基于GF-1卫星的县域冬小麦面积提取及年际变化监测
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摘要
利用遥感技术提取冬小麦种植面积及年际变化监测,可为农业经济的宏观决策提供可靠的依据.自2013年我国首颗高分卫星发射以来,高分一号卫星数据以其高分辨率的优势成为了我国农业遥感监测的主要数据源之一.本文以高分一号卫星携带的16m空间分辨率的宽视场(wide field view,WFV)数据为主要数据源,以开封县为例,采用Google Earth(以下简称GE)0.3m遥感影像为样本辅助数据,加上人工目视解译选择样本控制点,采用监督分类中的最大似然法提取冬小麦种植面积,并利用样本点对分类结果进行精度验证和年际变化监测.实验数据表明:采用冬小麦生长周期内的11月份到翌年4月份间的一幅高质量GF-1/WFV影像,利用本文给出的处理流程提取冬小麦面积均可达到农业使用的标准,且1月份为开封县冬小麦提取的最佳时像.同时,采用2013年12月29日、2015年2月25日及2016年2月20日的开封县GF1/WFV数据,面积提取精度均达到95%以上,kappa系数均大于0.96,种植面积提取的数据均略小于河南省统计局公布的数据,但两者呈现出的波动趋势是一致的.结果表明以GF-1/WFV影像为主要数据源,GE影像为辅助数据进行县域尺度上的冬小麦面积提取及年际变化监测的方法是有效可行的.
The remote sensing technology was used to extract the winter wheat planting area and interannual change monitoring,which can provide a reliable basis for the macroeconomic decision-making of agricultural economy.Since the launch of China's first high-resolution satellite in 2013,the high-resolution satellite data have become one of the major data sources for agricultural remote sensing monitoring in China.This paper took the wide field view(WFV)data of 16 meters spatial resolution carried by the high score satellite as the main data source.Taking Kaifeng County as an example,Google Earth(hereinafter referred to as GE)0.3 mremote sensing image was used.The sample-assisted data,coupled with manual visual interpretation of the selected sample control points,were used to extract the winter wheat planting area using the maximum likelihood method of supervised classification,and the sample points were used to verify the accuracy of the classification results and monitor the interannual variation.The experimental data show that using a high-quality GF-1/WFV image from November to April of the winter wheat growth cycle,the winter wheat area extracted using the processing procedure given in this paper can reach the agricultural use standard,and January is the best time for the extraction of winter wheat in kaifeng country.At the same time,using the GF1/WFV data of Kaifeng County on December 29,2013,February 25,2015 and February20,2016,the area extraction accuracy reaches above 95%,and the kappa coefficient is greater than 0.96.The planting area was extracted.The data are slightly smaller than the data released by the Henan Provincial Bureau of Statistics,but the fluctuations in the two are consistent.The results show that using GF-1/WFV image as the main data source,GE imagery as ancillary data for winter wheat area extraction and monitoring at county scale is effective and feasible.
The remote sensing technology was used to extract the winter wheat planting area and interannual change monitoring,which can provide a reliable basis for the macroeconomic decision-making of agricultural economy.Since the launch of China's first high-resolution satellite in 2013,the high-resolution satellite data have become one of the major data sources for agricultural remote sensing monitoring in China.This paper took the wide field view(WFV)data of 16 meters spatial resolution carried by the high score satellite as the main data source.Taking Kaifeng County as an example,Google Earth(hereinafter referred to as GE)0.3 mremote sensing image was used.The sample-assisted data,coupled with manual visual interpretation of the selected sample control points,were used to extract the winter wheat planting area using the maximum likelihood method of supervised classification,and the sample points were used to verify the accuracy of the classification results and monitor the interannual variation.The experimental data show that using a high-quality GF-1/WFV image from November to April of the winter wheat growth cycle,the winter wheat area extracted using the processing procedure given in this paper can reach the agricultural use standard,and January is the best time for the extraction of winter wheat in kaifeng country.At the same time,using the GF1/WFV data of Kaifeng County on December 29,2013,February 25,2015 and February20,2016,the area extraction accuracy reaches above 95%,and the kappa coefficient is greater than 0.96.The planting area was extracted.The data are slightly smaller than the data released by the Henan Provincial Bureau of Statistics,but the fluctuations in the two are consistent.The results show that using GF-1/WFV image as the main data source,GE imagery as ancillary data for winter wheat area extraction and monitoring at county scale is effective and feasible.
引文
[1]焦险峰,杨邦杰,裴志远.基于分层抽样的中国水稻种植面积遥感调查方法研究[J].农业工程学报,2006,22(5):105-110.JIAO X F,YANG B J,PEI Z Y.Paddy rice area estimation using a stratified sampling method with remote sensing in China[J].Transactions of the China Society of Agriculture Engineering,2006,22(5):105-110.
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[4]陈志杰,汪权方,王新生,等.基于GF-1的冬小麦遥感监测区域适用性分析[J].地理空间信息,2017,15(1):66-68.CHEN Z J,WANG Q F,WANG X S,et al.Regional applicability of winter wheat remote sensing monitoring based on GF-1[J].Geospatial Information,2017,15(1):66-68.
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[8]张健康,程彦培,张发旺.基于多时相遥感影像的作物种植信息提取[J].农业工程学报,2012,28(2):134-141.ZHANG J K,CHENG Y P,ZHANG F W.Crop planting information extraction based on multi-temporal remote sensing images[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(2):134-141.
[9]郝卫平,梅旭荣,蔡学良,等.基于多时相遥感影像的东北三省作物分布信息提取[J].农业工程学报,2011,27(1):201-207.HAO W P,MEI X R,CAI X L,et al.Crop planting extraction based on multi-temporal remote sensing data in Northeast China[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(1):201-207.
[10]李冰,梁燕华,李丹丹.多时相GF-1卫星PMS影像提取农作物种植结构[J].中国农业资源与区划,2017,38(9):56-62.LI B,LIANG Y H,LI D D.Multi-temporal GF-1satellite PMS image extraction crop planting structure[J].Chinese Journal of Agricultural Resources and Regional Planning,2017,38(9):56-62.
[11]游炯,裴志远.基于改进多元纹理信息模型和GF-1影像的县域冬小麦面积提取[J].农业工程学报,2016,32(13):131-139.YOU J,PEI Z Y.Area extraction of winter wheat at country scale based on modified multivariate texture and GF-1satellite images[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(13):131-139.
[12]李峰,谢磊,王昊.基于GF-1影像的冬小麦面积提取及年际变化动态监测[J].山东农业科学,2017,49(8):139-144.LI F,XIE L,WANG H.Extraction and annual variation dynamic monitoring of winter wheat area based on GF-1images[J].Shandong Agricultural Sciences,2017,49(8):139-144.
[13]王利民,刘佳,杨福刚.基于GF-1卫星遥感的冬小麦面积早期识别[J].农业工程学报,2015,31(11):194-201.WANG L M,LIU J,YANG F G.Early recognition of winter wheat area based on satellite[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(11):194-201.
[14]李润生,翟会楠.基于GPS的Google Earth的定位精度分析[J].影像技术,2011,23(1):15-18.LI R S,ZHAI H N.Positioning accuracy analysis of google earth based on GPS[J].Image Technology,2011,23(1):15-18.
[15]WILVER E S.Horizontal and vertical accuracy of google earth:Comment on‘positional accuracy of the google earth terrain model derived from stratigraphic unconformities in the big bend region,texas,USA’by S.C.Benker,R.P.Langford and T.L.Pavlis[J].Geocarto International,2014,29(6):625-627.
[16]CHRISTIANBENKER S,LANGFORD R,PAVLIS T.Positional accuracy of the google earth terrain model derived from stratigraphic unconformities in the big bend region,texas,USA[J].Geocarto International,2011,26(4):291-303.
[17]RUSLI N,MAJID M R.Google earth's derived digital elevation model:A comparative assessment with aster and SRTM data[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-6.
[18]NIRAV N,EMANUELE A.Multitemporal settlement and population mapping from landsat using google earth engine[J].International Journal of Applied Earth Observations and Geoinformation,2015,35(8):199-208.
[19]JOHN R,SARAH T L.Mapping public and private spaces of urban agriculture in Chicago through the analysis of highresolution aerial images in google earth[J].Landscape and Urban Planning,2012,108(1):57-70.
[20]FENG Q,WANG Y X.A new calculation method for shape coefficient of residential building using google earth[J].Energy&Buildings,2014,76(6):72-80.
[21]WARDAYA P D,RIDHA S.Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from google earth images[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-4.
[22]MARGHANY M,GENDEREN J V.Three-dimensional slum urban reconstruction in envisat and google earth egypt[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-5.
[23]TRUJILLO P,PIRODDI C,JACQUET J.Fish farms at sea:The ground truth from google earth[J].PLoS One,2012,7(2):1-5.
[24]NING L,ALEXANDER J H R,DOUGLAS R.Land cover dynamics monitoring with landsat data in Kunming,China:A cost-effective sampling and modelling scheme using google earth imagery and random forests[J].Geocarto International,2015,30(2):186-201.
[25]刘佳,王利民.Google Earth影像辅助的农作物面积地面样方调查[J].农业工程学报,2015,31(24):149-154.LIU J,WANG L M.Crop area ground sample survey using Google Earth image-aided[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(24):149-154.
[26]王利民,刘佳,杨玲波.基于无人机影像的农情遥感监测应用[J].农业工程学报,2013,29(18):136-145.WANG L M,LIU J,YANG L B.Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(18):136-145.
[27]吴健平,杨星卫.遥感数据分类结果的精度分析[J].遥感技术与应用,1995(1):17-24.WU J P,YANG X W.Accuracy analysis of remote sensing data classification results[J].Remote Sensing Technology and Application,1995(1):17-24.
[2]田海峰,周伯燕,陈燕芬,等.县域尺度上基于GF-1PMS影像的冬小麦种植面积遥感监测[J].中国农业大学学报,2017,22(10):139-146.TIAN H F,ZHOU B Y,CHEN Y F,et al.Extraction of winter wheat acreage based on GF-1PMS remote sensing image on country scale[J].Journal of China Agricultural University,2017,22(10):139-146.
[3]姬忠林,张月平,李乔玄,等.基于GF-1影像的冬小麦和油菜种植信息提取[J].遥感技术与应用,2017,32(4):760-765.JI Z L,ZHANG Y P,LI Q X,et al.Plantinginformation extraction of winter wheat and rape based on GF-1images[J].Remote Sensing Technology and Application,2017,32(4):760-765.
[4]陈志杰,汪权方,王新生,等.基于GF-1的冬小麦遥感监测区域适用性分析[J].地理空间信息,2017,15(1):66-68.CHEN Z J,WANG Q F,WANG X S,et al.Regional applicability of winter wheat remote sensing monitoring based on GF-1[J].Geospatial Information,2017,15(1):66-68.
[5]郝鹏宇.基于多时相遥感数据的农作物分类研究[D].北京:中国科学院大学,2017.HAO P Y.Crop classification using time series remote sensing data[D].Beijing:University of Chinese Academy of Sciences,2017.
[6]屠星月,赵冬玲.多时相遥感影像农作物识别方法的分析[C]//第四届“测绘科学前沿技术论坛”论文精选.北京:测绘出版社,2012.TU X Y,ZHAO D L.Analysis of crop identification method based on multi-temporal images[C]//Selected Papers of the4th“Foreing and Mapping Science Frontier Technology Forum”.Beijing:Surveying and Mapping Press,2012.
[7]屠星月,赵冬玲.多时相遥感影像农作物识别方法的分析[J].测绘通报,2012(S1):380-383.TU X Y,ZHAO D L.Analysis of crop identification method based on multi-temporal images[J].Survey Bulletin,2012(S1):380-383.
[8]张健康,程彦培,张发旺.基于多时相遥感影像的作物种植信息提取[J].农业工程学报,2012,28(2):134-141.ZHANG J K,CHENG Y P,ZHANG F W.Crop planting information extraction based on multi-temporal remote sensing images[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(2):134-141.
[9]郝卫平,梅旭荣,蔡学良,等.基于多时相遥感影像的东北三省作物分布信息提取[J].农业工程学报,2011,27(1):201-207.HAO W P,MEI X R,CAI X L,et al.Crop planting extraction based on multi-temporal remote sensing data in Northeast China[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(1):201-207.
[10]李冰,梁燕华,李丹丹.多时相GF-1卫星PMS影像提取农作物种植结构[J].中国农业资源与区划,2017,38(9):56-62.LI B,LIANG Y H,LI D D.Multi-temporal GF-1satellite PMS image extraction crop planting structure[J].Chinese Journal of Agricultural Resources and Regional Planning,2017,38(9):56-62.
[11]游炯,裴志远.基于改进多元纹理信息模型和GF-1影像的县域冬小麦面积提取[J].农业工程学报,2016,32(13):131-139.YOU J,PEI Z Y.Area extraction of winter wheat at country scale based on modified multivariate texture and GF-1satellite images[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(13):131-139.
[12]李峰,谢磊,王昊.基于GF-1影像的冬小麦面积提取及年际变化动态监测[J].山东农业科学,2017,49(8):139-144.LI F,XIE L,WANG H.Extraction and annual variation dynamic monitoring of winter wheat area based on GF-1images[J].Shandong Agricultural Sciences,2017,49(8):139-144.
[13]王利民,刘佳,杨福刚.基于GF-1卫星遥感的冬小麦面积早期识别[J].农业工程学报,2015,31(11):194-201.WANG L M,LIU J,YANG F G.Early recognition of winter wheat area based on satellite[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(11):194-201.
[14]李润生,翟会楠.基于GPS的Google Earth的定位精度分析[J].影像技术,2011,23(1):15-18.LI R S,ZHAI H N.Positioning accuracy analysis of google earth based on GPS[J].Image Technology,2011,23(1):15-18.
[15]WILVER E S.Horizontal and vertical accuracy of google earth:Comment on‘positional accuracy of the google earth terrain model derived from stratigraphic unconformities in the big bend region,texas,USA’by S.C.Benker,R.P.Langford and T.L.Pavlis[J].Geocarto International,2014,29(6):625-627.
[16]CHRISTIANBENKER S,LANGFORD R,PAVLIS T.Positional accuracy of the google earth terrain model derived from stratigraphic unconformities in the big bend region,texas,USA[J].Geocarto International,2011,26(4):291-303.
[17]RUSLI N,MAJID M R.Google earth's derived digital elevation model:A comparative assessment with aster and SRTM data[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-6.
[18]NIRAV N,EMANUELE A.Multitemporal settlement and population mapping from landsat using google earth engine[J].International Journal of Applied Earth Observations and Geoinformation,2015,35(8):199-208.
[19]JOHN R,SARAH T L.Mapping public and private spaces of urban agriculture in Chicago through the analysis of highresolution aerial images in google earth[J].Landscape and Urban Planning,2012,108(1):57-70.
[20]FENG Q,WANG Y X.A new calculation method for shape coefficient of residential building using google earth[J].Energy&Buildings,2014,76(6):72-80.
[21]WARDAYA P D,RIDHA S.Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from google earth images[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-4.
[22]MARGHANY M,GENDEREN J V.Three-dimensional slum urban reconstruction in envisat and google earth egypt[J].IOP Conference Series:Earth and Environmental Science,2014,18(1):1-5.
[23]TRUJILLO P,PIRODDI C,JACQUET J.Fish farms at sea:The ground truth from google earth[J].PLoS One,2012,7(2):1-5.
[24]NING L,ALEXANDER J H R,DOUGLAS R.Land cover dynamics monitoring with landsat data in Kunming,China:A cost-effective sampling and modelling scheme using google earth imagery and random forests[J].Geocarto International,2015,30(2):186-201.
[25]刘佳,王利民.Google Earth影像辅助的农作物面积地面样方调查[J].农业工程学报,2015,31(24):149-154.LIU J,WANG L M.Crop area ground sample survey using Google Earth image-aided[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(24):149-154.
[26]王利民,刘佳,杨玲波.基于无人机影像的农情遥感监测应用[J].农业工程学报,2013,29(18):136-145.WANG L M,LIU J,YANG L B.Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(18):136-145.
[27]吴健平,杨星卫.遥感数据分类结果的精度分析[J].遥感技术与应用,1995(1):17-24.WU J P,YANG X W.Accuracy analysis of remote sensing data classification results[J].Remote Sensing Technology and Application,1995(1):17-24.