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    张超, 彭道黎, 涂云燕, 党永峰, 智长贵. 利用TM 影像和偏最小二乘回归方法估测三峡库区森林蓄积量[J]. 北京林业大学学报, 2013, 35(3): 11-17.
    引用本文: 张超, 彭道黎, 涂云燕, 党永峰, 智长贵. 利用TM 影像和偏最小二乘回归方法估测三峡库区森林蓄积量[J]. 北京林业大学学报, 2013, 35(3): 11-17.
    ZHANG Chao, PENG Dao-li, TU Yun-yan, DANG Yong-feng, ZHI Chang-gui. Predicting forest volume in Three Gorges Reservoir Region using TM images and partial least squares regression.[J]. Journal of Beijing Forestry University, 2013, 35(3): 11-17.
    Citation: ZHANG Chao, PENG Dao-li, TU Yun-yan, DANG Yong-feng, ZHI Chang-gui. Predicting forest volume in Three Gorges Reservoir Region using TM images and partial least squares regression.[J]. Journal of Beijing Forestry University, 2013, 35(3): 11-17.

    利用TM 影像和偏最小二乘回归方法估测三峡库区森林蓄积量

    Predicting forest volume in Three Gorges Reservoir Region using TM images and partial least squares regression.

    • 摘要: 为进一步提高遥感模型预测森林蓄积量的精度和稳定性,分析了遥感特征因子、地形特征因子、郁闭度与森林蓄积量之间的相关关系。在此基础上,利用偏最小二乘回归方法构建了森林蓄积量遥感预测模型,生成了三峡库区森林蓄积量空间等级分布图,并与地面实测值进行比较。结果表明:该模型的最佳主成分数为3,且郁闭度、海拔、坡度、TM1、TM2、TM3、TM4、TM5、TM7、NDVI、RVI、TM7/ TM3、TM4 ⅹTM3/ TM2、亮度和湿度为预测森林蓄积量的入选变量;森林蓄积量预测的调整决定系数为0.524,相对误差为7.33%,均方根误差为1.763 m3 ;利用该模型计算出三峡库区森林总蓄积量约为1郾12 亿m3 ,总体预测精度达到89.58%。研究结果为提高森林蓄积量遥感预测的精度提供了一种有效手段,有利于大面积应用和推广。

       

      Abstract: China. In order to further improve the accuracy and stability of predicting forest volume by remote sensing, the study analyzed the relative relationship between remote sensing variables, topographic factor, forest canopy and forest volume. The partial least squares (PLS) regression model was generated from the significant variables and the space level distribution map of forest volume was constructed. The results indicated that for the PLS regression model, the number of the best principal components was 3, and canopy, elevation, slope, 6 single bands, normalized difference vegetation index ( NDVI), ratio vegetation index (RVI), TM7/ TM3, TM4 ⅹTM3/ TM2, brightness and wetness were identified as the predictors for predicting forest volume. The results showed that the determination coefficient (R2 ), relative error (RE) and the root mean square error (RMSE) between estimated value and measured one of forest volume were 0.524, 7.33% and 1.763 m3, respectively. The total forest volume in Three Gorges Reservoir Region was 1.12 ⅹ108 m3, while the total average prediction accuracy of PLS regression model reached 89.58%. The results indicate that PLS regression method can provide an effective way to improve the accuracy of predicting forest volume at large scale by remote sensing data.

       

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