摘要
为系统地研究特定区域的植被盖度信息提取技术,在不同的地形条件下,比较了目前流行的多种高光谱遥感植被盖度提取方法。结果表明:最优高光谱归一化植被指数(NDVI1)的建模和验证精度均高于其他两种归一化植被指数(NDVI),直接采用NDVI建立的回归模型对研究区植被盖度的估测能力低于像元二分模型;阴坡的最佳模型为基于一阶微分的偏最小二乘回归模型(PLSR模型),其建模决定系数(R2)为0.810,均方根误差(RMSE)为6.29,验证R2为0.773,RMSE为8.85;阳坡的最佳模型为基于二阶微分的PLSR模型,其建模R2为0.823,RMSE为6.04,验证R2为0.801,RMSE为7.35;平原的最佳模型为全受限的线性光谱混合分解模型(FCLS),其验证R2为0.852,RMSE为5.86。
Aims We compared a variety of vegetation cover extraction methods by hyperspectral remote sensing that are currently popular. Our aims were to systematically study vegetation information extraction technology and pro- vide a reference for further study of vegetation cover. Methods The methods of vegetation index, dimidiate pixel model, principal component regression （PCR）, par- tial least squares regression first order differential （PLSR） and linear spectral mixture decomposition model were used to extract vegetation information.Important findings The vegetation cover estimation ability of dimidiate pixel models established by different normalized differential vegetation index （NDVI） was higher than that of the regression models established by NDVI directly. The optimization model for shady slopes was PLSR model based on the first order differential （FD） with modeling R2 = 0.810, root mean square error （RMSE） = 6.29 and validation R2 = 0.773, RAISE = 8.85. The optimization model for sunny slopes was PLSR model based on the second order differential （SD）, with mod- eling R2 = 0.823, RAISE = 6.04 and validation R2 = 0.801, RAISE = 7.35. The optimization model for plains was full confine linear spectral mixture model （FCLS）, with validation R2 = 0.852, RMSE = 5.86.
出处
《植物生态学报》
CAS
CSCD
北大核心
2013年第1期18-25,共8页
Chinese Journal of Plant Ecology
基金
安徽省高等学校省级优秀青年人才基金项目(2011SQRL124)
滁州学院科研项目(2010-kj-027B)
关键词
干旱半干旱
高光谱
模型
植被盖度
arid and semi-arid, high spectrum, model, vegetation coverage
