中国地表覆盖异质性参数提取与分析
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摘要
地表异质性广泛存在于陆地表面各个尺度,是地表参数遥感反演不确定性的主要来源之一。基于高分辨率地表分类参考图,提取出低分辨率混合像元的端元数量和边界长度指标来描述地表异质性。然后以中国地区为例,使用全国30 m空间分辨率Global Land 30地表分类数据集提取出1 km尺度像元的描述混合结构和破碎程度的异质性指标。并基于提取出的异质性指标分析了中国区域在1 km尺度上非均质地表地物类型的组合特征、斑块特征和不同生态群系内部异质性特征。发现山地和生态交错区是主要的高异质性区域,稀树草原生物群系内部异质性最大(平均边界长度为7 426 m),其次依次为森林(4 323 m)、耕地/草地(3 160 m)和灌丛(1 779 m)。
Spatial heterogeneity exists in land surface at every scale,and it is one of key factors to bring uncertainty to land parameter retrieval from remote-sensed data. This paper proposed a methodology to use the boundary length among different land cover types to characterize and quantify land surface heterogeneity based on high-resolution land cover images. Then the heterogeneity feature at 1 kilometer scale in China was extracted from "GlobalLand30"land cover datasets with the spatial resolution of 30 m. The mixed structure,degree of fragmentation and intra-heterogeneity of eight main vegetation biomes from MODIS land cover product over heterogeneous surface in china were analyzed. Mountain area and ecotone are more heterogeneous than other regions. Savanna biome( average boundary length is 7 426 meters) is the most heterogeneous zone followed by forest,grass/crop and shrub biome with average boundary length of 4 323,3 160,1 779 meters,respectively.
Spatial heterogeneity exists in land surface at every scale,and it is one of key factors to bring uncertainty to land parameter retrieval from remote-sensed data. This paper proposed a methodology to use the boundary length among different land cover types to characterize and quantify land surface heterogeneity based on high-resolution land cover images. Then the heterogeneity feature at 1 kilometer scale in China was extracted from "GlobalLand30"land cover datasets with the spatial resolution of 30 m. The mixed structure,degree of fragmentation and intra-heterogeneity of eight main vegetation biomes from MODIS land cover product over heterogeneous surface in china were analyzed. Mountain area and ecotone are more heterogeneous than other regions. Savanna biome( average boundary length is 7 426 meters) is the most heterogeneous zone followed by forest,grass/crop and shrub biome with average boundary length of 4 323,3 160,1 779 meters,respectively.
引文
[1]Hintz M,Lennartz Sassinek S,Liu S,et al.Quantification of land-surface heterogeneity via entropy spectrum method[J].Journal of Geophysical Research,2014,119(14):8 764-8 777.
[2]Haralick R M,Shanmugam K S.Combined spectral and spatial processing of ERTS imagery data[J].Remote Sensing of Environment,1974,3(1):3-13.
[3]De Cola L.Fractal analysis of a classified Landsat scene[J].Photogrammetric Engineering and Remote Sensing,1989,55(5):601-610.
[4]Garrigues S,Allard D,Baret F,et al.Quantifying spatial heterogeneity at the landscape scale using variogram models[J].Remote Sensing of Environment,2006,103(1):81-96.
[5]Chen J M.Spatial scaling of a remotely sensed surface parameter by contexture[J].Remote Sensing of Environment,1999,69(1):30-42.
[6]Wu J,Jelinski D E,Luck M,et al.Multiscale analysis of landscape heterogeneity:Scale variance and pattern metrics[J].Geographic Information Sciences,2000,6(1):6-19.
[7]Gilabert M A,Garcia-Haro F J,MeliJ.A mixture modeling approach to estimate vegetation parameters for heterogeneous canopies in remote sensing[J].Remote Sensing of Environment,2000,72(3):328-345.
[8]Liang S.Numerical experiments on the spatial scaling of land surface albedo and leaf area index[J].Remote Sensing Reviews,2000,19(1/4):225-242.
[9]Qin W,Gerstl S A.3-D scene modeling of semidesert vegetation cover and its radiation regime[J].Remote Sensing of Environment,2000,74(1):145-162.
[10]Widlowski J-L,Pinty B,Lavergne T,et al.Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions:Simulated impact on canopy absorption[J].Remote Sensing of Environment,2006,103(4):379-397.
[11]Marshak A,Davis A.3D Radiative Transfer in Cloudy Atmospheres[M].Heidelberg:Springer Berlin Heidelberg,2005.
[12]Kobayashi H,Suzuki R,Nagai S,et al.Spatial scale and landscape heterogeneity effects on FAPAR in an open-canopy black spruce forest in Interior Alaska[J].IEEE Geosci Remote Sensing Letters,2014,11(2):564-568.
[13]Titov G A.Radiative horizontal transport and absorption in stratocumulus clouds[J].Journal of the Atmospheric Sciences,1998,55(15):2 549-2 560.
[14]Tian Y,Woodcock C E,Wang Y,et al.Multiscale analysis and validation of the MODIS LAI product:I.Uncertainty assessment[J].Remote Sensing of Environment,2002,83(3):414-430.
[15]Zeng Y,Li J,Liu Q,et al.A radiative transfer model for heterogeneous agro-forestry scenarios[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4 613-4 628.
[16]Yao Yanjuan,Liu Qiang,Liu Qinhuo,et al.LAI inversion uncertainties in heterogeneous surface[J].Journal of Remote Sensing,2007,11(6):763-770.[姚延娟,刘强,柳钦火,等.异质性地表的叶面积指数反演的不确定性分析[J].遥感学报,2007,11(6):763-770.]
[17]Tian Y,Wang Y,Zhang Y,et al.Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions[J].Remote Sensing of Environment,2003,84(1):143-159.
[18]Fang H,Li W,Myneni R.The impact of potential land cover misclassification on MODIS Leaf Area Index(LAI)estimation:A statistical perspective[J].Remote Sensing,2013,5(2):830-844.
[19]Chen Jun,Chen Jin,Liao Anping,et al.Concepts and key techniques for 30 m global land cover mapping[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):551-557.[陈军,陈晋,廖安平,等.全球30 m地表覆盖遥感制图的总体技术[J].测绘学报,2014,43(6):551-557].
[20]Chen J,Chen J,Liao A,et al.Global land cover mapping at 30m resolution:A POK-based operational approach[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,103:7-27.
[21]Friedl M A,Sulla-Menashe D,Tan B,et al.MODIS collection 5global land cover:Algorithm refinements and characterization of new datasets[J].Remote Sensing of Environment,2010,114(1):168-182.
[2]Haralick R M,Shanmugam K S.Combined spectral and spatial processing of ERTS imagery data[J].Remote Sensing of Environment,1974,3(1):3-13.
[3]De Cola L.Fractal analysis of a classified Landsat scene[J].Photogrammetric Engineering and Remote Sensing,1989,55(5):601-610.
[4]Garrigues S,Allard D,Baret F,et al.Quantifying spatial heterogeneity at the landscape scale using variogram models[J].Remote Sensing of Environment,2006,103(1):81-96.
[5]Chen J M.Spatial scaling of a remotely sensed surface parameter by contexture[J].Remote Sensing of Environment,1999,69(1):30-42.
[6]Wu J,Jelinski D E,Luck M,et al.Multiscale analysis of landscape heterogeneity:Scale variance and pattern metrics[J].Geographic Information Sciences,2000,6(1):6-19.
[7]Gilabert M A,Garcia-Haro F J,MeliJ.A mixture modeling approach to estimate vegetation parameters for heterogeneous canopies in remote sensing[J].Remote Sensing of Environment,2000,72(3):328-345.
[8]Liang S.Numerical experiments on the spatial scaling of land surface albedo and leaf area index[J].Remote Sensing Reviews,2000,19(1/4):225-242.
[9]Qin W,Gerstl S A.3-D scene modeling of semidesert vegetation cover and its radiation regime[J].Remote Sensing of Environment,2000,74(1):145-162.
[10]Widlowski J-L,Pinty B,Lavergne T,et al.Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions:Simulated impact on canopy absorption[J].Remote Sensing of Environment,2006,103(4):379-397.
[11]Marshak A,Davis A.3D Radiative Transfer in Cloudy Atmospheres[M].Heidelberg:Springer Berlin Heidelberg,2005.
[12]Kobayashi H,Suzuki R,Nagai S,et al.Spatial scale and landscape heterogeneity effects on FAPAR in an open-canopy black spruce forest in Interior Alaska[J].IEEE Geosci Remote Sensing Letters,2014,11(2):564-568.
[13]Titov G A.Radiative horizontal transport and absorption in stratocumulus clouds[J].Journal of the Atmospheric Sciences,1998,55(15):2 549-2 560.
[14]Tian Y,Woodcock C E,Wang Y,et al.Multiscale analysis and validation of the MODIS LAI product:I.Uncertainty assessment[J].Remote Sensing of Environment,2002,83(3):414-430.
[15]Zeng Y,Li J,Liu Q,et al.A radiative transfer model for heterogeneous agro-forestry scenarios[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4 613-4 628.
[16]Yao Yanjuan,Liu Qiang,Liu Qinhuo,et al.LAI inversion uncertainties in heterogeneous surface[J].Journal of Remote Sensing,2007,11(6):763-770.[姚延娟,刘强,柳钦火,等.异质性地表的叶面积指数反演的不确定性分析[J].遥感学报,2007,11(6):763-770.]
[17]Tian Y,Wang Y,Zhang Y,et al.Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions[J].Remote Sensing of Environment,2003,84(1):143-159.
[18]Fang H,Li W,Myneni R.The impact of potential land cover misclassification on MODIS Leaf Area Index(LAI)estimation:A statistical perspective[J].Remote Sensing,2013,5(2):830-844.
[19]Chen Jun,Chen Jin,Liao Anping,et al.Concepts and key techniques for 30 m global land cover mapping[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):551-557.[陈军,陈晋,廖安平,等.全球30 m地表覆盖遥感制图的总体技术[J].测绘学报,2014,43(6):551-557].
[20]Chen J,Chen J,Liao A,et al.Global land cover mapping at 30m resolution:A POK-based operational approach[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,103:7-27.
[21]Friedl M A,Sulla-Menashe D,Tan B,et al.MODIS collection 5global land cover:Algorithm refinements and characterization of new datasets[J].Remote Sensing of Environment,2010,114(1):168-182.