利用光谱变量、地表温度和土壤湿度估算中国东北地区针叶林的地上生物量。
Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture.
机构信息
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China; Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China.
Bangor College China, Bangor University, 498 Shaoshan Rd., Changsha, China.
出版信息
Sci Total Environ. 2021 Sep 1;785:147335. doi: 10.1016/j.scitotenv.2021.147335. Epub 2021 Apr 24.
As a crucial indicator of forest growth and quality, estimating aboveground biomass (AGB) plays a key role in monitoring the global carbon cycle and forest health assessments. Novel methods and applications in remote sensing technology can greatly reduce the investigation time and cost and therefore have the potential to efficiently estimate AGB. Random forest (RF), combined with remote sensing images, is a popular machine learning method that has been widely used for AGB estimation. However, the accuracy of the ordinary linear variable selection method in the AGB estimation of coniferous forests is challenging due to the complexity of these forest biomes. In this study, spectral variables (spectral reflectance and vegetation index), land surface temperature (LST) and soil moisture were extracted from the operational land imager (OLI) and thermal infrared sensor (TIRS) of Landsat 8, and optimized RF regressions were established to estimate the AGB of coniferous forests in the Wangyedian forest farm, Inner Mongolia, Northeast China. We applied one linear (Pearson correlation coefficient (PC)) and four nonlinear (Kendall's τ coefficient (KC), Spearman coefficient (SC), distance correlation coefficient (DC) and the importance index) indices to select variables and establish optimized RF regressions for AGB estimation. The results showed that all the nonlinear indices provided significantly lower estimation errors than the linear index, in which the minimum root mean square error (RMSE) of 40.92 Mg/ha was obtained by the importance index in the nonlinear indices. In addition, the inclusion of LST and soil moisture significantly improved AGB estimation. The RMSE of the models constructed through the five indices decreased by 12.93%, 7.31%, 8.33%, 6.28% and 10.78%, respectively, following the application of the LST variable. In particular, when LST and soil moisture were both added into the model, the RMSE decreased by 31.47%. This study demonstrates that combining the nonlinear variable selection method with optimized RF regression can improve the efficiency of AGB estimation to support regional forest resource management and monitoring.
作为森林生长和质量的关键指标，估算地上生物量（AGB）在监测全球碳循环和森林健康评估方面起着关键作用。遥感技术中的新方法和应用可以大大减少调查时间和成本，因此具有高效估算 AGB 的潜力。随机森林（RF）与遥感图像相结合，是一种广泛应用于 AGB 估算的流行机器学习方法。然而，由于这些森林生物群落的复杂性，普通线性变量选择方法在估算针叶林 AGB 方面的准确性具有挑战性。在这项研究中，从 Landsat 8 的操作陆地成像仪（OLI）和热红外传感器（TIRS）中提取了光谱变量（光谱反射率和植被指数）、地表温度（LST）和土壤湿度，并建立了优化的 RF 回归来估算中国东北地区内蒙古旺业甸林场的针叶林 AGB。我们应用了一个线性（皮尔逊相关系数（PC））和四个非线性（肯德尔 τ 系数（KC）、斯皮尔曼系数（SC）、距离相关系数（DC）和重要性指数）指标来选择变量，并建立优化的 RF 回归来估算 AGB。结果表明，所有的非线性指标都提供了显著低于线性指标的估计误差，其中非线性指标中的重要性指数获得了 40.92 Mg/ha 的最小均方根误差（RMSE）。此外，LST 和土壤湿度的纳入显著提高了 AGB 的估算。通过五个指标构建的模型的 RMSE 分别降低了 12.93%、7.31%、8.33%、6.28%和 10.78%，随后应用了 LST 变量。特别是，当 LST 和土壤湿度都被加入到模型中时，RMSE 降低了 31.47%。本研究表明，结合非线性变量选择方法和优化的 RF 回归可以提高 AGB 估算的效率，以支持区域森林资源管理和监测。
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