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Estimation of Grassland Height Based on the Random Forest Algorithm and Remote Sensing in the Tibetan Plateau
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2019.2954696
Jianpeng Yin , Qisheng Feng , Tiangang Liang , Baoping Meng , Shuxia Yang , Jinlong Gao , Jing Ge , Mengjing Hou , Jie Liu , Wei Wang , Hui Yu , Baokang Liu

Grassland height is one of the main factors used to evaluate grassland conditions. However, the retrieval of natural grassland height at the regional scale by remote sensing data and conventional statistical models will result in large errors, especially in the heterogeneous alpine grassland of the Tibetan Plateau (TP). In this article, we aimed to construct a model based on multiple variables (biogeographical, meteorological, and Moderate Resolution Imaging Spectroradiometer (MODIS) product) using a random forest (RF) algorithm to predict the spatial distribution of grassland height in the TP from 2003 to 2017. The results show the following conditions. 1) Seven variables (elevation, slope, aspect, enhanced vegetation index, reflectance in band seven of MODIS (B7), annual accumulated temperature (≥0 °C), and annual precipitation) that were selected by recursive feature elimination from 11 variables have high importance in the RF model. The final model exhibits good performance, with mean R2 and root mean squared error values of 0.51 and 6.15 cm, respectively, which were determined via 10-fold crossvalidation. 2) The mean grassland height (2003–2017) predicted by the RF model ranges from 5 to 10 cm in most areas of the TP, and the mean height is 10 cm. The grassland height in the east and southeast of the TP is significantly higher than that in other areas. 3) This article achieves a relatively accurate estimation of grassland height over a large spatial scale at 500-m spatial resolution, which plays an important role in accurately estimating aboveground biomass and evapotranspiration over grassland.

中文翻译:

基于随机森林算法和遥感的青藏高原草地高度估算

草地高度是评价草地状况的主要因素之一。然而,通过遥感数据和常规统计模型在区域尺度上反演天然草地高度会产生较大误差,尤其是在青藏高原(TP)的异质高寒草地中。在本文中,我们旨在使用随机森林 (RF) 算法构建基于多个变量(生物地理、气象和中分辨率成像光谱仪 (MODIS) 产品)的模型,以预测 2003 年以来青藏高原草地高度的空间分布。到 2017 年。结果显示以下条件。1) 七个变量(高程、坡度、坡向、增强植被指数、MODIS 七波段反射率(B7)、年积温(≥0 °C)、和年降水量)从 11 个变量中通过递归特征消除选择的在 RF 模型中具有很高的重要性。最终模型表现出良好的性能,平均 R2 和均方根误差值分别为 0.51 和 6.15 cm,这是通过 10 倍交叉验证确定的。2)RF模型预测的平均草地高度(2003-2017)在青藏高原大部分地区为5~10 cm,平均高度为10 cm。青藏高原东部和东南部草地高度明显高于其他地区。3)本文实现了500米空间分辨率下大空间尺度草地高度的相对准确估算,对准确估算草地地上生物量和蒸散量具有重要作用。
更新日期:2020-01-01
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