叶面积指数(leaf area index,LAI)是森林生态系统中最重要的参数之一。以多角度数据MISR和MODIS土地覆盖类型数据为数据源,采用5-Scale几何光学模型与统计模型相结合的方法反演小兴安岭地区植被叶面积指数。研究结果表明:比值植被指数(s...叶面积指数(leaf area index,LAI)是森林生态系统中最重要的参数之一。以多角度数据MISR和MODIS土地覆盖类型数据为数据源,采用5-Scale几何光学模型与统计模型相结合的方法反演小兴安岭地区植被叶面积指数。研究结果表明:比值植被指数(simple ratio,SR)与研究区LAI的相关性最好,最适合该区LAI的遥感提取。反演精度为95.7%,均方差为0.34,误差均在合理范围之内。反演精度较高,反演结果较好。研究区域植被LAI随着纬度的增加呈现递减趋势,LAI均值为1.21,最大值为9.28,最小值为0.83,以多角度数据为信息源采用5-Scale模型与统计模型相结合的方法为大区域LAI的遥感反演提供一个有效途径。展开更多
The leaf area index(LAI) is an important ecological parameter that characterizes the interface between vegetation canopy and the atmosphere.In addition,it is used by most process-oriented ecosystem models.This paper i...The leaf area index(LAI) is an important ecological parameter that characterizes the interface between vegetation canopy and the atmosphere.In addition,it is used by most process-oriented ecosystem models.This paper investigates the potential of HJ-1 CCD data combined with linear spectral unmixing and an inverted geometric-optical model for the retrieval of the shrub LAI in Wushen Banner of Inner Mongolia in the Mu Us Sandland.MODTRAN(Moderate Resolution Atmospheric Radiance and Transmittance Model) was used for atmospheric correction.Shrubland was extracted using the threshold of the normalized difference vegetation index,with which water bodies and farmland were separated,in combination with a vegetation map of the People's Republic of China(1:1000000).Using the geometric-optical model,we derive the per-pixel reflectance as a simple linear combination of two components,namely sunlit background and other.The fraction of sunlit background is related to the shrub LAI.With the support of HJ-1 CCD data,we employ linear spectral unmixing to obtain the fraction of sunlit background in an atmospherically corrected HJ image.In addition,we use the measured shrub canopy structural parameters for shrub communities to invert the geometric-optical model and retrieve the pixel-based shrub LAI.In total,18 sample plots collected in Wushen Banner of Inner Mongolia are used for validation.The results of the shrub LAI show good agreement with R2 of 0.817 and a root-mean-squared error of 0.173.展开更多
文摘叶面积指数(leaf area index,LAI)是森林生态系统中最重要的参数之一。以多角度数据MISR和MODIS土地覆盖类型数据为数据源,采用5-Scale几何光学模型与统计模型相结合的方法反演小兴安岭地区植被叶面积指数。研究结果表明:比值植被指数(simple ratio,SR)与研究区LAI的相关性最好,最适合该区LAI的遥感提取。反演精度为95.7%,均方差为0.34,误差均在合理范围之内。反演精度较高,反演结果较好。研究区域植被LAI随着纬度的增加呈现递减趋势,LAI均值为1.21,最大值为9.28,最小值为0.83,以多角度数据为信息源采用5-Scale模型与统计模型相结合的方法为大区域LAI的遥感反演提供一个有效途径。
基金supported by National Natural Science Foundation of China (Grant No.40871173)Special Grant for Prevention and Treatment of Infectious Diseases (Grant No.2008ZX10004-012)National State Basic Research Program of China (Grant No. 2007CB714404)
文摘The leaf area index(LAI) is an important ecological parameter that characterizes the interface between vegetation canopy and the atmosphere.In addition,it is used by most process-oriented ecosystem models.This paper investigates the potential of HJ-1 CCD data combined with linear spectral unmixing and an inverted geometric-optical model for the retrieval of the shrub LAI in Wushen Banner of Inner Mongolia in the Mu Us Sandland.MODTRAN(Moderate Resolution Atmospheric Radiance and Transmittance Model) was used for atmospheric correction.Shrubland was extracted using the threshold of the normalized difference vegetation index,with which water bodies and farmland were separated,in combination with a vegetation map of the People's Republic of China(1:1000000).Using the geometric-optical model,we derive the per-pixel reflectance as a simple linear combination of two components,namely sunlit background and other.The fraction of sunlit background is related to the shrub LAI.With the support of HJ-1 CCD data,we employ linear spectral unmixing to obtain the fraction of sunlit background in an atmospherically corrected HJ image.In addition,we use the measured shrub canopy structural parameters for shrub communities to invert the geometric-optical model and retrieve the pixel-based shrub LAI.In total,18 sample plots collected in Wushen Banner of Inner Mongolia are used for validation.The results of the shrub LAI show good agreement with R2 of 0.817 and a root-mean-squared error of 0.173.
