农业遥感；谷歌地球引擎；随机森林分类；种植结构提取；绿洲农业," /> 农业遥感；谷歌地球引擎；随机森林分类；种植结构提取；绿洲农业,"/> remote sensing in agriculture,Google Earth Engine,random forest classification,crop structure extraction,oasis agriculture,"/> 基于NDVI时间序列影像的天山北坡经济带农业种植结构提取
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干旱区地理 ›› 2019, Vol. 42 ›› Issue (5): 1105-1114.doi: 10.12118/j.issn.1000-6060.2019.05.16

• 生态与环境 • 上一篇    下一篇

基于NDVI时间序列影像的天山北坡经济带农业种植结构提取

熊元康1,2，张清凌1,3   

  1. 1中国科学院新疆生态与地理研究所，新疆乌鲁木齐830011； 2中国科学院大学,北京100049； 3中山大学航空航天学院，广州510006
  • 收稿日期:2019-01-14 修回日期:2019-04-22 出版日期:2019-09-25 发布日期:2019-09-19
  • 通讯作者: 张清凌（1974-），男，研究员，研究方向为农业遥感、夜光遥感、城市遥感等.
  • 作者简介:熊元康（1994-），男，硕士研究生，主要从事农业遥感研究. E-mail xiongyuankang16@mails.ucas.ac.cn
  • 基金资助:
    科技部国家重点研发计划（YFB0504204）；中国科学院百人计划（Y674141001）；新疆维吾尔自治区引进高层次人才专项（Y644151001）

Cropping structure extraction with [WTHX]NDVI[WTHZ] timeseries images in the northern Tianshan Economic Belt

XIONG Yuan-kang1,2,ZHANG Qing-ling1,3   

  1. 1 Research Center on Ecology and Environment of Central Asia,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,Xinjiang,China;

    2 University of Chinese Academy of Sciences,Beijing 100049,China; 3 School of Aeronautics and Astronautics,Sun Yat-sen University,Guangzhou 510006,Guangdong,China

  • Received:2019-01-14 Revised:2019-04-22 Online:2019-09-25 Published:2019-09-19

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摘要： 水资源匮乏是干旱区实现可持续发展的最大障碍。干旱区农业灌溉耗费大量的水资源，不同农作物在生长期所需的灌溉水量存在较大的差异，因此快速准确地了解干旱区的农业种植结构可以为节水型农业种植结构优化提供重要依据。以天山北坡经济带为研究区，以谷歌地球引擎（Google Earth Engine，GEE）云平台为支撑，以Sentinel 2以及Landsat 7-8的数据为遥感数据源，采取以下步骤进行研究区的农业种植结构提取：首先，为了简化农业种植结构提取的过程，利用一年最大NDVI值以及坡度信息构建耕地掩膜图层；然后，根据研究区内主要农作物的物候历，获取不同时间段内的最大NDVI值的时间序列数据以及农作物在一年中出现NDVI最大值的日期，并在此基础上构建一个包含10波段的特征波段影像；最后，结合野外实地考察获得的有效样本点以及经耕地掩膜图层掩膜后的10波段特征波段影像，利用随机森林分类器进行研究区的农业种植结构提取。分类结果表明：2018年研究区内棉花、玉米、小麦的总体分类精度为92.19%，Kappa系数为0.883。为了进一步将分类结果与统计数据进行对比，我们将训练得到的分类器应用于2017年的遥感影像，提取了研究区内2017年的农业种植结构信息，其分类结果表明2017年研究区内棉花、玉米、小麦的种植面积分别为5 270 km2、2 000 km2、2 340 km2，其相对精度分别为86.53%、77.54%、86.19%。

关键词: font-size:10.5pt, 农业遥感；谷歌地球引擎；随机森林分类；种植结构提取；绿洲农业')">">农业遥感；谷歌地球引擎；随机森林分类；种植结构提取；绿洲农业

Abstract:  

Limited water resources is the major factor affecting sustainable development in arid areas,and the water resources in arid areas are mostly used for agricultural irrigation.Rapidly and accurately mapping cropping structure in arid areas can provide an important basis for optimizing water use in agriculture.In this study,the Northern Tianshan Economic Belt,Xinjiang,China was chosen as the study area,and a method to map the cropping structure in this region with multi-source remote sensing data based on the Google Earth Engine (GEE) cloud platform was proposed.The Sentinel-2 and Landsat 7-8 data were chosen as the remote sensing data sources to extract the cropping structure in the study area through the following steps. First, in order to simplify the cropping structure extraction process and minimize the impacts from non-crop vegetation, a cropland mask was constructed by using the maximum NDVI value and slope information throughout the year in the study area.Second,according to the phenology calendars of the main crops in the study area, the time-series data of the maximum NDVI value and the corresponding date was calculated with remote sensing data. Then, the 10 feature bands were constructed. Third, the 10 feature bands were masked with the cropland mask. Based on these data together with the field samples, the random forest classifier was applied to cropping structure extraction. The accuracy evaluation results showed the overall accuracy of the classification results in 2018 was 92.19%,and the Kappa coefficient was 0.883.In order to further verify the accuracy of the classification algorithm, the crop structure in the study area in 2017 was also extracted, the classification results showed that the planted area of cotton, corn and wheat in the study area were 5 270 km2,2 000 km2 and 2 340 km2 respectively in 2017, and then compared it with the results of statistical yearbook data in 2017.The relative accuracy of cotton, corn and wheat planted area were 86.53%,77.54% and 86.19%,respectively.

 

Key words: remote sensing in agriculture')">remote sensing in agriculture, Google Earth Engine, random forest classification, crop structure extraction, oasis agriculture