Nasadem Global Elevation Model: Methods and Progress

Crippen, Robert E.; Buckley, S.; Agram, P. S.; Belz, E.; Gurrola, E. M.; Hensley, S.; Kobrick, M.; Lavalle, Marco; Martin, Jan M. L.; Neumann, Maxim; Nguyen, Q.; Rosen, P. A.; Shimada, J.; Simard, Marc; Tung, W.

doi:10.5194/isprsarchives-xli-b4-125-2016

Cited by 76 publications

(54 citation statements)

References 5 publications

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“…Whilst a significant improvement on SRTM, MERIT is still fundamentally based on SRTM data and is thus limited by the errors in SRTM. The next new edited DEM will be NASADEM (Crippen et al, 2016) set to be released in 2018, but again this is a reprocessing of SRTM.…”

Section: Dem Editingmentioning

confidence: 99%

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

et al. 2018

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Open-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30 m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in datasparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a 'must-have' for any flood model. A highaccuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation.

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Section: Dem Editingmentioning

confidence: 99%

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

et al. 2018

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“…Although acquisition dates vary, if topography is assumed to be stable, different sources can be combined. For example, MERIT [55], NASADEM [56], EarthEnv-DEM90 [57], and VFP [58] elevation models are fusion products, combining data from multiple elevation data products.…”

Section: Regional Topographymentioning

confidence: 99%

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Natijne

Lindenbergh

Bogaard

2020

Sensors

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Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

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“…DG level 1B data are radiometrically and sensor corrected, but not projected to a plane using map projection or datum [69]. We orthorectified the data using a 30 m NASADEM [70] and then converted into top-of-the-atmosphere reflectance for normalized difference vegetation index (NDVI) calculations. All the processing was done using Polar Geospatial Center's orthorectification tools [71] on the Advanced Data Analytics Platform (ADAPT) at NASA Goddard Space Flight Center (GSFC)'s NASA Center for Climate Simulation (NCCS) (http://www.nccs.nasa.gov/services/adapt).…”

Section: Satellite Imagerymentioning

confidence: 99%

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

2019

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The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

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Nasadem Global Elevation Model: Methods and Progress

Cited by 76 publications

References 5 publications

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

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