High-resolution Digital Elevation Models Research Articles

Evaluation of Simulated CO2 Point Source Plumes from High-Resolution Atmospheric Transport Model

Coal-fired power plants, as major anthropogenic CO2 emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO2 emissions from these point sources is crucial, as it will aid in quantifying CO2 emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO2 diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO2 simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO2 diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO2 diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R2 (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO2 point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO2 point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO2 concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO2 emissions using top-down approaches.

Mass-Movement Causes and Landslide Susceptibility in River Valleys of Lowland Areas: A Case Study in the Central Radunia Valley, Northern Poland

This work aims to analyse the mechanisms and factors contributing to shallow soil landslides in river valleys entrenched in lowlands on the example of the Central Radunia Valley. The combination of susceptibility analysis using geographic-information-system-based statistical models, field surveys, analysis of archival materials, and numerical modelling for the analysis of slope stability and hydrogeological processes allows for comprehensive landslide reconstruction, mass movement mechanism description, and an explanation of the role of triggering and causal factors. The results emphasise the need for cross-disciplinary studies of shallow soil landslides. The identification and prioritisation of the causal factors indicate that geomorphological conditions play a particularly important role. The current study shows that the greatest influence on landslide formation in the Central Radunia Valley is slope angle, as determined using a high-resolution digital elevation model. The slope angle factor is sufficient to produce a reliable susceptibility map (the areas under the curve of the success rate and prediction rate curves are 87.84% and 85.34%, respectively). However, numerical modelling of slope failure also clearly indicated that there was a significant influence of anthropogenic impacts on the landslide process. We determined that the main triggering factor causing the January 2019 Rutki landslide was related to the drilling of a borehole on 10 January 2019. The water used for drilling hydrated the soil and thus weakened the stability conditions.

Rainfall extremes under future climate change with implications for urban flood risk in Kathmandu, Nepal

Increased rainfall extremes cause severe urban flooding in cities with adverse socio-economic consequences, and Kathmandu city is no exception. Rainfall events are projected to become more intense and frequent in a warm and wet future, and they pose a major challenge to the sustainable development of Kathmandu city. This paper analyses historical extreme rainfall patterns across the city and uses these as the basis for future projections in combination with a range of General Circulation Models. Future projections of extreme rainfall are then fed into the numerical flood model HAIL-CAESAR (Lisflood), using a high-resolution digital elevation model of Kathmandu. We show that rainfall intensity, such as the annual maximum 1-day rainfall (RX1day), is projected to increase by up to 72% in the future, and the historical 100-year return period rainfall will become a 20 or 25-year return period rainfall. The flood modelling results show that the future flood hazard (magnitude and extent) will increase. The historical 100-year return period flood discharge will correspond to a 25-year return period future flood. A 100-year period flood discharge is likely to increase up to 72% (37% median) in the future. Area of land inundated by more than 1 m in a 100-year return period flood event could increase from 11.7 km2 to 23 km2 in the future. Furthermore, the location and timing of rainfall maxima affect the peak, timing, and location of flood hazards. This analysis can serve as a scientific basis to assess future flood-induced risk in Kathmandu in response to climate change.

Calibration of groundwater seepage against the spatial distribution of the stream network to assess catchment-scale hydraulic properties

Abstract. The assessment of effective hydraulic properties at the catchment scale, i.e., hydraulic conductivity (K) and transmissivity (T), is particularly challenging due to the sparse availability of hydrological monitoring systems through stream gauges and boreholes. To overcome this challenge, we propose a calibration methodology which only considers information from a digital elevation model (DEM) and the spatial distribution of the stream network. The methodology is built on the assumption that the groundwater system is the main driver controlling the stream density and extension, where the perennial stream network reflects the intersection of the groundwater table with the topography. Indeed, the groundwater seepage at the surface is primarily controlled by the topography, the aquifer thickness and the dimensionless parameter K/R, where R is the average recharge rate. Here, we use a process-based and parsimonious 3D groundwater flow model to calibrate K/R by minimizing the relative distances between the observed and the simulated stream network generated from groundwater seepage zones. By deploying the methodology in 24 selected headwater catchments located in northwestern France, we demonstrate that the method successfully predicts the stream network extent for 80 % of the cases. Results show a high sensitivity of K/R to the extension of the low-order streams and limited impacts of the DEM resolution as long the DEM remains consistent with the stream network observations. By assuming an average recharge rate, we found that effective K values vary between 1.0×10-5 and 1.1×10-4 m s−1, in agreement with local estimates derived from hydraulic tests and independent calibrated groundwater model. With the emergence of global remote-sensing databases compiling information on high-resolution DEM and stream networks, this approach provides new opportunities to assess hydraulic properties of unconfined aquifers in ungauged basins.

Lidar-Derived Rockfall Inventory—An Analysis of the Geomorphic Evolution of Rock Slopes and Modifying the Rockfall Activity Index (RAI)

Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or “RAI classes”. This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study’s outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments.

Spatiotemporal Variations of Glacier Mass Balance in the Tomur Peak Region Based on Multi-Source Altimetry Remote Sensing Data

Alpine glaciers are sensitive indicators of regional climate change, which can affect regional ecological stability and social development. Variations in glacier mass balance (GMB) are an important parameter in studying glacier change. In this study, data from the Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), and CryoSat-2 (Ku-band) were combined, and high-resolution ALOS DEM was employed to denoise. After that, the polynomial fitting method was used to analyze the characteristics of glacier surface elevation (GSE) variations from 2003–2020 in the Tomur Peak Region of the Central Asian Tianshan Mountains and the regional GMB was calculated. Research results showed that: (1) From 2003–2020, the GSE of the Tomur Peak Region had an overall −8.95 ± 4.48 m variation, the average rate of which was −0.53 ± 0.26 m/yr (/yr is /year). Overall, elevations of most glaciers in the Tomur Peak Region had downward trends, with a rate of change of −0.5 to 0 m/yr. The fastest rate of elevation decline in the Koxkar Glacier Tongue was −1.5 m/yr. The elevation of some altimetric points in the Eastern Tomur Peak Region showed a rising state, with a maximum rate of variation of 1.0 m/yr. (2) From 2003–2020, the average GMB in the Tomur Peak Region was −1.51 ± 0.04 Gt/yr. In the region of elevation below 4000 m, small glaciers dominated, with a GMB of −0.61 ± 0.04 Gt/yr. With increasing elevation, the melting rate of glaciers gradually slowed down, but overall, the mass balance remained in a state of decline. (3) Climate was the main driving factor of GMB change in the study area. From 2003–2020, in the Tomur Peak Region, the average annual temperature continued to increase at a rate of 0.04 ± 0.02 °C/yr, and this was the main influencing factor for the negative GMB in the Tomur Peak Region. In the same period, the annual precipitation showed a rising trend with a linear variation rate of 0.12 ± 0.06 mm/yr, and the rising precipitation was the influencing factor for the gradually slowing change in the GMB in the study area.

The use of budget UAV systems and GIS spatial analysis in cadastral and construction surveying for building planning

The uncertainty that comes with planning, constructing, and maintaining buildings is a constant issue for architects and civil engineers. As topography is the framework that unites architecture and landscape, the design and planning projects heavily rely on a range of monitoring, surveying methods and comprehensive field data. Along with the traditional topo-geodetic instrumentation used in land and construction surveying, unmanned aerial vehicles equipped with digital cameras and structure from motion software have been increasingly used recently in a variety of fields to create high-resolution digital elevation models. Despite this widespread use, in the majority of surveying projects it is considered that the topographic representations produced through this technology is inferior to that obtained with surveys conducted using conventional methods, along with other constraints imposed by legislation, environment and weather conditions. While certain limitations of unmanned aerial vehicle (UAV) systems are challenging, their advantage for gathering data from a different perspective and the generated outputs have the potential to significantly advance the construction industry. The present article provides an overview of the usefulness of budget UAV systems in developing a methodology that accompanies the conventional survey process for civil engineering applications. Thus, along with the established survey for cadastral and technical documentations necessary for the architectural process, a complementary UAV survey was developed, with subsequent spatial analysis in a geographic information system (GIS), in order to expand the array of deliverables. These include useful orthophoto map, larger-scale and denser representations of the topography, digital surface and terrain models, slope, aspect and solar radiation maps which will offer helpful information and instructions at the start of the construction planning process. The methodology contains two case studies with different degrees of terrain and vegetation challenges, and also presents an accuracy assessment and overall benefits discussion regarding the UAV implementation.

UnTDIP: Unsupervised neural network for DEM super-resolution integrating terrain knowledge and deep prior

Digital Elevation Models (DEMs) are essential for comprehending the three-dimensional (3D) structure of the Earth’s surface. When the resolution of airborne or satellite sensors fail to meet the requirements for high-precision terrain observation, super-resolution methods offer an alternative solution, which infers the high-resolution DEMs from coarse-resolution DEMs. This study aims to create an unsupervised DEM super-resolution method that utilizes untrained neural networks, integrating both deep prior from network structure and terrain knowledge, namely UnTDIP. Specifically, the untrained neural network iteratively processes a single DEM, and the deep prior from network structure learns low-order statistical information from the DEM that is then applied to the super-resolution task. Furthermore, the terrain knowledge for topographic consistency is integrated into the iterative process and synergies with the deep prior to reconstructing the DEM with the correct 3D structure. Most neural network-based DEM super-resolution methods are data-driven, while the proposed method does not require any training datasets or pre-trained network parameters, making it broadly applicable. The UnTDIP method was evaluated in three study areas with different spatial resolutions of 5 m, 10 m, and 30 m. The method performs well in multiple scale factors and spatial resolutions, even surpassing the fully supervised DEM super-resolution method in some evaluation metrics. Moreover, the integration of terrain knowledge reduces the Mean Absolute Error (MAE) by 6.08 % to 34.65 %. Overall, the proposed method is well-suited for DEM data and can be adapted for super-resolution tasks for other geospatial data with the integration of domain knowledge.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Simulation of Retrospective Morphological Channel Adjustments Using High-Resolution Differential Digital Elevation Models versus Predicted Sediment Delivery and Stream Power Variations

This work proposes a methodological approach applied to ephemeral gravel-bed streams to verify the change in the magnitude and frequency of hydrological events affecting the morphological dynamics and sediment budget in this type of channel. For the case study, the Azohía Rambla, located in southeastern Spain, was chosen, emphasizing the research on two reference riverbed sections (RCRs): an upper one, with a predominance of erosion, and a middle one, where processes of incision, transport, and deposition were involved. First, this approach focuses on relationships between peak discharges and sediment budgets during the period 2018–2022. For this purpose, water level measurements from pressure sensors, a One-Dimensional Hydrodynamic model, and findings from comparative analyses of high-resolution differential digital elevation models (HRDEM of Difference-HRDoD) based on SfM-MVS and LiDAR datasets were used. In a second phase, the GeoWEPP model was applied to the period 1996–2022 in order to simulate runoff and sediment yield at the event scale for the watersheds draining into both RCRs. During the calibration phase, a sensitivity analysis was carried out to detect the most influential parameters in the model and confirm its capacity to simulate peak flow and sediment delivery in the area described above. Values of NS (Nash–Sutcliffe efficiency) and PBIAS (percent bias) equal to 0.86 and 7.81%, respectively, were found in the calibration period, while these indices were 0.81 and −4.1% in the validation period. Finally, different event class patterns (ECPs) were established for the monitoring period (2018–2022), according to flow stage and morphological channel adjustments (overtopping, bankfull and sub-bankfull, and half-sub-bankfull), and then retrospectively extrapolated to stages of the prior simulated period (1996–2018) from their typical sequences (PECPs). The results revealed a significant increase in the number of events and PECPs leading to lower bed incision rates and higher vertical accretion, which denotes a progressive increase in bed armoring and bank erosion processes.

Defining multi-scale surface roughness of a coral reef using a high-resolution LiDAR digital elevation model

A decline in coral reef surface roughness may indicate that a coral reef ecosystem is approaching functional collapse. This is because surface roughness underpins many critical ecosystem metrics, such as, live coral cover and high fish biomass. Yet, we understand very little about how surface roughness changes at ecosystem scales due to the limited spatial extents of coral reef ecological surveys. Here, we investigate the surface roughness of a coral reef across multiple spatial scales on a high-resolution Light Detection and Ranging (LiDAR) derived digital elevation model (DEM, 0.25 m cells). We found that complex features dominated regions with high roughness (e.g., spur and groove systems on the reef slope) and increased the average roughness of geomorphic zones that are otherwise flat and featureless (e.g., lagoonal patch reefs in the sandy deep lagoon). Each geomorphic zone had a unique signature with high values of roughness usually expressed over finer spatial scales and, conversely, lower values of roughness observed over broader spatial scales. We allocated each geomorphic zone into one of four roughness equivalent habitats (REHs 1–4) that share similar surface roughness properties but are not necessarily geographically contiguous regions. These results identify fine scale features and geomorphic zones that are important for providing roughness to coral reef systems, such as spur and groove systems and the fore-reef slope respectively. They also suggest that morphodynamic processes operating at broader spatial scales influence the physical structure of coral reef ecosystems. The quantification of coral reef surface roughness is becoming increasingly important due to the wider availability of high (<1 m) and hyper (<0.1 m) resolution DEMs. Our approach and findings here can be used to provide greater surface textural information for common coral reef geomorphic zones and aid future management and research efforts, such as, monitoring of coral reef ecosystem response to environmental change and reef restoration and adaptation programs.

Experiences with the RTM Method in Local Quasi-Geoid Modeling

In local quasi-geoid modeling, the residual terrain modeling (RTM) method is often used to remove short-wavelength gravity field signals from the measured gravity on the ground in order to obtain a regularized and smooth gravity field that is suited for field interpolation and modeling. Accurate computation of RTM corrections plays a crucial role in computing an accurate local quasi-geoid, and it requires a set of fine-tuned parameters, including the combination of DEMs with different resolutions for suitably representing the real topography, the choice of integration radius for properly defining the extent of the computation zone, and the determination of reference topography to properly describe the RTM-reduced Earth’s surface. To our knowledge, this has not been systematically documented, despite its obvious importance. This study aims to systematically investigate the impact of these factors on RTM correction computation and, consequently, on local quasi-geoid modeling to provide practical guidelines for real-world applications. The tesseroid-based gravity forward modeling technique is employed to investigate the following issues existing in the practical use of the RTM method: ① Can the combination of a high-resolution DEM and a DEM with a lower resolution replace the single use of the high-resolution DEM for RTM correction computation while maintaining accuracy and improving efficiency? If it does, how do I properly choose the resolution of this coarse DEM as well as the integration radius r1 for the inner zone and r2 for the outer zone? ② How large would the differences between the RTM corrections computed by three types of reference topographies, which are obtained from the direct averaging (DA) approach, the moving averaging (MA) approach, and the spherical harmonic (SH) approach, be, and how large would their impact on quasi-geoid modeling be? To obtain objective findings, two research regions were selected for this investigation. One is the Colorado test area (USA) with rugged terrain, and the other is the Auvergne test area (France) with moderate terrain. The main numerical findings are: (1) the combination of the 3” resolution DEM (inner zone) and the 30″ resolution DEM (outer zone) is sufficient for accurate and efficient RTM correction computation; (2) if the resolution of the reference topography is 5′ or slightly lower, all three types of reference topographies are able to obtain local quasi-geoid models at a similar accuracy level, while the values of r1 and r2 are preferred to be at least 20 km and 111 km, respectively; (3) if the reference topography has a resolution of 30′ or lower, the MA or SH reference topography is recommended, especially for the latter one, and the values of r1 and r2 are suggested to be at least 20 km and 222 km, respectively. The above numerical findings can be taken as a reference for local quasi-geoid determination in areas with different topographic regimes than the two selected test areas.

Topography shapes the carbon allocation patterns of alpine forests

Topography plays a crucial role in determining the structure of alpine forests, as it restricts the availability of nutrients and water necessary for plant growth. Nevertheless, our information on how variations in forest carbon allocation patterns driven by fine-scale topography are influenced by broader-scale environmental contexts is limited. In the northern Tibetan Plateau, we combined field data from 89 forest plots with a high-resolution (1 m2) digital elevation model (DEM) and utilized a linear mixed-effects model to investigate how microtopography (characterized by slope, aspect, and topographic wetness index (TWI)) and broader-scale environmental context (characterized by elevation) and their interactions affect the carbon allocation patterns of alpine forest. Our results revealed that at low and high elevations with pronounced subsurface resource limitations, plants tend to allocate a higher proportion of carbon to the root system and have lower aboveground carbon stocks (ACS). Microtopographic heterogeneity significantly influenced the carbon allocation patterns of forest, with the intensity and direction of these effects varying across the environmental gradient. At low elevations, topographically wetter and northerly microhabitats had higher ACS and lower ratios of below- and aboveground carbon stocks (RBA); however, at high elevations, topographically drier and southerly microhabitats had higher ACS and lower RBA. TWI and aspect had the weakest effect on ACS and RBA in the mid-elevations. The relationship between slope and ACS and RBA was significantly positive but not evidently related to the broader-scale environmental gradient.

The influence of roads on depressional storage capacity estimates from high-resolution LiDAR DEMs in a Canadian Prairie agricultural basin

The Canadian Prairies are a post-glacial agricultural landscape, where millions of small depressions store surface water, form wetlands and control runoff contributing area. Their management is key to flood and drought hydrology, groundwater recharge, ecological integrity, migratory bird habitat and agricultural productivity. Depression drainage and infilling is common in the region, where it is often used to increase cropped area. The regularly spaced, rural ‘grid-road‘ network also impedes drainage, but associated culvert drainage can mitigate those effects. Management of depressions can be informed by hydrological modelling, but accurate surface water storage capacity estimates are needed to ensure accurate model results. Simple representation of road embankments in digital elevation models (DEMs) neglects the effects of culvert drainage. Here, a raster-based depression-filling algorithm was used to delineate depressions from three LiDAR-derived DEMs: a 10-m DEM with roads intact, a 2-m DEM with roads intact, and a 2-m DEM with roads breached at culvert locations. Road breaching was conducted manually in the 2-m DEM to remove artifact depressions that form alongside roads where culverts exist. Results indicated that increasing DEM resolution from 10-m to 2-m in a 393.5 km2 basin did not significantly change depression area or storage capacity estimates; however, breaching roads in the 2-m DEM decreased depression area by 29% (from 98.5 km2 to 69.8 km2) and estimated storage capacity by 48% (from 47.4 × 106 m3 to 23.8 × 106 m3), compared to leaving roads intact in the 2-m DEM. Depressions delineated from the 2-m roads-breached DEM also covered 48% more area and offered 53% more storage capacity than Canadian Wetland Inventory (CWI) aerial-photograph delineated wetlands, which occupied 47.1 km2 with an estimated storage capacity of 15.5 × 106 m3. The implications of these results for the ability of hydrological models to calculate runoff contributing areas and streamflow are discussed.

Quantification of Agricultural Terrace Degradation in the Loess Plateau Using UAV-Based Digital Elevation Model and Imagery

Agricultural terraces are important artificial landforms on the Loess Plateau of China and have many ecosystem services (e.g., agricultural production, soil and water conservation). Due to the loss of rural labor, a large number of agricultural terraces have been abandoned and then the degradation of terraces, caused by rainstorm and lack of management, threatens the sustainability of ecological services on terraces. Our previous study has found its geomorphological evidence (sinkhole and collapse). However, no quantitative indicators of terrace degradation are identified from the perspective of microtopography change. A framework for quantifying terrace degradation was established in this study based on unmanned aerial vehicle photogrammetry and digital topographic analysis. The Pujiawa terraces in the Loess Plateau were selected as study areas. Firstly, the terrace ridges were extracted by a Canny edge detector based on high-resolution digital elevation model (DEM) data. The adaptive method was used to calculate the low and high thresholds automatically. This method ensures the low complexity and high-edge continuity and accuracy of the Canny edge detector, which is superior to the manual setting and maximum inter-class variance (Otsu) method. Secondly, the DEMs of the terrace slope before degradation were rebuilt through the terrain analysis method based on the extracted terrace ridges and current DEM data. Finally, the degradation of terraces was quantified by the index series in the line, surface and volume aspects, which are the damage degrees of the terrace ridges, terrace surface and whole terrace. The damage degrees of the terrace ridges were calculated according to the extracted and generalised terrace ridges. The damage degrees of the terrace surface and whole terrace were calculated based on the differences of DEMs before and after degradation. The proposed indices and quantitative methods for evaluating agricultural terrace degradation reflect the erosion status of the terraces in topography. This work provides data and references for loess terrace landscape protection and its sustainable management.

Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration

Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (&lt;2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg−1, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management.

Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

ABSTRACT High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of flow barriers. Based on HRDEMs and aerial orthophotos, different Convolutional Neural Network (CNN) models were trained and compared to assess their effectiveness in image classification in four different watersheds across the U.S. Midwest. Our results show that most deep learning models can consistently achieve over 90% accuracies. The CNN model with HRDEMs as the sole input feature was found to be the best-fit one. The addition of aerial orthophotos and their derived spectral indices is insignificant to or even worsens the model’s accuracy. The selected best-fit model exhibits excellent transferability over different geographic contexts. This work can be applied to improve elevation-derived hydrography mapping at fine spatial scales.

Topographical dynamics based on global and UAV-SfM derived DEM products: a case study of transboundary Teesta River, Bangladesh

ABSTRACT Topographical changes in riverine floodplains need to be measured for assessing geomorphological dynamics and protecting floodplain areas, although topographic evaluation in disaster-prone floodplain in South Asia has been limited due to the lack of multitemporal, high-definition digital elevation models (DEMs) derived from modern techniques including airborne laser scanning, structure-from-motion (SfM) photogrammetry accompanied with Unmanned Aerial Vehicle (UAV, often referred as drone), and field-based mapping approaches. Here we conducted a preliminary study at two locations of Teesta River (Brahmaputra’s tributary) in Bangladesh using the UAV-SfM techniques and generated high-resolution DEMs. The selected locations represent dynamic changes of sediment and water on the floodplain over the years, and the UAV-SfM approach can be an effective method for monitoring those changes, but an archive of the past data has been unavailable. Here we evaluated the topographic changes by comparing the UAV-SfM-derived DEMs of 2022 with global DEM products (NASADEM of 1999), which are often the only available choice of DEMs in this river floodplain. The elevation differences of these two sets of DEMs were in the range of −5.23 to −84.66 m, and volumetric changes of −4.11 ± 0.15 to −86.25 ± 0.20 million m3, likely dominated by erosional processes towards the left side bank where the elevation errors are supposed to be several meters for UAV-DEM and ca. 5–12 m for NASADEM. Although it is not easy to accurately evaluate the absolute values of the changes, these changes may be associated with the upper basin’s morphodynamics.

Assessment of Using UAV Photogrammetry Based DEM and Ground-Measurement Based DEM in Computer-Assisted Forest Road Design

Computer-assisted forest road design mainly relies on a high-resolution digital elevation model (DEM), which provides terrain data for supporting the analysis of road design features. The resolution and accuracy of the DEM in representing the terrain structures vary depending on the preferred dataset, which then reflects some of the essential road features such as alignment, road slope, and earthwork. In this study, three forest road sections were designed by using high-resolution DEMs generated from UAV photogrammetry data, GNSS-GPS data and Total Station data. NetCAD 7.6 software, developed in Turkey and mostly used in road design applications, was used to perform the road design while calculating horizontal profiles, vertical profiles, curves, cross sections, and earthwork. The DEM generation capabilities for three datasets were compared based on spatial resolution, data collection and data processing stage. Then, the differences between three road sections were evaluated by considering specified road features such as alignment properties, road slope, and earthwork. The results indicated that the UAV (Unmanned Aerial Vehicles) based DEM generation method provided the highest resolution (10 cm), followed by the Total Station (56 cm) and GNSS-GPS (61 cm) based methods. When comparing the time for data collection procedure, it took 14 minutes, 70 minutes, and 110 minutes for UAV data, GNSS-GPS data, and Total Station data, respectively. On the other hand, UAV based method falls into a disadvantageous situation in data processing stage, due to high data processing time (3 hours). However, GNSS-GPS and Total Station based methods work only with spatial point data, so they require less processing time of 15 minutes and 25 minutes, respectively. The results indicated that road lengths were 294.8, 272.4 and 282.1 m and the average road slopes were 3.41%, 3.39%, and 3.31% for the road sections designed by using UAV, GNSS-GPS, and Total Station based DEMs, respectively. The excavation and landfill volumes were 369.16 m3 and 166.98 m3, 285.86 m3 and 201.83 m3, and 433.17 m3 and 183.95 m3, respectively. The results indicated that UAV photogrammetry data generates high-resolution DEMs that can be effectively used to design forest roads.

Multi-decadal erosion rates from glacierized watersheds on Mount Baker, Washington, USA, reveal topographic, climatic, and lithologic controls on sediment yields

Understanding land surface change in and sediment export out of proglacial landscapes is critical for understanding geohazard and flood risks over engineering timescales and characterizing landscape evolution over geomorphic timescales. We used automated Structure from Motion software to process historical aerial photographs and, with modern lidar data, generated a high-resolution DEM time series with coverage over 10 glacierized watersheds on Mount Baker, Washington, USA for the time period between 1947 and 2015. We measured basin-wide sediment yields and sediment redistribution on hillslopes and in stream channels. Slopes within most measured erosion sites are above theoretical and observed debris-flow thresholds. We observed significant erosion of hillslopes and limited deposition on hillslopes and in stream channels. Sediment delivery ratios during time periods with net erosion averaged 0.73. We determined, consistent with previous field observations, that debris flows originating from moraines are a primary erosion mechanism in proglacial zones on Mount Baker. Time series measurements indicate that temporal variability in erosion rates is associated with climate oscillations, with higher erosion rates during cooler-wetter periods. Basin-wide sediment yield is positively correlated with lithology (r2 = 0.54), hillslope angle (r2 = 0.52), drainage area (r2 = 0.82), and negatively correlated with stream channel slope (r2 = 0.67). Topographic differences between high and low yielding basins indicate that spatial variability in erosion on Mount Baker is sensitive to Pleistocene and Holocene glacial and volcanic activity. Specific sediment yields in six basins averaged 4600 ton/km2/yr, consistent with global measurements in glacierized catchments. Specific sediment yield decreased with increasing basin area, with total loads in the downstream main stem Nooksack River estimated between 480 and 820 ton/km2/yr. Proglacial sediment yields account for between 18 and 32 % of total sediment load in the main stem Nooksack River and exceed contributions by bluff and terrace erosion, which account for between 8 and 13 % of total load. Our findings indicate that erosion in glacierized basins is sensitive to decadal climate oscillations and that high proglacial sediment yields provide an important contribution to river systems downstream, particularly in catchments where upland topography and lithology is favorable.