Spatially continuous canopy height maps of forested ecosystems of Canada

This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution — one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada — the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI. The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.本数据集包含两套空间分辨率为250米的加拿大森林生态系统冠层高度图：一套基于星载激光雷达（spaceborne LiDAR）GEDI的观测数据，另一套来源于ICESat-2卫星。GEDI与ICESat-2在加拿大冠层高度信息的获取方面各具特性：前者植被信息反演精度更高，但无法实现加拿大全境覆盖；后者虽非专为植被信息采集设计，却具备全球覆盖能力。本研究依托ICESat-2卫星的ATL08激光雷达产品与GEDI的GEDI L2A产品，生成了全域覆盖的冠层高度图。数据采集时段设定为2020年生长季中期（6月与8月）。针对太阳背景噪声与大气散射效应，对原始点云数据进行滤波处理；经滤波与点抽稀操作后，ICESat-2与GEDI的有效点云数量分别为208554个与1249354个。将上述有效点云与14项辅助变量进行关联，这些变量主要反映地表结构特征，包括季相化的哨兵1号（Sentinel-1）VV、VH极化数据，季相化的哨兵2号（Sentinel-2）红波段与近红外（NIR）波段数据，以及年度PALSAR-2 HH、HV极化数据。随后，采用随机森林算法对激光雷达观测点开展空间外推，结合上述空间连续变量构建回归模型。以机载激光扫描（ALS）数据作为验证基准，GEDI的反演性能优于ICESat-2：两者的平均差（MD）分别为0.9米与2.9米，均方根误差（RMSE）分别为4.2米与5.2米。但由于GEDI与ALS均无法覆盖大部分半寒带森林区域，ICESat-2能够更精准地反演该区域预期的高大冠层高度，表现优于GEDI。