Aerial Surveys Research Articles

Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.

Developing digital twins of urban low-income communities in Sub-Saharan Africa: a case study in Ghana, West Africa

Low-income urban and rural communities in Sub-Saharan Africa are habitats for more than 556 million profoundly poor people, and the United Nations and the African Union are pessimistic that sustainable development goals will be met. The number of people falling into poverty is increasing, and policy initiatives to reduce poverty have been confounded by various economic, political, social, structural, and environmental issues. Despite a wealth of natural and human assets, there is no systematic approach to sustainable development for poverty alleviation in Sub-Saharan Africa. This case study of an urban community in Ghana, West Africa, investigates the potential role of digital twins in a systematic approach to sustainable development for poverty alleviation. Aerial and community surveys of the built environment and social and economic surveys of businesses and households were compiled to inform a virtual representation of the study area. A small e-commerce business intervention was introduced, and data was recorded for studies on the impact of the intervention. A 3D interactive view, extensive video, and fixed images provided a comprehensive view of the built environment. A limited view of the social and economic environment was obtained from a small population sample. It was observed that online transactions increased in the businesses receiving the e-commerce intervention, demonstrating a willingness of businesses and their customers to engage in e-commerce when incentives are provided. A single successful community-centric initiative has little value unless it can be generalized across the broader society. This limited case study focused on developing and testing virtual and physical constructs to enhance a deeper understanding of the community, community engagement, and pathways to sustainability. The scale of the intervention was too small to conclude generalizability. Future research will focus on improving the data collection processes, fidelity of virtual representations, visualization methods, and methodologies for constructing viable virtual interventions.

Robotics in Disaster Management: AI-Driven Rescue, Aerial Mapping, and Emerging IoT Applications for Effective Response

This review paper explores the growing role of robotics in disaster management, focusing on the integration of artificial intelligence (AI), Internet of Things (IoT) applications, and advanced simulation environments. Highlighting lessons from events like the Fukushima nuclear disaster and Wenchuan earthquake, it examines the deployment of aerial, ground, and underwater robots in tasks such as data collection, search and rescue, and hazard monitoring. AIoT frameworks enable these robotic systems to classify objects and make decisions on-site, supporting timely and efficient responses in hazardous conditions. Virtual platforms like the DARPA Virtual Robotics Challenge, using simulators such as Gazebo and CloudSim, facilitate realistic testing of robotic capabilities in disaster scenarios, advancing operational readiness through virtual training. This review synthesizes recent progress in AI-driven disaster robotics, identifies challenges, and discusses potential improvements for future applications.

Aerial Systems for Releasing Natural Enemy Insects of Purple Loosestrife Using Drones

Lythrum salicaria (purple loosestrife) is an invasive species that displaces native wetland flora in the USA. The detection and manual release of biological control agents for L. salicaria is challenging because L. salicaria inhabits many inaccessible areas. This study was conducted to develop aerial systems for the detection of L. salicaria and the release of its natural enemy, Galerucella calmariensis (Coleoptera: Chrysomelidae). We determined the optimal sensors and flight height for the aerial detection of L. salicaria and designed an aerial deployment method for G. calmariensis. Drone-based aerial surveys were conducted at various flight heights utilizing RGB, multispectral, and thermal sensors. We also developed an insect container (i.e., bug ball) for the aerial deployment of G. calmariensis. Our findings indicated that L. salicaria flowers were detectable with an RGB sensor at flight heights ≤ 15 m above the canopy. The post-release mortality and feeding efficiency of G. calmariensis did not significantly differ from the control group (non-aerial release), indicating the feasibility of the targeted release of G. calmariensis. This innovative study establishes a critical foundation for the future development of sophisticated aerial systems designed for the automated detection of invasive plants and the precise release of biological control agents, significantly advancing ecological management and conservation efforts.

The effect of Atmospheric Boundary Layer Meteorology in climate-related gases and VOCs concentrations over 3 European cities using an aerial platform

In the frame of the EU Horizon2020 ICARUS project, a N.A.S.A Awarded Light Manned Aircraft equipped with high-tech scientific instrumentation was used to perform an aerial mapping over Athens, Thessaloniki and Ljubljana greater areas. This study aimed to evaluate the effect of Atmospheric Boundary Layer (ABL) on Green House Gases (GHGs) and Volatile Organic Compounds (VOCs) concentrations over urban and rural areas of the above cities. Simultaneous ground-based measurements were performed in the respective regions. Air samples were pressurized with a stainless-steel bellows compressor into electropolished stainless steel canisters and analyzed by the use of a novel gas chromatographic system. The estimation of the mixing height (ΜΗ) of the ABL was based on the synoptic scale atmospheric circulation and the prevailing background wind. It was found that the MH variation, following the prevailing meteorological conditions, results in different concentration profiles in the lower troposphere over the examined regions. The pollutants concentrations were generally decreasing with altitude in the ABL. Under certain meteorological conditions, vertical mixing plus horizontal transport can cause a high pollution level at the top of ABL. The pollutants concentrations were low over less industrialized and upwind regions, suggesting that local emission sources play significant role on the GHGs and VOCs levels over the regions. However, due to the large scale of sampling area that the aircraft covers the above gradients in concentrations are relative low. AQ modelling activities for simulating the cases studied in the current or any relative future work could reduce operating costs and allow projections of potential impacts.

Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species.

Investigation of Radiation Exposure of Medical Staff During Lateral Fluoroscopy for Posterior Spinal Fusion Surgery

Background/Objectives: In spinal surgery, it is especially crucial to insert implants in the correct location. Intraoperative fluoroscopy is often necessary to safely perform spinal surgery because of serious complications that can occur if the screw deviates. However, the use of intraoperative fluoroscopy comes at the cost of radiation exposure to the surgeons and operating room staff. Therefore, it is desirable for spinal surgeons to understand the characteristics of radiation in order to minimize patient and medical staff exposure. This study aimed to create an aerial radiation dose distribution map for lateral fluoroscopy, a commonly used technique for posterior spinal fusion. Methods: A human body-equivalent phantom was placed in a prone position on the Jackson Table. The measurement method used was a lateral fluoroscopic evaluation, assuming posterior spinal fusion. Measurements were taken at three levels: 80 (gonadal), 100 (thoracoabdominal), and 150 cm (lens and thyroid). Results: The highest radiation doses were received by primary surgeons. The scrub nurse was the next most exposed. Conclusions: We developed an aerial dose distribution map for lateral fluoroscopy in posterior spinal fusion. Radiation exposure was the highest among primary surgeons.

Large-scale medieval urbanism traced by UAV-lidar in highland Central Asia.

Aerial light detection and ranging (lidar) has emerged as a powerful technology for mapping urban archaeological landscapes, especially where dense vegetation obscures site visibility1,2. More recently, uncrewed aerial vehicle/drone lidar scanning has markedly improved the resolution of three-dimensional point clouds, allowing for the detection of slight traces of structural features at centimetres of detail across large archaeological sites, a method particularly useful in areas such as mountains, where rapid deposition and erosion irregularly bury and expose archaeological remains3. Here we present the results of uncrewed aerial vehicle-lidar surveys in Central Asia, conducted at two recently discovered archaeological sites in southeastern Uzbekistan: Tashbulak and Tugunbulak. Situated at around 2,000-2,200 m above sea level, these sites illustrate a newly documented geography of large, high-altitude urban centres positioned along the mountainous crossroads of Asia's medieval Silk Routes (6th-11th centuryCE (Common Era)4,5. Although hidden by centuries of surface processes, our pairing of very-high-resolution surface modelling with semiautomated feature detection produces a detailed plan of monumental fortifications and architecture spanning 120 ha at Tugunbulak, thereby demonstrating one of the largest highland urban constellations in premodern Central Asia. Documentation of extensive urban infrastructure and technological production among medieval communities in Central Asia's mountains-a crucial nexus for Silk Road trade networks6-provides a new perspective on the participation of highland populations in the economic, political and social formation of medieval Eurasia.

Going digital: challenges in monitoring marine megafauna when comparing results from visual and digital aerial surveys

Since the first plans to develop offshore wind farms (OWFs), concerns have been raised about the impacts on marine megafauna. Today, it is required to assess these impacts over the whole lifecycle of the OWF. Before construction, initial assessments are often conducted by visual surveys, but subsequent monitoring over the lifecycle of the OWF has to be digital due to safety requirements, leading to challenges in data comparability. The aim of this study was to attempt to establish generalizable intercalibration factors for this transition between visual and digital monitoring methods. To this end, intercalibration surveys were conducted at five different sites and at different times of the year within a site, using both visual monitoring at low-altitude and digital monitoring at both low and high altitudes. We tested the potential for intercalibration of the results based on the ratio of abundance estimated from data collected by the different methods. We explored factors such as the species under study and site-specific conditions that may influence intercalibration. We computed more than 100 intercalibration factors and found that, on average, abundance estimates from digital methods were higher than those from visual methods and that flight altitude for digital monitoring did not significantly influence abundance estimates. Aside from divergent abundance estimates depending on monitoring method, the findings also revealed significant heterogeneity, only one-third of which was explained by contextual factors such as taxonomy or the sea conditions. This outcome presents a pessimistic outlook on the prospect for the intercalibration of results between an initial assessment carried out with visual observations and subsequent monitoring with digital methods after OWF construction and until decommissioning. The high heterogeneity prevents seamless transferability of intercalibration factors and highlights the importance of local context.

Картографирование пещер по фотограмметрическим данным

The authors present a new approach to mapping hard-to-reach caves. It relies on a modern framework of ground-based stereo photogrammetric and geodetic surveying, aerial photography from UAVs and computer modelling. A stereo system was designed and assembled, consisting of two GoProHero9 Black cameras and a powerful artificial light source with diffusing lens, mounted on the operator`s helmet. The site for underground filming was the Syanovskaya quarry (Moscow oblast, Domodedovo rayon). The aerial survey of the cave ground part was also made with DJIAir 2S UAV; it included a ravine with a concrete well, which was the entrance to the quarry. The article deals with the processes of geodetic georeferencing, external orientation of the surface model and underground parts of the cave, as well as the cartographic materials obtained in the course of the work

Spatial and temporal variation of marine megafauna off coastal beaches of south-eastern Queensland, Australia

Context Coastal beach environments provide habitats for marine megafauna, including turtles, rays, marine mammals and sharks. However, accessing these variable energy zones has been difficult for researchers by using traditional methods. Aims This study used drone-based aerial surveys to assess spatio-temporal variation of marine megafauna across south-eastern Queensland, Australia. Methods Drones were operated at five south-eastern Queensland beaches. Megafauna sightings and key variables including location, month and turbidity were analysed to assess variation across locations. Key results Overall, 3815 individual megafauna were detected from 3273 flights. There were significant differences in the composition of megafauna assemblages throughout the year and among beaches, with megafaunal sightings in >80% of flights conducted off North Stradbroke Island. Conclusions Strong temporal presence was found that is congruent with other studies examining seasonality. This supports the use of drones to provide ecological data for many hard-to-study megafauna species and help inform long-term sustainable management of coastal ecosystems. Implications Results indicated that environmental conditions can influence the probability of sighting marine megafauna during aerial surveys.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Identifying macrofloating debris hotspots in the Mediterranean Sea applying multiplatform methodologies

The Mediterranean Sea is considered the world's sixth greatest hotspot for marine litter; however, quantifying the extent of marine debris in the oceans is a challenge, especially due to variations in survey methodologies. This study aims to assess the spatial abundance and distribution of macrofloating debris (> 2.5 cm) in the Mediterranean basin through visual surveys carried out by methods (research vessel, sailing vessel, and aerial) and approaches (science and citizen science). Aerial and research vessel surveys estimated litter at 1.88 ± 2.3 items·km−2 and 0.89 ± 1.61 items·km−2 respectively for the whole Mediterranean; moreover both methods agreed that the main macrofloating debris hotspots were in the east of Algeria, Tyrrhenian, Adriatic and Alboran Seas. Likewise, for the common blocks analysed aerial surveys estimated greater amounts of macrofloating debris than research vessels (mean 1.92 ± 2.61 items·km−2 vs. 0.94 ± 1.69 items·km−2) highlighting the different detection capacities of the two methods. In the Spanish Mediterranean continental shelf, results obtained from research vessels showed mean values of 8.6 ± 7.8 items·km−2 for 2021 and 3.86 ± 3.96 items·km−2 for 2022. Sailing vessels along the Spanish coastline registered up to 70.87 ± 257.23 items·km−2 in waters of the Cabrera Island, which is a Marine Protected Area. No significant differences between citizen science and scientific methods were found, which suggests that the implementation of this tool could be very useful in obtaining greater datasets. Results on the abundance of macrofloating debris could be attributed to various factors, including the influence of mighty rivers, a dense population in these areas, especially during some seasons like summer, and the effect of some currents and eddies, such as the Algerian and the northern currents, which also influence the transboundary plastics.

Hybrid bottom-up and top-down framework resolves discrepancies in Canada’s oil and gas methane inventories

Estimating accurate oil and gas methane emissions has been a global challenge, highlighted by a twofold discrepancy between atmospheric measurement-based estimates and emission inventories. The principle of continuous improvement in Canada’s National Inventory Report has led to an unstable baseline in recent years for tracking emission reduction progress. The gaps between previous inventory estimates and inversions exceeded 60%. Here we show that incorporating new source-resolved information derived from low-altitude aerial survey data has narrowed this gap by 80%, reducing the discrepancy to 10% for the 2010–2014 baseline. This study proposes a hybrid emission reporting framework, complemented by an ensemble inversion top-down method using continuous tower-based atmospheric measurements, to establish a stable baseline and provide independent verification. As the 2030 target year for emission reduction approaches, we report a significant 27% decline (19%–34%) in inverse oil and gas methane emissions from 2010 to 2022 in Alberta and Saskatchewan, Canada, and a 41% decline (26%–56%) as calculated using the 2024 official inventory.

Synergy of UAV Aerial Survey Methods and LiDAR Scanning for the Study of Planar Objects of Historical and Cultural Heritage

Synergy of UAV Aerial Survey Methods and LiDAR Scanning for the Study of Planar Objects of Historical and Cultural Heritage

Unmanned aerial vehicle-based aerial survey of mines in Shanxi Province based on image data

Abstract Accurately monitoring the change of mine area in the mining process is beneficial to mine safety management. This paper briefly introduces the collection of remote sensing images by unmanned aerial vehicles (UAVs) and its application in measuring surface mining subsidence and surrounding vegetation in the mining area. A case study was carried out in some mining areas of Nanshan Mountain, Yuncheng City, Shanxi Province. The surface mining subsidence value and vegetation-related parameters were measured by comparing the digital elevation model and multi-spectral images collected on May 12 and June 12, 2023. The validity experiment verified that the UAV image data could be used to measure the mining subsidence and vegetation parameters. Moreover, it was found that mining underground coal could lead to significant ground subsidence and pollute the surrounding environment, reducing vegetation. The innovation of this article lies in using UAV-collected remote sensing images instead of manually collecting ground elevation data and vegetation distribution data, providing effective references for safe mining in mining areas.

A Proposed Method for Assessing the Spatio-Temporal Distribution of Carcharhinus melanopterus (Quoy and Gaimard, 1824) in Shallow Waters Using a UAV: A Study Conducted in Koh Tao, Thailand

In this study, we propose a method for assessing the temporal and spatial distribution of Carcharhinus melanopterus in shallow waters using unmanned aerial vehicles (UAVs). Aerial surveys were conducted in Tien Og Bay (Koh Tao, Thailand) thrice daily (morning, afternoon, evening) along a 360 m transect at a 30 m altitude. Environmental factors, including cloudiness, sea conditions, wind, tide, and anthropogenic disturbance, were recorded for each time slot. We developed a Python/AppleScript application to facilitate individual counting, correlating sightings with GPS data and measuring pixel-based length. Abundance varied significantly across time slots (p < 0.001), with a strong morning preference, and was influenced by tide (p = 0.040), favoring low tide. Additionally, abundance related to anthropogenic disturbance (p = 0.048), being higher when anthropogenic activity was absent. Spatial distribution analysis indicated time-related, sector-based abundance differences (p < 0.001). Pixel-based length was converted to Total Length, identifying juveniles. They exhibited a strong sector preference (p < 0.001) irrespective of the time of day. Juvenile abundance remained relatively stable throughout the day, constituting 94.1% of afternoon observations. Between 2020 and 2022, an underwater video survey was conducted to determine the sex ratio of the individuals. Only females and juveniles were sighted in the bay.

Comparison of thermal cameras and human observers to estimate population density of fallow deer (Dama dama) from aerial surveys in Tasmania, Australia

Context The population of introduced fallow deer (Dama dama) is thought to have increased exponentially across much of the island of Tasmania, Australia, since 2000. Historically, deer management decisions have relied on population trend data from vehicular spotlight surveys. Renewed focus on the contemporary management of the species requires development of more robust and precise population estimation methodology. Aims This study demonstrates two aerial survey methods – conventional counts by trained human observers, and thermal imaging footage recorded during the same flights – to inform future survey practices. Methods Conventional counts were carried out by three observers, two seated on the left side of the helicopter, and one on the right. A high-resolution thermal camera was fitted to the helicopter and was orientated to meet the assumptions of distance sampling methodologies. Both survey methods were used to generate deer population density estimates. Spatial distribution of deer was also analysed in relation to patches of remnant native vegetation across an agricultural landscape. Mark–recapture distance sampling was used to estimate density from human observer counts and provide a comparison to the distance sampling estimates derived from the thermal camera. Key results Human observer counts gave a density estimate of 2.7 deer per km2, while thermal camera counts provided an estimate of 2.8 deer per km2. Deer population density estimates calculated via both methods were similar, but variability of the thermal camera estimate (coefficient of variation (CV) of 36%) was unacceptably high. Human observer data was within acceptable bounds of variability (CV, 19%). The estimated population size in central and north-eastern Tasmania for 2019 approximated 53,000 deer. Deer were primarily congregated within 200 metres of the interface between canopy cover and open pasture. Conclusions The population density estimate provides a baseline for monitoring and managing the Tasmanian deer population. Human observer data was more precise than thermal camera data in this study, but thermal counts could be improved by reducing sources of variability. Implications Improvements for the collection of thermal imagery are recommended. Future control efforts may be more efficient if they preferentially target habitat edges at this time of year, paired with random or grid-based searches where population density is lower.

Morphological Traits Are Not Consistently Related to Population Size in Four Migratory Caribou Populations Across North America.

To develop effective management to maintain or restore populations of large herbivores, wildlife managers require sound empirical data on their variations in size and associated parameters. Many studies have highlighted links between morphological traits of individuals and population density; however, less attention has been devoted to whether or not morphological traits can reliably inform on population size in years when no population estimates are available. We evaluated the relationships between three morphological traits (hind foot length, body mass, and body fat) and population size interpolated over three decades, for four migratory caribou (Rangifer tarandus) herds in northern Canada and Alaska. Our sample included 8865 measurements of 4473 individuals. We used a Bayesian modeling approach to evaluate the relationships between morphology and population size across different sex and age classes, considering different temporal scales and, when possible, phases of population growth or decline. We found that morphological traits were not consistently linked to population size. Statistically significant relationships existed for some combinations of herd and age classes, but weak to absent relationships were more common. Our study suggests that morphological traits alone cannot replace data obtained from aerial surveys to approximate population size when population trends are unknown. We discuss the usefulness of morphological traits to explain population size, and recognize their role as complementary metrics to inform the management and conservation of large herbivores, but conclude that morphological data should not be used to predict population size without information on population trends.

Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary

Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.