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A new generation of space-borne LiDAR (Light Detection And Ranging) satellite ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) equipped with ATLAS (Advanced Topographic Laser Altimeter System) can perform earth observation. The main problem is to remove the noise photons from the data. The study proposes a main direction-based noise removal algorithm based on three sets of photon-counting LiDAR data. In order to extract the main direction, features in the spatial neighborhood (k) of photons are calculated, most of the initial noise is removed according to the angle between the main direction of photons and the along-track distance direction. Qualitative and quantitative evaluations are employed to validate the proposed algorithm. The obtained results and the performed analysis reveal that the proposed algorithm can process day and night data with different signal-to-noise ratios, while the accuracy of various surface types exceeds 96%. More specifically, the accuracy of the proposed algorithm for night data can reach 97.43%. Based on quantitative evaluations using SPL (Single photon LiDAR), MATLAS, and airborne LiDAR data, the average R, P, and F values are 0.951, 0.959, and 0.954, respectively. Meanwhile, the result of the proposed algorithm is compatible with the ATL03 photons with low, medium, and high confidence, and its accuracy is superior to ATL08 products. The proposed algorithm had fewer parameters and significantly outperformed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the improved local statistical distance algorithm. This algorithm is expected to provide a reference for subsequent photon-counting LiDAR data processing. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy of ATL08 is crucial for reliable time series analysis. However, fluctuations in the temporal accuracy of ATL08 data have been ignored in previous studies, leading to unknown uncertainties in existing time-series analyses. To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. Moreover, the study assesses accuracy at two official spatial resolutions, i.e., 100 m × 11 m and 20 m × 11 m, to evaluate the capability of ATL08 for the high-resolution height retrieval. For the terrain, the 100 m segment shows a bias of 0.04 m, a mean absolute error (MAE) of 0.44 m, and a root mean square error (RMSE) of 0.66 m, while the 20 m segment exhibits a bias of 0.10 m, a MAE of 0.35 m, and an RMSE of 0.49 m. For the surface height, the 100 m segment shows a bias of −0.59 m, a MAE of 3.06 m, an RMSE of 4.52 m, a bias% of −3.45 %, a MAE% of 21.26 %, and an RMSE% of 31.40 %. The 20 m segment exhibits a bias of −0.72 m, a MAE of 3.51 m, an RMSE of 5.23 m, a bias% of −5.81 %, a MAE% of 28.52 %, and an RMSE% of 42.47 %. The results indicate that improving segment resolution enhances terrain accuracy but reduces surface height accuracy. According to the error factor analysis, surface coverage and beam type are crucial for terrain retrieval accuracy, with their effects varying over time. Seasonal changes, particularly the presence of snow, affect terrain retrieval accuracy, with the lowest accuracy observed around March each year. This study confirms the critical impact of surface height on its retrieval accuracy and suggests avoiding the use of ATL08 for retrieving low target surface heights, especially in steep terrains. Nevertheless, the analysis affirms the applicability of ATL08 for canopy height estimation in boreal forests, primarily composed of coniferous species, highlighting its potential for extensive spatial and temporal research. This contributes to bridging the gaps between accurate estimates and large area coverage in global carbon budget and cycle studies. Additionally, the findings reveal that similar issues may exist in other satellite laser altimetry missions, emphasizing the important impacts of temporal fluctuations in surface and terrain accuracy when utilizing satellite laser altimetry datasets.

The ICESat-2 satellite equipped with a new photon-counting laser altimeter has received much attention as a source of accurate elevation observations. However, in this research field, there is a lack of an open-source high-accuracy elevation control point dataset with the specific quality requirements at a global scale. To this end, using ICESat-2 altimeter data as the main data source, we constructed and organized a dataset as a useful supplement for this research field. The dataset was generated by a methodology based on detection environment evaluation, photon spatial analysis, and the redundant observation statistics. The dataset includes more than 600 million elevation control points and covers the global land areas, except for Greenland and Antarctica. The dataset has been validated by multiple digital elevation models (DEMs) from around the world (sourced from airborne LiDAR data). The results show that the dataset has high-accuracy elevation control points. The overall root-mean-square error (RMSE) of the original elevations of ICESat-2 is about 1.384–4.820 m, but the overall RMSE of the elevation control points in the new dataset is about 0.279–0.642 m. Moreover, the results obtained in this study show that the dataset is suitable for application within high vegetation cover areas.

ABSTRACTHigh-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to

To verify the inversion accuracy of ground elevation and vegetation canopy height based on Land and Vegetation Height from ATL08 data of ICESat-2/ATLAS, a new generation of Ice, Cloud and Land Elevation satellite, in this study, the ICESat-2/ATLAS ATL08 product was taken as the research object, Maoer Mountain National Forest Park in Harbin City, Heilongjiang Province, China was taken as the research area, and high-precision airborne lidar data and sample data were taken as references to analyze the inversion accuracy differences of ground elevation and vegetation canopy height under different beam strength, time, slope and vegetation coverage. The results showed that:（1） for the ground elevation, the inversion accuracy RMSE and MAE of ATL08 strong beam were 1.9 m and 1.1 m, and the accuracy RMSE and MAE of weak beam were 4.1 m and 2.0 m.（2） For the vegetation canopy height, with the canopy height extracted from high-precision airborne Lidar data as a reference, the inversion accuracy of strong beam RMSE was 2.7 m, MAE was 2.3 m, weak beam RMSE was 5.4 m, MAE was 3.7 m. With the measured tree height data of the sample site as reference, the RMSE and MAE of all beams at night were 1.8 m and 1.6 m.（3） With the slope increasing from 0° to more than 20°, the RMSE of ground elevation increased from 2.3 m to 7.7 m, and the RMSE of vegetation canopy height inversion increased from 3.8 m to 10.4 m.（4） Within the range of medium and low vegetation coverage（0%-80%）, ATL08 product can measure the ground elevation well, and the RMSE was all less than 1 m. The inversion accuracy RMSE for high vegetation coverage（80%-100%） area was 3.5 m. Within the range of medium vegetation coverage（40%-80%）, ATL08 product can accurately measure the height of vegetation canopy with RMSE of 2.6 m. If the vegetation coverage was too high（80%-100%） or too low（0%-40%）, its accuracy would decrease, with RMSE of 4.6 m and 3.3 m. ATL08 data inversion of ground elevation and vegetation canopy height showed that the accuracy of strong beam was better than weak beam, night beam was better than daytime beam, and the night strong beam accuracy was the highest. With the increase of slope, the inversion errors of ground elevation and vegetation canopy height of different beams gradually increased, and the error increased with the increase of slope. The inversion of ground elevation was more accurate in the case of medium and low vegetation coverage, and the inversion accuracy of vegetation canopy height reached the highest in the case of medium vegetation coverage. [ABSTRACT FROM AUTHOR]

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams' segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides exceptional opportunities for characterizing the structure of ecosystems through the acquisition of along-track, three-dimensional observations. Focusing on canopy cover as a fundamental parameter for assessing forest conditions, the overall goal of this study was to establish a framework for generating a gridded 30 m canopy cover product with ICESat-2. Specifically, our objectives were to (1) Determine and compare ICESat-2-derived canopy cover with airborne lidar-derived and the 2016 National Land Cover Database (NLCD) cover product estimates, and (2) Evaluate a methodology for wall-to-wall mapping of canopy cover. Using two Southern US sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, four measures of canopy cover estimated with ICESat-2′s Land-Vegetation Along-Track Product, or ATL08, were evaluated at the 30 m pixel scale. Comparisons were made using spatially coinciding NLCD pixels and airborne lidar-derived reference canopy cover. A suite of Landsat and Landsat-derived parameters were then used as predictors to model and map each measure of canopy cover with Random Forests (RF), and their accuracies were assessed and compared. Correlations (r) between ICESat-2-derived and airborne lidar canopy cover at the pixel scale ranged from 0.57 to 0.78, and R2 up to 0.81 was produced between NLCD and ICESat-2-derived canopy cover. RF models developed for extrapolating ICESat-2-derived canopy cover estimate yielded R2 values between 0.50 and 0.61 (RMSEs between 16% and 20%) when evaluated with airborne lidar-derived canopy cover. With a demonstrated capability of ICESat-2 to estimate vegetation biophysical parameters, the findings serve to support the spatially comprehensive mapping of other vegetation attributes, especially forest aboveground biomass, and contribute to the development of an up-to-date gridded canopy cover product. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of ICESat-2 data capabilities for forest-height estimation and assessment of its spatial distribution over Russia. A brief overview of available current and historic lidar satellite-data products is carried out. The physical basis of the ATLAS instrument operation, as well as an approach to forming an ATL08 data product with information on vertical structure of vegetation, are described. An approach for automated download and preprocessing of ATL08 product is implemented, including filtering of missing/corrupted data, conversion to vector format, and delineation of borders of aggregated lidar footprints for which characteristics of vegetation-height distribution are provided. All the available ATL08 data over Russia for the period from October 14, 2018, to May 13, 2020, was processed to form a dataset of over 125 million lidar measurements of vegetation vertical structure, including over 50 million measurements of forest height. This study was performed at local and national spatial levels. Local-scale accuracy assessment of the ATL08 product (at the level of forest stands) included actualization of field survey data using models of forest growth, and satellite-data-based estimation of mean forest height and its uncertainty considering uncertainties of input lidar measurements and forest cover heterogeneity. Results of the local-scale accuracy assessment of ATL08 data are presented, showing agreement between ground-based and satellite measurements of mean forest height at the level of R2 = 0.67 and RMSE = 3.79 m. National level analysis included examples of potential use of the resulting dataset to study height and productivity of Russian forests. Estimates of mean forest-height spatial distribution over Russia, as well as distribution of mean height for different forest types, are presented. [ABSTRACT FROM AUTHOR]

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the first photon-counting laser altimetry satellite, is the most advanced on-orbit altimetry system in the world. The data obtained by it contain a large number of background photons, limited by sensitive photon detection system. In this study, a density-based adaptive method (DBAM) for photons detection of ground and canopy method is proposed, which is aimed at solving the problem of signal photons detection in vegetation areas. First, the photon density is homogenized according to the noise photon rate, to reduce the effect of uneven background noise. Then, the ground signal photons were extracted by the search ellipse adaptively changing direction and size along the slope direction to find the maximum density direction. The canopy signal photons were extracted again from the rest photons of the first step by using a vertical elliptical search area. At last of the DBAM, the photons between the ground and the canopy are extracted as vegetation signal photons. The effectiveness of DBAM is evaluated both quantitatively and qualitatively. The results show that the proposed method can effectively detect ground and canopy photons, with the Kappa coefficient of 0.88 and 0.91 of two selected datasets. Compared to the results of ATL08, DBAM can better adapt to the slope, the extracted ground photons have better continuity and can extract more accuracy vegetation photons. [ABSTRACT FROM AUTHOR]

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams’ segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale.

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides exceptional opportunities for characterizing the structure of ecosystems through the acquisition of along-track, three-dimensional observations. Focusing on canopy cover as a fundamental parameter for assessing forest conditions, the overall goal of this study was to establish a framework for generating a gridded 30 m canopy cover product with ICESat-2. Specifically, our objectives were to (1) Determine and compare ICESat-2-derived canopy cover with airborne lidar-derived and the 2016 National Land Cover Database (NLCD) cover product estimates, and (2) Evaluate a methodology for wall-to-wall mapping of canopy cover. Using two Southern US sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, four measures of canopy cover estimated with ICESat-2′s Land-Vegetation Along-Track Product, or ATL08, were evaluated at the 30 m pixel scale. Comparisons were made using spatially coinciding NLCD pixels and airborne lidar-derived reference canopy cover. A suite of Landsat and Landsat-derived parameters were then used as predictors to model and map each measure of canopy cover with Random Forests (RF), and their accuracies were assessed and compared. Correlations (r) between ICESat-2-derived and airborne lidar canopy cover at the pixel scale ranged from 0.57 to 0.78, and R2 up to 0.81 was produced between NLCD and ICESat-2-derived canopy cover. RF models developed for extrapolating ICESat-2-derived canopy cover estimate yielded R2 values between 0.50 and 0.61 (RMSEs between 16% and 20%) when evaluated with airborne lidar-derived canopy cover. With a demonstrated capability of ICESat-2 to estimate vegetation biophysical parameters, the findings serve to support the spatially comprehensive mapping of other vegetation attributes, especially forest aboveground biomass, and contribute to the development of an up-to-date gridded canopy cover product.

Current spaceborne lidar Ice, Cloud, and Land Elevation Satellite (ICESat)-2 provides ATL08 product for global terrain height, whose quality properties are yet to be fully understood. This article performs a comprehensive evaluation on its quality by using 3-D elevation program (3DEP) digital elevation model (DEM) and hundreds of survey marks of two counties in the USA. The evaluation is carried out in terms of data specification, survey marks, land cover, season and time (day or night) of acquisition, incidence angle, and terrain slope. The ATL08 height errors are further modeled as a function of laser incidence angle and canopy coverage. It is found out the height from ATL08 product lies between the 3DEP DEM and true ground surface. The uncertainty of ATL08 height is 0.2 m for plain terrain, and 2 m for mountainous terrain where the majority of ATL08 segments are not useful for terrain extraction. The terrain height gets more underestimated by ATL08 products in regions with large terrain slope or incidence angle and more overestimated where the terrain is covered by dense canopy. Furthermore, seasonal variation of terrain height error can be as high as 0.8 m, while the impact of acquisition time is less than 0.3 m. We expect these findings to be informative for the utilization of ATL08 terrain product by worldwide users, and for the improvement of future ATL08 product. [ABSTRACT FROM AUTHOR]

The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and will provide data which will offer the possibility of assessing AGB and forest carbon at multiple spatial scales. The primary goal of this study was to develop an approach for utilizing data similar to ICESat-2’s land-vegetation along track product (ATL08) to generate wall-to-wall AGB maps. Utilizing simulated daytime and nighttime ICESat-2 data from planned ICESat-2 tracks over vegetation conditions in south-east Texas, we investigated the integration of Landsat data and derived products for AGB model and map production. Linear regression models were first used to relate simulated photon-counting lidar (PCL) metrics for 100 m segments along ICESat-2 tracks to reference airborne lidar-estimated AGB over Sam Houston National Forest (SHNF) in south-east Texas. Random Forest (RF) was then used to create AGB maps from predicted AGB estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover and canopy cover data from the National Land Cover Database (NLCD). Using RF, AGB and AGB uncertainty maps produced at 30 m spatial resolution represented three data scenarios; (1) simulated ICESat-2 PCL vegetation product without the impact of noise (no noise scenario), (2) simulated ICESat-2 PCL vegetation product from data with noise levels associated with daytime operation of ICESat-2 (daytime scenario), and (3) simulated ICESat-2 PCL vegetation product from data with noise levels associated with nighttime operation of ICESat-2 (nighttime scenario). The RF models exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha with a separate test set. The adoption of a combinatory approach of simulated ICESat-2 and Landsat data could be implemented at larger spatial scales and in doing so, ancillary data such as climatic and topographic variables may be examined for improving AGB predictions.

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The ice, cloud, and land elevation satellite-2 (ICESat-2) is providing global elevation measurements to the science community. ICESat-2 measures the height of the Earth’s surface using a photon counting laser altimeter, ATLAS (advanced topographic laser altimetry system). As a photon counting system, the number of reflected photons per shot, or radiometry, is a function primarily of the transmitted laser energy, solar elevation, surface reflectance, and atmospheric scattering and attenuation. In this paper, we explore the relationship between detected scattering and attenuation in the atmosphere against the observed radiometry for three general forest types, as well as the radiometry as a function of day versus night. Through this analysis, we found that ATLAS strong beam radiometry exceeds the pre-launch design cases for boreal and tropical forests but underestimates the predicted radiometry over temperate forests by approximately half a photon. The weak beams, in contrast, exceed all pre-launch conditions by a factor of two to six over all forest types. We also observe that the signal radiometry from day acquisitions is lower than night acquisitions by 10% and 40% for the strong and weak beams, respectively. This research also found that the detection ratio between each beam-pair was lower than the predicted 4:1 values. This research also presents the concept of ICESat-2 radiometric profiles; these profiles provide a path for calculating vegetation structure. The results from this study are intended to be informative and perhaps serve as a benchmark for filtering or analysis of the ATL08 data products over vegetated surfaces.

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 assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and will provide data which will offer the possibility of assessing AGB and forest carbon at multiple spatial scales. The primary goal of this study was to develop an approach for utilizing data similar to ICESat-2's land-vegetation along track product (ATL08) to generate wall-to-wall AGB maps. Utilizing simulated daytime and nighttime ICESat-2 data from planned ICESat-2 tracks over vegetation conditions in south-east Texas, we investigated the integration of Landsat data and derived products for AGB model and map production. Linear regression models were first used to relate simulated photon-counting lidar (PCL) metrics for 100 m segments along ICESat-2 tracks to reference airborne lidar-estimated AGB over Sam Houston National Forest (SHNF) in south-east Texas. Random Forest (RF) was then used to create AGB maps from predicted AGB estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover and canopy cover data from the National Land Cover Database (NLCD). Using RF, AGB and AGB uncertainty maps produced at 30 m spatial resolution represented three data scenarios; (1) simulated ICESat-2 PCL vegetation product without the impact of noise (no noise scenario), (2) simulated ICESat-2 PCL vegetation product from data with noise levels associated with daytime operation of ICESat-2 (daytime scenario), and (3) simulated ICESat-2 PCL vegetation product from data with noise levels associated with nighttime operation of ICESat-2 (nighttime scenario). The RF models exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha with a separate test set. The adoption of a combinatory approach of simulated ICESat-2 and Landsat data could be implemented at larger spatial scales and in doing so, ancillary data such as climatic and topographic variables may be examined for improving AGB predictions. [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]

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

NASA’s ICESat-2 space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of ecosystem related research applications. Given the novelty of elevation and the derived data products from the ICESat-2 mission, the research community needs software tools that can facilitate photon-level analyses to support product validation and development new analysis methods. Here, we describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03). Developed in MATLAB, the GUI facilitates the reading and display of ATL03 Hierarchical Data Format (HDF) files, the manual labeling of individual photons into target classes of choice using a number of point selections tools and enables eventual saving of labeled data in ASCII format. Other capabilities include saving and loading of labeling sessions to manage labeling tasks over time. We expect labeled data generated using the application to serve two main purposes. First, serve as reference data for validating various products from ICESat-2 mission, especially for study sites around the world that do not have existing reference datasets such as airborne lidar. Second, serve as training and validation data in the development of new algorithms for generating various ICESat-2 data products. We demonstrate the first use case through a validation case study for the land and vegetation product (ATL08), which provides canopy and terrain height estimates, over two sites. For the first site, located in northwestern Zambia, we used ICESat-2 ATL03 data acquired at night and for our second site in Texas, US, we used ATL03 data acquired during the day. ATL08 canopy and terrain height data showed good agreement (mean R2 > 0.8) with corresponding height metrics generated from manually labeled data. A comparison between PhotonLabeler and ATL08 photon labels also showed good agreement −93.3% and 95.4% overall accuracies for the Texas and Zambia site, respectively. These results, while limited in scope, show how PhotonLabeler can facilitate photon-level analyses for ICESat-2 data products beyond the ATL08 product. The PhotonLabeler application is freely available as a compiled MATLAB binary to enable free access and utilization by interested researchers.

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]

The ice, cloud, and land elevation satellite-2 (ICESat-2) is providing global elevation measurements to the science community. ICESat-2 measures the height of the Earth's surface using a photon counting laser altimeter, ATLAS (advanced topographic laser altimetry system). As a photon counting system, the number of reflected photons per shot, or radiometry, is a function primarily of the transmitted laser energy, solar elevation, surface reflectance, and atmospheric scattering and attenuation. In this paper, we explore the relationship between detected scattering and attenuation in the atmosphere against the observed radiometry for three general forest types, as well as the radiometry as a function of day versus night. Through this analysis, we found that ATLAS strong beam radiometry exceeds the pre-launch design cases for boreal and tropical forests but underestimates the predicted radiometry over temperate forests by approximately half a photon. The weak beams, in contrast, exceed all pre-launch conditions by a factor of two to six over all forest types. We also observe that the signal radiometry from day acquisitions is lower than night acquisitions by 10% and 40% for the strong and weak beams, respectively. This research also found that the detection ratio between each beam-pair was lower than the predicted 4:1 values. This research also presents the concept of ICESat-2 radiometric profiles; these profiles provide a path for calculating vegetation structure. The results from this study are intended to be informative and perhaps serve as a benchmark for filtering or analysis of the ATL08 data products over vegetated surfaces. [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]

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research. [ABSTRACT FROM AUTHOR]

NASA's ICESat-2 space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of ecosystem related research applications. Given the novelty of elevation and the derived data products from the ICESat-2 mission, the research community needs software tools that can facilitate photon-level analyses to support product validation and development new analysis methods. Here, we describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03). Developed in MATLAB, the GUI facilitates the reading and display of ATL03 Hierarchical Data Format (HDF) files, the manual labeling of individual photons into target classes of choice using a number of point selections tools and enables eventual saving of labeled data in ASCII format. Other capabilities include saving and loading of labeling sessions to manage labeling tasks over time. We expect labeled data generated using the application to serve two main purposes. First, serve as reference data for validating various products from ICESat-2 mission, especially for study sites around the world that do not have existing reference datasets such as airborne lidar. Second, serve as training and validation data in the development of new algorithms for generating various ICESat-2 data products. We demonstrate the first use case through a validation case study for the land and vegetation product (ATL08), which provides canopy and terrain height estimates, over two sites. For the first site, located in northwestern Zambia, we used ICESat-2 ATL03 data acquired at night and for our second site in Texas, US, we used ATL03 data acquired during the day. ATL08 canopy and terrain height data showed good agreement (mean R2 > 0.8) with corresponding height metrics generated from manually labeled data. A comparison between PhotonLabeler and ATL08 photon labels also showed good agreement −93.3% and 95.4% overall accuracies for the Texas and Zambia site, respectively. These results, while limited in scope, show how PhotonLabeler can facilitate photon-level analyses for ICESat-2 data products beyond the ATL08 product. The PhotonLabeler application is freely available as a compiled MATLAB binary to enable free access and utilization by interested researchers. [ABSTRACT FROM AUTHOR]

Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due to fire also offer opportunities for mapping burned areas using three-dimensional (3D) datasets such as Light detection and ranging (LiDAR). This study focuses on the feasibility of using photon counting LiDAR data from National Aeronautics and Space Administration's (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat−2) mission to differentiate vegetation structure in burned and unburned areas and ultimately classify burned areas along mapped ground tracks. The ICESat−2 mission (launched in September 2018) provides datasets such as geolocated photon data (ATL03), which comprises precise latitude, longitude and elevation of each point where a photon interacts with land surface, and derivative products such as the Land Water Vegetation Elevation product (ATL08), which comprises estimated terrain and canopy height information. For analysis, 24 metrics such as the average, median and standard deviation of canopy height were derived from ATL08 data over forests burned by recent fires in 2018 in northern California and western New Mexico. A reference burn map was derived from Sentinel−2 images based on the differenced Normalized Burn Ratio (dNBR) index. A landcover map based on Sentinel−2 images was employed to remove non-forest classes. Landsat 8 based dNBR image and landcover map were also used for comparison. Next, ICESat−2 data of forest samples were classified into burned and unburned ATL08 100-m segments by both Random Forest classification and logistic regression. Both Sentinel−2 derived and Landsat 8 derived ATL08 samples got high classification accuracy, 83% versus 76%. Moreover, the resulting classification accuracy by Random Forest and logistic regression reached 83% and 74%, respectively. Among the 24 ICESat−2 metrics, apparent surface reflectance and the number of canopy photons were the most important. Furthermore, burn severity of each ATL08 segment was also estimated with Random Forest regression. R2 of predicted burn severity to observed dNBR is 0.61 with significant linear relationship and moderate correlation (r = 0.78). Overall, the reasonably high accuracies achieved in this study demonstrate the feasibility of employing ICESat−2 data in burned forest classification, opening avenues for improved estimation of burned biomass and carbon emissions from a 3D perspective. [ABSTRACT FROM AUTHOR]