Influence of DEM resolution on topographic correction models using spot satellite image

This study investigates the impact of different digital elevation model (DEM) resolutions on the topological attributes and simulated runoff, as well as the sensitivity of runoff parameters in the Mahabad Dam watershed in Iran. The watershed and streamlines were delineated in ArcGIS, and the hydrologic analyses were performed using the Soil and Water Assessment Tool (SWAT). The sensitivity analysis on runoff parameters was performed, using the Sequential Uncertainties FItting Ver. 2 algorithm, in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) program. The results indicated that the sensitivity of runoff parameters, watershed surface area, and elevations changed under different DEM resolutions. As the distribution of slopes changed using different DEMs, surface parameters were most affected. Furthermore, higher amounts of runoff were generated when DEMs with finer resolutions were implemented. In comparison with the observed value of 8 m3/s at the watershed outlet, the 12.5 m DEM showed more realistic results (6.77 m3/s). Comparatively, the 12.5 m DEM generated 0.74% and 2.73% more runoff compared with the 30 and 90 m DEMs, respectively. The findings of this study indicate that in order to reduce computation time, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

This study attempts to investigate the relationship between the accuracy of different Digital Elevation Model (DEM) and fractal dimension D and to solve the problem of determining the optimal catchment area threshold in plain watersheds. In this study, the fractal dimensions of the Shuttle Radar Topographic Survey Digital Elevation Model (SRTM) V4.1 DEM, Hydrology 1K (HYDRO1K) DEM, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with 90 m horizontal resolution and 30 m ASTER GDEM were calculated using the box dimension method, and the relationship between the horizontal resolution and accuracy of three data sources and fractal dimension D was studied. The optimal catchment area threshold in the study area was determined. The response of river network similarity and topographic features to DEM accuracy was explored, and the optimal catchment area threshold for the study area was verified. The result shows that, with the increase in the catchment area threshold, the fractal dimension D shows three stages of rapid decline, gentle fluctuation, and tend to 1. Compared with the horizontal resolution of DEM, the vertical accuracy has more influence on the fractal dimension D. The fractal dimension D accuracy increases with the increase in the vertical accuracy of DEM. The main order of influence of the three data sources is SRTM V4.1 DEM > ASTER GDEM > HYDRO1K DEM. The fractal dimension of the digital river network extracted by SRTM V4.1 DEM is 1.0245, the same as the fractal dimension of the actual river network. The optimal catchment area threshold of the study area is 4.05 km2, which has the highest coincidence with the actual river network. In summary, using the SRTM V4.1 DEM as the DEM data source is feasible to determine the optimal catchment area threshold in plain watersheds.

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from −772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines.

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

The use of preevent and postevent digital elevation models (DEMs) to estimate the volume of rock avalanches on glaciers is complicated by ablation of ice before and after the rock avalanche, scour of material during rock avalanche emplacement, and postevent ablation and compaction of the rock avalanche deposit. We present a model to account for these processes in volume estimates of rock avalanches on glaciers. We applied our model by calculating the volume of the 28 June 2016 Lamplugh rock avalanche in Glacier Bay National Park, Alaska. We derived preevent and postevent 2‐m resolution DEMs from WorldView satellite stereo imagery. Using data from DEM differencing, we reconstructed the rock avalanche and adjacent surfaces at the time of occurrence by accounting for elevation changes due to ablation and scour of the ice surface, and postevent deposit changes. We accounted for uncertainties in our DEMs through precise coregistration and an assessment of relative elevation accuracy in bedrock control areas. The rock avalanche initially displaced 51.7 ± 1.5 Mm3of intact rock and then scoured and entrained 13.2 ± 2.2 Mm3of snow and ice during emplacement. We calculated the total deposit volume to be 69.9 ± 7.9 Mm3. Volume estimates that did not account for topographic changes due to ablation, scour, and compaction underestimated the deposit volume by 31.0–46.8 Mm3. Our model provides an improved framework for estimating uncertainties affecting rock avalanche volume measurements in glacial environments. These improvements can contribute to advances in the understanding of rock avalanche hazards and dynamics.

The present study aimed to delineate a robust watershed boundary and extract its morphometric parameters in the Karnaphuli watershed, Bangladesh, using different digital elevation models (DEMs). Two DEMs, the shuttle radar topographic mission (SRTM) and the terra advanced spaceborne thermal emission and reflection radiometer (ASTER), were employed to delineate watershed boundaries and evaluate various morphometric characteristics. Raster data such as fill sinks, flow direction, and flow accumulation were utilized to delineate the watershed. The morphometric analysis included the estimation of linear, areal, and relief parameters. The findings revealed a noticeable difference between the datasets; ASTER delineated a larger watershed area than SRTM. Regarding stream order, there were streams ranging from 1st to 6th order, encompassing numerous small, medium, and main channels of the river. Drainage density was calculated as 0.52 km/km² for SRTM and 0.51 km/km² for ASTER, both having a spatial resolution of 30 m. The bifurcation ratio ranged from 1.94 for SRTM to 2.45 for ASTER, indicating varying degrees of structural disturbance influenced by geological factors. The form factor indicated an elongated shape of the study area. The overall dimensions of the streams and the watershed extent suggest moderate mean annual rainfall discharge. The watershed exhibited relatively high denudation rates, attributed to the basin relief. A low drainage density indicated the importance of infiltration processes. The findings of this study provide valuable insights into the spatial variability of watershed characteristics, highlighting the need for tailored management strategies for the Karnaphuli River. Implementing targeted conservation measures and watershed management practices based on these morphometric parameters can enhance water resource sustainability and ecological health in the region.

ABSTRACTIn this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.

Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

Evaluating short-term morphological changes in a gravel-bed braided river using terrestrial laser scanner

Terrain-neglected radiometric distortion remains a major challenge in quantitative remote sensing over slope terrain. While numerous topographic correction models have been developed and validated in specific regions or simulated settings, a systematic and quantitative evaluation of the satellite-derived reflectance bias introduced by terrain neglect is still lacking. A global whole-year multi-angular reflectance data set was produced for >15000 homogeneous 500 m single-slopes by coupling MODIS daily products with a physically based radiative-transfer model, including both terrain-neglected and terrain-corrected surface reflectance. The relative percent bias of terrain-neglected reflectance to terrain-corrected reflectance (rBias) was evaluated across four slope groups (10°–15°, 15°–20°, 20°–30°, ≥30°). The results show that rBias is primarily governed by slope-driven variation in relative solar zenith angles (rSZA). Positive and negative values of rBias flips with rSZA: terrain-neglected reflectance is totally overestimated at small rSZA (~0°–15°) while totally underestimated at high rSZA (~69°–90°), and the whole proportion of underestimation increases with slope, from 58.4% at 10°–15° to 71.5% at ≥30°. Magnitude of rBias escalates non-linearly with both slope and rSZA, as median rBias ranges from −29.5% to 4.2% at 10°–15° slopes and from −61.2% to 11.9% at slopes ≥30°. Also, with the rBias extrema expand from (−55.2%, 24.8%) at 10°–15° slopes to (−88.3%, 55.0%) at slopes ≥30°. These findings quantify terrain-neglected satellite-derived reflectance bias and highlight the need for topographic correction under steep slopes and high rSZA.

Solar radiation not only sustains the lives on the Earth, but also creates spatial and temporal variations of hydrological ingredients, such as vegetation, soil moisture, and snow. Precise quantification of spatial solar radiation incident on the Earth's surface which accounts for the topographic modulation, especially in complex terrain, underpins the study of many catchment hydro-meteorological and hydro-ecological processes. Topography is a key parameter that affects the spatial solar radiation pattern across different scales. This article addresses the issue of modelling spatial variability of actual solar radiation caused by topography from the hydrological perspective. Models with different algorithms and different complexities, from the simple empirical equations to process-based physical approach, have been developed to parameterize and calculate the potential radiation (under clear-sky condition) and the actual radiation (under overcast cloudy condition). Based on a review of the general steps of solar radiation modelling and the corresponding models for each step, two models with easily or globally available data for spatial solar radiation modelling in complex terrain, namely, the physically parameterized, remote-sensing-oriented Heliosat-2 model and the sunshine duration-based Angström–Prescott regression model are selected and implemented in a GIS framework. The capability of both models for simulation of cloudy-sky radiation on horizontal surfaces has been verified against observed station data showing an R 2 greater than 0.9. The validity of the models for modelling inclined surface is tested by comparing against each other, which has shown a satisfactory agreement and demonstrated that the simple Angström–Prescott method performed reasonably well compared with the more elaborate Heliosat-2 method. Scale sensitivity of the models and the shading effect are examined with different digital elevation model (DEM) resolutions from 30 to 500 m and reveal the existence of a threshold grid size to resolve the topography-induced spatial solar radiation variability. Spatial mapping of potential solar radiation and actual solar radiation has been demonstrated in a small catchment in Southern Germany, with a spatial difference up to 30% in winter and 5% in summer. This may lead to a significant difference for the energy-limited hydrological processes, such as snowmelt, and evapotranspiration.

Using historical aerial photographs to measure earthquake deformation: Testing the effects of scan resolution

This study applied existing methods of landslide susceptibility modelling of the mountainous area of the Cordillera Blanca (Peru), which is prone to landslides. In heterogeneous regions as in the Cordillera Blanca, the performance of a physically based approach Stability Index Mapping (SINMAP) was compared to empirical statistical models using logistic regression and a landslide density model. All models were applied to three different digital elevation models (DEMs): ASTER GDEM, SRTM (both 30-m spatial resolution), and TanDEM-X (12-m spatial resolution). Obtained results were evaluated using the area under the receiver operating characteristic curve (AUC) approach, once for a landslide inventory which extends over the whole study area and once using an inventory of a smaller area. The physically based approach (AUCs between 0.567 and 0.625) performed worse than the statistical models (AUCs from 0.672 to 0.759) over the large area. Additionally, all models received higher performances within the small area. This coincided with differences of the variability of the DEM-derived characteristics (e.g. slope angle and curvature) from the small to the large evaluation area. Using the smaller evaluation area, all models received higher AUC values (0.743–0.799), and the impact of the DEMs was less visible. The analysis of the susceptibility showed that mainly the same slopes are considered as most or least susceptible by all models, but SINMAP is classifying larger areas as unstable or stable. Overall, this study showed that regional-scale landslide susceptibility modelling can lead to reasonable results even in regions with scarce model input data, but performances of different DEMs and models need to be evaluated carefully.