ALS IntercomparisonWe used high-resolution gridded ALS to compare select 1 km and 6 km gridded GEDI metrics corresponding to the time period April 17, 2019 to March 16, 2023. We used the following ALS datasets for comparison:
(1)
National Ecological Observation Network27 (NEON), USA
(2)
NASA Carbon Monitoring System (CMS) Sonoma County, CA, USA28
(3)
USGS 3D Elevation Program (3DEP) Coconino National Forest, AZ, USA
(4)
NASA Carbon Monitoring System (CMS) Indonesia29
(5)
Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (EFForTS) Indonesia30,31
(6)
Stability of Altered Forest Ecosystem (SAFE) Malaysia32.
For the NEON dataset, we compared canopy height (RH98), height of median energy (RH50), total plant area index (PAI), and foliage height diversity (FHD). For the other five regions we compared canopy height (RH98) at a minimum, and in some cases (when other gridded ALS metrics were already available) we compared additional metrics (Table 3). We report the following statistics for each comparison:
adjusted R squared (R2) from a linear model of the form ALS~GEDI
Root mean squared error (RMSE)
Relative RMSE = 100 * (RMSE/mean(ALS))
Mean absolute error (MAE)
Table 3 Summary of ALS datasets used for comparison.NEONThe 1 km gridded GEDI product from the time period April 17, 2019 to March 16, 2023 was compared with NEON ALS data across a large range of latitudes and longitudes throughout the United States. First, we downloaded all ALS point cloud tiles for 31 NEON sites with > 30% forest cover. We queried all ALS tiles between 2020–2021, selecting the year with the best spatial coverage (tiles n), and where tied, selected the most recent year, resulting in approximately 1.5 TB of ALS across all sites. Second, we normalized all point clouds by tile using the lidR package33 in R. This process entailed instituting a multi-step noise removal algorithm consisting of (a) employing an isolated voxels filter that removes all 1 m voxels filter with fewer than 3 pts/m2; (b) determining the ground surface by estimating a digital terrain model (DTM) by interpolating a convex hull from all points classified as ground and removing all negative values; and (c) normalizing all point heights (z values) by subtracting the DTM from all points, and removing all negative values.Third, we determined a RH98 canopy height model (CHM) at 1 m spatial resolution as the 98th percentile of all points/m2. Concurrently, we generated PAI and FHD layers at an equivalent resolution of GEDI footprints by estimating plant area density for 25 m pixels using a universal extinction coefficient in the leafR package34 in R. We then calculated FHD as a function of PAI in 1 m vertical height bins. Fourth, we aligned all ALS rasters with corresponding gridded GEDI data by: (a) mosaicking all 1 m RH98 CHMs and 25 m PAI/FHD rasters across each NEON site; (b) masking water and urban classes from each ALS raster based on the 2019 National Land Cover Database35 (NLCD); (c) projecting and resampling all ALS mosaics to match those from gridded GEDI; (d) aggregating 1 m and 25 m rasters to 1 km by mean, median, SD, IQR, 95th percentile, and Shannon’s H; and (e) trimming all edge pixels so that only GEDI and ALS mosaic pixels with 100% overlap (i.e. “core” pixels) were retained. Finally, for comparison, we extracted all co-located ALS and GEDI pixels and assessed accuracy of GEDI relative to ALS. The mean (across all NEON sites) of each comparison statistic is shown in Table 4.Table 4 The mean (across all NEON sites) of each comparison statistic (RMSE, Rel. RMSE, MAE, and Adj. R2) for each GEDI metric and aggregation statistic at 1 km spatial resolution.Below (Figs. 3–4) we show comparison plots for GEDI PAI and FHD gridded at 1 km spatial resolution using the statistics mean, median, SD, IQR, 95th Percentile, and Shannon’s H. Additional NEON comparison plots and tables (RH98 and RH50) are shown in Supplementary Section D. Given the extents of the individual NEON sites and ALS surveys we did not perform comparisons at spatial resolutions coarser than 1 km.Fig. 3Comparison of NEON ALS PAI and GEDI PAI using mean, median, SD, IQR, 95th percentile, and Shannon’s H aggregation methods from the time period April 17, 2019 to March 16, 2023. The black line has a 1:1 relationship while the purple line corresponds to a linear fit (ALS~GEDI) of 1 km cells from all NEON sites.Fig. 4Comparison of NEON ALS FHD and GEDI FHD using mean, median, SD, IQR, 95th percentile, and Shannon’s H aggregation methods from the time period April 17, 2019 to March 16, 2023. The black line has a 1:1 relationship while the purple line corresponds to a linear fit (ALS~GEDI) of 1 km cells from all NEON sites.Additional ALSWe made use of other readily available ALS datasets in the USA and Southeast Asia for additional comparisons. Fine resolution canopy height models, and in some cases other gridded metrics, were distributed with some ALS datasets, specifically NASA CMS Borneo, EFForTS, and SAFE (Table 3). These metrics were computed with commonly used packages like lidR33, leafR34, and PDAL36,37. For USGS 3DEP Coconino we computed a high spatial resolution canopy height model by subtracting a digital surface model from a digital terrain model, both computed using PDAL. We uploaded the high-resolution ALS rasters along with associated gridded GEDI rasters from the time period April 17, 2019 to March 16, 2023 to Google Earth Engine38 where we developed a comparison script.Similar to the steps described for NEON comparison, we used a combination of masks to ensure a fair comparison between ALS and GEDI at spatial resolutions greater than or equal to 1 km. First we identified heavily urban or surface water pixels since these areas are not relevant for comparison. For the USA, we used NLCD 2021 land cover to determine urban and surface water pixels. For Southeast Asia, we used the mean GLAD annual surface water percentage39 and urban classification from Copernicus Global Land Service 100 m Land Cover to define water and urban masks40. Furthermore, considering the forest structure dynamics (especially in Southeast Asia) we added a mask to identify pixels which had undergone a stand-replacing disturbance41 during the year of or after the primary ALS acquisition year. We combined these three masks together to summarize the valid percent of each gridded pixel (i.e. not surface water, not urban, and not disturbed). In order for a gridded pixel to be eligible for comparison we required that at least 90% of the 30 m pixels used to determine the combined mask be valid. We extracted the corresponding ALS and GEDI gridded values for each metric, aggregation statistic, and pixel. We exported the resulting table to R and produced scatter plots and summary statistic tables.ALS data were acquired for Sonoma County, CA, USA in 2013. We used a 3 m spatial resolution canopy height model for comparison. Given the large extent of the County, we performed the comparison at 1 km (Fig. 5) and 6 km spatial resolution. Additional comparison plots and tables are shown in Supplementary Section D. Note that there is at least 6 years between ALS and GEDI lidar acquisition, so some error may be attributable to growth and/or non-stand-replacing disturbances.Fig. 5Comparison of Sonoma County ALS RH98 and GEDI RH98 using mean, median, SD, IQR, 95th percentile, and Shannon’s H aggregation methods from the time period April 17, 2019 to March 16, 2023. The black line has a 1:1 relationship while the purple line corresponds to a linear fit (ALS~GEDI) of 1 km cells.ALS data were acquired for Coconino National Forest, AZ, USA (and some surrounding areas) in 2019. We computed a 1 m spatial resolution canopy height model for comparison. Given the large extent of the National Forest, we performed the comparison at 1 km (Fig. 6) and 6 km spatial resolution. Additional comparison plots and tables are shown in Supplementary Section D.Fig. 6Comparison of Coconino National Forest ALS RH98 and GEDI RH98 using mean, median, SD, IQR, 95th percentile, and Shannon’s H aggregation methods from the time period April 17, 2019 to March 16, 2023. The black line has a 1:1 relationship while the purple line corresponds to a linear fit (ALS~GEDI) of 1 km cells.In addition to these temperate sites, we downloaded publicly available tropical forest ALS data associated with three projects in Southeast Asia, namely NASA CMS Borneo, EFForTS, and SAFE. As part of the NASA CMS Borneo project, ALS data were acquired for select regions of Kalimantan, Indonesia in 2014. We used a 3 m spatial resolution canopy height model for comparison. ALS data were acquired for the SAFE project landscape, Maliau Conservation Area and Danum Valley of Sabah, Malaysia in 2014. We used a 1 m spatial resolution canopy height model for comparison. We also used 20 m gridded maps of total PAI and FHD for comparison and present those results in Supplementary Section D. As part of the EFForTS project, ALS data were acquired for select regions of Jambi, Indonesia in 2020 and 2022. We mosaiced the 1 m canopy height models from the two years, giving priority to the data from 2020 since it covered more area. Rasters of additional ALS metrics (ZQ50, LAI, and FHD) were also available at 10 m spatial resolution. These additional ALS metrics were computed using slightly different equations relative to GEDI, but are still useful for preliminary comparison of gridded GEDI RH50, PAI, and FHD. We show comparison results for RH50, PAI, and FHD in Supplementary Section D. Given the relatively small collection extents of the three campaigns we only performed comparisons at 1 km spatial resolution. For the NASA CMS Borneo and SAFE projects there is at least 5 years between ALS and GEDI lidar acquisition, so some error may be attributable to growth and/or non-stand-replacing disturbances. Figure 7 shows comparison results for RH98 considering all three Southeast Asia projects, highlighting the impact of applying a higher per-pixel shot threshold. Supplementary Section D also includes comparison results for RH98, RH50, PAI, and FHD by individual project.Fig. 7Comparison of SE Asia ALS RH98 and GEDI RH98 using mean, median, SD, IQR, 95th percentile, and Shannon’s H aggregation methods from the time period April 17, 2019 to March 16, 2023. The black line has a 1:1 relationship while the purple line corresponds to a linear fit (ALS~GEDI) of 1 km cells. The left set of plots use a minimum of 2 shots per pixel while the right set of plots only use pixels which have at least 20 shots.