Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
复制标题
DOI：
10.1016/j.rse.2018.11.017
复制
发表时间：
2019-02
影响因子：
13.5
通讯作者：
Huabing Huang;Caixia Liu;Xiaoyi Wang;Xiaolu Zhou;P. Gong
中科院分区：
文献类型：
--
作者：
Huabing Huang;Caixia Liu;Xiaoyi Wang;Xiaolu Zhou;P. Gong
森林地上生物量密度（AGB）的量化对于森林碳循环研究、生物多样性保护和气候变化减缓行动很有用。然而，在中国国家层面还无法获得更高分辨率和空间连续的森林 AGB 地图。在这项研究中，我们基于 1607 个田地样地开发了森林类型和生态区特定的异速生长模型。将异速生长模型应用于地球科学激光高度计系统 (GLAS) 数据，以计算足迹级别的 AGB。然后，我们通过将 GLAS 足迹 AGB 与源自陆地卫星图像和相控阵 L 波段合成孔径雷达 (PALSAR) 数据的各种变量相关联，绘制了 30m 分辨率的国家森林 AGB。我们估计中国森林AGB平均为69.88Mg/ha，标准差为35.38Mg/ha，AGB总碳储量为5.44PgC。我们的 AGB 估计值与森林面积排名前十的省份的 AGB 清单相当吻合，确定系数和均方根误差分别为 0.73 和 20.65 毫克/公顷。我们发现 AGB 估计的主要不确定性可归因于异速生长模型和 GLAS 高度测量的误差。我们还发现，Landsat 衍生的变量优于 PALSAR 衍生的变量，并且 PALSAR 的纹理特征比后向散射强度更好地支持森林 AGB 估计。
Quantification of forest aboveground biomass density (AGB) is useful in forest carbon cycle studies, biodiversity protection and climate-change mitigation actions. However, a finer resolution and spatially continuous forest AGB map is inaccessible at national level in China. In this study, we developed forest type- and ecozone-specific allometric models based on 1607 field plots. The allometric models were applied to Geoscience Laser Altimeter System (GLAS) data to calculate AGB at the footprint level. We then mapped a 30 m resolution national forest AGB by relating the GLAS footprint AGB to various variables derived from Landsat images and Phased Array L-band Synthetic Aperture Radar (PALSAR) data. We estimated the average forest AGB to be 69.88 Mg/ha with a standard deviation of 35.38 Mg/ha and the total AGB carbon stock to be 5.44 PgC in China. Our AGB estimates corresponded reasonably well with AGB inventories from the top ten provinces in the forested area, and the coefficient of determination and root mean square error were 0.73 and 20.65 Mg/ha, respectively. We found that the main uncertainties for AGB estimation could be attributed to errors in allometric models and in height measurements by the GLAS. We also found that Landsat-derived variables outperform PALSAR-derived variables and that the textural features of PALSAR better support forest AGB estimates than backscattered intensity.