摘要
以福建省三明市将乐国有林场的杉木纯林为研究对象,利用高分2号遥感影像数据,将光谱因子、地形因子分别与全色波段纹理特征、融合数据纹理特征、衍生纹理指数组合为自变量,样地蓄积量为因变量,采用多元逐步回归算法构建不同窗口下的蓄积量估测模型。结果表明:全色波段纹理特征模型和融合数据纹理特征模型最佳窗口为3×3窗口,衍生纹理指数模型最佳窗口为7×7窗口;以最优窗口建立的蓄积量回归模型,拟合效果最优的是衍生纹理指数模型(R~2=0.705,R_(MSE)=37.183 m3/hm2),其估测精度为80.67%,而融合数据纹理特征模型和全色波段纹理特征模型的估测精度分别为72.89%、60.35%,说明衍生纹理指数能够有效地提升高分2号影像数据对杉木林分蓄积量估测的精度。
With Cunninghamia lanceolate forest in the Jiangle Forest Farm of Fujian Province,by multiple linear regression analysis,stand volume estimation models were established under different window based on GF-2 remote sensing image data by combination of the spectral reflectance,topographic factors respectively with panchromatic band texture,combined texture indices,derivative texture indices.The 3×3 window was the optimal texture generation window for both panchromatic band texture model and combined texture indices model,and 7×7 window was the optimal texture generation window for derivative texture indices model. The introduction of derivative texture indices yield highest estimate,as 70.5% of the variability in the field data was accounted for by the model( R~2= 0.705 and RMSE= 37.183 m~3/hm~2),and the estimated accuracy of the model reached 80.67%. The estimation accuracy of combined texture indices model and panchromatic band texture feature model is 72.89% and 60.35%,respectively. It's suggested that the obvious improvement for stand volume of the C.lanceolate estimation can be obtained using derivative texture indices based on the GF-2 satellite images.
引文
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