基于GF-2的油松人工林地上生物量反演

油松是黄土高原地区重要的造林树种.快速准确地估测其地上生物量,对开展该地区森林资源动态监测等具有重要作用.本研究选取陕西省黄龙山林区石堡林场的油松人工林为对象,结合国产卫星高分二号(GF-2)的多光谱遥感影像与野外同时段实测样地数据,对其地上生物量进行了估算.提取了5种植被指数和8种纹理信息,基于普通回归、逐步回归、岭回归、拉索回归与主成分回归5种方法在4种纹理窗口(3×3、5×5、7×7和9×9)下建模,使用留一法交叉验证测试了每个模型的估算精度.结果表明: 提取的遥感因子之间存在着较为严重的多重共线性关系,大部分遥感因子与油松人工林地上生物量有较为显著的相关性;GF-2数据在石堡林场油松人工林地上生物量的反演中可以实现较高精度,其中估算效果最好的是使用了9×9纹理窗口的主成分回归模型,估算效果最差的是使用了3×3纹理窗口的普通回归模型.利用国产高分辨率卫星影像对油松人工林地上生物量进行反演研究,可以为西北地区林业部门进行森林生物量监测、资源管理与可持续经营提供科学依据.

Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.