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Position: Home > Articles > Pixel Mean Variance Parabola Fitting of Pinus densata Abundance Based on Topographic Factors Forest Resources Management 2016 (5) 59-64

考虑地形因素的像元均方差抛物线拟合高山松丰度研究

作  者：
蒋胜昌;张加龙;陆驰;胥辉;黄传烯;罗云江
单  位：
西南林业大学
关键词：
高山松;混合像元分解;香格里拉;Landsat 8;像元均方差抛物线
摘  要：
基于香格里拉地区Landsat 8影像、外业调查和森林资源二类调查数据,采用坡度匹配法对遥感数据进行了地形校正,使用线性波谱分离(LSU)、匹配滤波(MF)、最小能量约束(CEM)、像元均方差抛物线(PMVP)等4种方法,提取了高山松丰度图。分析丰度结果,4个典型样区平均均方根误差值排序为LSU
译  名：
Pixel Mean Variance Parabola Fitting of Pinus densata Abundance Based on Topographic Factors
作  者：
JIANG Shengchang;ZHANG Jialong;LU Chi;XU Hui;HUANG Chuanxi;LUO Yunjiang;Faculty of Forestry,Southwest Forestry University;3S Technology and Engineering Research Center in Forestry of the Yunnan Universities,Southwest Forestry University;
关键词：
Pinus densata;;mixed pixel unmixing;;Shangri-La;;Landsat 8;;pixel mean variance parabola fitting
摘  要：
Four typical research sample areas which are boundary mixed were selected based on Landsat8 images. The method of slope matching was used to do topographic corrections. The abundance of the Pinus densat was extracted using the method of linear spectral separation( LSU),matched filtering( MF),the minimum energy constraint( CEM),the pixel mean variance parabola( PMVP). The results of the abundance show that the order of the average root mean square error values of the four typical sample areas is:LSU < PMVP < CEM < MF. The PMVP could better separate Pinus densat boundary with a good result. Using PMVP to extract abundance has achieved higher accuracy. It could also explore more suitable curve fitting methods applied to the extraction of forest tree species abundance and land cover classification in the future.

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