No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
On the use of global DEMs in ecological modelling and the accuracy of new bare-earth DEMs
Abstract
It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs' vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.
... Conducted by NASA and the National Geospatial-Intelligence Agency (NGA) in 2000, the SRTM utilized interferometric synthetic aperture radar (InSAR) technology to capture elevation data (Passini and Jacobsen, 2007) for more than 80% of Earth's landmass (Nikolakopoulos, Kamaratakis and Chrysoulakis, 2006;Ouerghi et al., 2015). SRTM derived products, such as SRTM30 and SRTM90 models, remain indispensable in geospatial analyses (Van Zyl, 2001), providing essential data for disciplines ranging from ecology (Moudrý et al., 2018;Silva, Gomes and Oliveira, 2023;Dian et al., 2024) and geology (Rossetti and Valeriano, 2007;EL-Omairi, Garouani and Shebl, 2024) to urban planning and hydrology (e.g., Cunha and Bacani, 2016;Arabameri et al., 2020;Gomes, 2020;Dell'Acqua and Gamba, 2002). ...
... The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is a high-resolution elevation dataset produced through collaboration between NASA and Japan's Ministry of Economy, Trade, and Industry (METI). ASTER GDEM provides near-global coverage at 30-meter spatial resolution, offering valuable insights for various applications including hydrology (Thakuri et al., 2022;AL-Areeq et al., 2023), geology (Asran, Emam and El-Fakharani, 2017), ecology (Moudrý et al., 2018), and urban planning. Since its first release in 2009 (Ouerghi et al., 2015), ASTER GDEM has been widely used in terrain analysis, particularly in mountainous and remote areas where high-resolution topographic data are essential but often limited. ...
... Several studies have tried to assess and compare the accuracy between freely available DEMs using either GPS-measured ground control points or finer-scale DEMs produced through photogrammetric processes [63][64][65][66][67][68][69]. The vertical accuracy of the world DEM files depends on the geomorphology of the ground and the terrain of each specific region, but, generally speaking, Copernicus GLO-30 and AW3D30 appear to outperform NASADEM, SRTM, and ASTERGDEM [67,70]. ...
... Several studies have tried to assess and compare the accuracy between freely available DEMs using either GPS-measured ground control points or finer-scale DEMs produced through photogrammetric processes [63][64][65][66][67][68][69]. The vertical accuracy of the world DEM files depends on the geomorphology of the ground and the terrain of each specific region, but, generally speaking, Copernicus GLO-30 and AW3D30 appear to outperform NASADEM, SRTM, and ASTERGDEM [67,70]. As a result, the Copernicus GLO-30 DEM was selected as the primary source of elevation information in the present research. ...
Coastal areas are currently exposed to numerous hazards exacerbated by climate change, including erosion, flooding, storm surges, and other sea level rise phenomena. Mediterranean countries, in particular, are facing a constant shrinking of coastal areas. This region also hosts significant cultural heritage assets, including several UNESCO World Heritage Sites. The present research demonstrates a methodological approach to assess the current and future exposure of Mediterranean coastal areas and heritage assets to Sea Level Rise using open access data regarding elevation, vertical ground motion, and Sea Level Change factors (e.g., ice sheets, glaciers, etc.). The future projections regard 2050 and 2100 and are based on RCP scenarios 2.6, 4.5 and 8.5. The datasets used include Copernicus GLO-30 DSM, the European Ground Motion Service’s dataset on Vertical Ground Motion, the Sea Level Change Projections’ Regional Dataset by NASA, and a hybrid coastline dataset created for the present research purposes to assist in delineating the study area. The research results demonstrate that Greece, Italy, and France’s mainland and cultural heritage assets already face SLR-related hazards but are expected to be further exposed in the future, always taking into consideration the high level of uncertainty regarding SLR projections and RCP scenarios’ hypotheses.
... These DEMs recently gained tremendous popularity among the scientific fraternity and helped to advance the understanding of Earth sciences. However, these DEMs include inaccuracies and model errors (namely voids, spikes, sinks, and artifacts) that resulted from various phases of the DEM generation process, from data acquisition to model computation [2][3][4]. Moreover, space-borne-based DEMs include elevation biases from the vegetation canopy and man-made structures that result in a Digital Surface Model (DSM), i.e., adding a positive offset to the true bare-earth model or a Digital Terrain Model (DTM). ...
... Previously, Bhardwaj [31], Dandabathula et al. [27], and Xu et al. [32] have evaluated FABDEM's tendency of a bare-earth model and concluded that it is a beneficial data resource for disaster-related applications, especially for flood hazard zonation and hydraulic simulations. Similarly, the performance of MERIT DEM and other open-access global DEMs was evaluated by various researchers [2,4,5,33,34,35,36]. The results from their investigations concluded that primarily, the algorithms implemented by Yamazaki et al. [9] to reduce the errors from SRTM were successful and evident in the form of MERIT DEM. ...
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.
... As a digital representation of topography, digital elevation models (DEMs) are widely used in various fields, such as geomorphology (Blanchard, Rogan, and Woodcock 2010), forestry (Gdulová et al. 2021), hydrology (Kiel, Alsdorf, and LeFavour 2006), and ecology (Moudrý et al. 2018;Amatulli et al. 2018). At present, several spaceborne global DEMs (GDEMs) with a resolution of 1-arcsec (approximately 30 m) have been freely accessible, including Shuttle Radar Topography Mission (SRTM) (Braun and Fotopoulos 2007), Advanced Land Observing Satellite (ALOS) World 3D Topographic Data (AW3D) , and Copernicus DEM (COPDEM) (Marešová et al. 2021). ...
... Similarly, Yamazaki et al. (2017) obtained the global VB with the global tree density map (Hansen et al. 2013) and the global tree height map (Simard et al. 2011); they produced a 'bare-earth' 90-m DEM (termed MERIT) by removing multiple error components, including VB. Nevertheless, such auxiliary data are too coarse to characterize vegetation parameters at a regional scale (Moudrý et al. 2018). Su and Guo (2014) employed a linear regression model with three LiDAR-derived independent variables of terrain slope, vegetation height, and leaf area index to compute VB. ...
To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs, including SRTM1, AW3D30, and COPDEM30. Taking LiDAR DTM as the ground truth, the accuracy of the GDEMs before and after VB correction is assessed, as well as two existing GDEMs including MERIT and FABDEM. Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types, with the largest biases of 21.5 m for SRTM1, 26.3 m for AW3D30, and 27.18 m for COPDEM30. Taking data randomly sampled from the corrected area as the training points, the proposed model reduces the mean errors (root mean square errors) of the three GDEMs by 98.8%−99.9% (55.1%−75.8%) in the three forests. When training data have the same forest type as the corrected GDEM but under different local situations, the proposed model lowers the GDEM errors by at least 76.9% (44.1%). Furthermore, our corrected GDEMs consistently outperform the existing GDEMs for the two cases.
... Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences including flood hazard mapping (Sampson et al 2016, Garrote 2022, hydrological modelling (O'Callaghan and Mark 1984, Beven and Freer 2001, Gharari et al 2020, shortwave radiation estimates (Garnier and Ohmura 1968, Dozier and Frew 1990, Marsh et al 2012 , landslide hazard assessment (Fenton et al 2013), ecology (Illés et al 2011, Moudrý et al 2018, geomorphology (Liu et al 2009), and landform classification (Reu et al 2013). However, many of these applications require the use of a 'bare-Earth' digital elevation model (DEM) versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas (O'Loughlin et al 2016). ...
... The complex topography present in mountain headwater catchments represents a mix of steep slopes and forested surfaces that could prove to be problematic with deforestation algorithms. Topographic indices derived from remote sense elevation data are commonly used as surrogates for field measurements (Moudrý et al 2018). ...
Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to debias the global-extent datasets. Recent advances in satellite platforms coupled with an increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Amongst the more challenging landscapes to quantify surface elevations are dense forested mountain catchments where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms has resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against an up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing de-forested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.
... In parallel, the EU Directive INSPIRE (Bartha & Kocsis, 2011) led to the diffusion of openaccess field databases that support calibration and validation of RS-based classifications (Rocchini et al., 2017), especially at a national scale, for which the cost of collecting field data would be too high. Although the quality of open data is considered heterogeneous, whether derived from RS (Moudr y et al., 2018) or field data (Suarez-Seoane et al., 2020), open data have recently been used to map grasslands and forest habitats (Agrillo et al., 2021) at a national scale. However, these two studies used a multi-classifier, which requires first masking the area of interest (e.g. ...
... LDA and classification based on open data provided results consistent with the ecological knowledge of heathland habitats. However, besides the usual local biases in the quality of bioclimatic (Cord et al., 2014) and DEM (Moudr y et al., 2018) variables in the mountains, the quality of the Sentinel-2 products used should be discussed more thoroughly. We used monthly cloud-free composite images (L3A processing level) to reduce the total amount of data collected ('only' 1.7 TB for France), but this choice influenced the results. ...
Abstract Mapping natural habitats remains challenging, especially at a national scale. Although new open‐access variables for vegetation and its environment and increased spatial resolution derived from satellite remote sensing data are available at the global scale, the relevance of these new variables for fine‐grained mapping of natural habitats at a national scale remains underexplored. This study aimed to map the fine‐grained pattern of four heathland habitats throughout France (550 000 km2). Environmental (bioclimatic, soil and topographic) and spectral (vegetation) variables derived from MODerate resolution Imaging Spectroradiometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Sentinel‐2 satellite data were analyzed using the MaxEnt classifier. Open‐access field databases were used to calibrate and validate the classification, based on the threshold‐independent area under the curve (AUC) index and the conventional F1‐score. For each heathland habitat, potential and actual areas were mapped using environmental and spectral variables, respectively. The results showed high classification accuracy for potential (AUC 0.92–0.99) and actual (AUC 0.88–0.99) suitability maps of the four heathland habitats. Visual interpretation of maps of the probability of occurrence indicated that the fine‐grained distribution of heathland habitat was detected satisfactorily. However, although the accuracy of the crisp map of combined classifications of actual heathland habitats was high (overall accuracy 0.72), estimated producer's accuracies in terms of proportion of area were low (
... This is, however, a very unlikely (if not impossible) combination. For example, multiple resolutions are typically tested when developing SDMs and this can considerably affect the propagation of errors from global CHMs McGarigal et al., 2016;Moudrý et al., 2018). Furthermore, deficiencies in species occurrence data, including positional accuracy (G abor et al., 2023) and sampling bias , must also be taken into account. ...
Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy height maps have been released, the global forest canopy height map (GFCH), the high‐resolution canopy height model of the Earth (HRCH), and the global map of tree canopy height (GMTCH). Here, we assessed their accuracy and usability for biodiversity modeling. We examined their accuracy by comparing them with the reference canopy height models derived from airborne laser scanning (ALS). Our results show considerable differences between the evaluated maps. The root mean square error ranged between 10 and 18 m for GFCH, 9–11 m for HRCH, and 10–17 m for GMTCH, respectively. GFCH and GMTCH consistently underestimated the height of all canopies regardless of their height, while HRCH tended to overestimate the height of low canopies and underestimate tall canopies. Biodiversity models using predicted global canopy height maps as input data are sufficient for estimating simple relationships between species occurrence and canopy height, but their use leads to a considerable decrease in the discrimination ability of the models and to mischaracterization of species niches where derived indices (e.g., canopy height heterogeneity) are concerned. We showed that canopy height heterogeneity is considerably underestimated in the evaluated global canopy height maps. We urge that for temperate areas rich in ALS data, activities should concentrate on harmonizing ALS canopy height maps rather than relying on modeled global products.
... Compared to airborne lidar DTMs [7], (1) they have larger vertical errors, (2) their resolution is lower, and (3) they often represent the Digital Surface Model (DSM) (vegetation and human-made structures are present) of an area instead of the terrain. These inherent issues stem from the primary measurement methods used in constructing global DEMs-either interferometry using C-band and X-band radar (SRTM, Tandem-X) or stereoscopy using passive optical imagery (ASTER, ALOS)-to measure elevation [8]; in contrast, lidar can penetrate the canopy. ...
Digital Elevation Models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of data from two spaceborne lidar altimetry missions, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric data collection patterns—as sources for rasterisation towards creating global DEMs. We validate the terrain measurements of both missions against airborne lidar datasets over three areas in the Netherlands, Switzerland, and New Zealand and differentiate them using land-cover classes. For our experiments, we use five years of ICESat-2 ATL03 data and four years of GEDI L2A data for a total of 252 million measurements. The datasets are filtered using parameter flags provided by the higher-level products ICESat-2 ATL08 and GEDI L3A. For all areas and land-cover classes combined, ICESat-2 achieves a bias of −0.11 m, an MAE of 0.43 m, and an RMSE of 0.93 m. From our experiments, we find that GEDI is less accurate, with a bias of 0.09 m, an MAE of 0.98 m, and an RMSE of 2.96 m. Measurements in open land-cover classes, such as “Cropland” and “Grassland”, result in the best accuracy for both missions. We also find that the slope of the terrain has a major influence on vertical accuracy, more so for GEDI than ICESat-2 because of its larger horizontal geolocation error. In contrast, we find little effect of either beam power or background solar radiation, nor do we find noticeable seasonal effects on accuracy. Furthermore, we investigate the spatial coverage of ICESat-2 and GEDI by deriving a DEM at different horizontal resolutions and latitudes. GEDI has higher spatial coverage than ICESat-2 at lower latitudes due to its beam pattern and lower inclination angle, and a derived DEM can achieve a resolution of 500 m. ICESat-2 only reaches a DEM resolution of 700 m at the equator, but it increases to almost 200 m at higher latitudes. When combined, a 500 m resolution lidar-based DEM can be achieved globally. Our results indicate that both ICESat-2 and GEDI enable accurate terrain measurements anywhere in the world. Especially in data-poor areas—such as the tropics—this has potential for new applications and insights.
... As well as the hydro-meteorological inputs, an integral requirement for such flood models is topographic data, ideally representing 'bare earth' ground elevations (Sampson et al. 2015;Sanders 2007). The vertical accuracy of these data is critical since the inundation depths and (Ciampalini et al. 2016), ecological modelling (Moudrý et al. 2018), and wetland carbon dynamics (Laudon et al. 2011). Until recently, there was a general consensus that the SRTM DEM was most accurate (Sampson et al. 2015;Sanders 2007), with AW3D30 sometimes preferred (Courty, Soriano-Monzalvo, and Pedrozo-Acuña 2019;Jain et al. 2018) and ASTER consistently found to be least accurate (Gesch 2018;Hirt, Filmer, and Featherstone 2010). ...
Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).
... The Shuttle Radar Topography Mission (SRTM) provides high-resolution digital elevation models (DEMs) extensively utilized across various fields, including geology, geomorphology, natural disaster assessment, and vegetation survey (Moudrý et al. 2018;Yang, Meng, and Zhang 2011). We adopted the 30 m resolution SRTM DEM to compute terrain slope and aspect, crucial inputs for forest height modeling. ...
Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides effective photon-counting light detection and ranging (LiDAR) data for estimating forest height across extensive geographical areas. Although prior studies have illustrated canopy conditions during leaf-on and leaf-off phases may influence ICESat-2 derived forest heights, a comprehensive understanding of this effect remains incomplete. This study seeks to comprehensively assess how varying canopy conditions (leaf-on/leaf-off) affect ICESat-2 forest height retrieval and modelling. First, the accuracies of ICESat-2 terrain and canopy heights under leaf-on and leaf-off conditions were validated. Second, random forest algorithm was utilized to model forest height by integrating ICESat-2, Sentinel-2, and other ancillary datasets. Finally, we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling. Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets. Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leaf-on conditions. Notably, the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover. Furthermore, the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.
... Offsets that experience altitude errors need to be removed to effectively convert DSM to DTM (Gallant et al. 2012). DSM registration and land cover will result in lower tree offset estimates (Moudrý et al. 2018). DSM SRTM response to tree cover changes is not sharp, but the transition is smooth at a distance of 3-4 cells (about 100 m). ...
Vertical accuracy evaluation is essential to determine in order to know the quality of a DTM. This quality will affect the scale type and utilization of the DTM. The study data use DTM ALOS PALSAR-2. This study evaluates the vertical accuracy of the DTM ALOS PALSAR-2 with different height reference fields in the Rote Dead Sea Area, Indonesia. Each DTM is made with the EGM 1996, EGM 2008, and WGM 2012. The three DTMs extracted based on the height reference field will have different orthometric heights; therefore, an evaluation of the vertical accuracy is needed to determine the quality of the three DTMs. They compare with field measurements from GNSS-levelling. This test is carried out at lowland and highland, using 10 test points. For the lowland area, the RMSE (z) in height at DTM is 1.363 m for WGM 2012, 2.017 m for EGM 2008, and 1.934 m for EGM 1996. For the highlands area, the RMSE (z) in height at DTM is 1.185 m for WGM 2012, 1.201 m for EGM 2008, and 1.432 m for EGM 1996. The DTM-WGM 2012 and DTM-EGM 1996 are recommended to use in this area because they have higher vertical accuracy. The vertical accuracy test in the Rote lowland corresponds to class 2 and class 3 on a scale of 1:10,000. The vertical accuracy test results in the Rote highland correspond to class 1 and class 2 on a scale of 1:10,000. The ALOS PALSAR-2 DTM vertical accuracy test results can be used for mapping scales of 1:10,000 – 1:25,000 in Rote.
... Compared to DEMs acquired with airborne lidar (Mallet and Bretar, 2009), (1) they have larger vertical errors, (2) their resolution is low, and (3) often they represent the digital surface model (DSM; vegetation and man-made structures are present) of an area instead of the terrain. These inherent issues stem from the measurement methods used by global DEMs-either interferometry using C-band and X-band radar (SRTM, Tandem-X) or stereoscopy using passive optical imagery (ASTER, ALOS)-to measure elevation (Moudry et al., 2018); lidar can penetrate canopy. ...
Digital elevation models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of two spaceborne lidar altimetry instruments, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric data collection patterns—as sources for rasterisation towards global DEMs. We validate the terrain measurements of both missions against airborne lidar datasets over three areas in the Netherlands, Switzerland, and New Zealand, and differentiate them using landcover classes. For our experiments, we use three and a half years of ICESat-2 ATL03 data and three years of GEDI L2A data, totalling 113 million measurements. The datasets are filtered using parameter flags provided by the higher-level products, respectively ICESat-2 ATL08 and GEDI L3A. For all areas and land cover classes combined, ICESat-2 achieves a bias of −0.06 m, a MAE of 0.46 m, and a RMSE of 1.39 m. We find that GEDI is less accurate and precise with a bias of 0.45 m, a MAE of 0.98 m and a RMSE of 5.66 m. Measurements in open land cover classes, such as “Cropland” and “Grassland”, result in the best precision for both missions. We also find that the slope of the terrain is a major influence on vertical accuracy, and more so for GEDI than ICESat-2, because of its larger horizontal geolocation error. Contrastingly, we find little effect of either beam power or background solar radiation, nor do we find noticeable seasonal effects on accuracy. Furthermore, we investigate the spatial coverage of ICESat-2 and GEDI by deriving a DEM at different horizontal resolutions and latitudes. GEDI has higher spatial coverage than ICESat-2 at lower latitudes due to its beam pattern and lower inclination angle, and a derived DEM can achieve a resolution of 700 m. ICESat-2 only reaches a DEM resolution of 2000 m at the equator but increases to almost 200 m at higher latitudes. When combined, a 500 m resolution DEM can be achieved globally. Our results indicate that both ICESat-2 and GEDI enable accurate terrain measurements anywhere in the world. Especially in data-poor areas—such as the tropics—this has potential for new applications and insights.
... The good performance of MERIT despite its relatively coarse resolution can be explained by the fact that MERIT has been generated by merging multiple elevation datasets and hydrologically corrected using advanced error removal techniques (Hirt, 2018;Yamazaki et al., 2019). Moudrý et al. (2018) found that removing speckle noise and vegetation offsets (tree height bias) can lead to a significant improvement in DEM quality by more accurately representing ground surface elevation and reducing errors. The benefits of using MERIT for hydrological modelings, such as flood modeling, have also been demonstrated in several studies (e.g., Hawker et al., 2018;Modi et al., 2022) in comparison to other global DEMs. ...
The digital elevation models (DEMs) are the primary and most important spatial inputs for a wide range of hydrological applications. However, their availability from multiple sources and at various spatial resolutions poses a challenge in watershed modeling as they influence hydrological feature delineation and model simulations. In this study, we evaluated the effect of DEM choice on stream and catchment delineation and streamflow simulation using the SWAT model in four distinct geographic regions with diverse terrain surfaces. Performance evaluation metrics, including Willmott's index of agreement, and nRMSE combined with visual comparisons were employed to assess each DEM's performance. Our results revealed that the choice of DEM has a significant impact on the accuracy of stream and catchment delineation, while its influence on streamflow simulation within the same catchment was relatively minor. Among the evaluated DEMs, AW3D30 and COP30 performed the best, closely followed by MERIT, whereas TanDEM-X and HydroSHEDS exhibited poorer performance. All DEMs displayed better accuracy in mountainous and larger catchments compared to smaller and flatter catchments. Forest cover also played a role in accuracy, mainly due to its association with steep slopes. Our findings provide valuable insights for making informed data selection decisions in watershed modeling, considering the specific characteristics of the catchment and the desired level of accuracy.
... The disadvantages, especially at higher flight heights above the ground (AGL), are a lower density of the point cloud, poorer vertical accuracy, and point distortion due to the size of the laser footprint. Comparisons of the results of ALS-based measurements with other methods are shown in [24][25][26]. The shortcomings of ALS can be largely eliminated by using unmanned aerial system (UAS)-borne lidar technology [27,28]. ...
There are numerous talus cones that have formed by long-term geological processes and sudden hydrological events in the Small Cold Valley (High Tatras National Park in Slovakia). Frequent hiking trails lead here; therefore, their safeness needs to be monitored due to recent rock avalanches and landslides. A complex methodology for monitoring changes in talus cones was developed to determine the extent, pace, nature, and origin of the morphological changes in the land in this complex high-mountain terrain. Non-contact UAS photogrammetry with SfM-MVS processing was applied as a quick, reliable, and environment-friendly data acquisition method. For proper georeferencing, a network of GCPs and stabilized surveying points were established by terrestrial geodetic surveying. Together with an evaluation of the methodology, the results comparing the actual state of a talus cone in 2018 and 2022 (after the significant hydrological event) are presented. Comparing and analyzing spatial models represented by point clouds, with an accuracy of centimeter level, was obtained. The detected morphological changes reached values in meters. A differential model expresses the distribution of the morphological changes. In conclusion, geodetic and geological knowledge is synthesized to evaluate the phenomena occurring in this territory.
... In addition, we calculated the distance to highways, roads, trails, and rivers. The terrain variables (Lecours et al., 2017) were derived from a high-accuracy digital terrain model at a 25-m resolution (Moudrý et al., 2018). The terrain attributes were derived using Horn's algorithm at that resolution and subsequently aggregated to the analysis grain using the mean value from each cell (Moudrý et al., 2019). ...
Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence-only SDMs on an example of a general-ist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1-km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures , (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006-2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling sub-populations independently (as they show distinct habitat preferences). In conclusion , SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the
environment at coarse analysis grains.
... Generation of high-resolution DEMs has recently led to a paradigm shift in geomorphometry (Drăguţ et al. 2011, Evans 2012, with growing interest to classify and map minor landforms, either natural or anthropogenic, at very fine scales (e.g., landform extraction and mapping from LiDAR DEMs (De Matos-Machado et al. 2019, Ortuño et al. 2017. Paradoxically, available global Digital Surface Models (DSM), with coarser resolution, such as those of the Shuttle Radar Topography Mission (SRTM) exist for a longer time than high-resolution DEMs and have proved efficient in the field of ecological modelling (Amatulli et al. 2018, Moudrý et al. 2018 but are still underused from this perspective of landform mapping. Although DSMs provide top of vegetation elevation data and are not Digital Terrain Model (DTM) i.e. elevations at ground level, their use for producing broad-scale geomorphological maps is worth considering (e.g., Iwahashi & Pike 2007, Bugnicourt et al. 2018. ...
An automatic method of landform mapping applicable to large continental areas is presented, based on 30-meter SRTM (Shuttle Radar Topography Mission) data and combining texture analysis using Fourier 2D periodograms (FOTO method) with a set of morphometric variables. This integrated strategy was applied to the whole Congo Basin and adjacent regions in Central Africa, where landscapes and landforms mapping remains heterogeneous and partial with existing expert maps differing in aims and scales. Through the FOTO method, a principal component analysis (PCA) on obtained Fourier r-spectra yielded six textural features, which were further combined with seven morphometric criteria into a global PCA. A k-means classification from these output results provided an automatic mapping of 12 landform classes (at a final resolution of 900 m) which were successfully interpreted in terms of geomorphological meaning together with some hydrological and soil attributes. Finally, comparison of our
landform map with existing, independent geomorphological sheets revealed a good spatial congruence. Overall, our method proved effective to depict landform assemblages at regional or continental scales based on complementary textural information and morphometric parameters. As such, it could serve as a sound basis for further predicting and mapping soil units at the landscape scale, given the close soil-landform imbrications and interactions at the
catena level. It could serve as well as a predictive variable for biodiversity measures and biomass estimates, especially in the humid tropics where environmental data are lacking whilst ecological modelling is urgently needed to support land planning and forest management.
... Following previous work (Rossetti and Valeriano, 2007;Bernal et al., 2011), we combine Google Earth, Landsat visual imagery, and bare-earth topography to identify abandoned channels on these megafans. For elevation data, we use the BEST (Bare-Earth SRTM Terrain) elevation model, which uses vegetation maps and satellite lidar to reveal bare-earth topography by correcting for vegetation elevations present in radar-derived topography (O'Loughlin et al., 2016;Moudrý et al., 2018). On top of each megafan, we overlaid a rasterized grid with square cells with dimensions that corresponded to roughly five channel widths, similar to the resolution of the cellular model that is described later. ...
River avulsions are an important mechanism by which sediment is routed and emplaced in foreland basins. However, because avulsions occur infrequently, we lack observational data that might inform where, when, and why avulsions occur and these issues are instead often investigated by rule-based numerical models. These models have historically simplified or neglected the effects of abandoned channels on avulsion dynamics, even though fluvial megafans in foreland basins are characteristically covered in abandoned channels. Here, we investigate the pervasiveness of abandoned channels on modern fluvial megafan surfaces. Then, we present a physically based cellular model that parameterizes interactions between a single avulsing river and abandoned channels in a foreland basin setting. We investigate how abandoned channels affect avulsion setup, pathfinding, and landscape evolution. We demonstrate and discuss how the processes of abandoned channel inheritance and transient knickpoint propagation post-avulsion serve to shortcut the time necessary to set up successive avulsions. Then, we address the idea that abandoned channels can both repel and attract future pathfinding flows under different conditions. By measuring the distance between the mountain front and each avulsion over long (106 to 107 years) timescales, we show that increasing abandoned channel repulsion serves to push avulsions farther from the mountain front, while increasing attraction pulls avulsions proximally. Abandoned channels do not persist forever, and we test possible channel healing scenarios (deposition-only, erosion-only, and far-field-directed) and show that only the final scenario achieves dynamic equilibrium without completely filling accommodation space. We also observe megafan growth occurring via ∼100000-year cycles of lobe switching but only in our runs that employ deposition-only or erosion-only healing modes. Finally, we highlight opportunities for future field work and remote sensing efforts to inform our understanding of the role that floodplain topography, including abandoned channels, plays on avulsion dynamics.
... DEM data can be obtained by ground surveying or by remote-sensing methods, including stereo photogrammetry, interferometric synthetic aperture radar (InSAR) interferometry and light detection and ranging (LiDAR) [1]. DEM data are an important input for many applications, such as: ecology and glaciology modeling [2,3], hydrologic and flood simulations [4][5][6][7][8][9][10][11][12][13][14], and engineering infrastructure modeling [15]. ...
A digital elevation model (DEM) represents the topographic surface of the Earth and is an indispensable source of data in many applications, such as flood modeling, infrastructure design and land management. DEM data at high spatial resolution and high accuracy of elevation data are not only costly and time-consuming to acquire but also often confidential. In this paper, we explore a cost-effective approach to derive good quality DEM data by applying a multi-channel convolutional neural network (CNN) to enhance free resources of available DEM data. Shuttle Radar Topography Mission (SRTM) data, multi-spectral imaging Sentinel-2, as well as Google satellite imagery were used as inputs to the CNN model. The CNN model was first trained using high-quality reference DEM data in a dense urban city—Nice, France—then validated on another site in Nice and finally tested in the Orchard Road area (Singapore), which is also an equally dense urban area in Singapore. The CNN model not only shows an impressive reduction in the root mean square error (RMSE) of 50% at validation site in Nice and 30% at the test site in Singapore, but also results in much clearer profiles of the land surface than input SRTM data. A comparison between CNN performance and that of an earlier conducted study using artificial neural networks (ANN) was conducted as well. The comparison within this limited study shows that CNN yields a more accurate DEM.
... We downloaded these at a resolution of 30 arc seconds (~ 1 km 2 ) and averaged them inside each mapping square to match the grid resolution of the species distribution data (~ 100 km 2 ). We also considered usage of elevation predictors such as maximum, minimum and range of elevation derived from Shuttle radar topography mission (SRTM, Farr et al. 2007, Moudrý et al. 2018) as they might be ecologically important to birds (Kosicki 2017). However, as these variables were highly correlated with the mean temperature in April-June, we eventually decided not to include them. ...
The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation.
... As space-based technology progresses, various satellite-based location estimates using remote sensing techniques (optical, microwave and lidar technologies) are delivering continuous maps and data of estimated positions (X, Y, and Z) in the form of digital elevation models (DEMs) [2]. DEMs from space, especially those available freely in the public domain, are a valuable and dependable data source for many scientific endeavors, such as hydrological mapping [3][4][5], extraction of hydrological parameters [6], flood flow mapping [7,8], flood flow modelling, production of orthoimageries [9], topographic characterization [10], ecological modelling [11], and fault kinematics and facet geomorphology [12]. The DEM is critical for tracking the movement and spread of dust plumes [13]. ...
Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies.
... The systematic bias stems from the influence of tree canopies, while random noise can be classed into speckle, stripe noise and absolute biases depending on their wavelengths (Rodríguez Ernesto 125 A4 -Morris, Charles S. A4 -Belz, J. Eric, 2006;Takaku et al., 2016). The Multi-Error-Removed Improved Terrain (MERIT) DEM (Yamazaki et al., 2017), at 3 resolution extended from 90 • N to 60 • S, was the first global product with a consistent systematic bias and random noise removal procedure, and is considered the best available, seamless DEM with global coverage (Hirt, 2018;Moudrỳ et al., 2018). MERIT DEM is a fusion of the National Aeronautics and Space Administration (NASA) SRTM3 version 2.1 (Farr et al., 2007), the Japan Aerospace Exploration Agency (JAXA) AW3D global high resolution 3D 130 map (version 1) (Tadono et al., 2015) and the Viewfinder Panorama's DEM (available at http://www.viewfinderpanoramas.org/ dem3.html). ...
The geographic distribution of streams and rivers drives a multitude of patterns and processes in hy-drology, geomorphology, geography and ecology. Therefore, a hydrographic network that accurately delineatesboth small streams and large rivers with equal precision, along with their topographic and topological proper-ties, would be indispensable in the earth sciences. However, no such hydrographic study has been publishedto date. Perhaps equally critical is the absence of small headwater streams in global hydrographies, as theseare estimated to contribute to more than 70 % of overall stream length. We aimed to fill this gap by using theMERIT Hydro Digital Elevation Model at 3 arc-sec (∼90 m at the equator) to derive a globally seamless, stan-dardised hydrographic network, the "Hydrography90m", with corresponding stream topographic and topologicalinformation. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation,of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels ingreat detail. By employing a suite of GRASS GIS hydrological modules, we calculated the range-wide upstreamflow accumulation and flow direction to delineate a total of 1.6 million drainage basins, and extracted globallya total of 726 million unique stream segments with their corresponding sub-catchments. In addition, we com-puted stream topographic variables comprising stream slope, gradient, length, and curvature attributes, as wellas stream topological variables to allow for network routing and various stream order classifications. We vali-dated the spatial accuracy and flow accumulation of Hydrography90m against NHDPlus HR, an independent,national high-resolution hydrographic network dataset of the United States. Our validation shows that the newlydeveloped Hydrography90m has the highest spatial precision, and contains more headwater stream channelscompared to three other global hydrographic datasets. This inclusive approach provides a vital, and long-overduebaseline for assessing actual streamflow in headwaters, and opens new research avenues for high-resolution stud-ies of surface water worldwide. Hydrography90m thus offers significant potential to facilitate the assessment offreshwater quantity and quality, inundation risk, biodiversity and conservation, as well as resource managementobjectives in a globally comprehensive and standardised manner. We provide all the computed layers for visual-isation and download in 20° × 20° tiles at https://public.igb-berlin.de/index.php/s/od7neyLcYgi5qRp. While the entire dataset can be used directly in standard GIS applications, we recommend its seamless integration with hydrological modules in open-source QGIS and GRASS GIS software to further customise the data and derive optimal utility from it.
... Although MERIT-DEM and MERIT-Hydro are quite recent, they have been used in a large number of recent studies (see, e.g. Lin et al. , 2019;Moudrý al. , 2018;Shin et al. , 2019Shin et al. , , 2020Wing et al. , 2020), generally showing the added value of these datasets. ...
... Although MERIT-DEM and MERIT-Hydro are quite recent, they have been used in a large number of recent studies (see, e.g. Lin et al. , 2019;Moudrý al. , 2018;Shin et al. , 2019Shin et al. , , 2020Wing et al. , 2020), generally showing the added value of these datasets. ...
Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data. This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.
... Following previous work (Rossetti and Valeriano, 2007;Bernal et al., 2011), we combine Google Earth, Landsat visual imagery, and bare-earth topography to identify 65 abandoned channels on South American megafans. For elevation data, we use the BEST (Bare-Earth Srtm Terrain) elevation model, which uses vegetation maps and satellite lidar to reveal bare-earth topography by correcting for vegetation elevations present in radar-derived topography (O'Loughlin et al., 2016;Moudrý et al., 2018). On top of each megafan, we overlaid a rasterized grid with square cells with dimensions that corresponded to roughly five channel widths, similar to the resolution of the cellular model that is described later. ...
River avulsions are an important mechanism by which sediment is routed and emplaced in foreland basins. However, because avulsions occur infrequently, we lack observational data that might inform where, when, and why avulsions occur and these questions are instead often investigated by rule-based numerical models. These models have historically simplified or neglected the effects of abandoned channels on avulsion dynamics, even though fluvial megafans in foreland basins are characteristically covered in abandoned channels. Here, we investigate the pervasiveness of abandoned channels on modern fluvial megafan surfaces. Then, we present a physically based cellular model that parameterizes interactions between a single avulsing river and abandoned channels in a foreland basin setting. We investigate how abandoned channels affect avulsion set-up, pathfinding, and landscape evolution. We demonstrate and discuss how the processes of abandoned channel inheritance and transient knickpoint propagation post-avulsion serve to shortcut the time necessary to set-up successive avulsions. Then, we address the idea that abandoned channels can both repel and attract future pathfinding flows under different conditions. By measuring the distance between the mountain-front and each avulsion over long (106 to 107 years) timescales, we show that increasing abandoned channel repulsion serves to push avulsions farther from the mountain-front, while increasing attraction pulls avulsions proximally. Abandoned channels do not persist forever, and we test possible channel healing scenarios (deposition-only, erosion-only, and far-field directed) and show that only the final scenario achieves dynamic equilibrium without completely filling accommodation space. We also observe megafan growth occurring via ~O:105 year lobe switching, but only in our runs that employ deposition-only or erosion-only healing modes. Finally, we highlight opportunities for future field work and remote sensing efforts to inform our understanding of the role that floodplain topography, including abandoned channels, plays on avulsion dynamics.
... Interpolation of tree offsets is multiplied by smoothed tree cover maps. It aims to produce estimates of tree offset that can be deducted from DSM to produce DEM of vacant land (Moudrý et al., 2018). Estimates of tree offsets depend on accurately identifying object heights in topography that are not covered by the tree. ...
The latest Digital Terrain Model (DTM) is seen as an upgradable DTM that is fitted to the latest combination of DTM master and its displacement. The latest DTM can be used to overcome the problem of static DTM weaknesses in displaying the latest topographic changes. DTM masters are obtained from InSAR and Digital Surface Model (DSM) ALOS PALSAR conversions. Meanwhile, the displacement is obtained from Sentinel-1 images, which can be updated every 6–12 days or at least every month. ALOS PALSAR data were the images acquired in 2008 and 2017, while Sentinel-1 data used were images acquired in 2018 and 2020. This study aims to reveal the importance of an upgradable DTM so called latest DTM which is combination of DTM master and its displacement in order to show the latest condition of study area. The case study is the dynamics analyze of the Semangko fault specifically in the Sianok and Sumani segments situated in Indonesia. The vertical accuracy assessment was done to evaluate the DSM to DTM conversion with a tolerance of 1.96σ. The result obtained is the latest DTM. It is derived from the integration of the DTM master with displacement. The latest DTM can be used to detect the dynamics of Semangko fault. The study area has vertical deformation at a value of –50 cm to 30 cm. The Semangko fault area is dominated by –25 to 5 cm deformation. In general, this region has decreased. The decline in this region ranges from 7.5 cm to 10 cm per year. The latest DTM vertical accuracy is 2.158 m (95% confidence level) with a scale of 1: 10,000 to 1: 20,000.
... Digital elevation models are fundamental to many applications in Earth sciences such as geomorphology, geology, ecology, and engineering [67][68][69]. Hence, the accuracy of DEM-derived elements must be deemed to reduce the inherent errors. ...
The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.
... Three study areas located in mountain ranges were selected, with the availability of highly accurate airborne laser scanning data for subsequent accuracy assessment being an additional selection criterion. We used a DSM as a reference as it is well-recognized that DEMs derived from SAR height measurement include trees, buildings, and other aboveground structures, thus not representing the bare ground elevation in vegetated and built-up areas [36,37]. However, we used digital terrain models (DTMs) to derive the slope and the aspect of the terrain. ...
Several global digital elevation models (DEMs) have been developed in the last two decades.
The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original
version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such
as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing
is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved
edited version, the Copernicus DEM, in three major European mountain ranges (the Alps,
the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning
data as a reference. In addition, to evaluate the applicability of data acquisition characteristics
(coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect,
altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X
DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically
found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The
editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE
for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from
16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively.
The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees
showed that acquisition characteristics provided as auxiliary data are useful for locating problematic
sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error
was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM
were filled using DEMs from different time periods and, hence, users performing multitemporal
analysis or requiring data from a specific time period in the mountain environment should be wary
when using TanDEM-X and its derivations. We suggest that when filling problematic sites using
alternative DEMs, more attention should be paid to the period of their collection to minimize the
temporal displacement in the final products.
... Although the remote-sensing-based variables used in this study cover the continental/global extent, their artefacts and moderate spatial resolution are two disadvantages for discriminating grassland habitats. Hence, spectral variables (i.e., monthly synthesis MOD13Q1) may contain aberrant values caused by haze or shadows (Didan, 2015), soil variables inevitably contain uncertainties because they are generated using machine-learning models (Hengl et al., 2017), and topographic variables from digital elevation models (DEMs) derived from optical and synthetic aperture radar (SAR) remote-sensing data may be biased in wooded or steep-slope areas (Moudrý et al., 2018). In addition, these variables were re-sampled at 250 m spatial resolution to be consistent with the research on MODIS-based grassland mapping (Hubert-Moy et al., 2019). ...
Grassland habitats provide many ecosystem services but are threatened by agricultural intensification and urbanization. While the lack of accurate and comprehensive inventories at the national scale makes them difficult to manage, advances in spatial modeling using open remote sensing data and open-source software, as well as the increasing use of ecological archives, provide new perspectives for mapping natural habitats. In this context, this study evaluated the contribution of spectral and environmental variables to discriminate and then map grassland habitats throughout France. To this end, 92 spectral variables derived from moderate-resolution imaging spectroradiometer data, 19 bioclimatic variables derived from WorldClim data, 4 topographic variables derived from the European Union Digital Elevation Model (DEM),
and 8 soil variables derived from SoilGrids data were combined at a spatial resolution of 250 m. Reference plots that characterized 6 and 21 grassland ecosystems at European Nature Information System (EUNIS) levels 2 (broad habitats) and 3 (habitats), respectively, were collected from vegetation archives. We first performed descriptive analysis that included habitat description, ordination, and pairwise separability. We then performed predictive analysis of grassland habitats using a cross-validated random forest model that included a spatial constraint. While environmental and spectral variables characterized most grassland habitats well and consistently, some confusion occurred between habitats with similar abiotic conditions. The main grassland habitat types were correctly mapped at EUNIS level 2 (F1 score 0.68), but not at EUNIS level 3 (F1 score 0.52). In addition, the two variables that contributed most to the model were the near-infrared spectral band in spring and the minimum temperature of the coldest month. The model’s prediction at EUNIS level 2 for mainland France provides the map of grassland habitats at a new spatial scale.
... Finescale environmental data are not readily available, but an alternative approach infers environmental conditions using topographically-derived variables from digital elevation models (DEMs; Leempoel et al. 2015;Wang et al. 2016). DEMs are available at finer resolutions than interpolated climate datasets (Leempoel et al. 2015;Kropáček et al. 2018), so they can be used to capture the underlying biophysical processes linked to species occurrence patterns, especially in topographically heterogeneous landscapes (Leempoel et al. 2015(Leempoel et al. , 2018. ...
Understanding species' habitat use is fundamental for conservation and management. However, quantifying habitat use for small cryptic species is limited by imperfect detection during field surveys and the lack of habitat data at meaningful spatial scales. Topographically-derived habitat variables from digital elevation models (DEMs) have the potential to overcome these limitations. Here we used DEM-derived topographic variables as fine-scale proxies for abiotic conditions to study site-occupancy patterns of the berg adder (Bitis atropos), a small-bodied cryptic viper. We carried out seven repeated field surveys across 219 hectares in a mountainous protected area in northeastern South Africa to estimate snake detection probability and occupancy using maximum likelihood methods. Although snakes occurred across a third of the surveyed habitat, they were only detected 40% of the time during the springtime when detection was highest. Results showed that these snakes preferred northwest facing, mid and upper slopes, which are exposed to afternoon sun and presumably higher ambient energy. Our results demonstrate the value of using DEM-derived topographic variables for ecological studies where habitat data are either unavailable or inappropriate, thereby providing valuable insights into habitat use of cryptic and difficult to detect species.
... One of the focuses in this research is on the satellite DEM, which is crucial in many applications of land surface modeling, such as hydrodynamics, flood simulations, volcanology, ecology, and glaciology modeling [4][5][6][7]. Several elevation data on an almost global scale were provided, for example, by GTOPO30, the Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) DEM, and TanDEM-X [8][9][10][11]. ...
The digital elevation model (DEM) is crucial for various applications, such as land manage-ment and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface.However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly,and often confidential. This paper explores a DEM improvement scheme using an artificial neuralnetwork (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). TheANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and thenapplied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-Xdata of the Nice area were used as the input data, while the ground truth observation data of Nicewere used as the target data. The applicability of iTanDEM-X was finally conducted at a differentsite in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean squareerror of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g.,vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the applicationof the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available;for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).
... The literature review indicates that variables derived from RS data can be influenced by the specific characteristics of the sensor, environmental conditions, or data processing. For examples, (i) Lopatin et al. [78] showed that shadows in very high spatial resolution images decrease classification accuracy; (ii) Moudrý et al. [12,113] found that variable topographic quality (e.g., spatial resolution, ability to penetrate vegetation cover, parameters for calculating topographic indices) influenced OCC accuracy greatly; (iii) Randin et al. [26] indicated that spectral values of the thermal bands used to generate bioclimatic variables are also influenced by the land-cover; (iv) Cord et al. [101] and Truong et al. [106] stated that the quality of the LULC variable (e.g., spatial resolution, thematic resolution, map reliability) often influences OCC accuracy and suggested replacing LULC categorical variable with continuous spectral variables. ...
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.
... As well as the point cloud, other outputs, such as orthomosaics, are also widely used [29]. Photogrammetry outcomes are usually evaluated using independent laser scanning or control points (CPs), the position of which is measured by ground surveys, which facilitate the assessment of model deformations [30][31][32]. Correct determination of the elements of internal and external orientation, traditionally performed using GCPs, is crucial for creating an accurate photogrammetric model. At present, the use of UAVs equipped with an onboard global navigation satellite system-real-time kinematic (GNSS RTK) receiver is on the rise. ...
Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.
... The Multi-Error-Removed Improved-Terrain (MERIT) [22,23] global elevation and Climate Central Coastal DEM [24] are recent attempts to improve vertical accuracy (i.e., eliminate vegetation cover), but they come at very different costs and resolutions. The NASADEM [25] is the most recent attempt to integrate all available global elevation data (e.g., GDEM, AW3D30, and ICESat laser altimeter) to improve and fill data voids, however, it is a digital surface model (DSM) product and therefore problematic for coastal SLR and storm surge modeling where tall and dense vegetation persists [26,27]. Some of these new products come with moderate to substantial price tags, which may be difficult for stakeholders in SIDS. ...
The Caribbean is affected by climate change due to an increase in the variability, frequency, and intensity of extreme weather events. When coupled with sea level rise (SLR), poor urban development design, and loss of habitats, severe flooding often impacts the coastal zone. In order to protect citizens and adapt to a changing climate, national and local governments need to investigate their coastal vulnerability and climate change risks. To assess flood and inundation risk, some of the critical data are topography, bathymetry, and socio-economic. We review the datasets available for these parameters in Jamaica (and specifically Old Harbour Bay) and assess their pros and cons in terms of resolution and costs. We then examine how their use can affect the evaluation of the number of people and the value of infrastructure flooded in a typical sea level rise/flooding assessment. We find that there can be more than a three-fold difference in the estimate of people and property flooded under 3m SLR. We present an inventory of available environmental and economic datasets for modeling storm surge/SLR impacts and ecosystem-based coastal protection benefits at varying scales. We emphasize the importance of the careful selection of the appropriately scaled data for use in models that will inform climate adaptation planning, especially when considering sea level rise, in the coastal zone. Without a proper understanding of data needs and limitations, project developers and decision-makers overvalue investments in adaptation science which do not necessarily translate into effective adaptation implementation. Applying these datasets to estimate sea level rise and storm surge in an adaptation project in Jamaica, we found that less costly and lower resolution data and models provide up to three times lower coastal risk estimates than more expensive data and models, indicating that investments in better resolution digital elevation mapping (DEM) data are needed for targeted local-level decisions. However, we also identify that, with this general rule of thumb in mind, cost-effective, national data can be used by planners in the absence of high-resolution data to support adaptation action planning, possibly saving critical climate adaptation budgets for project implementation.
... The Shuttle Radar Topography Mission (SRTM) DEM (1 arc second and 3 arc second) and TanDEM-X (3 arc second) acquired Earth quantitative model from InSAR technique and the Advanced Space Borne Thermal Emission and Reflection Radiometer (ASTER)-Global DEM (1 arc second), ALOS world-3D (1 arc second), CARTOSAT-1 an Indian national DEM (1 arc second) are acquired by the satellite stereo images. These DEMs are widely used in hydrology (Buakhao and Kangrang 2016;Pakoksung and Takagi 2020), geomorphology (Szypuła 2019;Garcia and Grohmann 2019), ecology (Moudrỳ et al. 2018), flood simulations (Sanyal and Lu 2004;Azizian and Brocca 2020;Muhadi et al. 2020), forestry (Akay et al. 2009), and volcanology (Favalli and Fornaciai 2017). However, the error generated due to the type of equipment, data acquisition method, the instrument's capability, resolution, and processing techniques, and the terrain type of the area limits their application (Li 1992;Gong et al. 2000;Chaplot et al. 2006;Fisher and Tate 2006). ...
The selection of suitable DEM from available open-source DEMs like SRTM, ALOS World 3D, CARTOSAT-1, ASTER-GDEM, TanDEM-X which are acquired through different techniques is difficult without prior guidelines, especially on the rugged mountainous terrain. Therefore, this article aimed to evaluate the role of land cover and altitude on the vertical accuracy of open-source DEMs with near to ground measurements taken by Ice Cloud and Land Elevation (ICESat) Geoscience Laser Altimetry System (GLAS) in and around Western Ghats (WG) of India. The SRTM (30 m) DEM outperformed other DEMs at the scale of WG and in the dense vegetation cover with least performance by ASTER DEM (30 m). The vertical accuracy of DEM is varying with different elevation ranges and land cover conditions and is found to be better than the vertical accuracy specified by the mission. The overestimation of elevation in low terrain relief area, and underestimation on higher elevation with steep terrain is substantive in all the DEMs. The role of land cover and altitude is significant on the elevation and slope more than the aspect and roughness. Good performance by 90-m resolution DEM over 30-m resolution DEMs proves the potential of InSAR in elevation measurement in vegetated areas with low cost and high accuracy. These results help in the selection of pertinent DEM for any geo-climatical applications and in development of merged DEM based on the terrain relief and land cover of the region.
... Besides, this area is also located close to Palangkaraya City, the capital of Central Kalimantan Province [20]. The dynamics in the form of land subsidence and uplift, which often change every period of the rainy and dry season, causes difficulties in monitoring [21], [22]. Also, in this area, there is a deep peat dome. ...
DEM is needed for monitoring peatland dynamics. Currently, available free DEMs have low vertical accuracy and are not up to date. Commercial DEMs have high resolution but is expensive. DEM Pleiades is an example of a commercial DEM. One solution to overcome this problem is to use the latest DTM, which has the advantage of being up to date. This study aims to compare the vertical accuracy of the latest DTM with DEM Pleiades on peatlands. The study area is located on the peatlands of the "Palangkaraya-Pulang Pisau" border. This region has relatively flat topography. The latest DTM is extracted from a combination of InSAR ALOS PALSAR/PALSAR-2 and DInSAR Sentinel. The latest DTM is the integration of the DTM master with the latest displacement. The vertical accuracy of the latest DTM needs to be tested on the DEM Pleiades data with a spatial resolution of 0.5 m and field measurement data using GNSS. DEM Pleiades, the latest DTM, and field measurements using the EGM 2008 for the height reference field. The height data on the DEM Pleiades and the latest DTM were extracted and adjusted for 15 field measurement points. The result obtained is the mean height differences between DEM Pleiades and the latest DTM which is ammounting 0.923 m. The mean height differences between DEM Pleaides and field measurements is 0.557 m. The mean height differences between the latest DTM and field measurements is 0.705 m. Furthermore, a longitudinal profile is made according to 15 field measurement points on the DEM Pleiades and the latest DTM. The results obtained are that DEM Pleiades still has more height errors than the latest DTM. The latest DTM can be an alternative to DEM Pleaides for peatlands mapping with relatively flat topography.
... Recently, new freely available global-scale DEM products have been released, such as the TanDEM-X DEM [15], the Multi-Error-Removed Improved-Terrain (MERIT) DEM [21], the ALOS Global Digital Surface Model (AW3D30) [22], and the NASADEM [23]. Because these products are so new (TanDEM-X has been freely available since late 2018, MERIT 2017, AW3D30 and NASADEM since the beginning of 2020), fewer studies have evaluated their accuracy [24][25][26][27][28]. If their accuracy can be confirmed, these new DEM products, which are based on more current remote sensing data and better processing methods, will be highly valuable because of their ability to capture natural or man-made changes in the topography of terrestrial surfaces [29]. ...
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.
... Several authors focused on the accuracy assessment of DEMs, e.g. [23][24][25][26][27][28]. The paper [29] evaluated the slope estimates based on the SRTM DEM against those estimated by interpolating topographic contours. ...
Abstract: Various Global Digital Elevation Models (DEM) are available freely on the Web. The
main objective of this work is to evaluate the latest digital elevation models towards the estimation of
morphological and topographic erosion parameters in the Wadi M’Goun watershed. We have
evaluated multiple DEMs: SRTM (3-arcsec resolution, 90 m), ASTER GDEM (1-arcsec resolution,
30 m), SRTMGL1 V003 (30 m), and ALOS-PALSAR (12,5 m). We have applied for this purpose
Open source GIS software. To compare and evaluate each DEM, different processing methods have
been applied to estimate the Wadi M’Goun watershed characteristics, namely Hypsometry,
topographic slope extraction, retrieval of Slope Length and Steepness factor (LS-factor) and
topographic wetness index. The accuracy of the ALOS-PALSAR and SRTMGL1 V003 (30 m)
DEMs met the requirements applying to the required morphometric parameters. DEMs vertical
accuracy has been evaluated by applying the root mean square error (RMSE) metric to DEM
elevations vs. actual heights of 353 sample points extracted from an accurate survey-based map
(toposheet). The RMSE was 1718 mm for ALOS-PALSAR, 1736 for SRTM 1-arcsec, 1958 for
ASTER GDEM 1-arcsec and, 3189 for SRTM 3-arcsec. These results indicate that best accuracy is
achieved with the high-resolution of the ALOS PALSAR DEM. This study suggests potential
uncertainties in the open-source DEMs, which should be taken into account when estimating
topographical and morphometric parameters related to erosion risk in the Wadi M’Goun watershed.
... The resulting MERIT-DEM product covers the land area between 90°N and 60°S at a spatial resolution of approximately ~90 m at the equator in the Plate Carrée projection on a WGS84 Ellipsoid, and is considered to be the optimal best-effort DEM that is currently available as free and open data on a global scale 19,20 . One of the unique aspects of our study is the reprojection of MERIT-DEM to Equi7 (based on a bilinear interpolation method and the projection parameters previously outlined), and the associated computation of geomorphometric variables with minimal distortions. ...
Topographical relief comprises the vertical and horizontal variations of the Earth’s terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.
Point clouds are now a standard way of describing objects in many engineering disciplines, whether they are man-made objects such as structures, buildings, or various types of structures. Commonly used methods of acquiring such data include ground, UAV, or even aerial photogrammetry, followed by terrestrial, UAV, and aerial scanning. After measurement (by the scanner) or calculation (from photogrammetry), the point cloud goes through extensive processing that essentially transforms the unordered mass of points into a usable data set. One of the important steps is removing points representing obstructing objects and features, including vegetation in particular. Here, many filtering methods based on different principles are available and suitable for application to different scenes. This paper presents a new method of filtering point clouds based on the visible spectrum color principle using vegetation indexes determined from RGB system colors only. Since each sensor has to some extent, an individual interpretation of the colors, it cannot be assumed to determine specific boundaries of what is and is no longer vegetation. Therefore, it was proposed to use means clustering to simplify the operator's work. The method was also designed in such a way that the entire evaluation could be implemented in the freely available CloudCompare software. The procedure was tested on three different sites with different terrain and vegetation characteristics showing, which demonstrated the applicability of this method to data where the color information (green) uniquely identifies vegetation. The selected vegetation filters ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily between vegetation and the rest of the point cloud without compromising the quality of the result. The method is practically implementable using the freely downloadable and usable CloudCompare software.
Accurate observations of river water surface elevation (WSE) are critical for hydrodynamic (HD) model calibration in poorly gauged regions. This study presents a new river WSE approach that combines ICESat-2 derived cross-sections and Sentinel-2 observed sub-pixel river widths to achieve an effective HD model calibration. The unknown channel Manning coefficient, river bed elevation and cross-sectional shape in the HD model are calibrated simultaneously through a Bayesian optimization algorithm. The complete workflow is implemented in a flood-prone reach in Yiluo River basin. Results show that the WSE retrieved by the new approach have a higher spatiotemporal resolution in narrower rivers than that of Sentinel-3 altimetry derived WSE, and the root-mean-square error (RMSE) varies from 0.25 to 0.59 m against in-situ WSE. The HD model calibrated by WSE retrieved by the new approach simulates the WSE with RMSE of 0.36 m and the flood extents with critical success index (CSI) of 0.60 in validation, outperforming the results of the HD model calibrated by Sentinel-3 altimetry derived WSE, with corresponding results of 0.49 m and 0.58, respectively. Additional case studies in the Maqu and Lingkou reaches further demonstrate the applicability of the WSE retrieval approach proposed in this study for HD model calibration in different types of rivers. This study highlights the potential of the proposed WSE retrieval approach in HD model calibration to improve flood forecasting in poorly gauged regions.
In urban areas, topography data without above-ground objects are typically preferred in wide-area flood simulation but are not yet available for many locations globally. High-resolution satellite photogrammetric DEMs, like ArcticDEM, are now emerging and could prove extremely useful for global urban flood modelling; however, approaches to generate bare-earth DEMs from them have not yet been fully investigated. In this paper, we test the use of two morphological filters (simple morphological filter – SMRF – and progressive morphological filter – PMF) to remove surface artefacts from ArcticDEM using the city of Helsinki (192 km2) as a case study. The optimal filter is selected and used to generate a bare-earth version of ArcticDEM. Using a lidar digital terrain model (DTM) as a benchmark, the elevation error and flooding simulation performance for a pluvial scenario were then evaluated at 2 and 10 m spatial resolution, respectively. The SMRF was found to be more effective at removing artefacts than PMF over a broad parameter range. For the optimal ArcticDEM-SMRF the elevation RMSE was reduced by up to 70 % over the uncorrected DEM, achieving a final value of 1.02 m. The simulated water depth error was reduced to 0.3 m, which is comparable to typical model errors using lidar DTM data. This paper indicates that the SMRF can be directly applied to generate a bare-earth version of ArcticDEM in urban environments, although caution should be exercised for areas with densely packed buildings or vegetation. The results imply that where lidar DTMs do not exist, widely available high-resolution satellite photogrammetric DEMs could be used instead.
The geographic distribution of streams and rivers drives a multitude of patterns and processes in hydrology, geomorphology, geography, and ecology. Therefore, a hydrographic network that accurately delineates both small streams and large rivers, along with their topographic and topological properties, with equal precision would be indispensable in the earth sciences. Currently, available global hydrographies do not feature small headwater streams in great detail. However, these headwaters are vital because they are estimated to contribute to more than 70 % of overall stream length. We aimed to fill this gap by using the MERIT Hydro digital elevation model at 3 arcsec (∼90 m at the Equator) to derive a globally seamless, standardised hydrographic network, the “Hydrography90m”, with corresponding stream topographic and topological information. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation, of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels in great detail. By employing a suite of GRASS GIS hydrological modules, we calculated the range-wide upstream flow accumulation and flow direction to delineate a total of 1.6 million drainage basins and extracted globally a total of 726 million unique stream segments with their corresponding sub-catchments. In addition, we computed stream topographic variables comprising stream slope, gradient, length, and curvature attributes as well as stream topological variables to allow for network routing and various stream order classifications. We validated the spatial accuracy and flow accumulation of Hydrography90m against NHDPlus HR, an independent, national high-resolution hydrographic network dataset of the United States. Our validation shows that the newly developed Hydrography90m has the highest spatial precision and contains more headwater stream channels compared to three other global hydrographic datasets. This comprehensive approach provides a vital and long-overdue baseline for assessing actual streamflow in headwaters and opens new research avenues for high-resolution studies of surface water worldwide. Hydrography90m thus offers significant potential to facilitate the assessment of freshwater quantity and quality, inundation risk, biodiversity, conservation, and resource management objectives in a globally comprehensive and standardised manner. The Hydrography90m layers are available at 10.18728/igb-fred-762.1, and while they can be used directly in standard GIS applications, we recommend the seamless integration with hydrological modules in open-source QGIS and GRASS GIS software to further customise the data and derive optimal utility from it.
Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at 1/12∘ derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (1/2∘) and the other with the new spatial resolution (1/12∘). It is shown that, overall, CTRIP at 1/12∘ outperforms CTRIP at 1/2∘, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (10.5281/zenodo.6482906, ), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.
Topography is the most important component of the geographical shell, one of the main elements of geosystems, and the framework of a landscape. geomorphometry is a science, the subject of which is modeling and analyzing the topography and the relationships between topography and other components of geosystems. Currently, the apparatus of geomorphometry is widely used to solve various multi-scale problems of the Earth sciences. As part of the RFBR competition “Expansion”, we present an analytical review of the development of theory, methods, and applications of geomorphometry for the period of 2016–2021. For the analysis, we used a sample of 485 of the strongest and most original papers published in international journals belonging to the JCR Web of Science Core Collection quartile I and II (Q1–Q2), as well as monographs from leading international publishers. We analyze factors caused a progress in geomorphometry in recent years. These include widespread use of unmanned aerial survey and digital photogrammetry, development of tools and methods for survey of submarine topography, emergence of new publicly available digital elevation models (DEMs), development of new methods of DEM preprocessing for their filtering and noise suppression, development of methods of two-dimensional and three-dimensional visualization of DEMs, introduction of machine learning techniques, etc. We consider some aspects of the geomorphometric theory developed in 2016–2021. We discuss new computational methods for calculating morphometric models from DEM, as well as the problems facing the developers and users of such methods. We consider application of geomorphometry for solving multiscale problems of geomorphology, hydrology, soil science, geology, glaciology, speleology, plant science and forestry, zoogeography, oceanology, planetology, landslide studies, remote sensing, urban studies, and archaeology.
Photogrammetric models constructed from unmanned aerial vehicle (UAV) data are nowadays used for determining areas or volumes in many scientific and practical applications. The presented study discusses the possibility of direct georeferencing of photogrammetricmetric models based on onboard navigational GNSS. It is widely expected that all UAV-derived data need to be georeferenced with a sub-decimeter accuracy. The aim of the work was to test whether it would not be enough to georeference photogrammetric models only using navigation GNSS. Imagery from several locations was used for the construction of photogrammetric models using GCPs or navigational GNSS only. All models were transformed into the same coordinate system using the same transformation key to facilitate comparisons. For the first comparisons, a 7-element transformation was performed to facilitate analysis of the systematic shift, rotation, and scale change between the models. It confirmed that the scale change of the photogrammetric model constructed using navigation GNSS data only from that constructed using GCPs was very small, with a maximum change of 2% (scale change of 0.98). The 7-element transformation also revealed that the models are mutually shifted and rotated. The tilts were below 2°, and the horizontal shifts are consistent with the (in)accuracy of the navigational GNSS (the highest deviation was 3.7m). The data were also transformed using a 4-element transformation (shifted and rotated along the Z-axis) to enable an analysis of the change of the shape. Subsequently, areas and volumes of both point clouds were calculated; the differences in volumes were below 10%, differences in areas even below 4%. For analysis of local deformations on an extensive site, an airfield area was used. The analysis of distances measured on an orthophotomosaic of a relatively large area (9.5 km2) revealed a mean absolute deviation between the GNSS and GCP data, which is a surprisingly good result. This method is suitable for identifying potential local deformations that can occur in photogrammetric models in the spaces between GCPs. The reported experiments demonstrate that the calculations of volumes and areas using only onboard UAV GNSS navigation data can be sufficient for many applications.
The objective of this research is to develop an approach to correct nonlinear errors in the SRTM (Shuttle Radar Topography Mission) elevations, which cannot be handled by most traditional methods. First, a set of uncorrelated feature attributes has been generated from the SRTM digital elevation model (DEM) together with the new freely available Sentinel-2 multispectral imagery, over a dense urban area in Egypt. Second, the SRTM DEM, Sentinel-2 image, and the generated attributes have been applied as input data in an artificial neural network (ANN) classification model to assign each pixel to each of 12 reference elevations. Finally, the posterior probabilities obtained for ANN have been combined based on an inverse probability weighted interpolation (IPWI) approach to estimate revised SRTM elevations. The results were compared with a reference DEM with 1-m vertical accuracy derived through image matching of the Worldview-1 stereo satellite imagery. The process of performance evaluation is based on various statistics such as scatter plots, correlation coefficient (R), standard deviation (SD), and root mean square error (RMSE). The results show that, using the SRTM DEM as a single data source, the RMSE of estimated elevations has improved to 3.04 m. On the other hand, including the Sentinel-2 image has improved the RMSE of elevations to 2.93 m. Including the generated attributes as well has improved the estimated RMSE of the elevations to 2.07 m. Compared with the results from the commonly used multiple linear regression (MLR) method, the improvement in RMSE of the estimated elevations can reach 45%.
O relevo é um dos principais fatores que controlam os processos que ocorrem na superfície terrestre. Dessa forma, a caracterização geomorfométrica é um elemento básico para o planejamento e gestão de bacias hidrográficas. Considerando que a resolução da grade do modelo altimétrico influencia as características topográficas extraídas, este trabalho teve como objetivo quantificar atributos geomorfométricos para a bacia hidrográfica do córrego Guariroba a partir de Modelos Digitais de Superfície (MDSs) baseados em imagens de radar – SRTM e Alos Palsar – e Modelo Digital de Elevação (MDE) gerado por aerofotogrametria, com tamanhos de grade de 30, 12,5 e 5 m, respectivamente, visando demonstrar o efeito da resolução espacial da grade nos parâmetros derivados, bem como subsidiar a escolha do modelo altimétrico apropriado para orientar o planejamento e o gerenciamento dos recursos solo e água nesta bacia hidrográfica. Os parâmetros geomorfométricos foram avaliados sob três perspectivas: rede de drenagem, geometria e características de relevo. A rede de drenagem é do tipo dendrítica; a hierarquia fluvial é de quinta ordem; a forma da bacia é menos alongada; o canal principal tende a ser retilíneo com alta influência da carga sedimentar; e a área possui baixo relevo e declives e é dissecada moderadamente. As bases de menor resolução espacial (MDS 30 e 12,5m) subestimaram as áreas planas (27,0 e 16,1%) e com relevo suave ondulado (4,4 e 29,2%); e superestimaram a área com relevo ondulado (34,9 e 70,9%) e com declive superior a 5% (29,3 e 77,0%), respectivamente. O modelo altimétrico que permitiu melhor delineamento dos parâmetros geomorfométricos foi o gerado a partir do levantamento aerofotogramétrico. Implications of the resolution and source of altimetric models in the quantitative analysis of geomorphometric attributes for the Guariroba watershed, Campo Grande, MS A B S T R A C TRelief is one of the main factors that control the processes that occur on the Earth's surface. Thus, geomorphometric characterization is a basic element for the planning and management of watersheds. The objective of this study was to investigate the impact of Digital Surface Models (DSMs) and Digital Elevation Model (DEM) resolution on the extraction and quantification of geomorphometric attributes for the Guariroba watershed. Elevation data base generated by radar (Alos Palsar and SRTM) and photogrammetry techniques with 30, 12.5 and 5m grid size, were used. The geomorphometric attributes were evaluated from three perspectives: drainage network, geometry, and relief characteristics. The drainage network is of the dendritic type; the river hierarchy, of fifth order; the shape of the basin, less elongated; the main channel, rectilinear with high influence of sedimentary load; and the area, dissected moderately with low relief and slopes. The grids with the lowest spatial resolution (DSM 30 and 12.5m) underestimated the flat areas (27.0 and 16.1%) and those with soft wavy relief (4.4 and 29.2%); and overestimated the areas with wavy relief (34.9 and 70.9%) and those with a slope of more than 5% (29.3 and 77.0%), respectively. The altimetric model which allowed a better determination of the geomorphometric attributes was generated from the aerophotogrammetric survey. The results constitute a baseline database to aid in planning and management of soil and water resources in this watershed.Keywords: Grid size, GIS, Soil and water conservation, Environmental planning.
O relevo é um dos principais fatores que controlam os processos que ocorrem na superfície terrestre. Dessa forma, a caracterização geomorfométrica é um elemento básico para o planejamento e gestão de bacias hidrográficas. O objetivo deste estudo foi investigar o impacto da resolução de Modelos Digitais de Superfície (MDSs) e elevação (MDE) na extração e quantificação de atributos geomorfométricos para a bacia hidrográfica do córrego Guariroba. Os dados base utilizados foram gerados por radar (Alos Palsar e SRTM) e técnicas aerofotogramétricas, com tamanhos de grade de 30, 12,5 e 5m. Os atributos geomorfométricos foram avaliados sob três perspectivas: rede de drenagem, geometria e características de relevo. A rede de drenagem é do tipo dendrítica; a hierarquia fluvial é de quinta ordem; a forma da bacia é menos alongada; o canal principal tende a ser retilíneo com alta influência da carga sedimentar; e a área possui baixo relevo e declives e é dissecada moderadamente. As bases de menor resolução espacial (MDS 30 e 12,5m) subestimaram as áreas planas (27,0 e 16,1%) e com relevo suave ondulado (4,4 e 29,2%); e superestimaram a área com relevo ondulado (34,9 e 70,9%) e com declive superior a 5% (29,3 e 77,0%), respectivamente. O modelo altimétrico que permitiu melhor delineamento dos atributos geomorfométricos foi o gerado a partir do levantamento aerofotogramétrico. Os resultados obtidos constituem um banco de dados de avaliação inicial para auxiliar o planejamento e o gerenciamento dos recursos solo e água nesta bacia hidrográfica.
Palavras-chave: Tamanho da grade, SIG, Conservação do solo e da água, Planejamento ambiental.
Implications of the resolution and source of altimetric models in the quantitative analysis of geomorphometric attributes for the Guariroba watershed, Campo Grande, MS
A B S T R A C T
Relief is one of the main factors that control the processes that occur on the Earth's surface. Thus, geomorphometric characterization is a basic element for the planning and management of watersheds. The objective of this study was to investigate the impact of Digital Surface Models (DSMs) and Digital Elevation Model (DEM) resolution on the extraction and quantification of geomorphometric attributes for the Guariroba watershed. Elevation data base generated by radar (Alos Palsar and SRTM) and photogrammetry techniques with 30, 12.5 and 5m grid size, were used. The geomorphometric attributes were evaluated from three perspectives: drainage network, geometry, and relief characteristics. The drainage network is of the dendritic type; the river hierarchy, of fifth order; the shape of the basin, less elongated; the main channel, rectilinear with high influence of sedimentary load; and the area, dissected moderately with low relief and slopes. The grids with the lowest spatial resolution (DSM 30 and 12.5m) underestimated the flat areas (27.0 and 16.1%) and those with soft wavy relief (4.4 and 29.2%); and overestimated the areas with wavy relief (34.9 and 70.9%) and those with a slope of more than 5% (29.3 and 77.0%), respectively. The altimetric model which allowed a better determination of the geomorphometric attributes was generated from the aerophotogrammetric survey. The results constitute a baseline database to aid in planning and management of soil and water resources in this watershed.
Significant inter-country transfers of ecological footprint are embodied in foreign trade. Previous research on these transfers mainly focused on historical characteristics of the flows based on historical data. However, the transfers of ecological footprints in foreign trade are dynamic and are influenced by environmental tax policies, whose future impacts have seldom been considered. In the present study, we explored the effects of an energy tax, a common form of environmental tax, on these transfers. We combined a multi-sector dynamic computable general equilibrium model with ecological footprint assessment, and analyzed the changes in the actual and virtual land uses that comprise the ecological footprint under different tax intensities. We used China as a case study to analyze the energy tax's effects. For the actual land use component of the ecological footprint, the energy tax increased the export of ecological footprint but decreased its import, and China's average net exports (exports minus imports) decreased by 4.56 × 10⁶ gha (global hectares) and 10.81 × 10⁶ gha from 2015 to 2020 under the low and high tax intensities, respectively. For the virtual land use component of the ecological footprint, the energy tax simultaneously decreased export and import of this footprint component; the average net import would increase by 3.84 × 10⁶ gha and 8.91 × 10⁶ gha. For the total ecological footprint, the energy tax also simultaneously reduced export and import, with the net import decreasing by 1.76 × 10⁶ gha and 4.45 × 10⁶ gha under the low and high taxes, respectively.
Study region
Thirlmere Lakes National Park, New South Wales, Australia
Study focus
Uncertainties in water balance calculations can arise from errors associated with each of the budget input terms: precipitation, evapotranspiration, and inflows/outflows. However, uncertainties associated with the accuracy of the surface storage calculation have seldom been the focus of previous water balance studies. Digital elevation models (DEMs) used in water balance studies typically rely on bathymetric/topographic surveys, with remote-sensing techniques including satellite imaging processing, Light-Detection-and-Ranging (LiDAR), and unmanned-aerial-vehicle photogrammetry. This study investigates the vertical errors in bathymetric DEMs obtained from various remote-sensing techniques and its implication on water balance estimates in an intermittent wetland under drying conditions with vegetation encroachment.
New hydrological insights
When bathymetry data obtained from different remote-sensing survey methods were adopted to calculate the water balance of a lake, variations in the model-predicted levels were attributed to the poor quality of photogrammetric DEMs. To improve the photogrammetric data, a new ground-filtering approach is developed, which reduces vertical errors induced by vegetation interference. The correlation (R²) of the DEMs, as compared to ground-truthed elevations, was improved from 0.5 before ground filtering to 0.9 after ground filtering. Using the ground-filtered DEM in the water balance calculation, a 70 % improvement was achieved in the water balance residuals. As such, uncertainties in lake and wetland bathymetry should be assessed in future water balance studies.
Remote sensing techniques are currently the main methods providing elevationdata used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope,orientation, rugosity) derived from DTMs are commonly used as surrogates of spe-cies or habitat distribution in ecological studies. While DTMs’ errors are known topropagate to terrain attributes, their impact on ecological analyses is howeverrarely documented. This study assessed the impact of data acquisition artefacts onhabitat maps and species distribution models. DTMs of German Bank (off NovaScotia, Canada) at five different spatial scales were altered to artificially introducedifferent levels of common data acquisition artefacts. These data were used in 615unsupervised classifications to map potential habitat types based on biophysicalcharacteristics of the area, and in 615 supervised classifications (MaxEnt) to predictsea scallop distribution across the area. Differences between maps and models builtfrom altered data and reference maps and models were assessed. Roll artefactsdecreased map accuracy (up to 14% lower) and artificially increased models’ per-formances. Impacts from other types of artefacts were not consistent, eitherdecreasing or increasing accuracy and performance measures. The spatial distribu-tion of habitats and spatial predictions of sea scallop distributions were alwaysaffected by data quality (i.e. artefacts), spatial scale of the data, and the selection ofvariables used in the classifications. This research demonstrates the importance ofthese three factors in building a study design, and highlights the need for errorquantification protocols that can assist when maps and models are used in deci-sion-making, for instance in conservation and management.
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.
Data acquisition artefacts are commonly found in multibeam bathymetric data, but their effects on mapping methodologies using geographic information system techniques have not been widely explored. Artefacts have been extensively studied in terrestrial settings, but their study in a marine context has currently been limited to engineering and surveying technology development in order to reduce their amplitude during data collection and postprocessing. Knowledge on how they propagate to further analyses like environmental characterization or terrain analysis is scant. The goal of this paper is to describe the contribution of different types of artefacts to marine terrain attributes at multiple scales. Using multibeam bathymetric data from German Bank, off Nova Scotia (Canada), digital bathymetric models (DBMs) were computed at five different spatial resolutions. Ten different amplitudes of heave, pitch, roll, and time artefacts were artificially introduced to generate altered DBMs. Then, six terrain attributes were derived from each of the reference and altered DBMs. Relationships between the amplitude of artefacts and the statistical and spatial distributions of: 1) altered bathymetry and terrain attributes surfaces and 2) errors caused by the artefacts were modeled. Spatial similarity between altered and reference surfaces was also assessed. Results indicate that most artefacts impact spatial similarity and that pitch and roll significantly impact the statistical distribution of DBMs and terrain attributes while time and heave artefacts have a more subtle impact. Results also confirm the relationship between spatial data quality and spatial scale, as finer-scale data were impacted by artefacts to a greater degree than broader-scale data.
Aim
Species distributions along an environmental gradient are often not symmetric but skewed towards one end of the gradient. Various explanations for this skewness have been proposed but the patterns of niche asymmetry along extensive environmental gradients have been rarely explored. In this study, we tested three predictions of asymmetric abiotic stress limitation (AASL) hypothesis that predicts a steeper decrease in the probability of occurrence towards the more stressful end of a plant distributional range.
Location
Ladakh, arid Himalayas, where drought stress dominates in the lower elevation, whilst the cold stress dominates in the upper elevations.
Methods
Using data from 4062 plots (2640–6150 m a.s.l.), we explored the shapes of response curves of 395 vascular plant species through Huisman‐Olff‐Fresco models. We compared the observed patterns of niche asymmetry along the elevational gradient with null models.
Results
Species with symmetric response curves (61.5%) prevailed at lower elevations, whilst species with left‐skewed responses (36.2%) were significantly underrepresented up to 3750 m a.s.l. and occurred significantly more frequently at 5150–5450 m a.s.l. Right‐skewed responses were rare (2.3%) along the whole gradient. The steepness of the response increased with elevation. Response types were found in similar proportions across different habitats and functional groups.
Main conclusions
Our results support the predictions of AASL hypothesis for cold limits, but not for dry limits. The low proportion of right‐skewed responses over the entire gradient suggests an effective adaptation of the local flora to arid conditions, or sufficient opportunity to avoid drought stress through the presence of favourable habitat patches. The accumulation of skewed responses at high elevations likely reflects shared physiological limits of many steppe species, whose distribution abruptly ends at the transition between steppe and alpine zones. Cold, therefore, represents a stronger barrier to species distribution than drought.
Remote sensing (RS)—taking images or other measurements of Earth from above—provides a unique perspective on what is happening on the Earth and thus plays a special role in biodiversity and conservation applications. The periodic repeat coverage of satellite-based RS is particularly useful for monitoring change and so is essential for understanding trends, and also provides key input into assessments, international agreements, and conservation management. Historically, RS data have often been expensive and hard to use, but changes over the last decade have resulted in massive amounts of global data being available at no cost, as well as significant (if not yet complete) simplification of access and use. This chapter provides a baseline set of information about using RS for conservation applications in three realms: terrestrial, marine, and freshwater. After a brief overview of the mechanics of RS and how it can be applied, terrestrial systems are discussed, focusing first on ecosystems and then moving on to species and genes. Marine systems are discussed next in the context of habitat extent and condition and including key marine-specific challenges. This is followed by discussion of the special considerations of freshwater habitats such as rivers, focusing on freshwater ecosystems, species, and ecosystem services.
Modelling provides an effective means of integrating the complementary strengths of biodiversity data derived from in situ observation versus remote sensing. The use of modelling in biodiversity change observation, or monitoring, is just one of a number of roles that modelling can play in biodiversity assessment. These roles place different levels of emphasis on explanatory versus predictive modelling, and on modelling across space alone, versus across both space and time, either past-to-present or present-to-future. One of the most challenging, yet vitally important, applications of modelling to biodiversity monitoring involves mapping change in the distribution and retention of terrestrial biodiversity. Unlike many structural and functional attributes of ecosystems, most biological entities at the species and genetic levels of biodiversity cannot be readily detected through remote sensing. Estimating change in these levels of biodiversity across large spatial extents is therefore benefiting from advances in both species-level and community-level approaches to model-based integration of in situ biological observations and remotely sensed environmental data.
Species distribution models (SDMs) are often limited by the use of coarse-resolution environmental variables and by the number of observations required for their calibration. This is particularly true in the case of elusive animals. Here, we developed a SDM by combining three elements: a database of explanatory variables, mapped at a fine resolution; a systematic sampling scheme; and an intensive survey of indirect observations. Using MaxEnt, we developed the SDM for the population of the Asiatic wild ass (Equus hemionus), a rare and elusive species, at three spatial scales: 10, 100, and 1000 m per pixel. We used indirect observations of feces mounds. We constructed 14 layers of explanatory variables, in five categories: water, topography, biotic conditions, climatic variables and anthropogenic variables. Woody vegetation cover and slopes were found to have the strongest effect on the distribution of wild ass and were included as the main predictors in the SDM. Model validation revealed that an intensive survey of feces mounds and high-resolution predictor layers resulted in a highly accurate and informative SDM. Fine-grain (10 and 100 m) SDMs can be utilized to: (1) characterize the variables influencing species distribution at high resolution and local scale, including anthropogenic effects and geomorphologic features; (2) detect potential population activity centers; (3) locate potential corridors of movement and possible isolated habitat patches. Such information may be useful for the conservation efforts of the Asiatic wild ass. This approach could be applied to other elusive species, particularly large mammals.
In this study, we developed a stable and temporally dynamic model for predicting tsetse (Glossina pallidipes) habitat distribution based on a remotely sensed Normalised Difference Vegetation Index (NDVI), an indicator of vegetation greenness, and topographic variables, specifically, elevation and topographic position index (TPI). We also investigated the effect of drainage networks on habitat suitability of tsetse as well as factors that may influence changes in area of suitable tsetse habitat. We used data on tsetse presence collected in North western Zimbabwe during 1998 to develop a habitat prediction model using Maxent (Training AUC = 0.751, test AU = 0.752). Results of the Maxent model showed that the probability of occurrence of tsetse decreased as TPI increased while an increase in elevation beyond 800 m resulted in a decrease in the probability of occurrence. High probabilities (>50%) of occurrence of tsetse were associated with NDVI between high 0.3 and 0.6. Based on the good predictive ability of the model, we fitted this model to environmental data of six different years, 1986, 1991, 1993, 2002, 2007 and 2008 to predict the spatial distribution of tsetse presence in those years and to quantify any trends or changes in the tsetse distribution, which may be a function of changes in suitable tsetse habitat. The results showed that the amount of suitable tsetse habitat significantly decreased (r² 0.799, p = 0.007) for the period 1986 and 2008 due to the changes in the amount of vegetation cover as measured by NDVI over time in years. Using binary logistic regression, the probability of occurrence of suitable tsetse habitat decreased with increased distance from drainage lines. Overall, results of this study suggest that temporal changes in vegetation cover captured by using NDVI can aptly capture variations in habitat suitability of tsetse over time. Thus integration of remotely sensed data and other landscape variables enhances assessment of temporal changes in habitat suitability of tsetse which is crucial in the management and control of tsetse.
The near-global elevation product from NASA’s Shuttle Radar Topographic Mission (SRTM) has been widely used since its release in 2005 at 3 arcsecond resolution and the release of the 1 arcsecond version in late 2014 means that the full potential of the SRTM DEM can now be realised. However the routine use of SRTM for analytical purposes such as catchment hydrology, flood inundation, habitat mapping and soil mapping is still seriously impeded by the presence of artefacts in the data, primarily the offsets due to tree cover and the random noise. This paper describes the algorithms being developed to remove those offsets, based on the methods developed to produce the Australian national elevation model from SRTM data.
The offsets due to trees are estimated using the GlobeLand30 (National Geomatics Center of China) and Global Forest Change (University of Maryland) products derived from Landsat, along with the ALOS PALSAR radar image data (JAXA) and the global forest canopy height map (NASA). The offsets are estimated using several processes and combined to produce a single continuous tree offset layer that is subtracted from the SRTM data.
The DEM products will be made freely available on completion of the first draft product, and the assessment of that product is expected to drive further improvements to the methods.
Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acoustic-based mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.
The near-global elevation product from NASA’s Shuttle Radar Topographic Mission (SRTM) has been widely used since its release in 2005 at 3 arcsecond resolution and the release of the 1 arcsecond version in late 2014 means that the full potential of the SRTM DEM can now be realised. However the routine use of SRTM for analytical purposes such as catchment hydrology, flood inundation, habitat mapping and soil mapping is still seriously impeded by the presence of artefacts in the data, primarily the offsets due to tree cover and the random noise. This paper describes the algorithms being developed to remove those offsets, based on the methods developed to produce the Australian national elevation model from SRTM data.
The offsets due to trees are estimated using the GlobeLand30 (National Geomatics Center of China) and Global Forest Change (University of Maryland) products derived from Landsat, along with the ALOS PALSAR radar image data (JAXA) and the global forest canopy height map (NASA). The offsets are estimated using several processes and combined to produce a single continuous tree offset layer that is subtracted from the SRTM data.
The DEM products will be made freely available on completion of the first draft product, and the assessment of that product is expected to drive further improvements to the methods.
We develop the first global ‘Bare-Earth’ Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) for all landmasses between 60N and 54S. Our new ‘Bare-Earth’ SRTM DEM combines multiple remote sensing datasets, including point-ground elevations from NASA's laser altimeter ICESat, a database of percentage of tree cover from the MODIS satellite as a proxy for penetration depth of SRTM and a global vegetation height map in order to remove the vegetation artefacts present in the original SRTM DEM. We test multiple methods of removing vegetation artefacts and investigate the use of regionalization. Our final ‘Bare-Earth’ SRTM product shows global improvements greater than 10 m in the bias over the original SRTM DEM in vegetated areas compared with ground elevations determined from ICESat data with a significant reduction in the root mean square error from over 14 m to 6 m globally. Therefore, our DEM will be valuable for any global applications, such as large scale flood modelling requiring a ‘Bare-Earth’ DEM.
Understanding the effects of scale is essential to the understanding of natural eco -
systems, particularly in marine environments where sampling is more limited and sporadic than in
terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic
habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to
better characterization of seafloor pattern and process. This review paper highlights the importance
of incorporating ecological scaling and geographical theories in benthic habitat mapping. It
reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial
scale influences patterns and processes in benthic habitats. We address how scale is represented
in geographic data, how it influences their analysis, and consequently how it influences our understanding
of seafloor ecosystems. We conclude that quantification of ecological processes at multiple
scales using spatial statistics is needed to gain a better characterization of species−habitat
relationships. We offer recommendations on more effective practices in benthic habitat mapping,
including sampling that covers multiple spatial scales and that includes as many environmental
variables as possible, adopting continuum-based habitat characterization approaches, using
statistical analyses that consider the spatial nature of data, and explicit statement of the scale at
which the research was conducted. We recommend a set of improved standards for defining
benthic habitat. With these standards benthic habitats can be defined as ‘areas of seabed that are
(geo)statistically significantly different from their surroundings in terms of physical, chemical and
biological characteristics, when observed at particular spatial and temporal scales’.
Abstract. The System for Automated Geoscientific Analyses
(SAGA) is an open source geographic information system
(GIS), mainly licensed under the GNU General Public
License. Since its first release in 2004, SAGA has rapidly
developed from a specialized tool for digital terrain analysis
to a comprehensive and globally established GIS platform
for scientific analysis and modeling. SAGA is coded
in CCC in an object oriented design and runs under several
operating systems including Windows and Linux. Key
functional features of the modular software architecture comprise
an application programming interface for the development
and implementation of new geoscientific methods, a
user friendly graphical user interface with many visualization
options, a command line interpreter, and interfaces to
interpreted languages like R and Python. The current version
2.1.4 offers more than 600 tools, which are implemented in
dynamically loadable libraries or shared objects and represent
the broad scopes of SAGA in numerous fields of geoscientific
endeavor and beyond. In this paper, we inform about
the system’s architecture, functionality, and its current state
of development and implementation. Furthermore, we highlight
the wide spectrum of scientific applications of SAGA in
a review of published studies, with special emphasis on the
core application areas digital terrain analysis, geomorphology,
soil science, climatology and meteorology, as well as
remote sensing.
Digital elevation models ( DEM s) are often used in landscape ecology to retrieve elevation or first derivative terrain attributes such as slope or aspect in the context of species distribution modelling. However, DEM ‐derived variables are scale‐dependent and, given the increasing availability of very high‐resolution ( VHR ) DEM s, their ecological relevance must be assessed for different spatial resolutions.
In a study area located in the Swiss Western Alps, we computed VHR DEM s‐derived variables related to morphometry, hydrology and solar radiation. Based on an original spatial resolution of 0·5 m, we generated DEM ‐derived variables at 1, 2 and 4 m spatial resolutions, applying a Gaussian Pyramid. Their associations with local climatic factors, measured by sensors (direct and ambient air temperature, air humidity and soil moisture) as well as ecological indicators derived from species composition, were assessed with multivariate generalized linear models ( GLM ) and mixed models ( GLMM ).
Specific VHR DEM ‐derived variables showed significant associations with climatic factors. In addition to slope, aspect and curvature, the underused wetness and ruggedness indices modelled measured ambient humidity and soil moisture, respectively. Remarkably, spatial resolution of VHR DEM ‐derived variables had a significant influence on models’ strength, with coefficients of determination decreasing with coarser resolutions or showing a local optimum with a 2 m resolution, depending on the variable considered.
These results support the relevance of using multi‐scale DEM variables to provide surrogates for important climatic variables such as humidity, moisture and temperature, offering suitable alternatives to direct measurements for evolutionary ecology studies at a local scale.
The European Union's Marine Strategy Framework Directive (MSFD) seeks to achieve, for all European seas, " Good Environmental Status " (GEnS), by 2020. Ecological models are currently one of the strongest approaches used to predicting and understanding the consequences of anthropogenic and climate-driven changes in the natural environment. We assess the most commonly used capabilities of the modelling community to provide information about indicators outlined in the MSFD, particularly on biodiversity, food webs, non-indigenous species and seafloor integrity descriptors. We built a catalogue of models and their derived indicators to assess which models were able to demonstrate: (1) the linkages between indicators and ecosystem structure and function and (2) the impact of pressures on ecosystem state through indicators. Our survey identified 44 ecological models being implemented in Europe, with a high prevalence of those that focus on links between hydrodynamics and biogeochemistry, followed by end-to-end, species distribution/habitat suitability, bio-optical (remote sensing) and multispecies models. Approximately 200 indicators could be derived from these models, the majority of which were biomass and physical/hydrological/chemical indicators. Biodiversity and food webs descriptors, with ∼49% and ∼43% respectively, were better addressed in the reviewed modelling approaches than the non-indigenous species (0.3%) and sea floor integrity (∼8%) descriptors. Out of 12 criteria and 21 MSFD indicators relevant to the abovementioned descriptors, currently only three indicators were not addressed by the 44 models reviewed. Modelling approaches showed also the potential to inform on the complex, integra-tive ecosystem dimensions while addressing ecosystem fundamental properties, such as interactions between structural components and ecosystems services provided, despite the fact that they are not part of the MSFD indicators set. The cataloguing of models and their derived indicators presented in this study, aim at helping the planning and integration of policies like the MSFD which require the assessment of all European Seas in relation to their ecosystem status and pressures associated and the establishment of environmental targets (through the use of indicators) to achieve GEnS by 2020.
virtualspecies is a freely available package for R designed to generate virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package combines the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. The package includes (1) generating the probability of occurrence of a virtual species from a spatial set of environmental conditions (i.e., environmental suitability), with two different approaches; (2) converting the environmental suitability into presence-absence with a probabilistic approach; (3) introducing dispersal limitations in the realised virtual species distributions and (4) sampling occurrences with different biases in the sampling procedure. The package was designed to be extremely flexible, to allow users to simulate their own defined species-environment relationships, as well as to provide a fine control over every simulation parameter. The package also includes a function to generate random virtual species distributions. We provide a simple example in this paper showing how increasing ecological realism of the virtual species impacts the predictive performance of species distribution models. We expect that this new package will be valuable to researchers willing to test techniques and protocols of species distribution models as well as various biogeographical hypotheses.This article is protected by copyright. All rights reserved.
The System for Automated Geoscientific Analyses (SAGA) is an open-source Geographic Information System (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular organized software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, an easily approachable graphical user interface with many visualization options, a command line interpreter, and interfaces to scripting and low level programming languages like R and Python. The current version 2.1.4 offers more than 700 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Further, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.
Urban environment is only rarely considered an exclusive refuge for rapidly declining bird species. Crested Lark (Galerida cristata) is a species in Central Europe whose synanthropization began in the second half of the 20th century due to dramatic changes in agriculture and landscape structure. We analyze how changes in these distribution patterns mirror landscape structures and demonstrate colonization potential from adjacent refuges. We used Czech Breeding Bird Atlas data from two periods, 1985–1989 and 2001–2003, to model distribution change patterns for Crested Larks in two parts of the country, Bohemia and Moravia. Mapping quadrats comprised the sampling units, and binomial species presence or absence was used to model the change patterns using generalized linear models while considering landscape structure, demographic data and dominant habitat attributes as predictors. Despite similar attributes of landscape structures in Bohemia and Moravia, Crested Larks clearly differed in their extinction patterns. Within isolated Bohemia, the remnant subpopulations were restricted to early successional stands around commercial areas in urban zones. In Moravia, only altitude appeared related to the species’ disappearance. Moravia is connected at the south to the Pannonian Plain with stable or increasing Crested Lark populations, which we consider responsible for the significantly higher number of newly occupied quadrats in Moravia. Our results indicate the more isolated Bohemian population is more prone to extinction than is the Moravian population with its greater colonization potential. The study notes a rare example of urban zones serving as refuges for a bird species demanding early successional stands.
Digital elevation model (DEM), deriving conventionally from contour data of topographic maps, provides sufficient information regarding the continuously varying topographic surface of the Earth. Though spaceborne DEMs are increasingly being used in earth-environmental-applications, suitability of various freely available spaceborne DEMs [e.g., advanced spaceborne thermal emission and reflection (ASTER), shuttle radar topography mapping mission (SRTM), global multi-resolution terrain elevation data (GMTED)] for topographic and geomorphometric analyzes in tropical regions is yet to be ascertained. In this paper, comparability of these spaceborne DEMs among themselves and also with the DEM (TOPO) prepared from digital contour data of topographic maps is assessed. Results show that various primary and secondary derivatives of ASTER and SRTM DEMs provide relatively better precision and substantial agreement with the corresponding parameters derived from TOPO. Among the spaceborne DEMs, SRTM has relatively higher vertical accuracy (root mean square error = 17.05 m), compared to ASTER (24.09 m) and GMTED (32.85 m). The vertical accuracy of all the spaceborne DEMs strongly depends on the relief and ruggedness of the terrain as well as the type of vegetation. It is proposed that in the absence of other available and acceptable elevation datasets, SRTM and ASTER are equally competent for geomorphometric analysis in tropical regions, while GMTED shows significant loss of terrain information due to coarser spatial resolution.