基于Landsat8影像时间序列NDVI的作物种植结构提取
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摘要
为提高内蒙古平原灌区作物种植结构遥感监测精度和效率,提出一种基于时序NDVI曲线的作物种植结构提取方法。以内蒙古土默特右旗平原区为研究区域,以2015年覆盖作物生育期的多时相Landsat影像为数据源,根据不同地物其NDVI值范围不同,将研究区地表分为植被覆盖地表,无植被覆盖地表和水体3类。在植被覆盖区域内,根据林地和荒草地时序NDVI曲线特征,提取林地和荒草地,其余区域即为农田。根据小麦、玉米、葵花和西葫芦的时间序列NDVI曲线特征差异构建分类决策树模型,在农田区域内提取上述作物的空间种植分布信息。研究区各类地物及作物遥感提取面积与实际统计面积接近,土地利用分类总体精度达到85.71%,作物分类总体精度达到82.69%。研究结果表明该方法提取作物种植信息的精度较高,能够实现区域作物种植信息的高效准确监测。
In order to improve the accuracy and efficiency of remote sensing monitoring of crop planting information in plain irrigation area in Inner Mongolia,China,this paper proposes a method of crop planting information extraction based on time-series NDVI profile.Selecting Tumoteyou County of Inner Mongolia as the study area,using the multi-temporal Landsat images covering crop growing season in 2015 as data sources,the study area was divided into vegetation covering area,non-vegetation covering area and water covering area according to the NDVI value range of ground objects.In the area of vegetation covering,the forest area and grass area were extracted successively according to the NDVI curve differences of forest and grass.The rest areas were farmland in vegetation covering area.According to the NDVI profile of wheat,corn,sunflower and zucchini,the decision-tree was built,and the spatial planting information of the crops was extracted successively according to the decision-tree.Building the decision-tree is mainly based on the difference of NDVI average curve characteristics between crops,forest and grass.However,the change range of each crop NDVI due to their own different growth at the same time should also be fully considered.Appling the decision-tree,the extraction area of land use and crop is close to the actual statistical area.The overall accuracy of land use classification was 85.71% and the Kappa coefficient is 0.75.The overall accuracy of crop classification was 82.69% and the Kappa coefficient was 0.77.The results show that the research method can monitor crop spatial planting information in the region accurately and effectively.
In order to improve the accuracy and efficiency of remote sensing monitoring of crop planting information in plain irrigation area in Inner Mongolia,China,this paper proposes a method of crop planting information extraction based on time-series NDVI profile.Selecting Tumoteyou County of Inner Mongolia as the study area,using the multi-temporal Landsat images covering crop growing season in 2015 as data sources,the study area was divided into vegetation covering area,non-vegetation covering area and water covering area according to the NDVI value range of ground objects.In the area of vegetation covering,the forest area and grass area were extracted successively according to the NDVI curve differences of forest and grass.The rest areas were farmland in vegetation covering area.According to the NDVI profile of wheat,corn,sunflower and zucchini,the decision-tree was built,and the spatial planting information of the crops was extracted successively according to the decision-tree.Building the decision-tree is mainly based on the difference of NDVI average curve characteristics between crops,forest and grass.However,the change range of each crop NDVI due to their own different growth at the same time should also be fully considered.Appling the decision-tree,the extraction area of land use and crop is close to the actual statistical area.The overall accuracy of land use classification was 85.71% and the Kappa coefficient is 0.75.The overall accuracy of crop classification was 82.69% and the Kappa coefficient was 0.77.The results show that the research method can monitor crop spatial planting information in the region accurately and effectively.
引文
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[2] 宋茜,周清波,吴文斌,等.作物遥感识别中的多源数据融合研究进展[J].中国农业科学,2015,48(6):1122-1135.[SONG Qian,ZHOU Qingbo,WU Wenbin,et al.Recent progresses in research of integrating multi-source remote sensing data for crop mapping[J].Scientia Agricultura Sinica,2015,48(6):1122-1135.]
[3] 吴炳方,李强子.基于两个独立抽样框架的作物种植面积遥感估算方法[J].遥感学报.2004,8(6):551-569.[WU Bingfang,LI Qiangzi.Crop acreage estimation using two individual sampling frameworks with stratification[J].Journal of Remote Sensing,2004,8(6):551-569.]
[4] 刘佳,王利民,姚保民,等.基于多时相OLI数据的宁夏大尺度水稻面积遥感估算[J].农业工程学报,2017,33(15) :200-209.[LIU Jia,WANG Limin,YAO Baomin,et al.Ningxia rice area remote sensing estimation on large scale based on multi-temporal OLI data[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(15):200-209.]
[5] 胡琼,吴文斌,宋茜,等.作物种植结构遥感提取研究进展[J].中国农业科学,2015,48(10):1900-1914.[HU Qiong,WU Wenbin,SONG Qian,et al.Recent progresses in research of crop patterns mapping by using remote sensing[J].Scientia Agricultura Sinica,2015,48(10):1900-1914.]
[6] 马丽,徐新刚,刘良云,等.基于多时相NDVI及特征波段的作物分类研究[J].遥感技术与应用,2008,23(5):520-524.[MA Li,XU Xingang,LIU Liangyun,et al.Study on crops classification based on multi-temporal NDVI and characteristic Bands[J].Remote Sensing Technology and Application,2008,23(5):520-524.]
[7] 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 Chinese Society of Agricultural Engineering,2014,30(9):148-156.]
[8] 张焕雪,曹新,李强子,等.基于多时相环境星 NDVI时间序列的作物分类研究[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.]
[9] 李晓东,姜琦刚.基于多时相遥感数据的农田分类提取[J].农业工程学报,2015,31(7):145-150.[LI Xiaodong,JIANG Qigang.Extraction of farmland classification based on multi-temporal remote sensing dada[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(7):145-150.]
[10] 陈君颖,田庆久.高分辨率遥感植被分类研究[J].遥感学报,2007,11(2):221-227.[CHEN Junying,TIAN Qingjiu.Vegetation classification based on high-resolution satellite image[J].Journal of Remote Sensing,2007,11(2):221-227.]
[11] 刘焕军,于胜男,张新乐,等.一年一季作物遥感分类的时效性分析[J].中国农业科学,2017,50(5):830-839.[LIU Huanjun,YU Shengnan,ZHANG Xinle,et al.Timeliness analysis of crop remote sensing classification one crop a year[J].Scientia Agricultura Sinica,2017,50(5):830-839.]
[12] 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.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.]
[13] 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 Chinese Society of Agricultural Engineering,2015,31(24):155-161.]
[14] 刘佳,王利民,杨福刚,等.基于HJ时间序列数据的作物种植面积估算[J].农业工程学报,2015,31(3):199-206.[LIU Jia,WANG Limin,YANG Fugang,et al.Remote sensing estimation of crop planting area based on HJ time-series images[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):199-206.]
[15] 张荣群,王盛安,高万林,等.基于时序植被指数的县域作物遥感分类方法研究[J].农业机械学报,2015,46(增刊):246-252.[ZHANG Rongqun,WANG Sheng’an,GAO Wanlin,et al.Remote-sensing classification method of county-level agricultural crops using time-series NDVI[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(Suppl):246-252 .]
[16] 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 Chinese Society of Agricultural Engineering,2013,29(2):169-176.]
[17] 黄健熙,贾世灵,武洪峰,等.基于GF-1 WFV影像的作物面积提取方法研究[J].农业机械学报,2015,46(增刊):253-259.[HUANG Jianxi,JIA Shiling,WU Hongfeng,et al.Extraction method of crop planted area based on GF-1 WFV image[J].Transactions of the Chinese Society for Agricultural Machinery,2015,(Suppl):253-259.]
[18] 贾海蜂,刘雪华.环境遥感原理与应用[M].北京:清华大学出版社,2006.[JIA Haifeng,LIU Xuehua.Principle and application of environmental remote sensing[M].Beijing:Tsinghua University Press,2006.]
[19] 白燕英.基于多时相遥感影像的盐渍化农田表层土壤水分反演研究[D].呼和浩特:内蒙古农业大学,2014.[BAI Yanying.Study on surface soil moisture inversion of salinized farmland based on multi-temporal remote sensing images[D].Hohhot :Inner Mongolia Agricultural University,2014.]