣 |
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| 作者单位： | 重庆师范大学地理与旅游学院,重庆 401331;GIS应用研究重庆市高校重点实验室,重庆 401331;西南大学地理科学学院遥感大数据应用重庆市工程研究中心,重庆 400715;重庆金佛山喀斯特生态系统教育部野外科学观测研究站,重庆 400715 |
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| 基金项目： | 国家自然科学基金（41571419）；重庆师范大学研究生科研创新项目（YKC19019） |
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| 摘 要： | 山区水稻种植呈现破碎分散的特点,中低分辨率的遥感影像分类效果不甚理想,需要寻找适用于山区水稻提取的遥感数据源和监测方法;水稻在不同生长阶段有不同的形态特征,适用的分类特征与得出的分类结果显然不同。该研究以Sentinel-2影像为数据源,对不同生长阶段的水稻进行提取。选取波段特征、植被指数、红边指数、水体指数、地形特征、纹理特征等58个分类特征,运用SEaTH算法进行筛选后,采用随机森林分类法进行分类,并构建误差矩阵比较分类结果。结果表明,分类特征经过筛选后,数量分别为发育期16个、生长期13个、成熟期12个;分类结果进行精度验证后,用户精度分别为发育期0.93、生长期0.88、成熟期0.85,水稻发育期为提取水稻的最佳时期。Sentinel-2影像和随机森林方法可作为理想的数据源和监测方法用于山区水稻时空信息的提取。
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| 关 键 词： | 水稻 生长 随机森林算法 特征优选 Sentinel-2 |
| 收稿时间： | 2020/1/13 0:00:00 |
| 修稿时间： | 2020/3/9 0:00:00 |
Rice recognition of different growth stages based on Sentinel-2 images in mountainous areas of Southwest China |
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| Chen Anxu and Li Yuechen.Rice recognition of different growth stages based on Sentinel-2 images in mountainous areas of Southwest China[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(7):192-199. |
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| Authors: | Chen Anxu and Li Yuechen |
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| Institution: | 1.School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China;3. Chongqing Key Laboratory of GIS Application, Chongqing 401331, China; and 1. School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China; 2. Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China;4. Ministry of Education Field Observation and Research Station of Chongqing Jinfoshan Karst Eco-environments, Chongqing 400715, China |
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| Abstract: | Rice is one of the most important food crops in China and even the world.Effective monitoring of rice is essential to ensure national food security,effective water resources management,and greenhouse gas emissions.In recent years,remote sensing technology has been increasingly applied to rice information extraction.To find remote sensing data sources suitable for rice extraction in the mountain area and classification features suitable for each growth stage of the rice,and to compare the classification accuracy differences of rice at different growth stages.The study was based on Sentinel-2 images to perform the extraction.The data included Sentinel-2 image data,ASTER GDEMV2 elevation data,study area boundary vector data,and field survey data.Based on the characteristics of the growth stages of rice,we selected the seeding period from May to June,the growth period from July to August,and the maturity period from the end of August to September.Land-use type was divided into 5 categories:rice,other vegetation(dryland crops,grassland,weed wasteland),forest land(various woody plant land),water bodies,construction land(including buildings,roads,bare Ground).After pre-processing such as atmospheric correction and re-sampling,58 classification features such as band features,vegetation index,red-edge index,water body index,topographical features,and texture features were selected.Classified samples were selected through outdoor field surveys and indoor visual interpretation.682 rice,557 other vegetation,335 water bodies,458 forest lands,and 514 construction land were collected.The mean and standard deviation were extracted and the classification features were screened using the SEaTH algorithm.After screening,the number of classification features was 16 during seeding,13 during growth,and 12 during maturity.The data of each period were classified by the random forest classification method,and an error matrix was constructed to compare the classification results.The user accuracy of each period was 0.93,0.88,and 0.85;the mapping accuracy was 0.93,0.96,and 0.93;the overall classification accuracy was 0.92,0.92,and 0.91;the Kappa coefficient was 0.90,0.90,and 0.88,respectively.The study drew the following conclusions that rice was easily confused with different land types at different growth stages.The separation between various ground objects was better during the seeding period.It was easy to be confused with forest land and other vegetation during the growth period.It was easily confused with construction land and other vegetation during the maturity period.The classification features could be screened by SEaTH algorithm to avoid data redundancy.Band characteristics and vegetation index were the main classification features in each period.The accuracy of the user during the rice seeding period was the highest,and it was the best period for rice extraction.The method provided a feasible mapping scheme for remote sensing extraction of rice cultivation areas in the mountain area.This study was only applicable to small-scale rice extraction.It was bound to increase work costs and reduce efficiency for large-scale rice extraction.GEE(Google Earth Engine)as a remote sensing big data cloud platform,relying on its strong cloud computing capabilities to expand the scope of the research area in the future;the user accuracy obtained during the rice growth period was 0.88,which also achieved high classification accuracy.Could the image development method be used to combine the data from the seeding period with the growth period to obtain higher classification accuracy?The accuracy of classification needed further study in the future.The classification method used in this research was the pixel-based image classification.It was difficult to avoided false pixels.In the future,images could be segmented and the object-oriented classification method was used to eliminate the influence of false pixels. |
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| Keywords: | rice growth random forest algorithm feature optimization Sentinel-2 |
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