基于Landsat8 OLI数据的富贵竹种植区域信息提取
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摘要
富贵竹作为一种观赏植物,在我国南方省份有大面积种植,具有良好的经济价值。为了解与监测区域富贵竹种植情况,以Landsat8 OLI遥感影像为数据源,构建了一种新的网体—水体差异增强指数(difference enhence between net and water index,DENWI)作为特征参数,通过面向对象的分类方法建立富贵竹信息提取规则集,得到研究区域内富贵竹的种植信息,并与2种传统信息提取方法进行对比研究。结果表明,相比于传统方法,基于DENWI的面向对象分类方法可以更有效地提取富贵竹种植信息,总体分类精度为98. 46%,Kappa系数为0. 97,该方法监测提取富贵竹种植信息是可行且有优势的,可以为富贵竹种植监测和管理提供科学依据。
The Dracaena sanderiana,as an ornamental plant,has been extensively planted in southern China,and has good economic value.In order to monitor the planting situation of Dracaena sanderiana,the authors constructed a new index-"difference enhence between net and water index"(DENWI) as a characteristic parameter based on Landsat8 OLI remote sensing image.Object-oriented classification method was used to establish the Dracaena sanderiana information extraction rule set,the Dracaena sanderiana planting information was obtained,and two kinds of traditional information extraction methods were adopted for comparative study.The results show that,compared with the traditional method,the object-oriented classification method based on DENWI can extract the information of Dracaena sanderiana,with the overall classification accuracy being 98.46% and the kappa coefficient being 0.97.Remote sensing monitoring and extraction of Dracaena sanderiana planting information is feasible and advantageous,and it can provide scientific basis for monitoring and management of Dracaena sanderiana.
The Dracaena sanderiana,as an ornamental plant,has been extensively planted in southern China,and has good economic value.In order to monitor the planting situation of Dracaena sanderiana,the authors constructed a new index-"difference enhence between net and water index"(DENWI) as a characteristic parameter based on Landsat8 OLI remote sensing image.Object-oriented classification method was used to establish the Dracaena sanderiana information extraction rule set,the Dracaena sanderiana planting information was obtained,and two kinds of traditional information extraction methods were adopted for comparative study.The results show that,compared with the traditional method,the object-oriented classification method based on DENWI can extract the information of Dracaena sanderiana,with the overall classification accuracy being 98.46% and the kappa coefficient being 0.97.Remote sensing monitoring and extraction of Dracaena sanderiana planting information is feasible and advantageous,and it can provide scientific basis for monitoring and management of Dracaena sanderiana.
引文
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[21]张连蓬,柳钦火,王德高,等.高光谱遥感植被指数的普适性分析[J].测绘通报,2010(9):1-4.Zhang L P,Liu Q H,Wang D G,et al.The universal analysis of vegetation indices for hyperspectral remote sensing data[J].Bulletin of Surveying and Mapping,2010(9):1-4.
[2]曹涤环,刘建武.富贵竹栽培管理技术[J].南方农业,2010,4(6):65-67.Cao D H,Liu J W.Techniques of Dracaena sanderiana’s culture and management[J].South China Agriculture,2010,4(6):65-67.
[3]Tao F L,Yokozawa M,Zhang Z,et al.Remote sensing of crop production in China by production efficiency models:Models comparisons,estimates and uncertainties[J].Ecological Modelling,2005,183(4):385-396.
[4]吴炳方.全国农情监测与估产的运行化遥感方法[J].地理学报,2000,55(1):25-35.Wu B F.Operational remote sensing methods for agricultural statistics[J].Journal of Geographical Sciences,2000,55(1):25-35.
[5]宋盼盼,杜鑫,吴良才,等.基于光谱时间序列拟合的中国南方水稻遥感识别方法研究[J].地球信息科学学报,2017,19(1):117-124.Song P P,Du X,Wu L C,et al.Research on the method of rice remote sensing identification based on spectral time-series fitting in southern China[J].Journal of Geo-information Science,2017,19(1):117-124.
[6]牛鲁燕,张晓艳,郑继业,等.基于Landsat8 OLI数据的山东省耕地信息提取研究[J].中国农学通报,2014,30(34):264-269.Niu L Y,Zhang X Y,Zheng J Y,et al.Extraction of cultivated land information in Shandong Province based on Landsat8 OLI data[J].Chinese Agricultural Science Bulletin,2014,30(34):264-269.
[7]陈杰,陈铁桥,刘慧敏,等.高分辨率遥感影像耕地分层提取方法[J].农业工程学报,2015,31(3):190-198.Chen J,Chen T Q,Liu H M,et al.Hierarchical extraction of farmland from high-resolution remote sensing imagery[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):190-198.
[8]刘旭拢,何春阳,潘耀忠,等.遥感图像分类精度的点、群样本检验与评估[J].遥感学报,2006,10(3):366-372.Liu X L,He C Y,Pan Y Z,et al.Accuracy assessment of thematic classification based on point and cluster sample[J].Journal of Remote Sensing,2006,10(3):366-372.
[9]田绍鸿,张显峰.采用随机森林法的天绘数据干旱区城市土地覆盖分类[J].国土资源遥感,2016,28(1):43-49.doi:10.6046/gtzyyg.2016.01.07.Tian S H,Zhang X F.Random forest classification of land cover information of urban areas in arid regions based on TH-1 data[J].Remote Sensing for Land and Resources,2016,28(1):43-49.doi:10.6046/gtzyyg.2016.01.07.
[10]李金香,李志强,李帅,等.高分辨率遥感影像居民地半自动提取方法研究[J].国土资源遥感,2017,29(3):17-24.doi:10.6046/gtzyyg.2017.03.03.Li J X,Li Z Q,Li S,et al.The method for semi-automatic extraction of residential area from high resolution remote sensing images[J].Remote Sensing for Land and Resources,2017,29(3):17-24.doi:10.6046/gtzyyg.2017.03.03.
[11]徐涵秋.一种快速提取不透水面的新型遥感指数[J].武汉大学学报(信息科学版),2008,33(11):1150-1153,1211.Xu H Q.A new remote sensing index for fastly extracting impervious surface information[J].Geomatics and Information Science of Wuhan University,2008,33(11):1150-1153,1211.
[12]陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报(信息科学版),2006,31(4):316-320.Chen Y H,Feng T,Shi P J,et al.Classification of remote sensing image based on object-oriented and class rules[J].Geomatics and Information Science of Wuhan University,2006,31(4):316-320.
[13]尹展,张建国,李宏斌.面向对象分类技术在高分辨率遥感影像信息提取中的应用研究[J].测绘与空间地理信息,2013,36(8):61-63.Yin Z,Zhang J G,Li H B.Application research on information extraction based on high-resolution remote sensing images using object-oriented analysis[J].Geomatics and Spatial Information Technology,2013,36(8):61-63.
[14]杨青山,张华.融合像元形状和光谱信息的高分遥感图像分类新方法[J].国土资源遥感,2016,28(4):64-70.doi:10.6046/gtzyyg.2016.04.10.Yang Q S,Zhang H.A new method for classification of high spatial resolution remotely sensed imagery based on fusion of shape and spectral information of pixels[J].Remote Sensing for Land and Resources,2016,28(4):64-70.doi:10.6046/gtzyyg.2016.04.10.
[15]e Cognition.User guide[EB/OL].Munich:Definiens Imaging GmbH[2017-04-26].http://www.ecognition.com.
[16]郭琳,裴志远,吴全,等.面向对象的土地利用/覆盖遥感分类方法与流程应用[J].农业工程学报,2010,26(7):194-198.Guo L,Pei Z Y,Wu Q,et al.Application of method and process of object-oriented land use-cover classification using remote sensing images[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(7):194-198.
[17]庄喜阳,赵书河,陈诚,等.面向对象的遥感影像最优分割尺度监督评价[J].国土资源遥感,2016,28(4):49-58.doi:10.6046/gtzyyg.2016.04.08.Zhuang X Y,Zhao S H,Chen C,et al.Supervised evaluation of optimal segmentation scale with object-oriented method in remote sensing image[J].Remote Sensing for Land and Resources,2016,28(4):49-58.doi:10.6046/gtzyyg.2016.04.08.
[18]Gorte B.Probabilistic segmentation of remotely sensed images[C]//ITC Publication Series,1998(63):8-10.
[19]马国锐,马艳丽,江满珍.结合颜色直方图和LBP纹理的遥感影像分割[J].国土资源遥感,2017,29(3):32-40.doi:10.6046/gtzyyg.2017.03.05.Ma G R,Ma Y L,Jiang M Z.Combinational color histogram and LBP textural features for remote sensing image segmentation[J].Remote Sensing for Land and Resources,2017,29(3):32-40.doi:10.6046/gtzyyg.2017.03.05.
[20]Hansen M,Dubayah R,Defries R.Classification trees:An alternative to traditional land cover classifiers[J].International Journal of Remote Sensing,1996,17(5):1075-1081.
[21]张连蓬,柳钦火,王德高,等.高光谱遥感植被指数的普适性分析[J].测绘通报,2010(9):1-4.Zhang L P,Liu Q H,Wang D G,et al.The universal analysis of vegetation indices for hyperspectral remote sensing data[J].Bulletin of Surveying and Mapping,2010(9):1-4.