基于多时相OLI数据的宁夏大尺度水稻面积遥感估算

为客观获取宁夏水稻面积空间分布信息,也为区域农作物遥感监测奠定技术基础,该文以宁夏回族自治区为研究区域,选择美国LandSat-8携带的陆地成像仪(operational land imager,OLI)数据,采用2016年3月11日-7月01日间的15景影像,基于水稻田耕地与水体特征反射率随着季节变化规律的分析,采用归一化植被指数(normalized difference vegetation index,NDVI)、近红外波段反射率(infrared reflectance,IR)、短波指数(short waved index,SWI)3个指数,以及多时相NDVI最大值、IR最小值、SWI最小值3个衍生指数,共6个指数为基础进行决策分类树构建,对全区水稻进行识别与提取,采用该区水稻面积本底遥感调查结果进行精度验证,水稻种植面积提取误差仅.4.22%,Kappa系数为0.83,水稻空间分布的用户分类精度分别为85.11%,制图精度为81.67%;同时与监督分类方法提取的水稻面积进行对比,该文方法提取水稻的用户精度提高了8.13个百分点,制图精度更是提高了20.01个百分点。研究结果表明,利用中高分辨率的OLI遥感时间序列卫星影像,在大宗农作物时间序列的变化规律分析基础上,构建分类决策树,可以准确地提取大宗农作物种植面积,是区域农作物面积遥感监测业务运行中具有潜力的方法。

Ningxia rice area remote sensing estimation on large scale based on multi-temporal OLI data

Current researches mostly focus on method and accuracy selection under the condition where data sources are rich but rarely study regional application of multiphase operation land imager (OLI). In this study, Ningxia Hui Autonomous Region was selected for analysis on regional application potential of multiphase OLI data. In order to objectively obtain Ningxia rice area spatial distribution information, to lay a technical foundation for regional crop remote sensing monitoring, and according to the principle of spectral consistency, this paper divided the study area into 6 ground types of rice, sparse forest and shrub, dry land/woodland, abandoned land, wetland/water bodies, and others. The period before July 10, 2016 was taken as the early stage of rice growth. The normalized difference vegetation index (NDVI), infrared reflectance (IR) and short waved index (SWI) were established by choosing the data of OLI carried by US LandSat-8 and using the images taken in the 6 periods of March 11th, April 12th, April 28th, May 30th, June 15th and July 1st. Based on the analysis of dynamic change of 3 indexes of NDVI, IR and SWI, especially on maximum NDVI, minimal IR, and minimal SWI, a decision tree was established, and the identification of rice types in the study area was conducted by using images between March 11 and July 1 of 2016. The basic processes of decision tree classification were: firstly the ground objects such as cities and towns and deserts were eliminated by using maximum NDVI from March to June; the sparse forest and shrubs were eliminated by using maximum NDVI from March to April; the dry land/woodlands were eliminated by using minimum IR from May to June; then wetland/water bodies were eliminated by using minimum IR from March to April; finally, the abandoned lands were eliminated by using minimum SWI from May to June. The remaining pixels were taken as rice. The accuracy verification was conducted by using the highly accurate GF-2 remote sensing (the resolution was 4 m) survey results of rice area background of this region. The extraction accuracy of GF-2 was as high as 99% above. The results showed that the planting area by GF-2 was 91910 hm2 and the rice planting area was 88030 hm2 by the OLI data. The total extraction error was only -4.22% with the Kappa coefficient of 0.83; the user's classification accuracy of rice spatial distribution was 85.11% with the mapping accuracy of 81.67%. Among the total rice area, the area in Pingluo, Helan, Yingchuan, Qingtunxia, Lingwu, Shapotou, Litong, Yongning, Zhongning, Dawukou and Huinong accounted for 27.71%, 16.76%, 13.69%, 11.87%, 9.93%, 6.72%, 5.34%, 3.27%, 2.24%, 1.60% and 0.87%, respectively. The rice was mostly distributed in the north of Yellow River Irrigation Area. The extraction area based on different phases was different. The rice area proportion of 129/033,129/034 and 130/034 images was 60.41%, 32.88% and 6.71%, respectively. Compared with the user's accuracy of maximum likelihood supervised classification algorithm on the rice area extraction of 76.98% and the mapping accuracy of 61.66% in this area, the method used in this paper showed an increase of 8.13 percentage points in the user's accuracy, and an increase of even 20.01 percentage points in the mapping accuracy. The result shows that, the method proposed here of establishment of decision classifying tree by using the satellite images of early stage OLI remote sensing time series of rice growth before July 10, and based on the analysis of changing pattern of time series of staple crops can accurately extract the staple crop planting area, and it is a potential method for regional crop area remote sensing monitoring operations.