Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors. Here we introduce a high‐accuracy global DEM at 3″ ...resolution (~90 m at the equator) by eliminating major error components from existing DEMs. We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques. After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill‐valley structures, became clearly represented. We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.
Key Points
A high‐accuracy global digital elevation model (DEM) was developed by removing multiple height error components from existing DEMs
Landscape representation was improved, especially in flat regions where height error magnitude was larger than actual topography variation
The improved‐terrain DEM is helpful for any geoscience applications which are terrain dependent, such as flood inundation modelling
Plain Language Summary
Terrain elevation maps are fundamental input data for many geoscience studies. While very precise Digital Elevation Models (DEMs) based on airborne measurements are available in developed regions of the world, most areas of the globe rely on spaceborne DEMs which still include non‐negligible height errors for geoscience applications. Here we developed a new high accuracy map of global terrain elevations at 3" resolution (~90m at the equator) by eliminating multiple error components from existing spaceborne DEMs. The height errors included in the original DEMs were separated from actual topography signals and removed using a combination of multiple satellite datasets and filtering techniques. After error removal, global land areas mapped with ±2m or better accuracy increased from 39% to 58%. Significant improvements were found, especially in flat regions such as river floodplains. Here detected height errors were larger than actual topography variability, and following error removal landscapes features such as river networks and hill‐valley structures at last became clearly represented. The developed high accuracy topography map will expand the possibility of geoscience applications that require high accuracy elevation data such as terrain landscape analysis, flood inundation modelling, soil erosion analysis, and wetland carbon cycle studies.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
We present a practical approach to inter-compare a range of candidate digital elevation models (DEMs) based on pre-defined criteria and statistically sound ranking approach. The presented approach ...integrates the randomized complete block design (RCBD) into a novel framework for DEMs comparison. The method presented provides a flexible, statistically sound and customizable tool for evaluating the quality of any raster - in this case a DEM - by means of a ranking approach, which takes into account a confidence level, and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six 1" global DEMs, considering a wide set of study sites, covering different morphological and landcover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1-5 m lidar-derived DEMs. Results confirmed significant superiority of CopDEM 1" and its derivative FABDEM as the overall best 1" global DEMs. They are slightly better than ALOS, and clearly outperform NASADEM and SRTM, which are in turn much better than ASTER.
Simple stacking of InSAR Digital Elevation Models (DEMs) has shown potential to increase DEM quality. In order to obtain a straight-forward stacking procedure that reduces the uncertainty of InSAR ...DEMs, in this contribution we evaluate in detail the impact of different stacking routines on the accuracy of stacked DEMs. For that end, we performed systematic tests in a region of Córdoba, Argentina. First, we produced a set of 54 Sentinel-1 and 10 SAOCOM-1 InSAR DEMs, which were evaluated with respect to a reference photogrammetric DEM. We then tested different stacking workflows, obtaining stacked DEMs with a higher average accuracy than that of the single-pair DEMs. This suggests that the uncertainty in the quality of the InSAR DEMs can be overcome by simple stacking techniques. Further post-processing of the stacked DEMs involved planimetric position correction by co-registration with the reference DEM, altimetric correction by linear regression adjustment to the national altimetric network, and multidirectional filtering to correct for speckling and outliers. The evaluation of the final stacked DEMs with respect to the reference shows that the SAOCOM-1 stacked DEM has a mean biased error of 0.39 m with a standard deviation of 3.77 m, whereas the Sentinel-1 stacked DEM has a mean biased error of 2.25 m with a standard deviation of 7.61 m. Both DEMs offered a smaller pixel size (15 m) than the available Argentine digital elevation model MDE-Ar (30 m), but with a lower accuracy. In turn, the combination of the SAOCOM-1 and Sentinel-1 stacked DEMs resulted in a 50 % and 33 % reduction in the mean biased error and the standard deviation, respectively, with respect to the SAOCOM-1 stacked DEM; an accuracy close to that of MDE-Ar, with a smaller pixel size. Although further improvements could be accomplished by exploring more sophisticated stacking and data-fusion techniques, these results constitute a significant step towards the systematization of a methodology to obtain reliable DEMs from SAR data.
•Simple stacking constitutes a reliable solution to overcome the quality uncertainty of InSAR DEMs.•SAOCOM-1 data proved to be useful for production of medium resolution, high quality DEMs.•The weighted combination of SAOCOM-1 and Sentinel-1 stacks improved DEM accuracy in terms of bias and standard deviation.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurate representation of global coastal topography is essential for numerous scientific disciplines, coastal management, and disaster risk assessment. Even with recent improvements to existing ...global digital elevation models (DEMs), high and persistent errors in these DEMs result in significant uncertainty when analyzing coastal processes. This results in low confidence for current sea level rise inundation risk assessments. We present DiluviumDEM, the first global DEM appropriate (i.e., the root mean square error (RMSE) is half the total water elevation in 2100 under a specific scenario) for mapping sea level rise inundation under the IPCC SSP2–4.5 and SSP5–8.5 scenarios, with an estimated RMSE of 1.13 m for coastal areas with elevations less than 2 m above mean sea level. Out of ten countries used for validation, DiluviumDEM has the lowest RMSE compared to three other DEMs analyzed, the lowest mean absolute error (MAE) for eight, and the mean error (ME) closest to zero for six. By reducing the error of the European Space Agency's Copernicus DEM using a gradient boosted decision tree model, we have created a new global coastal DEM with up to twice the accuracy compared to other global DEMs.
•A gradient boosted decision tree model is used to correct Copernicus DEM.•The accuracy of DiluviumDEM is up to twice the accuracy of other global DEMs.•DiluviumDEM has the highest confidence levels for sea level rise mapping.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The absence of a high-quality seamless global digital elevation model (DEM) dataset has been a challenge for the Earth-related research fields. Recently, the 1-arc-second Shuttle Radar Topography ...Mission (SRTM-1) data have been released globally, covering over 80% of the Earth’s land surface (60°N–56°S). However, voids and anomalies still exist in some tiles, which has prevented the SRTM-1 dataset from being directly used without further processing. In this paper, we propose a method to generate a seamless DEM dataset blending SRTM-1, ASTER GDEM v2, and ICESat laser altimetry data. The ASTER GDEM v2 data are used as the elevation source for the SRTM void filling. To get a reliable filling source, ICESat GLAS points are incorporated to enhance the accuracy of the ASTER data within the void regions, using an artificial neural network (ANN) model. After correction, the voids in the SRTM-1 data are filled with the corrected ASTER GDEM values. The triangular irregular network based delta surface fill (DSF) method is then employed to eliminate the vertical bias between them. Finally, an adaptive outlier filter is applied to all the data tiles. The final result is a seamless global DEM dataset. ICESat points collected from 2003 to 2009 were used to validate the effectiveness of the proposed method, and to assess the vertical accuracy of the global DEM products in China. Furthermore, channel networks in the Yangtze River Basin were also extracted for the data assessment.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A large and growing volume of repeat Digital Elevation Models (DEMs) obtained from multiple spaceborne sensors enables high-resolution mapping of the Earth's surface at continental scales. Mosaicking ...of individual DEMs to form a continuous surface is challenging due to variability data quality and positional accuracy, all of which can result in artifacts. Presented is a method for efficiently mosaicking sets of repeat, overlapping DEMs using their pairwise, translational offsets to remove poor quality DEMs and optimize their alignment prior to merging. The Coregistration, Adjustment and Median of Stacks (CAMS) approach is tested by mosaicking a set of 2-m resolution DEMs created from WorldView stereoscopic imagery and comparing the result to LiDAR data. CAMS produces a mosaic of substantially higher quality and accuracy than that obtained from the median of all overlapping DEMS, as commonly performed for mosaicking satellite derived DEMs. The method requires no sensor-specific information or ground control, making it applicable for large-area mosaic production using multiple datasets.
Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by ...very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.
•Rangeland function is influenced by fine-scale patterns of species & soil nutrients.•Range plant-soil-nutrient dynamics require very high spatial & spectral resolution.•UAV multispectral-photogrammetry fusion excels at functional cover classification.•UAV hyperspectral-LiDAR fusion excels at species & soil fertility classification.•LiDAR data detect soil fertility changes from ecological disturbance of fire.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) ...was applied to create Digital Surface Models (DSMs) of the landslide surface with an accuracy of 4-5 cm in the horizontal and 3-4 cm in the vertical direction. The accuracy of the co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, which minimized alignment errors to a mean of 0.07 m. Variables such as landslide area and the leading edge slope were measured and temporal patterns were discovered. Volumetric changes of particular areas of the landslide were measured over the time series. Surface movement of the landslide was tracked and quantified with the COSI-Corr image correlation algorithm but without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6630 m3. This study has demonstrated a robust and repeatable algorithm that allows a landslide's dynamics to be mapped and monitored with a UAV over a relatively long time series.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This paper reports new research progress on characterizing the burden surface particles of the blast furnace. An algorithm is proposed to extract the contour of the particles on the burden surfaces ...from their digital elevation models. The statistical distributions of particle size corresponding to the coke and sintered ore burden surfaces are counted from the extraction results of the particle contours. The statistical results obtained in the former research are compared with those of here. The particle surface height distributions can be approximated based on the burden particle size distributions. The peak positions of the estimated particle surface height distribution are consistent with that of the burden surface height distribution.
Lunar domes are common structures on the lunar surface and are important for studying the geological evolution of the moon. The distribution of spatial frequencies of lunar domes provides significant ...evidence for the evolution of lunar volcanoes. In recent years, deep learning methods have been rapidly developing in many fields. However, most of the existing dome detection algorithms use manual or semi-automatic traditional methods. In this paper, we propose an automatic deep learning recognition method to simplify the traditional dome identification process, which is an end-to-end detection method. We built a lunar dome dataset using digital elevation model data and compared eleven advanced deep learning target detection algorithms, which include three types of detection architecture. The region of Marius Hills was selected for validation to evaluate method performance. By comparing the results with manual identification, the proposed method has an identification precision of 88.7%. In addition, we detected 12 unrecorded potential domes/cones. The morphological characterization and visualization results indicate that the detected features may be domes/cones and our method may provide novel dome detection.
•Advanced deep learning methods are compared and applied to dome detection.•Precision of lunar dome detection using digital elevation model data was 88.7%.•Twelve new possible domes/cones are discovered.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
