Evaluation of the Vertical Accuracy of Open Global DEMs over Steep Terrain Regions Using ICESat Data: A Case Study over Hunan Province, China

The global digital elevation model (DEM) is important for various scientific applications. With the recently released TanDEM-X 90-m DEM and AW3D30 version 2.2, the open global or near-global coverage DEM datasets have been further expanded. However, the quality of these DEMs has not yet been fully characterized, especially in the application for regional scale studies. In this study, we assess the quality of five freely available global DEM datasets (SRTM-1 DEM, SRTM-3 DEM, ASTER GDEM2, AW3D30 DEM and TanDEM-X 90-m DEM) and one 30-m resampled TanDEM-X DEM (hereafter called TDX30) over the south-central Chinese province of Hunan. Then, the newly-released high precision ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) altimetry points are introduced to evaluate the accuracy of these DEMs. Results show that the SRTM1 DEM offers the best quality with a Root Mean Square Error (RMSE) of 8.0 m, and ASTER GDEM2 has the worst quality with the RMSE of 10.1 m. We also compared the vertical accuracies of these DEMs with respect to different terrain morphological characteristics (e.g., elevation, slope and aspect) and land cover types. It reveals that the DEM accuracy decreases when the terrain elevation and slope value increase, whereas no relationship was found between DEM error and terrain aspect. Furthermore, the results show that the accuracy increases as the land cover type changes from vegetated to non-vegetated. Overall, the SRTM1 DEM, with high spatial resolution and high vertical accuracy, is currently the most promising dataset among these DEMs and it could, therefore, be utilized for the studies and applications requiring accurate DEMs.

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VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b4-143-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 143-148 ◽

Cited By ~ 9

Author(s):

D. Gesch ◽

M. Oimoen ◽

J. Danielson ◽

D. Meyer

Keyword(s):

United States ◽

Land Cover ◽

Digital Elevation Model ◽

Ground Level ◽

Land Cover Type ◽

Mean Error ◽

Digital Elevation ◽

The Mean ◽

Vertical Accuracy ◽

Elevation Model

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

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Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

Remote Sensing ◽

10.3390/rs12213482 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3482

Author(s):

Evelyn Uuemaa ◽

Sander Ahi ◽

Bruno Montibeller ◽

Merle Muru ◽

Alexander Kmoch

Keyword(s):

Land Cover ◽

Reference Model ◽

Digital Elevation Models ◽

Systematic Evaluation ◽

Slope Aspect ◽

Research Fields ◽

Digital Elevation ◽

Geographic Regions ◽

Vertical Accuracy ◽

Dem Accuracy

Freely available global digital elevation models (DEMs) are important inputs for many research fields and applications. During the last decade, several global DEMs have been released based on satellite data. ASTER and SRTM are the most widely used DEMs, but the more recently released, AW3D30, TanDEM-X and MERIT, are being increasingly used. Many researchers have studied the quality of these DEM products in recent years. However, there has been no comprehensive and systematic evaluation of their quality over areas with variable topography and land cover conditions. To provide this comparison, we examined the accuracy of six freely available global DEMs (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM) in four geographic regions with different topographic and land use conditions. We used local high-precision elevation models (Light Detection and Ranging (LiDAR), Pleiades-1A) as reference models and all global models were resampled to reference model resolution (1m). In total, 608 million 1x1 m pixels were analyzed. To estimate the accuracy, we generated error rasters by subtracting each reference model from the corresponding global DEM and calculated descriptive statistics for this difference (e.g., median, mean, root-mean-square error (RMSE)). We also assessed the vertical accuracy as a function of the slope, slope aspect, and land cover. We found that slope had the strongest effect on DEM accuracy, with no relationship for slope aspect. The AW3D30 was the most robust and had the most stable performance in most of the tests and is therefore the best choice for an analysis of multiple geographic regions. SRTM and NASADEM also performed well where available, whereas NASADEM, as a successor of SRTM, showed only slight improvement in comparison to SRTM. MERIT and TanDEM-X also performed well despite their lower spatial resolution.

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Assessment of debris-flow susceptibility at medium-scale in the Barcelonnette Basin, France

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-627-2011 ◽

2011 ◽

Vol 11 (2) ◽

pp. 627-641 ◽

Cited By ~ 56

Author(s):

M. S. Kappes ◽

J.-P. Malet ◽

A. Remaître ◽

P. Horton ◽

M. Jaboyedoff ◽

...

Keyword(s):

Regional Scale ◽

Low Frequency ◽

Data Availability ◽

Limiting Factor ◽

Case Scenario ◽

Worst Case ◽

High Dependency ◽

Digital Elevation ◽

Elevation Model

Abstract. Debris flows are among the most dangerous processes in mountainous areas due to their rapid rate of movement and long runout zone. Sudden and rather unexpected impacts produce not only damages to buildings and infrastructure but also threaten human lives. Medium- to regional-scale susceptibility analyses allow the identification of the most endangered areas and suggest where further detailed studies have to be carried out. Since data availability for larger regions is mostly the key limiting factor, empirical models with low data requirements are suitable for first overviews. In this study a susceptibility analysis was carried out for the Barcelonnette Basin, situated in the southern French Alps. By means of a methodology based on empirical rules for source identification and the empirical angle of reach concept for the 2-D runout computation, a worst-case scenario was first modelled. In a second step, scenarios for high, medium and low frequency events were developed. A comparison with the footprints of a few mapped events indicates reasonable results but suggests a high dependency on the quality of the digital elevation model. This fact emphasises the need for a careful interpretation of the results while remaining conscious of the inherent assumptions of the model used and quality of the input data.

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State of Science Assessment of Remote Sensing of Great Lakes Coastal Wetlands: Responding to an Operational Requirement

Remote Sensing ◽

10.3390/rs12183024 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3024

Author(s):

Lori White ◽

Robert A. Ryerson ◽

Jon Pasher ◽

Jason Duffe

Keyword(s):

Remote Sensing ◽

Great Lakes ◽

Coastal Vegetation ◽

Extensive Literature ◽

Mapping Unit ◽

Great Lakes Coastal Wetlands ◽

Vertical Accuracy ◽

Dem Accuracy ◽

Elevation Model ◽

Accuracy Determination

The purpose of this research was to develop a state of science synthesis of remote sensing technologies that could be used to track changes in Great Lakes coastal vegetation for the Great Lakes-St. Lawrence River Adaptive Management (GLAM) Committee. The mapping requirements included a minimum mapping unit (MMU) of either 2 × 2 m or 4 × 4 m, a digital elevation model (DEM) accuracy in x and y of 2 m, a “z” value or vertical accuracy of 1–5 cm, and an accuracy of 90% for the classes of interest. To determine the appropriate remote sensing sensors, we conducted an extensive literature review. The required high degree of accuracy resulted in the elimination of many of the remote sensing sensors used in other wetland mapping applications including synthetic aperture radar (SAR) and optical imagery with a resolution >1 m. Our research showed that remote sensing sensors that could at least partially detect the different types of wetland vegetation in this study were the following types: (1) advanced airborne “coastal” Airborne Light Detection and Ranging (LiDAR) with either a multispectral or a hyperspectral sensor, (2) colour-infrared aerial photography (airplane) with (optimum) 8 cm resolution, (3) colour-infrared unmanned aerial vehicle (UAV) photography with vertical accuracy determination rated at 10 cm, (4) colour-infrared UAV photography with high vertical accuracy determination rated at 3–5 cm, (5) airborne hyperspectral imagery, and (6) very high-resolution optical satellite data with better than 1 m resolution.

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VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-143-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 143-148 ◽

Cited By ~ 1

Author(s):

D. Gesch ◽

M. Oimoen ◽

J. Danielson ◽

D. Meyer

Keyword(s):

United States ◽

Land Cover ◽

Digital Elevation Model ◽

Ground Level ◽

Land Cover Type ◽

Mean Error ◽

Digital Elevation ◽

The Mean ◽

Vertical Accuracy ◽

Elevation Model

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Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-869-2013 ◽

2013 ◽

Vol 13 (4) ◽

pp. 869-885 ◽

Cited By ~ 75

Author(s):

P. Horton ◽

M. Jaboyedoff ◽

B. Rudaz ◽

M. Zimmermann

Keyword(s):

Debris Flows ◽

Regional Scale ◽

Source Area ◽

Population Pressure ◽

Complex Nature ◽

Simplified Approach ◽

Susceptibility Maps ◽

Digital Elevation ◽

Elevation Model

Abstract. The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.

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Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites

Remote Sensing ◽

10.3390/rs13071304 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1304

Author(s):

Haijiao Han ◽

Qiming Zeng ◽

Jian Jiao

Keyword(s):

Land Cover ◽

Quality Assessment ◽

Normal Distribution ◽

Incidence Angle ◽

Strong Relationship ◽

Slope Aspect ◽

Aster Gdem ◽

Vertical Accuracy ◽

New Perspective

Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented accuracy. These global datasets are often used for local applications and the quality of DEMs affects the results of applications. Many researchers have assessed and compared the quality of global DEMs on a local scale. To provide some additional insights on quality assessment of 12- and 30-m resolution TanDEM-X DEMs, 30-m resolution ASTER GDEM and 30-m resolution SRTM, this study assessed differences’ performance in relation to not only geographical features but also the ways in which DEMs have been created on selected Chinese sites, taking ICESat/GLAS points with 14-cm absolute vertical accuracy but size of 70-m diameter and 12-m resolution TanDEM-X DEM with less than 10-m absolute vertical accuracy as the reference data for comprehensive quality evaluation. When comparing the three 30-m DEMs with the reference DEM, an improved Least Z-Difference (LZD) method was applied for co-registration between models, and Quantile–Quantile (Q-Q) plot was used to identify if the DEM errors follow a normal distribution to help choose proper statistical indicators accordingly. The results show that: (1) TanDEM-X DEMs have the best overall quality, followed by SRTM. ASTER GDEM has the worst quality. The 12-m TanDEM-X DEM has significant advantages in describing terrain details. (2) The quality of DEM has a strong relationship with slope, aspect and land cover. However, the relationship between aspect and vertical quality weakens after data co-registration. The quality of DEMs gets higher with the increasing number of images used in the fusion process. The quality in where slopes opposite to the radar beam is the worst for SRTM, which could provide a new perspective for quality assessment of SRTM and other DEMs whose incidence angle files are available. (3) Systematic deviations can reduce the vertical quality of DEM. The differences have non-normal distribution even after co-registration. For researchers who want to know the quality of a DEM in order to use it in further applications, they should pay more attention to the terrain factors and land cover in their study areas and the ways in which the DEM has been created.

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Quality of streams in the Bull Mountains region, south-central Montana

10.3133/wri822 ◽

1982 ◽

Keyword(s):

South Central ◽

Central Montana

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Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

Sustainability ◽

10.3390/su13147782 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7782

Author(s):

Wenjing Zeng ◽

Yongde Zhong ◽

Dali Li ◽

Jinyang Deng

Keyword(s):

Large Scale ◽

Regional Scale ◽

Hunan Province ◽

Social Settings ◽

Recreation Opportunity Spectrum ◽

City District ◽

Night Lights ◽

Recreational Resources ◽

Metropolitan Counties ◽

Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

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Understanding Completeness and Diversity Patterns of OSM-Based Land-Use and Land-Cover Dataset in China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090531 ◽

2020 ◽

Vol 9 (9) ◽

pp. 531

Author(s):

ShuZhu Wang ◽

Qi Zhou ◽

YuanJian Tian

Keyword(s):

Land Use ◽

Land Cover ◽

Quality Assessment ◽

Socioeconomic Factors ◽

Reference Dataset ◽

Diversity Pattern ◽

Diversity Patterns ◽

Intrinsic Quality ◽

Accuracy Measures

OpenStreetMap (OSM) data are considered essential for land-use and land-cover (LULC) mapping despite their lack of quality. Most relevant studies have employed an LULC reference dataset for quality assessment, but such a reference dataset is not freely available for most countries and regions. Thus, this study conducts an intrinsic quality assessment of the OSM-based LULC dataset (i.e., without using a reference LULC dataset) by examining the patterns of both its completeness and diversity. With China chosen as the study area, an OSM-based LULC dataset of the country was first generated and validated by using various accuracy measures. Both its completeness and diversity patterns were then mapped and analyzed in terms of each prefecture-level division of the country. The results showed the following: (1) While the overall accuracy was as high as 82.2%, most complete regions of China were not mapped well owing to a lack of diverse LULC classes. (2) In terms of socioeconomic factors and the number of contributors, higher correlations were noted for diversity patterns than completeness patterns; thus, the diversity pattern is a better reflection of socioeconomic factors and the spatial patterns of contributors. (3) Both the completeness and the diversity patterns can be combined to better understand an OSM-based LULC dataset. These results indicate that it is useful to consider diversity as a supplement for intrinsically assessing the quality of an OSM-based LULC dataset. This analytical method can also be applied to other countries and regions.

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