Terrain Elevation Research Articles

AVALIAÇÃO COMPARATIVA DA QUALIDADE DOS DADOS DE ELEVAÇÃO OBTIDOS COM GNSS, GOOGLE EARTH E SRTM PARA PROJETOS DE ESTRADAS

Obtaining terrain elevation data is essential for numerous engineering applications. However, regardless of the technique used to obtain the elevation data, there will be errors intrinsic to the instruments and to the measurement technique used, as well as depending on the model of Earth representation, which are plane, spherical or ellipsoidal. In this perspective, this work proposes a comparative approach between the elevation data obtained from the geodesic coordinates, provided by Global Navigation Satellite System (GNSS), and those obtained from Google Earth (GE) and Shuttle Radar Topography Mission (SRTM). The data collection was carried out in the urban section between km 125 to km 145,500 of Dom Pedro-I Highway (SP-065), State of São Paulo, Brazil. The quality of the results is evaluated based on the Mean Quadratic Error (RMSE). The main results showed that the elevation data obtained through the SRTM show greater agreement or adjustment to the GNSS data when compared to the higher quality and less variation than those obtained through the GE, since they have smaller RMSE and are closer to the average elevations calculated for the highway segment analyzed. However, it is worth noting that the use of GE can be a good alternative for low-cost preliminary studies.

Remote detection of upland surface water storage capacity in deglaciated terrain

AbstractA notable characteristic of terrain in non‐urbanized deglaciated areas of northeastern North America is the microtopography created by processes related to surficial geology, deglaciation and mechanical disturbances to surface materials from excavating events, most of which are caused by tree throw in the modern landscape. The features are often on the scale of 1–4 m across and decimetres to a metre in depth, appearing as ‘puddles’ during intense or high‐magnitude precipitation events. Generalized storage capacity values have been summarized in textbooks for varied landscape conditions, but surprisingly little information is available about how microtopography and related surface water storage varies in dominant physiographic settings in deglaciated landscapes defined by slope, surficial geology and land cover conditions. The increasing availability of elevation data at a horizontal resolution of 2 m or higher has made it possible to remotely evaluate differences in terrain elevation and quantify upland surface water storage capacity from relatively small topographic depressions. Here, we describe and quantify these topographic features in several coastal and inland watersheds in the state of Maine (USA) with measurements of depression volume calculated from digital elevation models (DEMs) using a pit filling approach. Results show that microtopographic storage capacity varies with slope and land cover conditions in deglaciated terrain of northeastern North America. Basin‐average surface water depression storage capacity estimates range from ~4 mm to as low as 0.2 mm. Human interventions such as clearing land for agriculture are associated with lower microtopographic surface water storage capacity than forested landscapes in the region.

Correction of ICESat-2 terrain within urban areas using a water pump deployment criterion with the vertical contour of the terrain

Previous studies have shown that the terrain elevation of the ICESat-2's ATL08 product is of good accuracy, overall, but in urban areas under complex surface conditions, the terrain elevation is vulnerable to the influence of the effect of dense buildings, resulting in deviations and even outliers. However, the previous studies have not effectively corrected these deviations and outliers. To this end, we propose a method for correcting the terrain elevation of the ATL08 product in urban areas, which is an auto-refinement method for the terrain elevation. Based on the data characteristics of this type of terrain elevation data and the distribution characteristics of urban buildings, the proposed method innovatively corrects the deviated part of the terrain elevation by a water pump deployment criterion with the vertical contour of the terrain. To increase its applicability, the proposed method uses a strategy of mixing the terrain elevation data that are from the strong- and weak-beam observations, allowing the deviated terrain in one beam with a higher missing data rate to be corrected with the aid of the other beam with a lower missing data rate. We used airborne LiDAR data from several areas (New York, Amsterdam, and Palmerston North) with different city sizes to validate the corrected results. The results show that the root-mean-square error (RMSE) of the terrain elevation in urban areas is reduced from ∼4 m to ∼1 m after correction. The correction results also show that the proposed method can correct the deviated terrain elevation, while ensuring that the high-quality terrain elevation can be effectively retained.

UWB and MB-OFDM for Lunar Rover Navigation and Communication

This paper presents a comprehensive study of ultra-wideband (UWB) and multi-band orthogonal frequency-division multiplexing (MB-OFDM) technologies for lunar rover navigation and communication in challenging terrains. Lunar missions pose unique challenges, such as signal propagation in the lunar environment, terrain elevation, and rover movement constraints. To address these challenges, we propose a hybrid communication and navigation system that leverages UWB technology for high-precision positioning and MB-OFDM for robust and high-throughput communication. We develop a realistic simulation framework that incorporates terrain elevation, obstacles, and rover movement constraints, along with a simple fading model for communication. Simulation results demonstrate the effectiveness of the proposed system in navigating lunar rovers to their target locations while maintaining reliable communication links with a lunar lander. A novel approach based on game theory for rover navigation is also presented. The study provides valuable insights into the design and optimization of communication and navigation systems for future lunar missions, paving the way for seamless integration of advanced terrestrial technologies in extraterrestrial environments.

Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

ABSTRACT Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China’s first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu’er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

ABSTRACT The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

Boundary‐layer plumes over mountainous terrain in idealized large‐eddy simulations

AbstractCoherent plume structures in the convective boundary layer over non‐flat terrain are investigated using large‐eddy simulation. A conditional sampling method based on the concentration of a decaying passive tracer is implemented in order to identify the boundary‐layer plumes objectively. Conditional sampling allows quantification of the contribution of plume structures to the vertical transport of heat and moisture. A first set of simulations analyzes the flow over an idealized valley, where the terrain elevation only varies along one horizontal coordinate axis. In this case, vertical transport by coherent structures is the dominant contribution to the turbulent components of both heat and moisture flux. It is comparable in magnitude to the advective transport by the mean slope‐wind circulation, although it is more important for heat than for moisture transport. A second set of simulations considers flow over terrain with a complex texture, drawn from an actual digital elevation model. In this case, conditional sampling is carried out by using a simple domain‐decomposition approach. We demonstrate that thermal updrafts are generally more frequent on hill tops than over the surroundings, but they are less persistent on the windward sides when large‐scale winds are present in the free atmosphere. Large‐scale upper level winds tend to reduce the vertical moisture transport by the slope winds.

Influence of Urban Spatial Structure on the Spatial Distribution of Gaseous Pollutants

The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June).

Historical residues of iron ore mining in environs of the Holy Cross Mountains (the Góry Świętokrzyskie) are recognizable on the Digital Terrain Elevation Model (DEM) derived from the LIDAR data

Historical residues of iron ore mining in environs of the Holy Cross Mountains (the Góry Świętokrzyskie) are recognizable on the Digital Terrain Elevation Model (DEM) derived from the LIDAR data

A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry

Designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines’ operations and terrain features as well as the trade-offs between various objectives. With the overall goal to minimize the operational costs of the logging operation, this paper proposes a mathematical optimization model for the trail network design problem and a greedy heuristic method based on different randomized search scenarios aiming to find the optimal location of machine trails —with potential to reduce negative environmental impact. The network is designed so that all trees can be reached and adapted to how the machines can maneuver while considering the terrain elevation’s influence. To examine the effectiveness and practical performance of the heuristic and the optimization model, it was applied in a case study on four harvest units with different topologies and shapes. The computational experiments show that the heuristic can generate solutions that outperform the solutions corresponding to conventional, manual designs within practical time limits for operational planning. Moreover, to highlight certain features of the heuristic and the parameter settings’ effect on its performance, we present an extensive computational sensitivity analysis.

A Geographic Information System (GIS) based approach for drainage and morphometric characterization of Beki river basin, India

Geographic Information Systems and remote sensing, have proved to be efficient tools in delineation of drainage pattern and different geometric methodology of geomorphologic, watershed management even GIS has been widely used in several flood management, and environmental applications. The river Beki with an area of 19,354.35 sq.km2 originates at Himalayan glacier (Kula Kangri glacier in Bhutan) 26.18° N latitudes and 90.53° E longitudes and flows though the plains of Assam and finally to the mighty Brahmaputra at 26.48° N latitudes and 91.02° E longitudes has been selected for detailed morphometric analysis. Morphometric parameters via; Stream order, Stream length, Bifurcation ratio, Drainage density, Drainage frequency, Drainage texture, Form factor, Circularity ratio, Elongation ratio and Compactness ratio etc. were measured for prioritization and compound parameter values were calculated. This study will help the local people to utilize the resources in right manner for Sustainable Water Resource Development of the Basin area. Moreover, the study can also be referred as a benchmark for studies on temporal change in geomorphology due to climate change. Different Morphometric analysis provides the explanation of physical characteristics of the watershed which are useful for the areas of land use planning, soil conservation, terrain elevation and soil erosion.

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.

A turbulent orographic form drag scheme accounting for anisotropy and orientation for kilometer‐ to subkilometer‐scale models

AbstractThis paper presents a turbulent orographic form drag (TOFD) parameterization scheme that takes into account the directional effects stemming from the angle between the low‐level wind and the principal axis of small‐scale orography. Suitable for models with both kilometer and subkilometer horizontal resolutions, this scheme builds upon prior theoretical and numerical studies to formulate surface TOFD based on the slope and direction of the sinusoidal hills. In this study, we proposed a straightforward function to calculate the surface TOFD for various orographic aspect ratios and directional parameters. The vertical decay of the drag is modeled with a scale twice the standard deviation of the sub‐grid orography. A comparison with numerous large‐eddy simulations featuring a single ellipsoidal hill demonstrates that our scheme effectively captures the dependence of drag on factors such as maximum slope, aspect ratio, the angle between low‐level wind and the principle axis, and hill height. We recommend calculating the sub‐grid orographic parameters using a well‐established method and digital terrain elevation data with a horizontal resolution less than a 100 m. This will allow for representation on orographic scales of both kilometers and subkilometers. Real‐case numerical weather prediction tests are conducted and verified with dense surface wind observations. The proposed scheme improves surface wind simulation compared to a renowned TOFD scheme, and also effectively exhibits the wind response to orographic anisotropy.

Investigation of spatial variability in power law index of wind profiles above the urban area around Tokyo Bay based on local objective analysis data

By utilizing meteorological local objective analysis (LA) data on a local scale, this study investigated the spatial distribution of the power law index (PLI) of the mean wind speed profile in an urban area around Tokyo Bay in Japan. First, the reliability of the LA data was verified by comparison with observational data. The PLIs of the area for the 16 wind directions at a spatial resolution of 5 km were then estimated using LA data. Under neutral atmospheric conditions, the effects of the roughness length and terrain elevation on the PLI were investigated, and the spatial distribution characteristics of the PLI were analyzed. The results showed that the LA data were consistent with the observational data, indicating that high-precision LA data produced by data assimilation can be a practical supplement to observational data over urban areas. The influence of wind direction on the PLI was evident in the urban area. Furthermore, the metropolis appeared to affect the wind profile significantly when it was within the fetch of other cities, resulting in a larger PLI in the surrounding area. Because the PLI has complex spatial heterogeneity, in addition to urban substrate roughness, changes in terrain elevation and wind direction are thought to affect the PLI significantly. Therefore, generalizing the wind profile characteristics of large-area urban regions using local on-site observational data is not recommended.

An Improved Future Land-Use Simulation Model with Dynamically Nested Ecological Spatial Constraints

A land-use simulation model oriented toward ecological constraints is effective for evaluating the ecological impact of urban spatial planning. However, few studies have incorporated dynamically nested ecological spatial constraints into the model or fully considered the urban development, agricultural production, and ecological function among the ecological spatial constraints. Therefore, this study developed an improved land-use simulation model with dynamically nested ecological spatial constraints (LSDNE). We fully considered the multilevel ecological spatial constraints from the perspectives of ecological (ecological protection red line, EPRL), production (capital farmland, CF), and living (urban development land-use suitability, UDLS). Five scenarios in terms of future land-use distribution in 2030 were set, namely, inertial development (S1), considering EPRL (S2), considering CF (S3), considering EPRL and CF (S4), and considering EPRL, CF, and UDLS (S5). This new approach was implemented in the rapidly developing provincial capital city of Changchun, China. The results show that, due to the occupation of arable land, Changchun had the largest increase in built-up land (2019.75 km2 to 3036.36 km2) from 2010 to 2020. Terrain elevation was the most significant factor in all kinds of land expansion. According to future land spatial distribution results in 2030, under S4, Changchun’s built-up land will be more compact compared with S1–S3 and S5, which showed more scattered built-up land. These predicted results show that Changchun’s spatial planning put forward high requirements for the efficient use of land and constraints in red-line areas. Due to a clear evaluation of the impact of ecological spatial constraints on future land expansion, the LSDNE model provides more accurate support for the efficient use of land resources and future territorial spatial planning.

Flooding risk of cropland areas by repiquetes in the western Amazon basin: A case study of Peruvian Tamshiyacu City

Study regionThe western Amazon basin at Tamshiyacu gauging station (near the Iquitos City) hosts floodplain agriculture that can be affected by the sudden reversal in direction of water levels known as “repiquetes” that produce intermittent flooding. Study focusThis study assesses repiquete flooding risk in riparian crop areas based on statistical analyses of repiquete events registered from 1996 to 2018, hydraulic modeling to estimate flooded extension, and assessment of climatological characteristics during the formation of repiquetes. New hydrological insightsFloods (≥ 20 cm) produced by repiquetes in riparian crop areas between 83.00 and 88.00 m above sea level (masl) occur 1.8 times per year. However, not all elevation ranges have the same flooding risk to crops. Terrain elevations between 85.31 and 87.00 masl have a reduced flooding risk of 0.35 per year. Likewise, areas with elevations between 87.00 and 88.00 masl (43% of the total area) were not affected by repiquetes. Extreme repiquetes (study cases of 2002 and 2008) have been influenced by the increase of atmospheric moisture flux convergence and precipitation over both the northern Ucayali and Marañón basins through the six previous days. Flood impacts from the extreme event of 2002 (2008) could have reached 40% (25%) of the available area for agriculture at the initiation of the repiquete.

Groundwater flooding hazard assessment in a semi-urban aquifer through probability modelling of surrogate data

Many areas of the world are experiencing groundwater flooding (GF) events with serious impacts on the environment and human health. In Europe, GF has received specific attention with the Directive 2007/60/EC, which requires Member States to map GF hazard and risk and propose mitigation measures. In Italy, despite the commitment assigned by the Legislative Decree 49/2010 to River Basin District Authorities, no models have been developed for groundwater flooding hazard (GFH) evaluation also for the lack of standard methods based on commonly available data. In this paper, a new method has been developed through a specific procedure that accounts for i) probability of GF events estimation based on available time series of groundwater levels and ii) probability mapping through a newly developed MATLAB™ code, high-resolution topographic data and groundwater table model. The methodology has been tested and validated in the Metropolitan City of Naples (Italy), affected by GF since 2010, for which i) sixty-two-year long time series of groundwater levels, ii) high-resolution Digital Elevation and Digital Terrain Models and iii) basement/building geometry, are available. The results show that about 59% of the buildings present in the study area have potentially floodable underground structures. In the 2010–2021 period, only 2% of these buildings were flooded and most of them were built in the period 1980–2000, when the piezometric levels were deeper due to over-pumping of the groundwater. The obtained GFH map shows that only 2% of the built area is involved by 1- to 2- year events, while 6% is affected by 1- to 10- year events. A good spatial correspondence between the return period and locations of GF events occurred during 2010–2021 period was observed, thus indicating the significant performance of the model. The obtained GFH map output can support decisions of authorities and policy-makers to reduce the GF risk.

Geometry and topology of a Polish Outer Carpathian digital-elevation-model-interpreted lineament network in the context of regional tectonics

Abstract. The Polish part of the Western Outer Carpathian lineament network was analysed based on the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) digital elevation model. Lineaments were identified in the visual screening of the hillshade model. To the best of our knowledge, no one has studied the geometrical properties of the network in relation to the topological ones. The NetworkGT QGIS toolbox was applied to identify the nodes and branches of the network as well as to calculate the topology parameters. Our aim was to find differences between the western and eastern parts of the Western Outer Carpathians; therefore, the analyses were carried out in six sectors chosen based on the geographical subdivision in the geological context: three in the north, mainly the Silesian unit, and three in the south, mainly the Magura unit. We found general agreement of the identified network with the photo-lineament map; however, some of the photo-lineaments are not confirmed by a digital elevation model (DEM). We found that the topological parameters of the networks change from west to east but not from north to south. There are areas of increased interconnectivity, especially the Nowy Sącz Basin, where the lineament network may reflect a complicated system of cross-cutting, deep-rooted fault zones in the basement.

Extraction of tree heights in mountainous natural forests from UAV leaf-on stereoscopic imagery based on approximation of ground surfaces

The application of high-resolution stereoscopic imagery acquired by Unmanned Aerial Vehicle (UAV) on the extraction of forest heights has grown rapidly in recent years. Most existing studies either required auxiliary terrain data, e.g., Digital Terrain Model (DTM) provided by lidar data, or focused on flat terrains. It is still a great challenge to extract tree heights in mountainous forests only using UAV leaf-on stereoscopic imagery. An algorithm referred to as AGAR (i.e., Approximation of Ground using Allometric Relationship) is proposed in this study to estimate individual heights of visible trees on UAV stereoscopic imagery in mountainous natural forests. The central idea of the AGAR algorithm is firstly to approximate the understory terrain elevations (i.e., DTM) based on attributes of tree crowns (e.g., crown area) and the iterative adjustment of allometric equation coefficients. Then individual tree heights are determined by differencing the elevation of crown tops with that of the approximated ground surface. The proposed algorithm was demonstrated at five sites with different terrain conditions by taking field measurements and ICESat-2 data as references, respectively. Results showed that the AGAR algorithm worked well on the estimation of tree heights at all sites. In contrast, the classical progressive triangulation filter (PTF) algorithm was susceptible to terrains and forest structures. The root mean square error (RMSE) and relative RMSE (rRMSE) of tree heights estimated by the PTF algorithm were 4.4 m ∼ 6.3 m and 32.6% ∼ 37.6%, respectively. They were decreased by the AGAR algorithm to 1.7 m ∼ 2.5 m and 12.6% ∼ 15.2%, respectively. The AGAR algorithm will substantially advance the application of UAV stereoscopic imagery on the extraction of tree heights in the absence of other available terrain data, and will also open new horizons for application of decimeter or even centimeter spaceborne stereoscopic imagery on forest vertical structures in the future.

Breeding Population and Nesting Habitat of Skuas in the Harmony Point Antarctic Specially Protected Area

Regular monitoring of breeding population abundance and environmental factors related to the nesting habitat has proven fundamental for seabird conservation. Harmony Point (Nelson Island) is an Antarctic Specially Protected Area (ASPA) designated to manage and protect the high biological value of seabirds’ richness and abundance. However, due to the remote location of their breeding sites, many species lack updated population counts. Skuas (Catharacta sp.) exhibited a two-decade gap since the last census was conducted in Harmony Point. The abundance and spatial distribution of the nests of skuas were studied during the austral summer of 2019/2020. Through an exhaustive search, we counted and mapped active nests. Nesting habitat was assessed by the use of an unmanned aerial vehicle to take aerial pictures and build an orthomosaic image to determine vegetation in the area. Additionally, a digital elevation model was built to calculate a series of geomorphological-related variables. Suitability models were used to estimate the importance of variables to the nesting of skuas. A total of 71 brown skua (Catharacta antarctica) and 3 south polar skua (C. maccormicki) nests were counted. Two of the seven variables (terrain slope and vegetation cover) accounted for 57.5% ± 14.1% of the models’ variability; sun radiation incidence, and wind shielding were of secondary importance. Water flow accumulation, distance from penguin colonies, and terrain elevation were the least important variables. Skuas selected for nesting flat terrains (slope < 10°) with a vegetation cover of above 20%, slightly higher sun incidence (270 to 280 kW/h), and intermediary windshielding (45% to 55% of exposition). Considering previous estimates, the skua species at Harmony Point has kept an apparently stable population size over the last 25 years. However, expected changes in nesting habitat availability, i.e., increased snow-free area, increased wind intensity, changes in vegetation cover, and reduction of penguin populations, might change population size in the mid to long term.