LiDAR-derived Digital Elevation Models Research Articles

Influence of the spatial resolution of Digital Elevation Models on water-balance estimations in temporary shallow lakes

Many shallow lakes are strongly degraded and their hydroecological functioning has been altered. At the same time, their rate of disappearance is remarkably high compared to other ecosystem types. Moreover, due to their relatively small size and shallow depth these lakes are highly sensitive to meteorological variability, making them highly vulnerable to climate change. The accurate estimation of their water balance is key to identifying proper management strategies. To carefully model the water balance, it is necessary to use a Digital Elevation Model (DEM) of a high-enough spatial resolution to understand the bathymetric relationships of the basin; however, it is not always possible to generate DEMs with field data. Even when DEMs are available, it is still unclear what spatial resolution is adequate to obtain an accurate water balance in shallow lakes. The aim of this work was to analyze the influence that bathymetries from different DEMs may have on shallow lake-water-balance estimations, using Lake Alcahozo (central Spain) as a case study. To do this, hydrological balances were developed with Lidar-derived DEMs with different spatial resolutions, and these balances were compared with one developed from bathymetric field data. The hydrological models obtained were calibrated with limnimetric measurements and validated with estimates of flooded areas calculated via remote-sensing imagery. The main results showed that, to a certain extent, the different resolutions of bathymetries do not have a strong influence on the result of water-balance estimations: DEMs up to a spatial resolution of 50 m (a pixel size representing 0.3 % of the basin area) have shown similar goodness-of-fit in the modeling of the hydrological balances in our case study. Therefore, depending on the objective of the study, Lidar data with different spatial resolutions can be used, up to a minimal resolution that should be determined on a case-by-case basis.

Channel morphological change monitoring using high-resolution LiDAR-derived DEM and multi-temporal imageries

Natural processes and human activities both cause morphological changes in channels. Remote sensing products are often used to assess planform changes, but they tend to overlook vertical changes. However, considering both planform and vertical changes is crucial for a comprehensive evaluation of morphological changes. Using spatiotemporal aerial imagery and topographic data, remote sensing plays a vital role in evaluating channel morphological changes and flood-carrying capacity. This study aimed to investigate the morphological changes of a creek in an urban catchment using very high-resolution remote sensing products. In this study, we developed a new framework for investigating overall channel morphology change by employing very high-resolution aerial imagery and a LiDAR-derived digital elevation model (DEM). By digitizing channel boundaries using ArcGIS Pro 3.0, and analyzing various morphological parameters, erosion, and deposition patterns, we examined the impact of urban expansion and infrastructure development on channel adjustments. Channel adjustments have been performed in the case study catchment (Dry Creek, South Australia, Australia) due to urban expansion and development of infrastructure in the downstream reaches. Our findings revealed a significant southwest shift in the planform of the channel, with a maximum shift of 478 m and an average shift of 217 m since 1998. This alteration resulted in an increase in the sinuosity index reaching 1.2. Over the period from 2018 to 2022, the channel experienced a net deposition depth of 3.4 cm to 3.6 cm in downstream reaches. The annual deposition volume in the downstream reaches was 1963 m3, necessitating regular desilting to prevent channel capacity loss and flooding in the surrounding environment. This study also highlights the incremental growth of riparian vegetation within the channel, which affects surface roughness, channel slope, and carrying capacity. These findings provide a valuable baseline for future investigations into stream channel morphology changes and emphasize the importance of implementing appropriate measures such as desilting and vegetation management to mitigate deposition levels, reduce flood risks, and enhance the overall health and functionality of Dry Creek. The framework used in this study can be applied to other case studies employing reliable and high-resolution remote sensing data products.

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

Comparing roughness maps generated by five typical roughness descriptors for LiDAR-derived digital elevation models

<abstract> <p>Terrain surface roughness, often described abstractly, poses challenges in quantitative characterization with various descriptors found in the literature. In this study, we compared five commonly used roughness descriptors, exploring correlations among their quantified terrain surface roughness maps across three terrains with distinct spatial variations. Additionally, we investigated the impacts of spatial scales and interpolation methods on these correlations. Dense point cloud data obtained through Light Detection and Ranging technique were used in this study. The findings highlighted both global pattern similarities and local pattern distinctions in the derived roughness maps, emphasizing the significance of incorporating multiple descriptors in studies where local roughness values play a crucial role in subsequent analyses. The spatial scales were found to have a smaller impact on rougher terrain, while interpolation methods had minimal influence on roughness maps derived from different descriptors.</p> </abstract>

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.

Inside Late Bronze Age Settlements in NE Romania: GIS-Based Surface Characterization of Ashmound Structures Using Airborne Laser Scanning and Aerial Photography Techniques

The identification and delineation, through aerial photography, of the archaeological structures that present temporal resolution, as well as their characterization based on high-resolution LiDAR (Light Detection and Ranging)-derived DEMs (Digital Elevation Models) are modern techniques widely used in the archaeological prospecting of various landscapes. In this study, we present an application of Airborne Laser Scanning (ALS) and aerial photography (AP) techniques, used in order to compute geomorphometric indices specific to the ashmound structures of Late Bronze Age (LBA) archaeological sites that are visible on the soil surface. The necessity of determining the ashmounds’ geoarchaeological description stems from the fact that despite the majority of archaeologists weighing in on the subject, there is still no accepted explanation regarding their initial functionality. Thus, we believe that the GIS-based high-resolution characterization of 200 ashmound features identified in 21 Noua Culture (NC) archaeological sites will contribute to a better understanding of the ashmounds’ functionality and evolution in the heterogeneous landscape of the study area (NE Romania). Therefore, various shape indices, such as the area (A), perimeter (P), length (L), form factor (RF), circularity ratio (RC), and elongation ratio (RE) were computed for microlevel characterizations of the visible ashmounds’ structures. Additionally, LiDAR-derived DEMs with a 0.5 m resolution were used to generate more surface characteristics such as the slope (S) and hypsometric indices (HI). The outcomes indicate that the ashmounds have relatively diverse shapes (an RF range from 0.37 to 0.77; a RC range from 0.79 to 0.99; a RE range from 0.68 to 0.99), and the micro-relief slightly varies from positive to negative landforms (HI range from 0.34 to 0.61) depending on the erosion intensity (S range from 1.17° to 19.69°) and anthropogenic impact (e.g., current land use and agriculture type). Furthermore, each morphometric parameter is an indicator for surface processes, aiding in the identification of the geomorphologic and surface-erosion aspects that affect the archaeological remains, contributing to the assessment of the conservation status of the ashmound structures within the current landscape configuration. In this regard, this article presents and discusses the remote sensing (RS) techniques used, as well as the morphometric data obtained, exploring the implications of our findings for a better characterization of the NC in Romania.

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.

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 (<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.

Lidar-Derived Road Profiles

ABSTRACTDespite considerable developments in the archaeological application of lidar for detecting roads, less attention has been given to studying road morphology using lidar. As a result, archaeologists are well equipped to locate but not thoroughly study roads via lidar data. Here, a method that visualizes and statistically compares road profiles using elevation values extracted from lidar-derived digital elevation models is presented and illustrated through a case study on Chaco roads, located in the US Southwest. This method is used to establish the common form of ground-truthed Chaco roads and to measure how frequently this form is across non-ground-truthed roads. This method is an addition to the growing suite of tools for documenting and comparing roads using remotely sensed data, and it can be particularly useful in threatened landscapes where ground truthing is becoming less possible.

High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

Use of High-Resolution Multi-Temporal DEM Data for Landslide Detection

Landslides in urban areas have been relatively well-documented in landslide inventories despite issues in accuracy and completeness, e.g., the absence of small landslides. By contrast, less attention has been paid to landslides in sparsely populated areas in terms of their occurrences and locations. This study utilizes high-resolution and LiDAR-derived digital elevation models (DEMs) at two different times for landslide detection to (1) improve the localization and detection accuracies in landslide inventories, (2) minimize human intervention in the landslide detection process, and (3) identify landslides that cannot be easily documented in the current state of the practice. To achieve this goal, multiple preprocessing steps were used to ensure the spatial alignment of the multi-temporal DEMs. Map algebra was then used to calculate the vertical displacement for each cell and create a DEM of Difference (DoD) to obtain a quantitative estimation of ground deformations. Next, the elevation changes were filtered via an appropriate Level of Detection (LoD) threshold to mark potential landslide candidates. The landslide candidates were further assessed with the aid of customized topographic maps as auxiliary data and pattern recognition to distinguish landslides (true positive changes) from construction, erosion, and deposition (false positives). The results from the proposed method were compared with existing landslide inventories and reports to evaluate its performance. The new method was also validated with temporal high-resolution Google Earth images. The results showed the successful application of the method in landslide detection and mapping. Compared with traditional methods, the proposed method provides a semi-automatic way to obtain landslide inventories with publicly available yet lowly utilized DEM data, which can be valuable in preliminary analysis for landslide detection.

Combining Deep Semantic Edge and Object Segmentation for Large-Scale Roof-Part Polygon Extraction from Ultrahigh-Resolution Aerial Imagery

The roofscape plays a vital role in the support of sustainable urban planning and development. However, availability of detailed and up-to-date information on the level of individual roof-part topology remains a bottleneck for reliable assessment of its present status and future potential. Motivated by the need for automation, the current state-of-the-art focuses on applying deep learning techniques for roof-plane segmentation from light-detection-and-ranging (LiDAR) point clouds, but fails to deliver on criteria such as scalability, spatial predictive continuity, and vectorization for use in geographic information systems (GISs). Therefore, this paper proposes a fully automated end-to-end workflow capable of extracting large-scale continuous polygon maps of roof-part instances from ultra-high-resolution (UHR) aerial imagery. In summary, the workflow consists of three main steps: (1) use a multitask fully convolutional network (FCN) to infer semantic roof-part edges and objects, (2) extract distinct closed shapes given the edges and objects, and (3) vectorize to obtain roof-part polygons. The methodology is trained and tested on a challenging dataset comprising of UHR aerial RGB orthoimagery (0.03 m GSD) and LiDAR-derived digital elevation models (DEMs) (0.25 m GSD) of three Belgian urban areas (including the famous touristic city of Bruges). We argue that UHR optical imagery may provide a competing alternative for this task over classically used LiDAR data, and investigate the added value of combining these two data sources. Further, we conduct an ablation study to optimize various components of the workflow, reaching a final panoptic quality of 54.8% (segmentation quality = 87.7%, recognition quality = 62.6%). In combination with human validation, our methodology can provide automated support for the efficient and detailed mapping of roofscapes.

PyVF: A python program for extracting vertical features from LiDAR-DEMs

Coastal and riverine flooding is one of the most common environmental hazards that affect billions of people worldwide. A coupled hydrologic and coastal storm surge simulation is required to develop an improved understanding of the individual and collective mechanisms that can cause flooding within watersheds. These simulations are dependent on an accurate digital elevation model (DEM); however, it is a challenge to include numerical model resolution as fine as contemporary DEMs due to the enormous computational cost. Therefore, significant vertical features (VFs) such as roadbeds, levees, railroads, and natural ridges must be identified and considered in developing the model representation of the DEM since the VFs can affect flow propagation. PyVF is an open-source program to extract significant VFs from a high-resolution, bare-earth, LiDAR-derived DEM automatically. This paper introduces the methods and shows the automated extraction of VFs for a coastal, urban, mountain and beach area.

Subsampling Large-Scale Digital Elevation Models to Expedite Geospatial Analyses in Coastal Regions

Murphy, K.A.; Zawada, D.G., and Yates, K.K., 2022. Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions. Journal of Coastal Research, 38(6), 1236–1245. Coconut Creek (Florida), ISSN 0749-0208. Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km2 to 242 km2.

Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia

ABSTRACT Digital Elevation Model (DEM) is the most vital data to generate drainage networks and to provide critical terrain factors and hydrologic derivatives, such as slope, aspect, and streamflow. The accuracy of generated drainage features is extensively dependent on the quality and resolution of DEM, such as LiDAR-derived DEM. Contrary, it has a high level of roughness and complexity. Thus, smoothing methods are sometimes employed to conquer the roughness. This paper presents feature-preserving DEM smoothing (FPDEM-S) and edge-preserving DEM smoothing (EPDEM-S) approaches to smooth surface complexity in kind of preserving small drainage features using the 0.5 m – resolution LiDAR DEM of the Kedungbener River area in Kebumen Regency, Indonesia. Entangling linear morphometric factors, those smoothing approaches delivered a slight difference of stream number, with the FPDEM-S stream length ratio performing 7% better tendencies. The FPDEM-S method perormed better than EPDEM-S in this study area to provide an optimal smoothed LiDAR DEM at certain parameter values. Summarising that two smoothing methods approaches performed similar characteristics of watershed as an oval structure close to the circular shape. Also, it can be revealed that the watershed did not reach maturity phase.

Geostatistical Resampling of LiDAR-Derived DEM in Wide Resolution Range for Modelling in SWAT: A Case Study of Zgłowiączka River (Poland)

A digital elevation model (DEM) is an essential element of input data in the model research of watersheds. Recently, progress in measurement techniques has led to the availability of such data with high spatial resolution. Therefore, simplification of DEMs to shorten the time of their processing is a significant, but insufficiently investigated issue. This study, gradually and with various methods, carried out a great simplification of a detailed LiDAR-derived DEM. Then, the impact of that treatment on the precision of the selected elements for modeling a watershed was assessed. The simplification comprised a reduction in resolution, with the use of statistical resampling methods, namely giving an average, modal, median, minimum, maximum, or the closest value to the pixels. This process was carried out in a wide range of pixel sizes, increasing by 50% each time (from 1 m to 1.5, 2.3, 3.4, 5.1, 7.6, 11, 17, 26, 38, 58, and 86 m, respectively). The precision of the obtained DEMs and the precision of the delineation of boundaries of the watershed and watercourses were assessed. With the systematic reduction in the resolution of a DEM, its precision systematically decreased. The changes in the precision of determining the watercourses and boundaries of a watershed were irregular, ranging from being very small, to mild, to significant. A method of giving the minimum value, that was simple with regard to computing, was singled out. In the determination of both the watercourses and the boundaries of a watershed, this method produced one of the best results for the higher resolution and for the lower resolution—considerably better than the other methods tested. The research was conducted on a flat agricultural catchment, and it can be assumed that the obtained conclusions can be considered for similar cases. For catchments with different characteristics, further research is advisable.

Application of LiDAR-Derived Data using Multi-Criteria Evaluation (MCE) and Stochastic Modelling; A Flood Risk Analysis of the Mersey River, Nova Scotia

The Maritime province of Nova Scotia has seen coastal flooding become a more frequent phenomenon during the past decades due to the changing climate regime. This has influenced the interest provincial and federal governments have in flood risk modelling, who often incorporate Geographic Information Systems (GIS) as useful tools in their analysis. Incorporating LiDAR-derived digital elevation models (DEMs) in their workflows is the next step in hydrological analysis, as LiDAR-derived DEMs offer high resolution data for the analysis of flood risk without the need to rely on biotic or hydrological data. This study aims to follow this theme in order to model the effects of inland flooding in the low relief landscape of the Mersey River, located in Queen’s County, Nova Scotia, and its effects on the infrastructure built along the river network. The analysis included multi-criteria evaluation (MCE) methods coupled with a stochastic simulation approach in order to determine areas where vulnerability is the most certain. Results indicated that high flood risk is present in urbanized areas within 1 km of the Mersey River at a low degree of uncertainty, making them the best candidates for flood-preventive measures. The accuracy provided by LiDAR-derived DEMs supported a high-quality workflow for the MCE and DEM error analysis, proving their utility for floodplain delineation. The addition of historical and hydrological data to future projects could build on the results presented in this study, adding more to the literature on flood risk modelling along the Mersey River.

Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software

Significant declines in longleaf pine ecosystems during the last 150 years have motivated research and conservation programs focused on their restoration to perceived historical reference conditions. However, the ecological impacts of the historical naval stores industry on forest structure, fire regimes, and fuel conditions are not currently considered in reference conditions constructed from 18th, 19th, and 20th century sources. We present the Tar Kiln Feature Detection workflow (TKFD), an open-access, scripted, and replicable process developed in R and FIJI to identify archaeological tar kilns within high-resolution digital elevation models derived from aerial LiDAR datasets. The workflow is developed and validated on the entirety of the Francis Marion National Forest in coastal South Carolina. The TKFD has identified and measured over 2,700 tar kilns within our 420,000-acre study area and validation studies demonstrate a balanced identification accuracy of 90.6%. This is the most comprehensive dataset of tar production sites in North America and has implications for understanding the historical distribution of longleaf pine stands, anthropogenic impacts on fire and fuels, and the nature of these unique archaeological sites.