ICESat Research Articles

Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

• ICESat-2 has the potential to improve the estimation accuracy and efficiency of forest aboveground biomass on a large scale. • The Google Earth Engine cloud platform was used for Sentinel-2 images acquisition and composition. • The optimized extreme learning machine achieved the highest estimation accuracy and reasonable forest aboveground biomass spatial distribution. Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R 2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment.

Multi-temporal analysis of inland water level change using ICESat-2 ATL-13 data in lakes and dams.

Accurate and frequent monitoring of inland water body levels is essential for water sources and their ecological protection and management. Water bodies such as natural lakes and manmade dams provide an ecological environment for endangered animals and endemic plants and serve as irrigation and water source for human activities. However, in situ measurements or fixed observation stations are not always available for small to large water bodies. Here, we investigate water level changes at a regional level where endangered endemic species live in the Ramsar site, a nationally protected park, and inland waters using the National Aeronautics and Space Administration's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) satellite launched in 2018. Most of the inland water bodies show high R2 values ranging from 0.28 to 0.99 and the root mean square error values are widely distributed lower than 1m. This study shows the potential and limitations of the new altimetry data source ICESat-2 ATL13 product for monitoring the long-term behavior of inland water reservoirs for sustainable monitoring.

ICESat-2 laser data denoising algorithm based on a back propagation neural network.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.

Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

ABSTRACT The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides an extraordinary opportunity to support global large-scale forest carbon mapping, but further research is needed in order to obtain wall-to-wall forest aboveground biomass (AGB) maps with this technology. The effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature. In this study, we combined recent ICESat-2 vegetation (ATL08) data, Airborne Laser Scanning (ALS)- and field-based estimates, and a multi-sensor earth observation composite for extrapolation of AGB estimates and AGB mapping. A diverse gradient of forest Mediterranean ecosystems, distributed over 19,744.15 km2 of forest area in the region of Extremadura (Spain), with different species and structural complexity forming 5 different forest types (3 Quercus spp. dominated and 2 Pinus spp. dominated forests), was used to (i) evaluate the precision of ICESat-2 canopy height estimations, (ii) develop ICESat-2-based AGB models, and (iii) generate a spatially continuous prediction of AGB by using data from the satellite missions Sentinel-1 (S1), Sentinel-2 (S2), Phased Array L-band Synthetic Aperture Radar (ALOS2/PALSAR2), and Shuttle Radar Topography Mission (SRTM). First, ALS- and ICESat-2-derived metrics that best described canopy height (p98 and rh98, respectively) were compared at the ATL08 segment level. Second, ALS-based AGB values were derived at the ATL08 segment scale. Third, ALS-based AGB estimates at the ICESat-2 segment level were used as dependent variables to fit ICESat-2-based AGB models. Fourth, a multi-sensor approach was then implemented to predict ICESat-2-derived AGB, by means of a Random Forest (RF) modeling technique, with predictors retrieved from S1, S2, ALOS2/PALSAR2, and SRTM. Finally, RF was used to generate wall-to-wall AGB maps that were compared with field-, ALS- and ICESat-2-based observations. The agreement between the ALS- and ICESat-2-derived metrics related to the canopy height distribution was higher for Pinus spp. forest than for the Quercus spp-dominated forests. The ICESat-2-based AGB models yielded model efficiency (Mef) values between 0.56 and 0.80, with a RMSE ranging from 7.76 to 17.71 Mg ha−1 and rRMSE from 19.04 to 55.21%. The multi-sensor RF models provided the following results when compared with the ICESat-2- and ALS-based AGB observations: R 2 values of 0.63 and 0.64, and RMSE values of 11.10 Mg ha−1(rRMSE = 28.15%) and 12.28 Mg ha−1 (rRMSE = 31.45%), respectively, and an approximately unbiased result (0.03 Mg ha−1 and 0.09 Mg ha−1). When applied to the field-based validation data set (4th Spanish National Forest Inventory (SNFI-4) plots = 508), the RF-derived AGB model showed a relatively lower predictive capacity (R 2 = 0.45), a higher RMSE value (25.88 Mg ha−1) and slightly biased results (−1.47 Mg ha−1), especially for larger field-derived AGB intervals. The results of this study serve to provide an initial quantitative assessment of the ICESat-2 ATL08 data for large-scale AGB estimation. The findings suggest that a multi-sensor approach may be feasible for extrapolating ICESat-2-derived AGB estimates over areas where field or ALS reference data are not available.

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R2 of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m−2. Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R2 of 0.64 and a RMSE of 2.32 kg C m−2. The carbon stocks within the fifth ring road of Beijing were 8.28 × 108 kg in total, with the mean carbon density of 3.52 kg C m−2. Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.

Characterizing canopy cover with ICESat-2: A case study of southern forests in Texas and Alabama, USA

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.

Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Accurate measurements of terrain elevation are crucial for many ecological applications. In this study, we sought to assess new global three-dimensional Earth observation data acquired by the spaceborne Light Detection and Ranging (LiDAR) missions Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). For this, we examined the “ATLAS/ICESat-2 L3A Land and Vegetation Height”, version 5 (20 × 14 m and 100 × 14 m segments) and the “GEDI Level 2A Footprint Elevation and Height Metrics”, version 2 (25 m circle). We conducted our analysis across four land cover classes (bare soil, herbaceous, forest, savanna), and six forest types (temperate broad-leaved, temperate needle-leaved, temperate mixed, tropical upland, tropical floodplain, and tropical secondary forest). For assessment of terrain elevation estimates from spaceborne LiDAR data we used high resolution airborne data. Our results indicate that both LiDAR missions provide accurate terrain elevation estimates across different land cover classes and forest types with mean error less than 1 m, except in tropical forests. However, using a GEDI algorithm with a lower signal end threshold (e.g., algorithm 5) can improve the accuracy of terrain elevation estimates for tropical upland forests. Specific environmental parameters (terrain slope, canopy height and canopy cover) and sensor parameters (GEDI degrade flags, terrain estimation algorithm; ICESat-2 number of terrain photons, terrain uncertainty) can be applied to improve the accuracy of ICESat-2 and GEDI-based terrain estimates. Although the goodness-of-fit statistics from the two spaceborne LiDARs are not directly comparable since they possess different footprint sizes (100 × 14 m segment or 20 × 14 m segment vs. 25 m circle), we observed similar trends on the impact of terrain slope, canopy cover and canopy height for both sensors. Terrain slope strongly impacts the accuracy of both ICESat-2 and GEDI terrain elevation estimates for both forested and non-forested areas. In the case of GEDI the impact of slope is, however, partly caused by horizontal geolocation error. Moreover, dense canopies (i.e., canopy cover higher than 90%) affect the accuracy of spaceborne LiDAR terrain estimates, while canopy height does not, when considering samples over flat terrains. Our analysis of the accuracy and precision of current versions of spaceborne LiDAR products for different vegetation types and environmental conditions provides insights on parameter selection and estimated uncertainty to inform users of these key global datasets.

Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

BackgroundFast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.ResultsThe results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.ConclusionCompared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.

Deriving vertical profiles of chlorophyll-a concentration in the upper layer of seawaters using ICESat-2 photon-counting lidar.

Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs<15%) and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data in the along-track direction (average R2>0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.

The ATL08 as a height reference for the global digital elevation models

ABSTRACT High-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 <20 m. The evaluations of ASTER, SRTM, and TDX DEMs by ATL08 strong and weak beams are close to those assessed by LiDAR DTM points within 0.6 m for the strong beams. These findings indicate that ATL08 strong beams can be used as a height reference over vegetated mountainous regions.

Converting along-track photons into a point-region quadtree to assist with ICESat-2-based canopy cover and ground photon detection

The advanced laser altimetry satellite—the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2)1—is a potentially feasible, rapid and effective tool for monitoring carbon cycle and carbon storage to support the sustainable development of Earth system at landscape-to-global scales. To assist with these scientific goals, accurate signal photon detection is required. In this study, we attempted to develop an effective ICESat-2-based signal detection algorithm for different land covers by converting the along-track photons into a point-region (PR)2 quadtree. Firstly, we performed photon homogenization of the background photon. The total number of divisions in the process of converting the spatial distribution of the photons into a point-region quadtree was then calculated. Finally, the signal photons were detected with the appropriate thresholds. For complex woodland areas, the above steps should be performed twice. The results showed that the signal detection algorithm performs well in preserving signal photons and identifying noise photons. The value of F-measure (F)3 obtained at medium and low noise rates are all above 0.9. For more complex woodland areas, the strategy of calculating twice is helpful, giving a high-accuracy performance (F greater than 0.97). The proposed signal detection algorithm enriches an available reference for extracting signal photons from ICESat-2 altimetry data to meet the challenge of achieving carbon neutrality.

Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data

Anthropogenic carbon emissions directly contribute to global warming, which has induced severe environmental concerns like extreme droughts and devastating fires. To evaluate the effects of fires on carbon cycling and climate change, it is crucial to accurately estimate the amount of carbon released during fires. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission offers spaceborne light detection and ranging (LiDAR) measurements of global forests, providing excellent opportunities for monitoring forest dynamics and carbon emissions. The ICESat-2 Land and vegetation height product (ATL08), Landsat 8 data, and Sentinel-1 data were integrated to estimate biomass burning emissions with a three-phase hierarchy model. In Phase 1, the least absolute shrinkage and selection operator (LASSO) regression was used to establish the relationship between ATL08 LiDAR metrics and reference aboveground biomass (AGB), R2 0.77 and RMSE 56.67 Mg ha−1. In Phase 2, the LASSO regression predicted AGB for all ATL08 segments. A Random Forest Regression model was trained with Landsat 8 reflectance data and derived vegetation indices, Sentinel-1 data, and terrain variables as predictors and with the LASSO predicted AGB as the response variable, R2 0.71 and RMSE 45.91 Mg ha−1. Wall-to-wall maps of pre-fire and post-fire AGB were produced with the Random Forest model. In Phase 3, the difference between the pre-fire and the post-fire AGB was converted to carbon emissions. The estimated biomass burning emissions of the 2018 Carr fire group, the 2018 Camp fire, and the 2019 Walker fire in California are 3.54 (± 0.067) Tg C, 1.66 (± 0.041) Tg C, and 0.45 (± 0.022) Tg C, respectively. Forests have the highest biomass burning emissions with an average emission of 30.09 Mg ha−1, approximately twice and four times the average emissions from shrubs and grasses. This study highlights the merit of integrating spaceborne remote sensing data, including LiDAR data, optical data, and radar data, for estimating biomass burning emissions, opening an avenue for accurate forest biomass and carbon dynamics monitoring, as well as climate change mitigation.

Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea

Accurate bathymetric and topographical information is crucial for coastal and marine applications. In the past decades, owing to its low cost and high efficiency, satellite-derived bathymetry has been widely used to estimate the depth of shallow water in coastal areas. However, insufficient spectral bands and availability of in situ water depths limit the application of satellite-derived bathymetry. Currently, the investigation about the bathymetric potential of hyperspectral imaging is relatively insufficient based on datasets of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). In this study, Zhuhai-1 hyperspectral images and ICESat-2 datasets were utilized to perform nearshore bathymetry and explore the bathymetric capability by selecting different bands based on classical empirical models (the band ratio model and the linear band model). Furthermore, experimental results achieved at the South China Sea indicate that the combination of blue (2 and 3 band) and green (9 band) bands and the combination of red (10 and 12 band) and near-infrared (29 band) bands are most suitable to achieve nearshore bathymetry. Correspondingly, the highest accuracy of bathymetry reached root mean square error values of 0.98 m and 1.19 m for different band combinations evaluated through bathymetric results of reference water depth. The bathymetric accuracy of Zhuhai-1 image is similar with that of Sentinel-2 when employing the blue and green bands. The combination of red and near-infrared bands has a higher bathymetric accuracy for Zhuhai-1 image than that for Sentinel-2 image.

A new digital elevation model (DEM) dataset of the entire Antarctic continent derived from ICESat-2

Abstract. Antarctic digital elevation models (DEMs) are essential for fieldwork, ice motion tracking and the numerical modelling of the ice sheet. In the past 30 years, several Antarctic DEMs derived from satellite data have been published. However, these DEMs either have coarse spatial resolution or aggregate observations spanning several years, which limit their further scientific applications. In this study, the new generation satellite laser altimeter Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is used to generate a new Antarctic DEM for both the ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2018 to November 2019 are used to estimate surface elevations at resolutions of 500 m and 1 km based on a spatiotemporal fitting method. Approximately 74 % of Antarctica is observed and the remaining observation gaps are interpolated using the normal kriging method. The DEM is formed from the estimated elevations in 500 m and 1 km grid cells, and is finally posted at the resolution of 500 m. National Aeronautics and Space Administration (NASA) Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter called the ICESat-2 DEM) in individual Antarctic regions and surface types. Overall, a median bias of −0.19 m and a root-mean-square deviation of 10.83 m result from approximately 5.2 × 106 OIB measurement points. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to the surface slope and roughness, and more reliable estimates are found in the flat ice sheet interior. The ICESat-2 DEM is comparable to other DEMs derived from altimetry, stereophotogrammetry and interferometry. Similar results are found when comparing to elevation measurements from kinematic Global Navigation Satellite System (GNSS) (GPS and the Russian GLONASS) transects. The elevations of high accuracy and ability of annual updates make the ICESat-2 DEM an addition to the existing Antarctic DEM groups, and it can be further used for other scientific applications. The generated ICESat-2 DEM (including the map of uncertainty) can be downloaded from National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at https://data.tpdc.ac.cn/en/disallow/9427069c-117e-4ff8-96e0-4b18eb7782cb/ (last access: 27 June 2022) (Shen et al., 2021a, https://doi.org/10.11888/Geogra.tpdc.271448).

A Review on Global and Localised Coverage Elevation Data Sources for Topographic Application

As the need for elevation data grows, it is more vital than ever for users to match the data degree of dependability, precision, and spatial resolution to their specific uses to produce a useful and cost-effective product. This article will describe several sources of elevation data, ranging from space-based to aerial-based techniques, and classify the data according to its respective quality and accuracy. The elevation data sources can be classified into two namely localised or can also be referred to as regional, and global coverage. Among the example of localised sources of elevation data are Light Detection and Ranging (LiDAR) and Interferometry Synthetic Aperture Radar (InSAR). The global sources of elevation data are Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER), Advanced Land Observing Satellite (ALOSW3D), Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), TerraSAR-X add on for daily Digital Elevation Measurement (TanDEM-X), The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), Radar Satellite (RADARSAT) Constellation Mission (RCM) and Satellite-Derived Bathymetry (SDB). The characteristics of each elevation data source were discussed in terms of its launch date, period of observation, spatial resolution, horizontal and vertical datum, and coverage. Its reliability was described in detail for future topographic applications.

Assessment of the potential use of ICESat-2 data for bathymetric mapping of small lakes of Kazakhstan

Lake bathymetry is of great importance for water resources management and hydrological modeling. Bathymetric mapping of lakes was predominantly conducted with the use of highly-priced methods such as airborne lidars, active imaging sonars, multibeam echosounders. With the advancements in GIS and emergence of remotely sensed data new approaches for bathymetry extraction were developed. However, despite a high motivation to obtain bathymetric information for small lakes from remotely sensed data, there is a lack of reliable methods that can be implemented under various climate conditions and on a wide scale. In this paper several remote-sensing-based methods for bathymetry mapping of small lakes are discussed. The new Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data was used to extract bathymetric information on three small lakes of Kazakhstan. The assessment of ICESat-2 for lake bathymetry extraction was conducted using field measurements as the validation data.

Satellite observed recent rising water levels of global lakes and reservoirs

Monitoring global lake/reservoir water level changes is needed to understand the global water cycle and investigate its potential drivers. The existing global water level products only cover lakes/reservoirs with large sizes (>100 km2). Here, Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 altimetry data with small footprints are employed to examine global water level changes for 22 008 lakes/reservoirs greater than 1 km2. We report that 77.56% of them exhibited rising water levels over 2003–2021. Across the globe, 78.84% of lakes exhibit a rising water level, while the proportion for reservoirs is only 56.01%. Global lake/reservoir is estimated to experience a median water level change rate of +0.02 ± 0.02 m yr−1 over 2003–2021, and lakes have a larger water level rise (+0.02 ± 0.02 m yr−1) than reservoirs (+0.008 ± 0.14 m yr−1). We detect large-scale rising water levels in the Tibetan Plateau, the Mississippi River basin, and high-latitude regions of the Northern Hemisphere. Our calculation also suggests a negative relationship between the percentage of water level rise in lakes/reservoirs and population density for global river basins (r = −0.41, p-value < 0.05) and 11 hotspots (r = −0.48, p-value < 0.05). Our result suggests that inland water level has tended to rise in recent years under natural processes while human activities (i.e. with higher population density) can balance the water level rise via reservoir regulation. We find the existing datasets underestimated global water level rise, which may be caused by the exclusion of numerous small lakes/reservoirs. Our estimated global water level change rates (that include numerous small lakes with areas of 1–10 km2) can improve the understanding of global hydrological cycle and water resource management under the double pressure of climate warming and human activities.

Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of [Formula: see text]. [Formula: see text] is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data-RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high [Formula: see text] may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high [Formula: see text] ([Formula: see text] ≥ 0.3) appear to have a stronger capacity to recover than low-[Formula: see text] ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science, citizen science, and education program through which volunteers in participating countries collect environmental data in support of Earth system science. Using the program’s software application, GLOBE Observer (GO), volunteers measure tree height and optional tree circumference, which may support the interpretation of NASA and other space-based satellite data such as tree height data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation instrument. This paper describes tree heights data collected through the GO application and identifies sources of error in data collection. We also illustrate how the ground-based citizen science data collected in the GO application can be used in conjunction with ICESat-2 tree height observations from two locations in the United States: Grand Mesa, Colorado, and Greenbelt, Maryland. Initial analyses indicate that data location accuracy and the scientific relevance of data density should be considered in order to align GLOBE tree height data with satellite-based data collections. These recommendations are intended to inform the improved implementation of citizen science environmental data collection in scientific work and to document a use case of the GLOBE Trees data for the science research community.

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.