基于GF-1/WFV时间序列的绿洲作物类型提取
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摘要
当前基于中等空间分辨率时序数据的农作物种植结构提取成为研究热点,但农作物季相节律特征在不同气候背景下存在较大差异,绿洲作为干旱区具有明显小气候效应的生态景观,其农作物种植结构的遥感提取具有较强的典型性和代表性。选取宁夏河套平原绿洲典型区域,通过构建高分一号(GF-1/WFV)时间序列数据,结合不同作物耕作方式及生长物候,分析不同作物在整个生长季内的归一化植被指数(NDVI)和归一化水体指数(NDWI)的时间序列特征,构建不同决策树提取研究区农作物种植结构信息,并验证了不同方法的适用性。结果表明,对具有明显小气候效应的干旱区绿洲,利用时间分辨率和空间分辨率都较优的GF1-WFV时间序列数据,对其农作物种植结构进行遥感提取具有较强的实用性和代表性。
The rapid extraction of regional crops is of great significance for agricultural production management,planting structure adjustment and optimization,and food security. The use of time series data of remote sensing image for extracting crops is an important means,and the time resolution and spatial resolution are constraints. The time resolution of medium spatial resolution of remote sensing data is significantly improved with the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System. However,the seasonal rhythm features of crops differ greatly under different climate backgrounds. The Hetao Oasis is irrigated by the Yellow River. It has an ecological landscape with obvious microclimate effect. The extraction of remote sensing data of crops is typical and representative. The Hetao Oasis in Ningxia is selected as the study area to analyze and evaluate the applicability of GF-1 satellite data in extracting crops. According to the characteristics of crop planting in the study area,the four main crops including rice,corn,wheat and alfalfa were extracted. Firstly,the GF-1/WFV image was calculated by band,and the NDVI and NDWI time series of remote sensing data sets were obtained. The values of NDVI and NDWI of the main crops were extracted,and the time series curves of NDVI and NDWI were constructed. The time series data of remote sensing indexes of the main crops were analyze,the farming information was determined to extract the phases and thresholds of the crops,construct the decision tree and extract the crops. The time series data of natural grasslands,reed and grape and of other vegetation types were combined to establish the CART decision tree so as to verify the classification effect of the correlation time series and the decision tree constructed by the farming information. The results are as follows:(1) The morphological characteristics of time series curves were different from different farming ways,and the NDVI time series data could be used to describe the growth status of crops in different periods; the NDWI time series data obtained from GF-1/WFV could be used to describe the soil moisture status of different crops and reflect the different irrigation systems of different crops in the study area. The NDVI time series and the NDWI time series could be used to accurately express the surface dynamic change information of the surrounding areas of the study area,correlate the crop time series with farming information,and effectively recognize different crops;(2) Compared with the CART decision tree,the decision tree of classification phase,classification threshold and classification accuracy constructed by combining different crop farming information and main crop curve features were similar. These indicated that the farming ways were different from different crops in the study area,and the time series of corresponding NDVI and NDWI values were significantly different.
The rapid extraction of regional crops is of great significance for agricultural production management,planting structure adjustment and optimization,and food security. The use of time series data of remote sensing image for extracting crops is an important means,and the time resolution and spatial resolution are constraints. The time resolution of medium spatial resolution of remote sensing data is significantly improved with the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System. However,the seasonal rhythm features of crops differ greatly under different climate backgrounds. The Hetao Oasis is irrigated by the Yellow River. It has an ecological landscape with obvious microclimate effect. The extraction of remote sensing data of crops is typical and representative. The Hetao Oasis in Ningxia is selected as the study area to analyze and evaluate the applicability of GF-1 satellite data in extracting crops. According to the characteristics of crop planting in the study area,the four main crops including rice,corn,wheat and alfalfa were extracted. Firstly,the GF-1/WFV image was calculated by band,and the NDVI and NDWI time series of remote sensing data sets were obtained. The values of NDVI and NDWI of the main crops were extracted,and the time series curves of NDVI and NDWI were constructed. The time series data of remote sensing indexes of the main crops were analyze,the farming information was determined to extract the phases and thresholds of the crops,construct the decision tree and extract the crops. The time series data of natural grasslands,reed and grape and of other vegetation types were combined to establish the CART decision tree so as to verify the classification effect of the correlation time series and the decision tree constructed by the farming information. The results are as follows:(1) The morphological characteristics of time series curves were different from different farming ways,and the NDVI time series data could be used to describe the growth status of crops in different periods; the NDWI time series data obtained from GF-1/WFV could be used to describe the soil moisture status of different crops and reflect the different irrigation systems of different crops in the study area. The NDVI time series and the NDWI time series could be used to accurately express the surface dynamic change information of the surrounding areas of the study area,correlate the crop time series with farming information,and effectively recognize different crops;(2) Compared with the CART decision tree,the decision tree of classification phase,classification threshold and classification accuracy constructed by combining different crop farming information and main crop curve features were similar. These indicated that the farming ways were different from different crops in the study area,and the time series of corresponding NDVI and NDWI values were significantly different.
引文
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[2]张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015,30(2):304-311.[Zhang Huanxue,Cao Xin,Li Qiangzi,et al.Research on crop identification using multi-temporal NDVI HJ images[J].Remote Sensing Technology and Application,2015,30(2):304-311.]
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[6]刘吉凯,钟仕全,梁文海.基于多时相Landsat 8OLI影像的作物种植结构提取[J].遥感技术与应用,2015,30(4):775-783.[Liu Jikai,Zhong Shiquan,Liang Wenhai.Extraction on crops planting structure based on multi-temporal Landsat 8OLI images[J].Remote Sensing Technology and Application,2015,30(4):775-783.]
[7]李鑫川,徐新刚,王纪华,等.基于时间序列环境卫星影像的作物分类识别[J].农业工程学报,2013,29(2):169-176.[Li Xinchuan,Xu Xingang,Wang Jihua,et al.Crop classification recognition based on time-series images from HJ satellite[J].Transactions of the CSAE,2013,29(2):169-176.]
[8]张峰,吴炳方,刘成林,等.利用时序植被指数监测作物物候的方法研究[J].农业工程学报,2004,20(1):155-159.[Zhang Feng,Wu Bingfang,Liu Chenglin,et al.Methods of monitoring crop phonological stages usingtime series of vegetation indicator[J].Transactions of the CSAE,2004,20(1):155-159.]
[9]Pe1a J,Gutiérrez P,Hervás-Martínez C,et al.Object-based image classification of summer crops with machine learning methods[J].Remote Sensing,2014,6(6):5 019-5 041.
[10]张晶,占玉林,李如仁.高分一号归一化植被指数时间序列用于冬小麦识别[J].遥感信息,2017,32(1):50-56.[Zhang Jing,Zhan Yulin,Li Ruren.Application of GF-1 NDVI time series in winter wheat identification[J].Remote Sensing Information,2017,32(1):50-56.]
[11]杨闫君,占玉林,田庆久,等.基于GF-1/WFVNDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.[Yang Yanjun,Zhan Yulin,Tian Qingjiu,et al.Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the CSAE,2015,31(24):155-161.]
[12]李梦莹,胡勇,王征禹.基于C5.0决策树和时序HJ-1A/BCCD数据的神农架林区植被分类[J].长江流域资源与环境,2016,25(7):1 070-1 077.[Li Mengying,Hu Yong,Wang Zhengyu.Study on vegetation classification in Shengnongjia forest district based on C5.0 decision tree and HJ-1 A/B data[J].Resources and Environment in the Yangtze Basin,2016,25(7):1 070-1 077.]
[13]梁明,孙毅中,罗荣,等.基于综合多尺度特征决策树模型的土地利用变化分析[J].农业工程学报,2014,30(17):259-267.[Liang Ming,Sun Yizhong,Luo Rong,et al.Analysis on land use change based on decision-tree model with comprehensive multiscale characteristics[J].Transactions of the CSAE,2014,30(17):259-267.]
[14]周静平,李存军,史磊刚,等.基于决策树和面向对象的作物分布信息遥感提取[J].农业机械学报,2016,47(9):318-326,333.[Zhou Jingping,Li Cunjun,Shi Leigang,et al.Crops distribution information extracted by remote sensing based on decision tree and object-oriented method[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):318-326,333.]
[15]朱永森,曾永年,张猛.基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取[J].农业工程学报,2017,33(14):258-265.[Zhu Yongsen,Zeng Yongnian,Zhang Meng.Extract of land use/cover information based on HJ satellites data and objectoriented classification[J].Transactions of the CSAE,2017,33(14):258-265.]
[16]康峻,侯学会,牛铮,等.基于拟合物候参数的植被遥感决策树分类[J].农业工程学报,2014,30(9):148-156.[Kang Jun,Hou Xuehui,Niu Zheng,et al.Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data[J].Transactions of the CSAE,2014,30(9):148-156.]
[17]那晓东,张树清,李晓峰,等.基于QUEST决策树兼容多源数据的淡水沼泽湿地信息提取[J].生态学杂志,2009,28(2):357-365.[Na Xiaodong,Zhang Shuqing,Li Xiaofeng,et al.Fresh water marsh wetland information extraction based on QUEST tree integrating with multi-source data[J].Chinese Journal of Ecology,2009,28(2):357-365.]
[18]吴健生,潘况一,彭建,等.基于QUEST决策树的遥感影像土地利用分类---以云南省丽江市为例[J].地理研究,2012,31(11):1 973-1 980.[Wu Jiansheng,Pan Kuangyi,Peng Jian,et al.Research on the accuracy of TM images land-use classification based on QUEST decision tree:A case study of Lijiang in Yunnan[J].Geographical Research,2012,31(11):1 973-1 980.]
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