基于随机森林和Sentinel-2影像数据的低山丘陵区土地利用分类——以重庆市江津区李市镇为例
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摘要
精准的土地利用信息是土地资源监测和管理的基础。为提高低山丘陵区域的土地利用分类精度,选取重庆市江津区李市镇为研究案例,基于随机森林方法,以Sentinel-2影像数据和地形因子为数据源,提取3种变量(传统遥感数据,红边遥感数据和地形因子),合计23个特征指标,构建3个具有不同输入变量的组合模型,以提取研究区土地利用信息,分析变量的重要性。结果表明:(1)传统遥感数据模型中顺序添加红边遥感数据和地形因子,总体分类精度分别为86.54%,87.19%,88.61%;Kappa系数分别为0.800 9,0.810 2,0.831 4;(2)对模型精度有重要影响的特征指标依次是波段B2(Blue),B4(Red),B3(Green),改进归一化差异水体指数(MNDWI)和波段B5(Vegetation Red Edge 1);(3)基于随机森林的遥感数据和地形因子的组合方法,是获取研究区高精度土地利用信息的一种有效手段。研究成果可以为地形复杂区域的土地利用分类提供参考。
Accurate and efficient information of land use types is of great importance for monitoring and management of land resources. In order to improve the accuracy of land use classification in low mountain and hilly areas, the current study applied random forest with Sentinel-2 images and terrain indicators to classify land use types in Lishi town, Jiangjing, Chongqing. A total of 23 features involved three kind of variables, namely, traditional remote sensing indices, red-edge remote sensing indices and topographic indices were derived from the Sentinel-2 images and digital elevation model. Three models with different inputs were developed and compared. The accurate map of land use types and the relative importance of these indices to the classification were obtained by the best model. The results showed that the values of overall accuracy and Kappa coefficient were 86.54% and 0.800 9 for the model with traditional remote sensing indices, 87.17% and 0.810 2 for the model with traditional remote sensing indices and red-edge remote sensing indices, 88.61% and 0.831 4 for the model with traditional remote sensing indices, red-edge remote sensing indices, and terrain indicators, respectively. The top five importance variables was ranked in order of B2(Blue), B4(Red), B3(Green), Modified Normalized Difference Water Index(MNDWI), and B5(Vegetation Red Edge(1) The random forest with Sentinel-2 images and terrain indicators could be a suitable tool for producing accurate land use information over the study area. The results could provide valuable information for land use classification in the areas with complex topography.
Accurate and efficient information of land use types is of great importance for monitoring and management of land resources. In order to improve the accuracy of land use classification in low mountain and hilly areas, the current study applied random forest with Sentinel-2 images and terrain indicators to classify land use types in Lishi town, Jiangjing, Chongqing. A total of 23 features involved three kind of variables, namely, traditional remote sensing indices, red-edge remote sensing indices and topographic indices were derived from the Sentinel-2 images and digital elevation model. Three models with different inputs were developed and compared. The accurate map of land use types and the relative importance of these indices to the classification were obtained by the best model. The results showed that the values of overall accuracy and Kappa coefficient were 86.54% and 0.800 9 for the model with traditional remote sensing indices, 87.17% and 0.810 2 for the model with traditional remote sensing indices and red-edge remote sensing indices, 88.61% and 0.831 4 for the model with traditional remote sensing indices, red-edge remote sensing indices, and terrain indicators, respectively. The top five importance variables was ranked in order of B2(Blue), B4(Red), B3(Green), Modified Normalized Difference Water Index(MNDWI), and B5(Vegetation Red Edge(1) The random forest with Sentinel-2 images and terrain indicators could be a suitable tool for producing accurate land use information over the study area. The results could provide valuable information for land use classification in the areas with complex topography.
引文
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[13] 陈元鹏,郧文聚,周旭,等.基于MESMA和RF的山丘区土地利用信息分类提取[J].农业机械学报,2017,48(7):136-144.CHEN Y P,YUN W J,ZHOU X,et al.Classification and extraction of land use information in hilly area based on MESMA and RF Classifier[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):136-144.
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[16] DUBEAU P,KING D J,UNBUSHE D G,et al.Mapping the Dabus Wetlands,Ethiopia,using random forest classification of Landsat,PALSAR and topographic data[J].Remote Sensing,2017,9(105610).
[17] 陈元鹏,罗明,彭军还,等.基于网格搜索随机森林算法的工矿复垦区土地利用分类[J].农业工程学报,2017,33(14):250-257,315.CHEN Y P,LUO M,PENG J H,et al.Classification of land use in industrial and mining reclamation area based grid-search and random forest classifier[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE) ,2017,33(14):250-257,315.
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[19] TIGGES J,LAKES T,HOSTERT P.Urban vegetation classification:Benefits of multitemporal RapidEye satellite data[J].Remote Sensing of Environment,2013,136(5):66-75.
[20] KIM H O,YEOM J M.Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data[J].International Journal of Remote Sensing,2014,35(19):7046-7068.
[21] SCHUSTER C,F?RSTER M,KLEINSCHMIT B.Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data[J].International Journal of Remote Sensing,2012,33(17):5583-5599.
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[23] RADOUX J,CHOMé G,JACQUES D C,et al.Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection[J].Remote Sensing,2016,8(6):488.
[24] IMMITZER M,VUOLO F,ATZBERGER C.First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe[J].Remote Sensing,2016,8(3):166.
[25] 刘丽雅.基于国产GF-1的高寒山区土地利用/覆盖分类研究[D].浙江大学,2016.LIU L Y.Study on land use/cover classification in alpine mountainous area based on GF-1[D].Zhejiang UniversityROUSE J W J,HAAS R H,SCHELL,2016.
[26] ROUSE J W J,HAAS R H,SCHELL J A,et al.Monitoring vegetation systems in the Great Plains with Erts[J].Nasa Special Publication,1973,351:309.
[27] XIAO X,BOLES S,LIU J,et al.Mapping paddy rice agriculture in southern China using multi-temporal MODIS images[J].Remote Sensing of Environment,2005,95(4):480-492.
[28] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.XU H Q.A Study on information extraction of water body with the modified normalized difference water Index(MNDWI)[J].Journal of Remote Sensing,2005,9(5):589-595.
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[30] DELEGIDO J,VERRELST J,ALONSO L,et al.Evaluation of Sentinel-2 Red-Edge Bands for empirical estimation of green LAI and chlorophyll content[J].Sensors,2011,11(7):7063-7081.
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