南方红壤典型水土流失区马尾松林地上林木碳储量的遥感监测——以长汀县河田镇为例

森林碳储量动态变化对揭示区域水土流失治理成效具有重要指示意义。以长汀县河田镇为例，2017年随机设置34个马尾松林样本作为建模集，分别与同期Landsat影像的原始波段、植被指数及主成分因子进行回归分析，构建马尾松（（Pinus massoniana））林地上林木碳储量的最佳反演模型，基于伪不变特征原理的线性归一化法实现该模型在2003、2010年影像上的适用性校正转换，实现研究区2003、2010、2017年马尾松林地上林木碳储量的反演及时空分异特征的研究。结果表明：研究区2017年马尾松林地上林木碳储量最佳遥感反演模型是以绿色植被指数（GNDVI）为自变量构建的指数模型：C₂₀₁₇=0.006e^{14.357GNDVI₂₀₁₇}，该模型拟合的决定系数为0.57，平均相对精度为82.19%；2003年、2010年马尾松林地上林木碳储量遥感估测模型为：C₂₀₀₃=0.006e^{（16.4086GNDVI₂₀₀₃+1.1428）}、C₂₀₁₀=0.006e^{（15.1677GNDVI₂₀₁₀+1.5821）}，两期校正模型的决定系数均在0.85以上；2003、2010及2017年碳储量分别为8.24 t/hm²、11.34t/hm²、16.14 t/hm²，整体呈上升趋势；地上林木碳储量随海拔、坡度的升高而增加，向阳坡地上林木碳储量高于背阴坡；碳储量增长率随海拔、坡度的升高而降低，背阴坡碳储量增长率高于向阳坡。

Remote sensing monitoring of aboveground carbon storage of Pinus massoniana forests in a typical red soil erosion area in Southern China: Hetian, Changting County of Fujian Province

The dynamic changes of forest carbon storage have important indications for revealing the effectiveness of regional soil erosion control. Take Hetian Town, Changting County as an example. In 2017, 34 Masson pine (Pinus massoniana) forests samples were randomly set as the modeling set, and the original waveband, vegetation index and principal component factor regression analysis of the Landsat images of the same period were used to construct the best inversion model for the aboveground forest carbon storage of Masson pine forests. The best inversion model, the linear normalization method based on the principle of pseudo invariant feature (PIF), realizes the applicability correction conversion of the model on the images of 2003 and 2010, and realizes the inversion of the carbon storage of the Masson pine forests in the study area in 2003, 2010 and 2017. Research on spatio-temporal differentiation characteristics. The results show that the best remote sensing inversion model for the aboveground forest carbon storage of Masson pine forests in the study area in 2017 is an index model constructed with the Green Normalized Vegetation Index (GNDVI) as the independent variable:C₂₀₁₇=0.006e^{14.357GNDVI₂₀₁₇},the fitting coefficient of determination of this model is 0.57, and the average relative accuracy is 82.19%; In 2003 and 2010, the remote sensing estimation models of the aboveground forest carbon storage of Masson pine forests are: C₂₀₀₃=0.006e^{(16.4086GNDVI₂₀₀₃+1.1428)},C₂₀₁₀=0.006e^{(15.1677GNDVI₂₀₁₀+1.5821)}, The coefficient of determination of the two-period calibration model is above 0.85; Carbon storage in 2003, 2010 and 2017 were 8.24 t/hm², 11.34 t/hm² and 16.14 t/hm², respectively, showing an overall upward trend; the aboveground forest carbon storage increases with the elevation and slope, and the aboveground forest carbon storage on the sunny slope is higher than that on the shady slope; The growth rate of carbon storage decreases with the increase of altitude and slope, and the growth rate of carbon storage on the shady slope is higher than that on the sunny slope.