Carbon stock map and uncertainty in plants of forested areas of Canada, 250m spatial resolution
收藏DataCite Commons2022-08-30 更新2024-07-03 收录
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This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with total C stored in plants of forested areas in Canada (AGB, BGB and dead plants) in kg/m² and C stock uncertainty. To estimate the C stored in plants of forest areas, we used 47,967 ground measurements of AGB measures and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- <i>included in the other dataset</i>), soil type map and radiation flux data. Different models were trained using a recursive feature elimination, random forest scheme and a 5-fold cross-validation assessment. The model with higher R² and lowest root mean square error (RMSE) was used for spatial prediction of AGB in forest areas while 1<sup>st</sup> and 3<sup>rd</sup> quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the maps in kg C m<sup>-2</sup>.
本项目旨在以250米空间分辨率生成加拿大境内植物与土壤碳储量的首张全覆盖估算结果。本数据集包含加拿大森林区域植物总碳储量(地上生物量(Aboveground Biomass, AGB)、地下生物量(Belowground Biomass, BGB)及死亡植物碳库)的碳密度图(单位:kg/m²)以及碳储量不确定性数据。为估算森林区域的植物碳储量,研究团队采用了47967份地上生物量实地测量数据,以及58项协变量,主要包括光学数据、地形参数、结构参数(例如合成孔径雷达(Synthetic Aperture Radar, SAR)数据、聚集指数、基于卫星激光雷达(Light Detection and Ranging, LiDAR)生成的冠层高度——详见另一数据集)、土壤类型图与辐射通量数据。研究采用递归特征消除结合随机森林方案训练多类模型,并通过5折交叉验证进行评估。最终选取决定系数R²最高且均方根误差(RMSE)最低的模型,用于森林区域地上生物量的空间预测;同时利用随机森林分位数回归的第一与第三分位数构建不确定性分布图。生成地上生物量分布图后,研究依据森林类型,通过地上生物量与地下生物量的关联关系计算得到森林区域的地下生物量;死亡植物碳库则通过实地测量得到的活生物量与死亡生物量间的线性回归关系计算得出。最终,将地上生物量、死亡植物碳库与地下生物量均乘以0.5,得到单位为kg C m⁻²的碳储量分布图。
提供机构:
4TU.ResearchData
创建时间:
2021-05-11



