Unmanned Aerial Vehicle Surveys Research Articles

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The article presents the results of research on the creation of a detailed soil map (scale 1:2500) using the example of a land plot of the Federal State Budgetary Educational Institution of Higher Education Perm State Agrarian University. The purpose of the research is to improve the methodology of detailed soil mapping using an unmanned aerial vehicle (UAV) and methods of geoinformation analysis of aerial photographs. The total area of the plot is 13.4 hectares, on which a UAV survey was carried out on September 12, 2023, and a soil survey was carried out on a detailed scale from June 1 to 24, 2024. Soil mapping was carried out on the basis of field data and a digital elevation model created by photogrammetry methods based on UAV survey materials. The map creation process is automated by creating geoprocessing models in QGIS model builder. Based on the results of creating a detailed soil map, a conclusion was made about the close relationship of soils with characteristic relief elements, which are reflected in classes according to the topographic position index TPI. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL MAP

Heading and maturity date prediction using vegetation indices: A case study using bread wheat, barley and oat crops

Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs.

Reconstruction method for a three-demensional discrete element numerical model of landslides using an integrated multi-electrode resistivity tomography method and an unmanned aerial vehicle survey

To analyze the hazard-causing modes of landslides, this paper proposes a three-dimensional discrete element model reconstruction method that employs an unmanned aerial vehicle survey and multi-electrode resistivity tomography method. To convert the resistivity profile into a material profile, we adopt the peak of the probability density method for material classification and utilize the Haar wavelet transform for image denoising. Subsequently, inverse distance weighting interpolation and the curtain-point method are used to transform two-dimensional profiles into a 3D visualization model. Similarly, the triangular mesh boundary can be extracted from the 3D visualization model using the curtain-point method. A mapping function f including the macroscopic parameters, was defined to populate the particles within the boundaries. Using the iterative method and defining the loss function L for parameter calibration, the targeted 3D discrete element model was constructed after setting the velocity threshold. This method was applied to the Changhe landslide (September 14, 2019) in Gansu Province, China, which had a typical damaged soil layer due to earthquake and rainfall factors. The results indicate that the lower part first exhibits significant displacement, followed by the upper and middle parts, which is consistent with the on-site inspections and UAV findings.

Surface topography modelling and ice flow velocity mapping of Dalk Glacier, East Antarctica: application of UAV remote sensing

Abstract. The Antarctic Ice Sheet is the largest potential contributor to global sea level rise due to global climate warming, making it important to monitor changes in the marginal outlet glacier dynamics and surface topography. The unmanned aerial vehicle (UAV), a new platform, has been proven to be a useful tool in glaciological applications because it can collect data in a variety of ways and has a high spatial and temporal resolution compared to traditional ground-based measurements and remote sensing from space. In this study, we used the combined UAV platform composed of the DJI Mavic 3 Enterprise (M3E) and M300 (L1) versions to perform high-accuracy on-site investigations of Dalk Glacier, a typical outlet glacier near the Zhongshan Station in East Antarctica. During the 39th Chinese Antarctic Expedition (CHINARE) in 2022–2023, UAV surveys with multiple sensors were performed. The M3E optical camera makes it possible to reconstruct high-resolution ortho-mosaic and digital elevation models (DEMs) of Dalk Glacier with high accuracy based on photogrammetric principles, helping us to extract the features of glacier surface topography, while dense point clouds derived from the L1 LiDAR camera are used to validate and improve the three-dimensional accuracy. To further monitor the dynamics and stability of Dalk Glacier, we generate ice flow velocity maps using ortho-mosaics for the years 2019, 2020, and 2023. The spatiotemporal changes in ice flow velocities were further compared with the products derived from satellite remote sensing data.

Monitoring and Quantifying Soil Erosion and Sedimentation Rates in Centimeter Accuracy Using UAV-Photogrammetry, GNSS, and t-LiDAR in a Post-Fire Setting

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams.

GNSS-supported direct georeferencing for UAV photogrammetry without GCP in Antarctica: a case study in Larsemann Hills

Unmanned aerial vehicle (UAV) has become an increasingly popular remote sensing platform in Antarctica. Due to the challenging natural conditions and lack of global navigation satellite system (GNSS) references in Antarctica, GNSS-supported direct georeferencing holds great potential for remote sensing applications in this region. Based on UAV surveys and GNSS observations we performed in Larsemann Hills, Antarctica, four GNSS-supported direct georeferencing schemes for UAV photogrammetry without ground control point (GCP) were designed and evaluated. Three of the schemes can generate high-accuracy photogrammetric products (horizontal accuracy: ∼0.7 ground sampling distance (GSD), vertical accuracy: ∼2.6 GSD). The fourth scheme, while exhibiting a lower accuracy at the meter-level, could offer high flexibility, and the accuracy of its derived products could be improved by post-flight transformation. The selection of an appropriate georeferencing scheme should be contingent upon the given application scenario, which can enhance the quality and accuracy of UAV photogrammetry in Antarctica. Potential applications of UAV remote sensing in Antarctica were discussed. It’s proven that UAV photogrammetry constitutes a reliable tool for Antarctic expedition path planning and ice morphology evolution monitoring. Our study demonstrated that direct georeferencing can generate high-accuracy UAV products in a reliable and feasible way in Antarctica.

Thermal photogrammetry on a permafrost rock wall for the active layer monitoring

Permafrost and active layer models often cannot explain the high spatial variability, especially in heterogeneous environments like the mountainous regions due to their scarce resolution, paucity of climatic data and topographic details. In this study, we want to introduce a new application of the unmanned aerial vehicle (UAV) in thermal photogrammetry to model the active layer thickness (ALT) of an alpine rock wall through the computation of the thermal inertia and compare the results with a widespread ALT model. On the Gran Zebrù South rock wall, 8 thermal UAV surveys has been conducted in 4 different summer days during 2021-2022 in order to have two 3D thermal models per day at different solar radiation inputs. By analyzing topographic data, visible imagery and the thermal models, the apparent thermal inertias (ATIs) have been converted into heat transfer coefficients (HTCs) and then into ALT of 2021 and 2022. These maps have been validated through the placement of thermistors at different elevations and with variable depths (2, 15 and 40 cm from the rock surface).The resulting ALT has been compared with the Stefan's solution and the alpine permafrost index map (APIM), which showed large underestimations and a noncorrespondence with permafrost occurrence. The average ALT increase of 29.3 cm from 2021 to 2022 has been discussed regarding permafrost formation/degradation future trend under the climatic change and potential risks of alpine areas.

Assigning resistivity values to rock quality designation indices using integrated unmanned aerial vehicle and 2D electrical resistivity tomography in granitic rock

Rock quality designation (RQD) is a standard technique in mining and geotechnical investigation for quantifying the quality and degree of jointing of the rock mass. However, the need for expeditious and inexpensive geotechnical site characterization is the prime motivation for modifying RQD. This research work integrates 2D Electrical Resistivity Tomography (2D ERT) and Unmanned Aerial Vehicle (UAV) conducted at Site 1 and Site 2. The UAV survey was performed to calculate RQD on the rock surface using volumetric Joint Counts (Jv) at Sites 1 and 2. A 2D ERT survey was conducted to obtain the corresponding subsurface resistivity values. Using k-nearest neighbours (k-NN), resistivity values were assigned to various RQD indices such as very poor rocks (<350 Ωm), poor rock (350–1150 Ωm), fair rock (1150–1850 Ωm), good rock (1850–2500 Ωm) and >2500 Ωm excellent rock. Based on the established correlation of RQD and resistivity, the subsurface rock mass quality at Site 1 was predominantly good and excellent rock, while at Site 2, poor rock was significantly reported. This research concluded that rock mass characterization using RQD is more rapid and inexpensive than the direct or indirect calculation of RQD. The combined 2D ERT and UAV deployed in this research study provide an environmentally non-destructive approach for slope stability assessment.

On the use of MTInSAR data and UAV photogrammetry to monitor the behavior of existing bridge portfolios

Bridges play a vital role in road networks, and ensuring their safety and preservation is of utmost importance to both management companies and the scientific community. On the other hand, ensuring a continuous assessment of the bridge portfolios health state requires high costs (e.g., sensor-based monitoring), which leads to employ different techniques to identify the most critical cases and implementing risk mitigation measures. With this goal in mind, this paper presents the application of two of the most actual and attractive cost-effective techniques for purpose of existing bridge portfolios health state monitoring: (a) multitemporal interferometry via synthetic aperture radar (MTInSAR) data; (b) unmanned aerial vehicle (UAV) photogrammetry. In detail, the paper deals with the use of the two above methodologies, where MTInSAR data can be employed to perform a qualitative assessment of the spatial displacements and velocities characterizing the focused bridges, while UAV flight surveys can be used to identify the occurrence of displacement phenomena (e.g., landslides) in the area immediately close around the observed structures. After describing pros and cons of the two approaches, an application is provided on an existing bridge, for which MTInSAR temporal series are analyzed over 1 year of observation, while UAV surveys are performed at the start and at the end of the considered period. From the combination and the comparison of the techniques, some insights are provided, opening new scenarios in the field of the structural health monitoring of existing bridges portfolio.

Is It More Visible from Above? Comparison of the Effectiveness of Methods for Locating the Bobak Marmot Marmota bobak (Rodentia, Sciuridae) Burrows in Ukraine

Burrow systems detection is a reliable method for counting Bobak marmots. We compared the effectiveness and labour costs of three methods of burrow detection: ground survey, unmanned aerial vehicle survey and satellite survey. The research was conducted in 2021 (Kyiv region). Ground surveys mapped 42 (12 main and 24 secondary) burrows, while UAV imagery identified 45 burrows. The proportion of burrows detected by satellite imagery was 48 % of the number of burrows mapped by the ground survey. Secondary burrows were more difficult to detect than main burrows. The largest area of marmot family group territories was calculated from UAV data (0.27 and 0.08 ha), the smallest from satellite imagery data (0.11 and 0.01 ha). To obtain complete information on marmot colonies, it is best to use either UAV surveys or traditional ground surveys. If it is necessary to find potential areas where animals may live, or to estimate their distribution, then the best option for detecting burrows is the analysis of satellite imagery. The most promising approach is to study colonial burrowing mammals using a combination of ground and remote sensing methods. This provides the opportunity to obtain different types of data.

Monitoring the filling of the landfill using the results of aerial photography

The important task of landfill current residual capacity assessment was solved in this work. The modern methods of high-precision three-dimensional reconstruction were used by the results of unmanned aerial vehicle survey, equipped with a global navigation satellite system receiver. The main stages of work were combining the project and field survey data into one coordinate and height system, reconstruction of designed underground and above-ground parts of landfill, project and actual model comparison, the calculation. As a result, the estimation of the landfill residual capacity becomes possible, which allows to develop recommendations for further landfill operation. The proposed approach allows rapid and high-quality active monitoring of the engineered facility. Photogrammetric processing of the results of low-altitude aerial photography makes it possible to obtain objective data on the current actual state of the landfills, to carry out competent and valid management of the landfill functioning. Such studies can significantly extend the landfill’s lifetime, minimize the negative impact on the environment and predict the attainment of projected capacity much more accurately.

The application potential of unmanned aerial vehicle surveys in grassland plant diversity

The application potential of unmanned aerial vehicle surveys in grassland plant diversity

Analysis of Earthquake-Triggered Landslides through an Integrated Unmanned Aerial Vehicle-Based Approach: A Case Study from Central Italy

Landslides are one of the most hazardous(危险) secondary(二次) effects of earthquakes(地震) due(由于) to the potential(潜在) for large-scale(规模) damage(损伤) and long-term(学期) alterations to landscapes. During the 2016–2017 seismic sequence(序列) in Central Italy, many earthquake(地震)-triggered landslides (EQTLs) affected the road network(网络) and mountain trails. In this study, a methodological approach(方法) for analysing EQTLs, based on data(数据) derived from Unmanned Aerial Vehicle (UAV) surveys, is shown. The approach(方法) is applied to investigate(探讨) the geometric, structural(结构), geomechanical, and kinematic features of the Foce rockslide, which is introduced in the back analysis(分析). The investigation(调查) involved three main(主要) steps: (i) set up of UAV-based Virtual Outcrop Models (VOMs) of the slope(边坡), (ii) a geomechanical characterisation of the rock mass(大众) through the VOM interpretation(解释) and conventional(常规) field data(数据), and (iii) 3D Limit Equilibrium (LE) slope(边坡) stability(稳定性) analyses(分析). This study highlights the potential(潜在) of UAV surveys for providing valuable(宝贵) data(数据) for stability(稳定性) analyses(分析), especially in emergency(紧急) conditions such(这样) as in the aftermath of seismic events.

Review of discrete fracture network characterization for geothermal energy extraction

Geothermal reservoirs are highly anisotropic and heterogeneous, and thus require a variety of structural geology, geomechanical, remote sensing, geophysical and hydraulic techniques to inform Discrete Fracture Network flow models. Following the Paris Agreement on reduction of carbon emissions, such reservoirs have received more attention and new techniques that support Discrete Fracture Network models were developed. A comprehensive review is therefore needed to merge innovative and traditional technical approaches into a coherent framework to enhance the extraction of geothermal energy from the deep subsurface. Traditionally, statistics extracted from structural scanlines and unmanned aerial vehicle surveys on analogues represent optimum ways to constrain the length of joints, bedding planes, and faults, thereby generating a model of the network of fractures. Combining borehole images with seismic attributes has also proven to be an excellent approach that supports the stochastic generation of Discrete Fracture Network models by detecting the orientation, density, and dominant trends of the fractures in the reservoirs. However, to move forward to flow modelling, computation of transmissivities from pumping tests, and the determination of hydraulically active fractures allow the computation of the hydraulic aperture in permeable sedimentary rocks. The latter parameter is fundamental to simulating flow in a network of discrete fractures. The mechanical aperture can also be estimated based on the characterization of geomechanical parameters (Poisson’s ratio, and Young’s modulus) in Hot Dry Rocks of igneous-metamorphic origin. Compared with previous review studies, this paper will be the first to describe all the geological and hydro-geophysical techniques that inform Discrete Fracture Network development in geothermal frameworks. We therefore envisage that this paper represents a useful and holistic guide for future projects on preparing DFN models.

Aerial visual data-driven approach for berthing capacity estimation in restricted waters

Coastal ship berth planning and management necessitate accurate identification of ship position and size, yet this is complicated by the presence of diverse ship types and mixed distribution patterns. Traditional survey-based and position-tracking ship monitoring methods have limitations in reflecting the precise spatial information of coastal ships and their surroundings. This paper introduces a novel aerial visual data-driven methodology to estimate the berthing capacity of sheltered space for stationary ships in geographically dispersed water areas. To measure the berthing capacity, image data from an unmanned aerial vehicle (UAV) survey is collected to reflect the spatial information of all types of ships. A deep learning-based computer vision algorithm is used to automatically detect, identify, and classify ships in sample images. Additionally, we introduce the occupancy factor to empirically quantify the spatial proportional correlation between the ship size and the requisite berthing capacity. A validation test of the proposed method is conducted across all sheltered spaces in Hong Kong, and the UAV survey yields over 7,000 informative images of all types of local ships. The ship identification algorithm achieves an average identification rate of 98.6%, demonstrating higher accuracy and reduced labor and time consumption than observational survey and manual counting methods. Findings indicate that the suggested sheltered space for individual ships is 7.75 times the ship size. The approach provides greater flexibility in estimating berthing capacity for mixed berthing forms in extensive or geographically dispersed water bodies, thereby having the potential to support the design of berths, anchorage areas, and typhoon shelter facilities in coastal regions.

The first use of a photogrammetry drone to estimate population abundance and predict age structure of threatened Sumatran elephants

Wildlife monitoring in tropical rainforests poses additional challenges due to species often being elusive, cryptic, faintly colored, and preferring concealable, or difficult to access habitats. Unmanned aerial vehicles (UAVs) prove promising for wildlife surveys in different ecosystems in tropical forests and can be crucial in conserving inaccessible biodiverse areas and their associated species. Traditional surveys that involve infiltrating animal habitats could adversely affect the habits and behavior of elusive and cryptic species in response to human presence. Moreover, collecting data through traditional surveys to simultaneously estimate the abundance and demographic rates of communities of species is often prohibitively time-intensive and expensive. This study assesses the scope of drones to non-invasively access the Bukit Tigapuluh Landscape (BTL) in Riau-Jambi, Indonesia, and detect individual elephants of interest. A rotary-wing quadcopter with a vision-based sensor was tested to estimate the elephant population size and age structure. We developed hierarchical modeling and deep learning CNN to estimate elephant abundance and age structure. Drones successfully observed 96 distinct individuals at 8 locations out of 11 sampling areas. We obtained an estimate of the elephant population of 151 individuals (95% CI [124, 179]) within the study area and predicted more adult animals than subadults and juvenile individuals in the population. Our calculations may serve as a vital spark for innovation for future UAV survey designs in large areas with complex topographies while reducing operational effort.

Slope visualisation and stability study using geographic information science

High-precision visualised three-dimensional (3D) slope modelling and stability analysis have benefited from the quick development of big data technology. This research aims to utilise unmanned aerial vehicle survey data to obtain influencing information on slope topography. A high-precision 3D surface model is established, which is then overlaid with 3D geological models and 3D mineral models constructed using geographic information system technology. This approach enables more efficient and accurate 3D visualisation modelling of slopes. Based on the 3D model, the search for the most dangerous sliding surface under rainfall conditions is completed. The average absolute error of sample points in the surface modelling effect verification experiment is less than 0.5 m, and the average relative error is less than 2%. These results indicate that the overall modelling effect of the 3D slope model can meet the requirements of subsequent stability analysis. According to the results of random simulation, the minimum safety factor on the most neutral surfaces is less than 1, which indicates that the slopes at some locations in this site do have the possibility of landslide. The neutral surface where the lowest safety factor appears can be used to pinpoint the position of the most hazardous sliding surface.

Low-amplitude brittle deformations revealed by UAV surveys in alluvial fans along the northwest coast of Lake Baikal: Neotectonic significance and geological hazards

Many large deltas and other areas, underlain by unconsolidated sediments, are heavily populated but impacted by various natural deformational processes. The causes and mechanisms of the deformation are often obscure because of difficulties in the identification of their geological source. We used an unmanned aerial vehicle (UAV) to survey an area of up to several km2 to detect and map surface discontinuities with displacements of a few centimeters, which can indicate the presence of initial geological deformations. We encountered such offsets in the alluvial fans on the northwestern coast of Lake Baikal, overlying the damage zones of several faults. These deformations at the different sites are referred to as primary or secondary co-seismic ruptures, brittle creep, or cryogenic fissures, which have specific origins. They are located a few hundred meters from a principal fault plane, from junctions of faults, and in the peripheral parts of alluvial fans, making these areas potentially hazardous. We analyzed the digital surface models of local segments of one of the delta plains between 2020 and 2021 and established that its periphery has subsided by an average of 5–10 cm. In areas of accumulation of the river sediment, the thickness of coarse clastic deposits has increased by approximately the same amount. Locally and in the coastal zone, the vertical surface changes are larger. In the axial parts of some seismically induced gravitational failures, the subsidence reached 33 cm over a period of 11 months and 19 days. Our results show that sediments of alluvial fans are very susceptible to various tectonic and exogenous deformational processes. The interpretation of the ultra-high resolution UAV's images can help the recognition the low-amplitude brittle deformations at an early stage of their development. Therefore, such UAV surveys are critical in the discernment of neotectonic activity and its related hazards over short observation periods.

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

Primarily investigation with multiple methods on permafrost state around a rapid change lake in the interior of the Tibet Plateau

Changes of the lakes on high-altitude regions of the Tibet Plateau influence the state of the surrounding permafrost. Due to the climate warming and wetting trend, extreme events including lake outburst has occurred more frequent. In 2011, an outburst event occurred on the Zonag Lake and this event changed the water distribution in the basin, leading a rapid expansion of the Tailwater lake, named as the Salt Lake. However, the construction of the drainage channel in the Salt Lake ended the expansion process and the shrinkage of the lake started since 2020. To investigate the permafrost state around the Salt Lake, multiple methods, including drilling boreholes, the unmanned aerial vehicle survey and the ground penetrating radar detection have been applied. By integrating these multi-source data, the thermal regime, topography and the spatial distribution of the permafrost around the Salt Lake were analyzed. The result showed that the permafrost state around the Salt Lake was related to the distance from the lake water. The permafrost table appears at 90 m away from the Salt Lake and interrupted by a nearby thermokarst lake at 220 m. The ground temperature in the natural field is 0.2 °C lower than the temperature in the lake at a depth of −5 m.