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Abstract The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched on September 15th, 2018 and this mission offers an extraordinary opportunity to contribute to an assessment of forest resources at multiple spatial scales. This study served to develop a methodology for utilizing ICESat-2 data over vegetated areas. The specific objectives were to: (1) derive a simulated ICESat-2 photon-counting lidar (PCL) vegetation product using airborne lidar data, (2) examine the use of simulated PCL metrics for modelling aboveground biomass (AGB) along ICESat-2 profiles using a simulated ICESat-2 PCL vegetation product and reference AGB estimated from airborne lidar data, and (3) estimate forest canopy cover using simulated PCL canopy product data and airborne lidar-derived canopy cover. Using existing airborne lidar data for Sam Houston National Forest (SHNF) in Texas and known ICESat-2 track locations, PCL simulations were carried out. Three scenarios were analyzed in this study; 1) simulated data without the addition of noise, 2) processed simulated data for daytime, and 3) nighttime scenarios. Segments measuring 100 m along the proposed ICESat-2 tracks were used to extract simulated PCL metrics from each of the three data scenarios and spatially coincident, reference airborne lidar-estimated AGB and airborne lidar canopy cover estimates. Linear regression models were then developed with a subset of the simulated PCL segments to estimate AGB and canopy cover and their performance assessed using separate testing sets. AGB model testing with the simulated dataset without noise, nighttime and daytime scenarios resulted in R2 values of 0.79, 0.79 and 0.63 respectively, with root mean square error (RMSE) values of 19.16 Mg/ha, 19.23 Mg/ha, and 25.35 Mg/ha. Predictive models for canopy cover (4.6 m) achieved R2 values of 0.93, 0.75 and 0.63 and RMSE values of 6.36%, 12.33% and 15.01% for the simulated dataset without noise, nighttime and daytime scenarios respectively. Findings from this study suggest the potential of ICESat-2 for estimating AGB, given the photon detection rate and background noise anticipated by ICESat-2 under temperate forest settings, and especially in the data format provided by standard ICESat-2 data vegetation products. Highlights • ICESat-2 photon-counting lidar (PCL) data are simulated using airborne lidar data. • Forest aboveground biomass and canopy cover are predicted with simulated PCL data. • Improved predictions with simulated ICESat-2 parameters for the nighttime scenario • Potential for characterizing forest structure with ICESat-2 is demonstrated. [ABSTRACT FROM AUTHOR]

The ICESat-2 data products work well in extracting terrain elevations and canopy heights in boreal forests. However, identifying ground photons in mountainous dense forests remains a challenge due to high canopy covers, complex terrains, and limited penetrability, particularly in tropical areas. Accurate identification of ground photons is crucial for improving the performance of ICESat-2 data. This study proposes an algorithm called OPIC to optimize ground photons provided by ICESat-2 data products. The OPIC algorithm involves removing outliers of ground photons, reclassifying photons misclassified as canopies or noises, and extracting optimized ground elevations. The OPIC algorithm is evaluated in four distinct forests located in subtropical southern China, tropical Indonesia, temperate Estonia and tropical America. The results show significant improvements in the number of valid segments and the accuracy of canopy height estimations. The ICESat-2 product of Land and Vegetation Height (ATL08) provides canopy heights by segments with an along-track distance resolution of 20 meters. In forests with canopy covers ≥85% on terrains with slopes ≥15°, the ATL08 is only able to provide canopy height estimations for 10 out of 368 segments (i.e., 2.7%) at the Chinese test site, but the OPIC algorithm improves this to 157 segments (i.e., 42.7%). At the Indonesian test site, the OPIC algorithm improves the number of valid segments from 11 to 369 out of 453 segments (i.e., from 2.4% to 81.5%). Similarly, the OPIC algorithm improves the number of valid segments from 353 to 1148 out of 2776 segments (i.e., from 12.7% to 41.4%) at the American test site. The airborne lidar data is utilized as reference data to evaluate the performance of OPIC. For valid segments provided in ATL08, the OPIC can reduce the relative root mean square error (%RMSE) of canopy height estimations from 31.64% m to 24.97% at the Chinese site, from 25.74% to 18.84% at the Indonesian site, from 7.27% to 2.47% at the Estonian site, and from 27.97% to 23.87% at the American site. For all segments provided by ATL08 in each test site disregarding terrains and canopy covers, the OPIC algorithm is able to increase the number of valid segments from 383 to 1244 out of 2252 (i.e., from 17.0% to 55.2%) at the Chinese site, from 173 to 1276 out of 1482 (i.e., from 11.7% to 86.1%) at the Indonesian site, from 8638 to 10,149 out of 10,344 (i.e., from 83.5% to 98.1%) at the Estonian site, and from 6145 to 9383 out of 16,150 (i.e., from 38.0% to 58.1%) at the American site, while maintaining approximately the same estimation accuracy of canopy heights as the valid segments of ICESat-2 data products. These results demonstrate the potential of the OPIC algorithm to provide more valid segments and more accurate estimations of canopy heights in mountainous dense forests. • ICESat-2 faces challenges in identifying ground photons in mountainous dense forest. • An algorithm (OPIC) is proposed to optimize ground photons of ICESat-2 data product. • Reclassifying ground photons misclassified as canopies and noises, and outliers are removed. • OPIC can improve ratio of valid segments from 11.7% to 86.1% in tropical forests. • Estimation errors of canopy heights can be decreased by OPIC from 25.74% to 18.84%. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the terrain elevation of the ICESat-2's ATL08 product is of good accuracy, overall, but in urban areas under complex surface conditions, the terrain elevation is vulnerable to the influence of the effect of dense buildings, resulting in deviations and even outliers. However, the previous studies have not effectively corrected these deviations and outliers. To this end, we propose a method for correcting the terrain elevation of the ATL08 product in urban areas, which is an auto-refinement method for the terrain elevation. Based on the data characteristics of this type of terrain elevation data and the distribution characteristics of urban buildings, the proposed method innovatively corrects the deviated part of the terrain elevation by a water pump deployment criterion with the vertical contour of the terrain. To increase its applicability, the proposed method uses a strategy of mixing the terrain elevation data that are from the strong- and weak-beam observations, allowing the deviated terrain in one beam with a higher missing data rate to be corrected with the aid of the other beam with a lower missing data rate. We used airborne LiDAR data from several areas (New York, Amsterdam, and Palmerston North) with different city sizes to validate the corrected results. The results show that the root-mean-square error (RMSE) of the terrain elevation in urban areas is reduced from ∼4 m to ∼1 m after correction. The correction results also show that the proposed method can correct the deviated terrain elevation, while ensuring that the high-quality terrain elevation can be effectively retained. • With about 50%–77% accuracy improvement of corrected ICESat-2's terrains in urbans. • Correcting deviated terrains while effectively retaining the high-quality terrains. • Improving application by the profile projection of strong and weak beam terrains. • Obtaining the terrain vertical contour by cumulative analysis of terrain elevations. • Improving identification of deviated terrains by deploying water pumps iteratively. [ABSTRACT FROM AUTHOR]

Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt. • ICESat-2 level3a datasets can be used to map watershed-scale snow depth gradients. • ATL08 ground elevations must be corrected for slope-dependent bias. • ATL08 ground elevation uncertainty increases with slope to >1 m above 20 degrees. • Mass balance gradients from ATL06 data require reference elevation timeseries. • ATL06 mass balance gradients can capture seasonal variability in maritime climates. [ABSTRACT FROM AUTHOR]

The acquisition of elevation measurements from NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and availability of airborne lidar data over the US present an exceptional opportunity to understand vegetation structural estimates provided by ICESat-2. Although a critical forest biophysical attribute, canopy cover is not yet reported from ICESat-2. Thus, there is a need to better understand the application of ICESat-2 for providing canopy cover information. The overall goal of this study was to investigate methods to derive canopy cover and characterize the predictive capability, with ICESat-2. Given availability of dedicated vegetation products (ATL08) and custom noise filtering algorithms developed for ICESat-2 photon data, multiple datasets were evaluated in this study. With focus on two study sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, specific objectives were to: (1) Evaluate equations for estimating canopy cover using custom-processed geolocated photon data, ATL03, and ICESat-2's vegetation product data, ATL08, (2) Compare ICESat-2-derived estimates of canopy cover with airborne lidar canopy cover, and (3) Evaluate a modeling-based approach to improve predictions of canopy cover by leveraging available canopy metrics from ATL08 and custom-processed datasets. A total of six (6) potential measures of canopy cover were investigated in this study, based on 100-m ATL08 segments: (i) percentage of photons above 2 m, (ii) percentage of photons above 4.6 m, (iii) percent of canopy (inside canopy) and top-of-canopy photons of total canopy and ground photons, (iv) percent of canopy of total ground and canopy photons, (v) percent of top-of-canopy of total top-of-canopy and ground photons, and (vi) mean of (iii), (iv) and (v). While strongest correlations were produced from canopy cover computed with (ii) (r between 0.72 and 0.84), similar relationships were indicated from using (iii) and (v) (r values between 0.70 and 0.79). Considering a suite of available canopy parameters, the best R2 and RMSE values from canopy cover estimation models were produced from the use of custom-processed ICESat-2-derived metrics (R2 and RMSE values of 0.77 and 13%). Findings from this study highlight methods for estimating canopy cover with ICESat-2 that may be suitable to a range of cover types, in addition to vegetation settings where cut-offs based on tree heights (e.g., 2 m and 4.6 m) are not applicable. With multiple approaches to characterizing canopy cover, results serve to better understand the applicability of ICESat-2 as a data source for canopy cover information and ultimately inform ongoing efforts for deriving an updated gridded product. • ICESat-2 data were examined for deriving canopy cover for southern United States sites. • Six formulas for computing canopy cover with ICESat-2 were investigated. • Four formulas may be used to derive canopy cover in low-height vegetation. • Correlations with reference canopy cover estimates range from 0.72 to 0.84. • Canopy parameters used in regression models yield R2 up to 0.77 (RMSE-13%). [ABSTRACT FROM AUTHOR]

The ICESat-2 ATL08 land and vegetation product includes several flags that can be used for the assessment of LiDAR-environment interactions and can help select data of the highest quality. However, the usability of these flags has not been sufficiently studied to date. Here, we aimed to evaluate the effects of atmospheric scattering, the presence of snow, canopy cover, terrain slope, beam strength, and solar angle on the accuracy of terrain and canopy height of the ATL08 product as well as on providing recommendations on how to filter data in order to minimize errors. We evaluated the vertical accuracy of ATL08 terrain and canopy height in European mountains by comparing them with the digital terrain model and canopy height model derived from airborne laser scanning data. Our results indicate that the assessment of atmospheric effects using the cloud confidence flag (cloud_flag_atm ; i.e. number of cloud layers) is better than the previously used multiple scattering warning flag (msw_flag). Day acquisitions with more than one layer of clouds yielded a terrain elevation RMSE of 3.22 m in forests while night acquisitions with no more than a single layer of clouds resulted in RMSE of 1.73 m. The increasing atmospheric scattering effects increased the photons' path length, resulting in terrain height underestimation. The presence of snow had a strong positive effect on the number of identified ground photons, independently of the canopy cover, but resulted in an overestimation of terrain height in higher altitudes. Accordingly, the presence of snow cover resulted in a significant underestimation of canopy height in forests. The canopy height in broadleaf/mixed as well as coniferous forests was in summer underestimated on average by 2.1 m (%ME of −15.3%) and 1.2 m (%ME of −8.2%), respectively; in winter, however, the underestimation increased to 8.5 m (%ME of −56.8%) and 5.7 m (%ME of −38.3%), respectively. Canopy height estimates had better accuracy for the strong beam (RMSE of 5.09 m; %RMSE of 35.4%) than for the weak beam (RMSE of 7.03 m; %RMSE of 51.3%). Our results show that the ATL08 terrain height accuracy decreases with uneven distribution of signal photons within individual segments and further deteriorates with increasing terrain slope. Filtering out segments with poor distribution of photons, more than one layer of clouds during the day, and snow cover in high altitudes is the best approach for minimizing the error while maximizing the number of segments left for subsequent analysis. [Display omitted] • Cloud confidence flag is useful for assessing atmospheric effects on ATL08 accuracy. • High number of cloud layers led to low number of photons and terrain underestimation. • Error of canopy height estimates is considerably larger than that of terrain height. • Canopy height is underestimated due to snow-cover and loss of tree foliage in winter. • We provide users with an approach for detecting problematic measurements. [ABSTRACT FROM AUTHOR]

Forests represent the world's largest terrestrial ecosystem and their monitoring is therefore critical from scientific, ecological, and management perspectives. Present day sustainable forest management practices go beyond forest inventory and increasingly include aspects such as carbon accounting and regeneration assessments. Such monitoring requires often unavailable, spatially exhaustive and up-to-date information on forest attributes over broad areas. Recent developments in the acquisition of broad-scale forest attribute information from remotely sensed data has included the use of multiple technologies that take advantage of globally available data products to derive forest attribute layers. However, less is known about the applicability and performance of such products when used to produce broad-scale, accurate, and up-to-date forest information products. This study aimed to evaluate the agreement between two broad-scale forest canopy height products – Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) and the National Terrestrial Ecosystem Monitoring System (NTEMS) imputed canopy height layers for Canada – across a variety of ecological gradients. Overall, the two datasets showed high correspondence, with a root-mean-square difference of 4.87 m, and 85% of ICESat-2 canopy heights falling within the 95% confidence interval of the NTEMS height estimate. Across ecozones, canopy heights in the Taiga Shield West and Boreal Shield West had stronger agreement (91% of ICESat-2 segments within the 95% confidence interval of NTEMS), while the Taiga Cordillera and Taiga Shield East had lower agreement (< 75% of ICESat-2 segments within the 95% confidence interval of NTEMS). Interestingly, we found that the modeled heights based upon optical satellite data had a less generalized distribution than heights from ICESat-2 as well as achieving a greater representation for the taller (overall and by ecozone) height classes. An increase in absolute difference between data products was also found as a function of increasing slope. Finally, the correspondence between products was evaluated across disturbed areas (35 to 10 years since disturbance) to assess the agreement of the two products in areas of regenerating forest. In general, the analysis found that burned areas, which tend to be more structurally heterogeneous, had lower agreement between products then harvested areas. The high overall correspondence between the data products demonstrate the potential for integration of ICESat-2 to inform (via calibration / validation) or update height products based upon optical satellite data. • Numerous remotely sensed datasets are available for monitoring of forest resources. • The agreement between two broad-scale forest canopy height products was evaluated. • Overall, there was high agreement between the data sources. • Correspondence between products varied across ecological attributes. • These data sources could be integrated to inform or update forest height products. [ABSTRACT FROM AUTHOR]

Despite its critical importance to carbon storage modeling, forest vertical structure remains poorly characterized over large areas. Canopy height estimates from current satellite missions such as ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) offer promise to close this knowledge gap, but their validation is critically important to inform their measurement uncertainties and scientific utility. Using existing airborne laser scanning (ALS) data, the agreement of a variety of terrain and aboveground canopy height metrics including summary height statistics and percentiles, from ICESat-2' Land, Water and Vegetation Elevation product (ATL08) product was assessed in 12 sites across six major biomes in the United States. The agreement between ATL08 and ALS heights was assessed using the mean bias (Bias, ATL08 – ALS), the mean absolute error (MAE) and their percent equivalents, percent bias (pBias) and percent MAE (pMAE), respectively. In general, the agreement between ATL08 and ALS terrain heights was high (Bias 0.18 m, pBias 0.1%) while canopy heights showed lower agreement (Bias −1.71 m, pBias −15.9%). Analyses by biome, time of acquisition and beam strength of the ICESat-2 photon data also showed generally higher agreement for ATL08 terrain than canopy heights. Analyses also showed the performance of ATL08 heights varied with canopy cover with ATL08 terrain heights showing the best agreement when canopy cover was between 40 and 70% while the best performance for ATL08 canopy heights was observed when canopy cover was greater than 80%. This observation, coupled with analyses by biome, indicate that ATL08 canopy heights are more suitable in relatively dense canopy environments such as conifer and broadleaf forests than relatively sparse environments such a temperate grassland and Savannas. Higher level canopy height percentiles (95th and 98th) showed higher agreement (mean Bias −12.5%) with ALS heights than lower percentiles (minimum, 25th, mean pBias ~39.2%). These findings indicate that ATL08 canopy heights show more promise for routine canopy height characterization using the 95th and 98% percentiles but is limited in characterizing intermediate vertical structure. The observed performance differences between ATL08 terrain and canopy heights are attributed to differences in photon sampling rates over terrain and canopy surfaces which, compounded with background noise in ICESat-2 photon data, led to different effectiveness for ATL08 processing routines in filtering terrain and off-terrain points. This assessment of the impact of a variety of factors provides the vegetation community with an understanding of the capabilities and limitations of height estimates from the ICESat-2 ATL08 product. • Assessed ICESat-2' ATL08 terrain and canopy heights across multiple sites in the US. • Evaluated ATL08 heights by site, biome, acquisition time and beam strength. • ATL08 terrain heights agreed closely with airborne lidar data across sites/factors. • ATL08 canopy heights varied widely compared to lidar across sites or factors. • These results inform usability of ATL08 data across a variety of factors. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in the fall of 2018 and is collecting vegetation canopy height and terrain measurements globally. In this paper we validate the terrain and canopy heights estimated from 11 months of ICESat-2 data using airborne lidar data collected in southern Finland. Overall, the terrain heights from the ATL08 data product agreed with the airborne lidar with vertical errors less than 75 cm (mean = −0.07 m; MAE = 0.53 m, RMSE = 0.73 m; n observations = 909,467). ATL08 terrain heights had positive bias (33 cm) when permanent snow cover was present compared to the airborne lidar. Canopy heights derived from ICESat-2 varied significantly by the beam selection (i.e. strong versus weak). Use of the weak beams increased the underestimation of canopy height by 1.06 m and increased RMSE% by 8.08%. The lowest canopy height errors were associated with strong beam/night/summer ATL08 acquisitions, which underestimated canopy height by 0.56 m (Bias% = 3.18; RMSE% of 13.75%), followed by strong beam/day/summer acquisitions (Bias% = 2.85%; RMSE% = 14.77%). For this study area with managed, coniferous-dominated boreal forests, we found that ATL08 canopy height errors were lowest for canopy cover ranging from 40 to 85% and within this range, we found that on average, ICESat-2 missed the top 11–13% of canopy heights. At low canopy cover (<40%), photons are more likely to be reflected from the terrain surface rather than the canopy due to the lower amount of vegetation present. Although not designed for canopy height retrievals, herein we have demonstrated that ICESat-2 can provide a useful source of canopy height information in boreal forests. By quantifying the accuracy with which terrain and canopy heights are characterized by ATL08, we provide insights on the capabilities and limitations of these data for these applications. Our results indicate that end users interested in canopy height retrievals in particular should avoid the use of weak beam data in boreal forest conditions. • Eleven months of ICESat-2 data over southern Finland were evaluated for accuracy. • ICESat-2 derived terrain heights were accurate to better than 75 cm • ICESat-2 derived canopy heights underestimated boreal canopy height by 0.56 m • ICESat-2 canopy heights were most accurate when canopy cover ranged from 40 to 85% [ABSTRACT FROM AUTHOR]