Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons

<p>Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), part of NASA's Earth Observing System, is a satellite mission for measuring ice sheet elevation as well as land topography. ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), a spaceborne lidar that provides topography measurements of land surfaces around the globe. This study intends to utilize ICESat-2 ATL03 elevation data to identify the outdated part in Taiwan’s Digital Elevation Model (DEM). Because the update of DEM takes time and is relatively expensive to renew by airborne LiDAR, a screen of elevation change is crucial for planning the flight route. ICESat-2 has not only a dense point cloud of elevation but also a short revisit time for data collection. That is, ICESat-2 may have a chance to provide a reference for the current condition of terrain formation.</p><p>In this study, we aim to verify the 20-meter DEM from the Ministry of the Interior, Taiwan, by ICESat-2 elevation data. The goal is to find out the patches that have experienced significant changes in elevation due primarily to landslides. We select a typical landslide hillside in southern Taiwan as an example, and compare the DEM with ICESat-2 ATL03 photon-based heights before and after the occurrence of landslide events. In our preliminary results, the comparison of DEM and ICESat-2 ATL03 heights has a high degree of conformity inaccuracy (within meter level), indicating ICESat-2’s ability for DEM renewal.</p>

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.

The mapping of cropland helps to make decisions due to the intensification of its use, where the conditions of the crops change due to climatic variability and other socio-economic factors. In this way, the implementation of modern sustainable agriculture is essential to prevent soil degradation as measures to guarantee food security, propose sustainable rural development and protect the provision of different ecosystem services associated with the soil. NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) launched September 15, 2018, offers new possibilities for the mapping of global terrain and vegetation. An additional science objective is to measure vegetation canopy height as a basis for estimating large-scale biomass and biomass change. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 utilizes a photon-counting LIDAR and ancillary systems (GPS and star cameras) to measure the time a photon takes to travel from ATLAS to Earth and back again and to determine the photon’s geodetic latitude and longitude. ICESat-2 ATL08 (Along-Track-Level) data product is developed for vegetation mapping with algorithms for along-track elevation profile of terrain and canopy heights retrieval of the from ATLAS point clouds. Thus, this study presents a brief look at the ATL08 product highlight the broad capability of the satellite for vegetation applications working with data of study area Seis de Julio de Cuellaje (SDJC), province of Imbabura, Ecuador. The study used Normalized Difference Vegetation Index (NDVI) by the year 2020 time-series at 30 m resolution by employing a Machine Learning (ML) approach. The results of this research indicate that the ATL08 data from the ICESat-2 product provide estimates of canopy height, show the potential for crop biomass estimation, and a machine learning land cover classification approach with a precision of 95.57% with Digital Elevation Model (DEM) data.

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

Improving InSAR elevation models in Antarctica using laser altimetry, accounting for ice motion, orbital errors and atmospheric delays

The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy. ICESat-2 data is delivering global estimates of forest structure; however, analysis of the performance of ICESat-2 on-orbit data across a range of forest conditions remains limited. This study derives structural estimates of (i) canopy height, (ii) canopy cover and (iii) canopy height variability from ICESat-2 data acquired in snow-free and low atmospheric scattering conditions over different boreal forest structural types in Ontario, Canada. ICESat-2 structural estimates were derived from the Global Geolocated Photon Data (ATL03) and Land and Vegetation Height (ATL08) data products and compared against single-photon detection airborne LiDAR (Leica SPL100). An extensive network of ground plots were used to stratify the study area into three distinct forest structural groups, each resulting from different stand development stages. ICESat-2 and SPL100 estimates of canopy height were compared at the ATL03 photon level, whereas estimates of height variability and canopy cover were compared for spatial analysis units (AU; mean size = 1287 m2). ICESat-2 photons returned from the top of the canopy underestimated canopy height relative to SPL100 by an average of 2.3 m overall and corresponded most strongly to the 90th percentile (P90) of coincident airborne SPL100 returns (root mean square difference (RMSD) = 2.9 m and correlation coefficient (r) = 0.84). The lowest average underestimation of SPL P90 was observed in homogeneous stands that were relatively simple, and single-layered with a single dominant species (RMSD = 2.5 m, r = 0.84). We observed the least agreement of ICESat-2 and SPL forest structural metrics in over-mature stands with complex structure and greater variability in canopy heights (RMSD = 3.5 m, r = 0.64). For the AUs, the strength of the relationship between SPL100 and ICESat-2 canopy height percentiles increased with increasing height percentiles (e.g. P25 RMSD% = 77.8%; P95 RMSD% = 23.7%). ICESat-2 generally underestimated canopy height variability relative to the SPL100 data, with both data having similar absolute variability (standard deviation of canopy heights RMSD% = 26.8%, r = 0.75), but lower agreement in relative variability (coefficient of variation of canopy heights RMSD% = 33.9%, r = 0.45). Herein we propose the use of the vegetation fill index as a method to estimate canopy cover with ICESat-2. Comparison of SPL100 and ICESat-2 vegetation fill indices at the AU level resulted in strong agreement overall (RMSD% = 19.7%; r = 0.57). These observations and results contribute to the overall objective of building a comprehensive understanding of the performance of ICESat-2 for characterizing vegetation structure in boreal forest environments.

The Ice, Cloud, and Land Elevation Satellite – 2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System

A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: A case study in the South China Sea

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

In 2018, the National Aeronautics and Space Administration (NASA) is scheduled to launch the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), with a new six-beam, green-wavelength, photon-counting lidar system, Advanced Topographic Laser Altimeter System (ATLAS). The primary objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass. However, if bathymetry can be reliably retrieved from ATLAS data, this could assist in addressing a key data need in many coastal and inland water body areas, including areas that are poorly-mapped and/or difficult to access. Additionally, ATLAS-derived bathymetry could be used to constrain bathymetry derived from complementary data, such as passive, multispectral imagery and synthetic aperture radar (SAR). As an important first step in evaluating the ability to map bathymetry from ATLAS, this study involves a detailed assessment of bathymetry from the Multiple Altimeter Beam Experimental Lidar (MABEL), NASA’s airborne ICESat-2 simulator, flown on the Earth Resources 2 (ER-2) high-altitude aircraft. An interactive, web interface, MABEL Viewer, was developed and used to identify bottom returns in Keweenaw Bay, Lake Superior. After applying corrections for refraction and channel-specific elevation biases, MABEL bathymetry was compared against National Oceanic and Atmospheric Administration (NOAA) data acquired two years earlier. The results indicate that MABEL reliably detected bathymetry in depths of up to 8 m, with a root mean square (RMS) difference of 0.7 m, with respect to the reference data. Additionally, a version of the lidar equation was developed for predicting bottom-return signal levels in MABEL and tested using the Keweenaw Bay data. Future work will entail extending these results to ATLAS, as the technical specifications of the sensor become available.

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program.

The Advanced Topographic Laser Altimeter System on Ice, Cloud and land Elevation Satellite 2 (ICESat‐2) offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat‐2/Advanced Topographic Laser Altimeter System data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution, and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat‐2 employs a year‐round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat‐2 observations may be used to investigate time‐varying, dynamic, and thermodynamic sea ice processes.

The ATL08 land and vegetation product for the ICESat-2 Mission

Spaceborne sensors are now providing invaluable datasets for the Earth’s surface studies. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) with the Advanced Topographic Laser Altimeter System (ATLAS) was launched by NASA on 15 September 2018, to measure the elevation of the Earth’s surface using a laser wavelength of 532 nm and pulse repetition frequency of 10 kHz giving a footprint of approximately 70 cm on the ground. The ICESat-2 datasets are used in this study for the visualization and investigation of the complex Himalayan terrain in the parts of the Kinnaur district and surroundings, which are prone to frequent landslides due to the geology of the region as observed during the recent landslide events. The ICESat-2 elevation datasets were compared with the openly accessible DEM datasets namely, ALOS PALSAR RTC HR (12.5 m) and TanDEM-X (90 m) at ICESat-2 footprint locations. The preprocessing of datasets was completed for selecting ICESat-2 footprints (Track ID: 325, 1270, 828, 386) at locations of high-quality datasets for analysis. The analysis of pre-processed 19,755 ICESat-2 footprints (out of 20,948 footprints) was performed with ALOS PALSAR RTC HR (12.5 m) and TanDEM-X (90 m) datasets. The visualization of the region in the Google earth and OpenAltimetry 3D viewer depicts that the mountain slopes are very steep indicating rugged terrain difficult to access and challenging for construction of transport facilities. The results of Track ID: 325, show that the range of elevations in ICESat-2 elevation values in the study area is from 3409.75 m to 5976.31 m. The standard deviation representing terrain ruggedness using ICESat-2 elevation values is found as 432.06 m. Considering higher accuracy ICESat-2 values for the difficult terrain as a reference, the mean error (ME), mean absolute error (MAE), and RMSE for TanDEM-X were found as 0.26 m, 12.92 m, and 17.4 m, respectively. Whereas the ME, MAE, and RMSE for ALOS PALSAR RTC HR DEM were found as 0.20 m, 9.50 m, and 13.88 m, respectively. Thus, for the study site, using ICESat-2 ATL06 products, ALOS PALSAR RTC HR DEM is found more suitable than TanDEM-X 90 m openly accessible datasets for any kind of application in such a terrain.