Accuracy assessment of digital bare-earth model using ICESat-2 photons: analysis of the FABDEM

Dandabathula, Giribabu; Hari, Rohit; Ghosh, Koushik; Bera, Apurba Kumar; Srivastav, S. K.

doi:10.1007/s40808-022-01648-4

Cited by 14 publications

(13 citation statements)

References 58 publications

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“…The choice of DEM affects the calculation of these factors (Warren et al, 2004), as well as the prediction of landslide susceptibility (Rabby et al, 2020). This study selected seven (Uuemaa et al, 2020) 6.73 m (Li et al, 2022) 3 -14 m (Dandabathula et al, 2022) 8.7 m (Mouratidis et al, 2010) activities. Loess, characterized by its high collapsibility, vertical joints and susceptibility to external forces, is a fundamental material for landslides.…”

Section: Study Areamentioning

confidence: 99%

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Lu,

Tang,

Yan

et al. 2024

Earth Surf Processes Landf

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In this study, the application of open‐source digital elevation model (DEM) is explored for regional landslide susceptibility mapping (LSM), and the potential impact of different DEM choices on the mapping accuracy is also examined. With the advancements in remote sensing technology, an increasing number of global open‐source DEMs have been available, with improvement in the accuracy. However, the latest released data are rarely evaluated in LSM research. In this paper, DEM‐based factors, including elevation, aspect, slope, plan curvature and profile curvature, were generated from seven open‐source DEMs, including Advanced Spaceborne Thermal Emission and Reflection (ASTER) V2, ASTERV3, ALOS World 3D‐30 m (AW3D30), Copernicus DEM 30 m (COP) Forest and Buildings removed Copernicus DEM (FABDEM), NASADEM, and Shuttle Radar Topography Mission (SRTM). DEM‐based factors were coupled with the distance to road, distance to river, land use, lithology, rain and normalized difference vegetation index (NDVI). The significant difference between DEMs is determined by comparing the area proportion. Slope, plane curvature and profile curvature are found to have a maximum difference of 15%–20%. Then, K‐Nearest Neighbours (KNN) and Random Forest (RF) were used to predict landslide susceptibility with two sampling methods, namely, 70% for training and 30% for testing (S1); 67% for training and 33% for testing (S2). For KNN with S1, the prediction rate is range from 0.8299 to 0.8701, with a difference of 0.0402. The difference of prediction rate is decreased to 0.0207 for S2 and 0.0258 for RF. COP has the highest prediction rate of 0.8701, 0.9254 and 0.9461 for KNN with S1 and RF with S1 and S2, respectively. ASTERV2 is the worst with prediction rate of 0.8897 and 0.8996 for KNN with S2 and RF with S1, respectively. The research result provides valuable insights for the selection of open‐source DEMs in future LSM.

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Section: Study Areamentioning

confidence: 99%

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Lu,

Tang,

Yan

et al. 2024

Earth Surf Processes Landf

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“…This section describes the approach developed to assess the water depth estimates computed in Section 2, using as a benchmark the altimetric data acquired by the ICESat-2 satellite mission of NASA (https://icesat-2.gsfc.nasa.gov). Specifically, ICESat-2 features ATLAS, an accurate laser altimeter designed for worldwide recording along six parallel tracks of ground points at a sampling frequency of up to about 1 point/m and a revisit time of 91 days (Neuenschwander and Pitts, 2019;Dandabathula et al, 2023;Wang et al, 2019;Li et al, 2021).…”

Section: Evaluation Of Water Depth Estimates With Icesat-2mentioning

confidence: 99%

Water depth estimate and flood extent enhancement for satellite-based inundation maps

Betterle,

Salamon

2024

Preprint

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Abstract. Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems, and alter the relationship of humans and animals with the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause billions of euros in direct damages and inestimable indirect losses, in both economical and in life-quality terms. Monitoring the spatio-temporal evolution of floods is of fundamental importance in order to reduce their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor large areas consistently and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a global scale. Synthetic Aperture Radar (SAR) sensors in particular have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in certain conditions, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing satellite-based flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or areas that are excluded from flood mapping. In particular, the method relies on simple morphological arguments to expand flooded areas to cover excluded regions, and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service.

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“…Moreover, the GPS-based survey needs planned and rigorous fieldwork followed by post-processing [23]. To overcome these limitations and also, due to the advantage of high accuracy, height information from LiDAR-based acquisitions is suggested as reference data to evaluate a DEM [24][25][26][27][28]. Importantly, using a set of sparse reference points is not scientifically viable if dealing with 30 m or 90 m grid-spaced DEMs, especially in undulating terrains, because a single point cannot judge the spatial variations in the height represented by a grid cell.…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, using a set of sparse reference points is not scientifically viable if dealing with 30 m or 90 m grid-spaced DEMs, especially in undulating terrains, because a single point cannot judge the spatial variations in the height represented by a grid cell. Dandabathula et al have suggested using profiles derived from lidar-based acquisitions to overcome this; moreover, profile-based evaluation methods have an advantage in detecting errors at slopes [27]. The recent NASA space-based laser altimetry mission, the Ice, Cloud, and land Elevation Satellite -2 (ICESat-2), hosts a solo sensor, namely, Advanced Topographic Laser Altimeter System (ATLAS) [29].…”

Section: Introductionmentioning

confidence: 99%

“…A successful surface-reflected photon event counted by the ATLAS sensors results in the accumulation of geolocated photon data, primarily comprising attributes like height above the ellipsoid, time, latitude, and longitude. The trend to evaluate a DEM using ICESat-2 geolocated photon data has proven much more effective in understanding DEM quality [24,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Validation of MERIT DEM’s Performance as a Bare-Earth Model Using ICESat-2 Geolocated Photons

Dandabathula,

Hari,

Sharma

et al. 2023

EARTH

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Digital elevation models represent the Earth's surface and play a key role in earth sciences by enabling the possibility of deriving terrain variables; the terrain variables are essential inputs for environmental modeling. The availability of open-access digital surface models has significantly advanced the understanding of earth system dynamics and also allowed researchers to generate digital terrain models, aka bare-earth models. These bare-earth models are essential data sets for applications related to hydrology and geomorphology, especially for disaster management. Under the category of open-accessible bare-earth models, Multi-Error-Removed Improved-Terrain DEM or MERIT DEM is the first kind of product unfolded by applying numerous error removal algorithms from existing DEM sources. This research reports the results after validating the MERIT DEM's performance by emphasizing its tree-height bias removal algorithm. Towards this, ground-reflected photons accrued from the ICESat-2 mission were used as reference data due to their attribution of high accuracy. Two test sites, one located in the rugged terrain of the outer Himalayas, the Lacchiwala Reserve forest, and the other, rolling hills at the Bhadra wildlife sanctuary located in the Western Ghats of the Indian sub-continent were used as test sites for validating the MERIT DEM's accuracy. The results derived after computing statistical formulae like RMSE, MAE, MBE, and profile-based visual analytics helped understand the performance of the MERIT DEM as a bare-earth model. The RMSE, MAE, and MBE for the Lachhiwala Reserve forest are 10.28 m, 7.78 m, and 0.69 m, respectively. Similarly, the RMSE, MAE, and MBE values for the Bhadra wildlife sanctuary are 4.52 m, 3.82 m, and 3.04 m, respectively. The assessment confirms that the accuracies are within the MERIT DEM's specifications and assured the successful implementation of MERIT DEM's tree-height removal algorithm since the elevations from the MERIT DEM are always lesser than the canopy height in both the test sites. Our research also investigated the reasons for the inaccuracies obtained at both the test sites and suggested using improved tree-height estimations from high-resolution canopy height data in the future version of MERIT DEM.

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Accuracy assessment of digital bare-earth model using ICESat-2 photons: analysis of the FABDEM

Cited by 14 publications

References 58 publications

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

Water depth estimate and flood extent enhancement for satellite-based inundation maps

Validation of MERIT DEM’s Performance as a Bare-Earth Model Using ICESat-2 Geolocated Photons

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