Terrain Elevation Research Articles

Error modelling method of extended Kalman filter‐based terrain referenced navigation system for integrity assurance under nominal conditions

Navigation safety is further emphasised for terrain referenced navigation (TRN) systems used in middle-range unmanned aerial vehicles (UAVs) than those in cruise missiles flying through mountainous areas, because middle-range UAVs may fly to regions and cities containing both combatants and civilians. For safety assurance of TRN systems, this paper proposes a method to compute protection levels (PLs) to assure the required level of integrity under nominal conditions. For obtaining PLs of TRN systems, all the components of the TRN measurement errors should be identified and overbounded. This study models every error component: the radar altimeter error, the vertical error in the terrain elevation database, the lateral offset induced error, and the interpolation error. Based on the characteristics of TRN systems using terrain information for obtaining navigation solutions, this study developed new models for the lateral offset induced error and the interpolation error as a function of terrain roughness by examining the effect of terrain roughness on the error sources. The simulation results demonstrate that the latitude and longitude PLs obtained by applying the proposed models can sufficiently overbound the navigation solution error for TRN systems under nominal conditions; especially the proposed method shows remarkable performance in regions with low terrain roughness.

ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

Abstract. Active remote sensing systems orbiting the Earth are only a small portion of the current constellation of satellites and will increase in number and advance in technology in the future. The launch of the GEDI sensor in December 2018, for an expected life-span period of about 2 years, is a fundamental step of this revolution, as it is the first spaceborne full-waveform lidar specifically designed for measuring the structure of ecosystems, providing information of the vertical profile of forests.Accuracy assessment of GEDI height metrics in the context of an Alpine forest environment in steep terrain scenarios has been conducted in this study. We used discrete return lidar from a recent aerial laser scanner survey as reference to analyse differences of heights of terrain elevation and maximum canopy height of the vegetation detected in each GEDI footprint. The height metrics differences between the discrete lidar and the GEDI data were then analysed to verify any correlation with the following factors: morphology (terrain slope), land cover (land cover type, fraction of canopy cover, vegetation density), GEDI laser beam characteristics (day/night-time acquisition, full power vs coverage laser beam, beam ID, laser sensitivity). Further analysis involved shifting the footprints’ location in 8 different direction and 4 distances to assess the impact of geolocation errors on accuracy and precision.Results show that what most influences accuracy in this study is the terrain slope, very likely linked to the uncertainty of geolocation of the GEDI footprints, suggesting caution in using single GEDI footprints if located in steep environments. Other than slope, terrain height accuracy varies mostly with forest type (conifer vs broadleaves), but not significantly with other factors. Canopy height instead is affected by most factors; high vegetation canopy is overestimated by ∼3 m in GEDI, and underestimated by 3 m over heath and bushes (median difference). Higher sensitivity pulses and night-time pulses provide better accuracy. Laser beams with full power also have better accuracy; beams with id 1000 and 1011 provide the most accurate canopy heights. Shifting the footprint position decreased accuracy except at 15 m and 270° with respect to orbit direction (left-looking).

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

On the accuracy of an emitter localization method based on multipath exploitation in realistic scenarios

This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures systems. Previously, the authors targeted only the theoretical development of the localization method. In fact, this could be a serious concern in practice since there is no evidence regarding its accuracy under the real data gathered from realistic scenarios. Therefore, an accurate ray-tracing algorithm is adapted to enable the implementation of the method in practice. Then, realistic scenarios are determined based on the geographic information system map generated to collect high-resolution digital terrain elevation data, as well as realistic localization problems for radar emitters. Next, simulations are performed to test the localization method. Thus, the performance of the method is verified for practical implementation in the electronic warfare context for the first time. Lastly, the performance bounds of the method are discussed.

The Sierra Nevada de Santa Marta (Colombia) and Nevado de Famatina (Argentina): The effects of tectonic syntaxis on the topography of the Andes

The Sierra Nevada de Santa Marta (Santa Marta) in Colombia and Nevado del Famatina (Famatina) in Argentina are two isolated peaks within the Andes of South America, having elevations of about 5,500 and 6,000 m, respectively. Both massifs are bounded by major wrench faults of opposite kinematics but comparable magnitude in their lateral displacement. Despite the volumes of research available, little has been said in the literature about the tectonics surrounding these particular systems. In this context, digital terrain elevation (DEM), regional geology, satellite imagery and geophysical data were analysed to illustrate how a network of transcurrent faults uplifted the peaks. We propose a model to account for topography by the opposed movement of faults operating in two compartments: internal (acute) and external (obtuse). Fault motion caused uplift of the internal compartments: a) the Santa Marta tectonic syntaxis is generated by the sinistral Santa Marta-Bucaramanga Fault on the west and the dextral Oca-Pilar Fault to the north whilst, b) the Famatina tectonic syntaxis is caused by the Valle Fértil sinistral fault on the western side and the dextral Tucumán lineament to the north. Elevation maps documenting the lateral displacement of modern alluvial fans provide evidence of the existence and kinematics of these faults. Active since the Neogene, the transcurrent faults resulted in displacements in the order of 25–60 km, with velocities of approximately 1–2 cm/year. Seismic records suggest that the syntaxis occurred after reactivation of oblique discontinuities in the basement due to the convergence of the Nazca, Caribbean, and South American plates. This tectonic framework led to the formation of a remarkable positive relief and the development of internal positive compartments subject to constrictive forces. Simultaneously, external distensive and negative compartments controlled the syntectonic deposition of thick sedimentary sequences. The significant topographic difference between the internal positive compartments and their external negative counterparts triggered high rates of erosion, transport, and ultimately sedimentation. Regional subsurface surveys indicate strong anomalies of the underlying bedrock influenced by tectonic syntaxis.

Mixed analysis-synthesis approach for estimating airport noise from civil air traffic

This paper presents an important upgrade of a modelling tool devised by the authors for estimating aircraft noise around civil airports, which is based on a best-practice noise computation method and flight tracking data collected from the Internet and complemented by datasets of aircraft models, runways, and terrain elevation. Targeting more accurate noise predictions , the upgraded model introduces reasoned degrees of freedom in the standard flight procedures, and the tracked altitudes and speeds of each flight event are used to fit these modified procedures, which leads to an original mixed analysis-synthesis approach. Additional improvements include better runway assignment and consideration of actual weather conditions. Comparison of July 2019 results with official data for Heathrow, Gatwick and Schiphol airports shows an improved noise prediction, with an average underestimation lowered to 1.5 dB(A) for exposure-based metrics. Therefore, this upgraded modelling tool can be a reliable support to civil aviation authorities and policy-makers.

Cloud water path, precipitation amount, and precipitation efficiency derived from multiple datasets on the Qilian Mountains, Northeastern Tibetan Plateau

Orographic clouds and precipitation are critical to the water cycle and ecology of arid and semiarid mountainous regions. This study intended to investigate the spatial and seasonal distribution of the cloud water path (CWP), precipitation amount, and precipitation efficiency (PE) on the Qilian Mountains (QM) of the northeastern Tibetan Plateau, as well as the related mechanisms linked with topographic distribution and water vapor transport. To solve the limitations of rain gauges and radar observations at high elevations of the QM, we used the latest multiple datasets, including the Global Precipitation Measurement (GPM), Advanced Very High Resolution Radiometer, and ECMWF Reanalysis v5 data from 2016 to 2019. The amount of precipitation retrieved from the two GPM products was evaluated using rain gauge data. The results show that the GPM data generally overestimate the precipitation amount on the QM and have a high correlation with rain gauge data in warm seasons and a low correlation in winter seasons, primarily due to the significant influences of snow and ice coverage. The terrain height and water vapor supply below 600 hPa are two critical factors that determine the spatial and seasonal distributions of clouds and precipitation in the QM. The precipitation generally increased with an increase in terrain elevation below 3000 m and decreased above 3000 m on the QM. CWP, precipitation amount, and PE in the mountainous areas are much higher than those in the surroundings and decrease from the southeast to the northwest because of the decrease in low-level water vapor supply. CWP, precipitation amount, and PE were the highest in summer because of the strong moisture transport from the southeast and the lowest in winter because of the weakest water vapor transport. Water vapor fluxes from the eastern, southern, and northern boundaries contributed to the wetness of the eastern and northeastern slopes of the QM. The lack of low-level water vapor in the westerly winds was the main reason for drought in the western QM.

A Terrain Elevation Map Generation Method Based on Self-Attention Mechanism and Multifeature Sketch

To address the issues of low efficiency in manual terrain feature map annotating and poor realism in terrain elevation map generation, this paper proposes a terrain elevation map generation method based on self-attention mechanism and multifeature sketch. Firstly, the proposed method extracts features from a terrain elevation map using an adaptive feature enhancement method. Afterwards, our method adds a self-attention mechanism to the generator and discriminator of conditional generative adversarial network to capture the global spatial features and generates a realistic terrain elevation map. Finally, a level of detail method is used to visualize the three-dimensional terrain, and an interactive terrain editing tool for roaming interaction is implemented. Experimental data show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.

Where do Energy-Poor Households Live? Empirical Evidence from Indonesia

Empirical analysis on the links between geography and energy access in archipelago setting is still limited. In particular, the territorial identification of energy poverty in Indonesia is still missing. Our study maps geographical location and estimates factors that determines the probability of being energy poor household in relation to electricity. We used the OLS (Ordinary Least Square) estimation and utilized the socioeconomic survey (Susenas) combined with data on terrain elevation, presence of geographic features such as mountainside, topography characteristics, ocean and forest obtained from the village census (PODES). The results show that energy poverty in Eastern part of Indonesia is larger than in the Western. In Eastern Indonesia, we estimate that 13.5% of the total households are energy poor compared to the Western which only 7.21%. Households located in the forest area was the dominant factor to influence prevalence of energy poverty among geographic constraints, with magnitude of influence at 22-23 percentage point to non-forest residents. Secondly, the presence of steep-sloped terrain is the next meaningful geographical constraint with contribution effect to energy poverty prevalence at around 18 percentage point. The result highlighted priority of locations in which resource and policy to reduce energy deprivation need to be allocated.

Improving UAV Mission Quality and Safety through Topographic Awareness

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just ±5 m.

GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps

Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection.

Climatically induced cyclicity recorded in the morphology of uplifting Tihu coastal ridgeplain, Hiiumaa Island, eastern Baltic Sea

Holocene relative sea level changes and palaeogeography of the uplifting Tihu ridge-swale beach system in the functionally tideless Baltic Sea were reconstructed using airborne LiDAR elevation data, ground-penetrating radar surveys, sedimentological proxies, recent models of Fennoscandian uplift due to glacial isostatic adjustment, and GIS-based terrain modelling. Considering that aeolian contribution to the ridge growth was relatively small and uniform along the studied profiles, it was possible to utilize an age estimation method based on terrain elevation and pre-determined RSL curves. The tested relative sea level curves converged around a linear trend during the past 7 ka, the rate of which (3.1–3.2 mm/a) is strongly correlated with relative-to-geoid uplift model output. We found that the Tihu system started to emerge from the sea ~5.4 ka ago. In its older part, it includes a complex of elevated barrier spits where both the isostatic uplift and gradual sediment aggradation due to longshore transport contributed to its nucleation and initial progradation. In its lower (younger) section, a forced progradation “staircase” of palaeocoastlines was formed in a regime of sediment deficit and low-energy hydrodynamic conditions. Analysis of near-cyclic occurrences of beach/foredune ridges and larger ridge sets within the Tihu ridgeplain revealed time intervals of enhanced storminess during 5.4–4.9, 4.8–4.5, 4.6–4.3, 4.1–3.8, 3.7–3.4, 3.2–2.9, 2.7–2.4, and 2.2–1.9 ka BP. Serving as a new independent proxy for past storminess and high sea level events in Estonia, these phases match well with some previous European Atlantic storminess reconstructions for Mid- to- Late Holocene.

A new Middle Pleistocene interglacial occurrence from Ejby, Sjælland, Denmark

Despite more than a century of investigations, parts of the Quaternary stratigraphy of Denmark with their fragmented record of deposits remain ambiguous. Here we describe a newly found interglacial clay deposit from Ejby on Sjælland, Denmark, from a borehole at 55.695°N, 11.839°E (terrain elevation 5.7 m above sea level). We place the new occurrence on record and provide details of the macrofossil analysis of the sample. The clay contains remains of the present-day temperate bivalve Corbicula fluminalis and the caddis fly Hydropsyche contubernalis – both inhabiting rivers. The presence of C. fluminalis indicates that the deposit most probably is of Middle Pleistocene age, older than the last interglacial, the Eemian.

MIP mapping the virtual environment for computer games

The paper describes how MIP-mapping and paging can be used to represent not only terrain imagery, but also terrain elevation. Previously the only things missing to implement Earth coverage were computing power, input/output bandwidth, graphics processing units (GPUs) and techniques to deal with large data sets. The article describes the rendering method that uses the GPU for most of the calculations. Modern graphics accelerators for personal computers allow you to solve problems that require the generation of images of the visual environment of photographic quality in real time. High-quality visualization of large continuous spaces of 3D vegetation is one of the requirements when creating most land transport simulators, as well as many virtual reality applications. Representation of large forest spaces in the form of free-standing tree models that have an acceptable visual quality for close-up display from the observer, it is impossible due to the huge number of primitives required for visualization. The article proposes use parallel calculations in GPU to accelerate rendering. We successfully integrated proposed visualization method into the standard rendering pipeline. For considered tests the application with GPU average ten times faster, than the version using only CPU. The proposed method takes into account the features of the architecture of modern GPUs and allows you to distribute the load on the display between the CPU and GPU. In this case, the method does not require significant resources for visualization. The implementation of the method showed that received speed in most cases is sufficient for the effective application of the method in computer games. The paper also proposes a method for high-quality visualization of large continuous spaces of 3D vegetation.

Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information

Earth scientists increasingly deal with ‘big data’. For spatial interpolation tasks, variants of kriging have long been regarded as the established geostatistical methods. However, kriging and its variants (such as regression kriging, in which auxiliary variables or derivatives of these are included as covariates) are relatively restrictive models and lack capabilities provided by deep neural networks. Principal among these is feature learning: the ability to learn filters to recognise task-relevant patterns in gridded data such as images. Here, we demonstrate the power of feature learning in a geostatistical context by showing how deep neural networks can automatically learn the complex high-order patterns by which point-sampled target variables relate to gridded auxiliary variables (such as those provided by remote sensing) and in doing so produce detailed maps. In order to cater for the needs of decision makers who require well-calibrated probabilities, we also demonstrate how both aleatoric and epistemic uncertainty can be quantified in our deep learning approach via a Bayesian approximation known as Monte Carlo dropout. In our example, we produce a national-scale probabilistic geochemical map from point-sampled observations with auxiliary data provided by a terrain elevation grid. By combining location information with automatically learned terrain derivatives, our deep learning approach achieves an excellent coefficient of determination (R^{2} = 0.74) and near-perfect probabilistic calibration on held-out test data. Our results indicate the suitability of Bayesian deep learning and its feature-learning capabilities for large-scale geostatistical applications where uncertainty matters.Graphic

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases.

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3–5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more than 90% under some scenarios, except for deciduous broadleaf forests, where an optimal algorithm could provide low biased estimations. In contrast, the improvements of stacking in R2 and RMSE were not significant. The stacking of MARS, MLP, and SVR provided improved results compared with the optimal base learner, and the average RI in R2 was 3.54% when we used all data without separating forest types. Finally, the optimal stacking model was used to generate global forest AGB maps.

Topographic SLAM Using a Single Terrain Altimeter in GNSS-Restricted Environment

In a Global Navigation Satellite System (GNSS)-restricted area, a mobile robot navigation system exploits surrounding environment information. For an aerial or underwater vehicle, undulating terrain of a land or seabed surface is a valuable information resource that leads to the development of terrain-referenced navigation (TRN) algorithms. However, due to the vast amount of a vehicle’s activity area, surveying all the regions to obtain a high-resolution terrain map is impractical and requires simultaneous localization and mapping (SLAM) as a highly desirable capability. This paper presents a topographic SLAM algorithm using only a single terrain altimeter, which is low-cost, computationally efficient, and sufficiently stable for long-term operation. The proposed rectangular panel map structure and update method enable robust and efficient SLAM. As terrain elevation changes are inherently nonlinear, an extended Kalman filter (EKF)-based SLAM filter is adopted. The feasibility and validity of the proposed algorithm are demonstrated through simulations using terrain elevation data from a real-world undersea environment.

Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case Investigation in Altay, Northwest China

Snow depth (SD) is an indispensable parameter for many studies. Launched in 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is designed to obtain global glacial elevations, but it can also acquire canopy and terrain elevations. Whether the depth of seasonal snow can be estimated by directly comparing the difference in elevations in snow-cover and snow-free cases, many people may reasonably ask. In this letter, we conduct such an investigation in Altay, Northwest China, using ICESat-2 ATL08 elevation products. Our investigation suggests: 1) in mountainous areas, the answer maybe is no because the estimation is obviously affected by rugged topography; 2) but in flat regions, SDs have been effectively estimated. (The R² is up to 0.88 between estimates and ground measurements.); and 3) as expected, land-cover types also affect the accuracy of the results, and the best estimation happens over the type of bare land. Therefore, estimating the depth of seasonal snow from the ICESat-2 product may be feasible, but we must check the results carefully.

МЕТОДИЧНІ ЗАСАДИ ВИЗНАЧЕННЯ ОПТИМАЛЬНОГО ЧАСУ РОБОТИ РЯТУВАЛЬНИКА ГІРСЬКОГО

Goal. Development of methods for studying the impact of dangerous and harmful factors on the mountain rescuer during search and rescue operations at low ambient temperatures. Method. The article presents the results of the study according to the methods of Euro standards, namely: experiments, theoretical analysis. Results. The article examines in detail the problem of protection of mountain rescue workers during search and rescue operations in mountainous areas, which is characterized by limited transport accessibility, and proposes methodological principles for studying the impact of low temperatures on the functional state of rescue workers. A certain algorithm for conducting a study of the level of protection of mountain rescuers is proposed and the need to standardize the time of search and rescue operations for effective work and minimal damage to the health of the mountain rescue service. Scientific novelty. The article for the first time uses the method of determining the degree of influence of low temperatures on the human body, in particular on the functional state of a mountain rescue worker, taking into account the level of his protection by electroencephalogram (EEG). Practical significance. The recommended method will ensure the formation of requirements for the regulations of all search and rescue operations by mountain rescue workers in areas with rugged terrain and relative elevations of 500 m and more within a radius of 25 km, and areas above 900 m and above sea level. Accordingly, the implementation of the proposed requirements of the regulation will ensure the rationing of the time of work of varying degrees of load at low temperatures that affect the human body.