Complex Mountainous Terrain Research Articles

Evaluation of Three Gridded Precipitation Products to Quantify Water Inputs over Complex Mountainous Terrain of Western China

This study evaluates the capacity of three gridded precipitation products (MSWEP V2.2, TRMM-3B42 V7, and GPM-IMERG V6) to detect precipitation in the Min Jiang watershed, a data-scarce and mountainous region in western China. A set of statistical and contingency indices is calculated for the precipitation products and compared with rain gauge observations at 23 ground stations from July 2000 to May 2016. Consistency between gridded and ground precipitation datasets is examined at different temporal (i.e., daily, monthly, seasonally, and annually) and spatial (i.e., site level, sub-regional level, and watershed level) resolutions. We identify possible reasons for discrepancies among precipitation datasets. Our results indicate that: (1) the MSWEP product is best suited for the study of long-term mesoscale rainfall, rather than short-term light or extreme rainfall; (2) the IMERG product represents stable performance for the simulation of rainfall spatial variability and detection capability; and (3) Composition of the datasets, climatic systems, and regional topography are key factors influencing the consistency between gridded and ground precipitation datasets. Therefore, we suggest using MSWEP V2.2 and GPM-IMERG V6 as potential precipitation data sources for hydrometeorological studies over the Min Jiang watershed. The findings of this study inform future hydrometeorological and climate applications in data-scarce regions with complex terrain.

Physical processes controlling the diurnal cycle of convective storms in the Western Ghats

Diurnal variation of convective storms (CSs) during monsoon season and associated physical mechanisms are significantly important for accurate forecast of short-time and extreme precipitation. The diurnal cycle of CSs is investigated using ground-based X-band radar, Tropical Rainfall Measuring Mission Precipitation Radar, and reanalysis data during the summer monsoon (June–September of 2014) over complex mountain terrain of Western Ghats, India. Diurnally, CSs show a bimodal distribution in the coastal areas, but this bimodality became weak along the upslope regions and on the mountain top. The first occurrence mode of CSs is in the afternoon–evening hours, while the second peak is in the early-morning hours. The diurnal cycle’s intensity varies with location, such that it reaches maximum in the afternoon–evening hours and early morning on the mountain top and coastal areas, respectively. Two possible mechanisms are proposed for the observed diurnal variation in CSs (a) the radiative cooling effect and (b) the surface wind convergence induced by the interaction between land-sea breeze, local topography and large-scale monsoon winds. It is also observed that the CSs developed on the mountain top during afternoon–evening hours are deeper than those along the coast. The higher moisture in the lower- and mid-troposphere, higher instability and strong upward motion facilitate deeper CSs during afternoon–evening hours.

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System.

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

Modeling tree canopy height using machine learning over mixed vegetation landscapes

Although the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (>95% quantile) and low (<median), respectively. This study provides a critical insight into the relationship between tree canopy height and predictive abilities of random forest models when taking account of spatial non-stationarity. Conclusions indicate that a trade-off approach based on the actual need of project should be taken when selecting an optimal model integrating both local and global effects in modeling attributes such as canopy height from remotely sensed data.

Identification of erosion-prone areas using morphometric parameters, land use land cover and multi-criteria decision-making method: geo-informatics approach

Soil erosion is the main driving force of several devastating natural hazards in the complex mountainous terrain of the Himalayas where the Teesta River basin is located. The present study focused on GIS-based multi-criteria analytical approach (MCA) that integrates morphometric parameters with land cover categories for identification of erosion hotspot areas through sub-watersheds prioritization. The general character of the eight sub-watersheds was derived from the linear, areal and relief aspects, while seven land cover types derived from maximum likelihood classification were evaluated in the MCA. Before combination of the studied parameters in MCA, each parameter was individually ranked and compound value (Cp) was calculated which produced four classes: low, medium, high and very high priority. Later, these ranks were integrated into MCA to give eight major classes classified from 1 to 8. Lowest Cp ranked 1 gets very high priority, while the highest Cp is ranked 8, i.e., lowest priority. The results revealed a predominance of dense forest account for 31.73% of the total area, the basin is a sixth-order river dominated by high relief and marginal slope, elongated in shape, and mean bifurcation ratio (Rb) was 3.879 which indicates an undistorted natural drainage system. Final MCA priority ranking indicated that: SW-4 and 5 having lowest Cp values (3.39 and 4) ranked 1 and 2 suggest very high erosion susceptibility, SW-8 and 3 (4.22 and 4.39) rated 3 and 4, i.e., high priority; SW-6 and 7 ranked 5 and 6, i.e., medium priority; and SW-1 and SW-2 ranked 7 and 8 because of their highest Cp (5.39 and 5.67), i.e., least priority. The prioritization result identified critical areas that are indispensable for sustainable use and management of water and land resources.

Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

Weather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

Thirty years of forest-cover change in Western Rwanda during periods of wars and environmental policy shifts

Human activities, exacerbated by periods of armed conflict, have impacted the biodiversity-rich forests in Western Rwanda during recent decades, creating a need to enhance forest protection and restoration policies that address widespread environmental degradation in this mountainous region. Contrasting observations and the lack of recent forest-cover maps make reliable assessments of forest-cover change in Western Rwanda difficult. We investigate forest-cover dynamics across Western Rwanda between 1986 and 2019, using the random forest algorithm to classify a suite of Landsat-5, Landsat-7, and Landsat-8 images to produce forest-cover maps at quadrennial intervals. Approximately 19% (23,412 ha) of the initial forest extent was lost between 1986 and 2006, primarily due to pastureland expansion and forest conversions associated with armed conflicts. Forest loss peaked between 2010 and 2014, a period corresponding to rapid extension of public infrastructures and overexploitation of forest products to supply the growing urban areas. Western Rwanda experienced a 17% increase in forest cover between 1986 (144,792 ha) and 2019 (169,197 ha), due to the establishment of tree plantations around protected areas and throughout agricultural land-uses. The published forest datasets for Western Rwanda either overestimate forest area due to confusions with agricultural land cover or they underestimate deforestation due to the complex mountainous terrain. Ongoing forest losses and increasing isolation of natural forests pose significant threats to the biodiversity habitats and the sustainable supply of ecosystem services in this densely populated, globally important biodiversity hotspot region.

Patterns of tree species richness in Southwest China

As a region known for its high species richness, southwest China plays an important role in preserving global biodiversity and ensuring ecological security in the Yangtze, Mekong, and Salween river basins. However, relatively few studies focus on the response of tree species richness to climate change in this part of China. This study determined the main tree species in southwest China using the Vegetation Map of China and the Flora of China. From simulations of 1970 to 2000 and three forecasts of future benign, moderate, and extreme climate warming anticipated during 2061 to 2080, this study used a maximum entropy model (MaxEnt) to simulate main tree species richness in southwest China. Regions with a peak species richness at intermediate elevations were typically dominated by complex mountainous terrain, such as in the Hengduan Mountains. Likewise, regions with the smallest richness were low-elevation areas, including the Sichuan Basin, and the high-elevation Sichuan-Tibet region. Annual precipitation, minimum temperature of the coldest month, temperature seasonality, and elevation were the most critical factors in estimating tree species richness in southwest China. During future 2061 to 2080 climate scenarios, tree species tended to migrate towards higher elevations as mean temperatures increased. For climate change scenarios RCP2.6-2070 (benign) and RCP4.5-2070 (moderate), the main tree species richness in the study area changed little. During the RCP8.5-2070 extreme scenario, tree species richness decreased. This study provides useful guidance to plan and implement measures to conserve biodiversity.

Research on Digital Mapping Method Based on Complex Terrain

Based on UAV low-altitude remote sensing technology and tilt photography technology, combined with highway projects, the UAV operating process, quality inspection and accuracy, and positioning accuracy in the mountainous complex terrain conditions are analyzed, and operating experience is summarized for subsequent navigation. The production provides guidance. At the same time, it also provides a reference for the exploration of digital mapping methods, and lays a theoretical foundation for the research of mapping methods. The combination of mapping methods in complex terrain with high technology is highly innovative.

Wind characteristics of a bridge site and wind-resistance key technology in complex mountains

The random variability of mountainous area terrain and the mutual aerodynamic interference between different terrain features make the wind field in bridge sites extremely complicated, which results in considerable challenges to the wind resistance of large-span bridges. In this study, the wind characteristics of a bridge site in a typical mountainous area were determined by field measurements. The design reference wind speed was derived from historical statistics, numerical wind tunnel simulations, field measurements, etc. According to the wind characteristics of the mountainous area, the flutter performances of a bridge under non-uniform and non-stationary inflow were analyzed, and the effects of common aerodynamic measures on flutter stability were analyzed at large angles of attack. The results showed that the integrated wind speed standard in complex mountainous terrains can derived in various ways, which is significantly different from a conventional plain area. The non-uniform, large angle of attack, and non-stationary inflow has a significant impact on the flutter performance of a bridge.

3D matching positioning method for landslide using infrasound signal received by triangular pyramid vector array, based on ray theory

Several hours before landslides occur, part of the rock and soil body begin to burst, rub, and fracture, generating infrasound signals propagate outward. 3D advanced positioning of the landslide has remained unsolved, which is important for disaster prevention. Using the infrasound signal send from location of the landslide, this work aimed at proposing an advanced 3D matching positioning method, based on ray theory. We first proved vectority of the instantaneous sound pressure field from the perspective of air dynamics, and used ray theory to deduce a point’s instantaneous sound pressure in the air as the sum of all characteristic sound ray vectors. Based on this, the 3D vector acoustic field model was established through simulation. We also found that the triangular pyramid array has vertical and horizontal sampling capabilities, which can collect the vector signals due to the directional anisotropy between the elements. Consequently, the 4-element triangular pyramid vector array was designed to be placed in the acoustic field to receive infrasound signals. Since the infrasound signal is issued before the landslide, we then proposed the 3D matching positioning method: by calculating the cost function of the measurement signal of the triangular pyramid vector array and the copy signals calculated by the propagation model in the predetermined space, point by point, the maximum value was predicted as the infrasound source position, also as the predicted location of landslide. We performed simulations in complex mountainous terrain and applied the method to localization of infrasound sources before the occurrence of the landslide. As a result, by searching and matching in massive space points, we obtained the accurate infrasound source coordinates. This study shows the application prospects of this method for predicting geohazards position several hours in advance.

Retrieving snow wetness based on surface and volume scattering simulation

Wetness is one of the important physical parameters of snowpack. Its spatial and temporal changes play a key role in snowmelt runoff forecast, regional climate change, and agricultural irrigation. In this study, we proposed a new method to retrieve snow wetness from full-polarimetric synthetic aperture radar (SAR) data. First, the dominant scattering components, i.e. surface and volume scattering, in wet-snow conditions were obtained by polarimetric decomposition. The random rough surface scattering models and the dense media radiative transfer (DMRT) model were then used to establish the surface and volume scattering models, through which the surface and volume scattering lookup tables were created, respectively. Based on the lookup tables and the polarimetric decomposition results, the snow surface and volume wetness were retrieved, respectively. Finally, the effective snow wetness was derived from the weighted summation of surface and volume wetness. The advantage of this method mainly comes from the full consideration the snow surface roughness, the local incidence angle on complex mountain terrain, and the polarimetric information. In experiments, the GaoFen-3 data obtained on January 17, 2018 in the Kelan River Basin and the Radarsat-2 data obtained on March 19, 2014 in the Manasi River Basin were selected to verify the applicability of the proposed method at different conditions. From the analysis of experiment results, the correlation coefficient between the estimated and the ground measured snow wetness in the Kelan River Basin is 0.72. The mean absolute error (MAE) and the root mean square error (RMSE) are 3.35% and 3.89%, respectively. The correlation coefficient between the snow wetness estimated by Radarsat-2 and the measured values in the Manasi River Basin is 0.62. MAE and RMSE are 1.32% and 1.62%, respectively. These results proved that the proposed method can effectively retrieve snow wetness under different SAR data, different areas, and different snow periods.

Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review

The snow cover is a key component of land surface hydrology, especially in mountain areas where it governs the amount and timing of water availability in downstream areas. It is involved in relevant climate feedbacks and natural hazards such as avalanches and floods. Monitoring and forecasting snow cover characteristics is challenging. While snow cover extent is relatively easy to retrieve from satellite data, remote sensing retrievals of the snow water equivalent (SWE) is often inaccurate, particularly in complex mountainous terrain. Model-based snow cover estimates, driven by meteorological data, often bear significant uncertainties due to both input data and model errors. Data assimilation can combine both approaches to improve SWE estimates. In this paper, we review current state-of-the-art data assimilation methodologies used to optimally combine measurements with snow cover models in order to reduce uncertainties. The suitability of a given data assimilation method varies with the numerical complexity of snow models as well as the availability and the type of observations. This review describes the issues and challenges associated with data assimilation applied to the mountain snow cover, providing recommendations for existing and upcoming monitoring and prediction systems of snow hydrology in mountainous regions.

Khảo sát tỷ lệ nhiễm Rickettsia và Orientia tsutsugamushi trên bệnh nhân sốt không rõ nguyên nhân tại Hà Giang

Rickettsial fever is one of a zoonotic disease which is caused by bacteria genus Rickettsia. The ectoparasites such as ticks, mites, fleas, lice... were demonstrated as the main transmited vectors through host reservoirs are rodents and small animals including mice, squirrels, mink... These is the main cause of Rickettsia infection in human. The prevalence of Rickettsia and Orientia tsutsugamushi in rodents and ectoparasites and patients in Vietnam were reported. However, the data still was not clearly and fully understanding. Ha Giang is one of the important provinces with complex mountainous terrain where there is a high risk of zoonotic diseases. The O. tsutsugamushi was detected in patients collected in Ha Giang province. In current paper, we describe the result for investigating the Rickettsia infection rate as well as the seroprevalence of scrub typhus, Rickettsia typhi and Rickettsia spotted fever group in fever of unknown origin patient collecting in Ha Giang province general Hospital. The ELISA result in 162 patients indicates that seroprevalence of Orientia tsutsugamushi, Rickettsia typhi, and Rickettsia spotted fever group are 33.33%, 8.02%, và 1.85%, respectively. The real-time PCR result shows that the positive rates of Orientia tsutsugamushi, Rickettsia typhi and Rickettsia spotted fever group are 24.69%, 5.63%, và 1.41%, respectively.

Wind characteristics in the high-altitude difference at bridge site by wind tunnel tests

With the development of economy and construction technology, more and more bridges are built in complex mountainous areas. Accurate assessment of wind parameters is important in bridge construction at complex terrain. In order to investigate the wind characteristics in the high-altitude difference area, a complex mountain terrain model with the scale of 1:2000 was built. By using the method of wind tunnel tests, the study of wind characteristics including mean wind characteristics and turbulence characteristics was carried out. The results show: The wind direction is affected significant by the topography, the dominant wind direction is usually parallel to the river. Due to the sheltering effect of the mountain near the bridge, the wind speed and wind attack angle along the bridge are both uneven which is different from that at flat terrain. In addition, different from flat terrain, the wind attack angle is mostly negative. The wind profiles obey exponential law and logarithmic law. And the fitting coefficient is consistent with the code which means that it is feasible to use the method of wind tunnel test to simulate complex terrain. As for turbulence characteristics, the turbulence intensity is also related to the topography. Increases sheltering effect of mountain increases the degree of breaking up the large-scale vortices, thereby increasing the turbulence intensity. Also, the value of turbulence intensity ratio is different from the recommended values in the code. The conclusions of this study can provide basis for further wind resistance design of the bridge.

Atmospheric vapour and precipitation are not in isotopic equilibrium in a continental mountain environment

AbstractIsotopic exchange with atmospheric vapour can strongly influence the isotopic values of evaporating surface water bodies (e.g., lakes), influencing our understanding of hydrological processes across aquatic and terrestrial environments. Rather than measure the isotopic values of the atmosphere directly, it is much more common to estimate values by assuming equilibrium with local precipitation. This assumption may introduce large errors, thereby biasing hydrological inferences and understanding. The pattern and magnitude of this error has been quantified only in a few circumstances. We compared observations of vapour and precipitation isotope values over a four‐year record collected in a montane environment in the central Rocky Mountains of North America. We further investigated factors and conditions promoting disequilibrium. Scenario comparisons assessed the impact of theoretical and methodological elements on the accuracy of the equilibrium assumption. We found that the equilibrium assumption was not well supported by direct and continuous observations of vapour isotopes using tower‐based laser isotope spectroscopy, particularly during the summer months. Across all scenarios, errors associated with the equilibrium assumption were high, credibly ranging from 14 to 154 ‰ for δ2H and 1.5 to 16.3 ‰ for δ18O. Environmental covariates (e.g., vapour pressure deficit, air pressure) helped explain some of the apparent disequilibrium. Although the equilibrium assumption for estimating atmospheric vapour isotope values may not be applicable in a continental montane environment, our findings highlight opportunities for using direct vapour isotope measurements to better understand vapour sources, air mass mixing, and phase changes over complex mountainous terrain, which in turn may better constrain regional‐ to global‐scale hydrological process estimates, such as evapotranspiration rates and the water budgets of mountain lakes.

Assessment of high-resolution satellite rainfall products over a gradually elevating mountainous terrain based on a high-density rain gauge network

ABSTRACT High-resolution satellite rainfall products (SRPs) provide forcing inputs for hydrologic applications. Complex mountainous terrains have a significant effect on the occurrence and intensity of rainfall. This study focuses on the assessment of errors and rainfall detection capability of SRPs over a complex terrain with an elevation ranging from −95 to 3091 m based on a high-density rain gauge network over the Taihang Mountains of North China. The performance of four high-resolution SRPs (rain gauge bias-corrected Climate Prediction Center morphing technique (CMORPH CRT), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) 3B42V7; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS)) was validated using 104 rain gauge stations from 1 January 2016 to 31 December 2017, and the results show that the annual rainfall varied from 375 mm to 1400 mm on average in 2016 and 2017. At the monthly scale, all SRPs except PERSIANN-CCS performed well. The spatial pattern of the annual rainfall variation, which was highest in the southeast and lowest in the northwest, was adequately captured by IMERG and 3B42 but not CMORPH CRT and PERSIANN-CCS. As indicated by the statistical metrics, all SRPs except PERSIANN-CCS exhibited better performance in the regions in the downward direction of the East-Asian Monsoon. In terms of rainfall detection, all SRPs exhibited moderate rainfall detection capability while IMERG exhibited the lowest false alarm ratio (FAR) equal to 0.41. Compared with 3B42, a significant improvement was found in IMERG, which presented increased correlation coefficient (r) and decreased FAR values over the study areas, and the improvement rate was 75% and 95%, respectively. All SRPs underestimated the no/light rainfall (0–1 mm day−1) events. IMERG and PERSIANN-CCS exhibited poor performance with significant underestimation of the 1–2 mm day−1 rainfall class and overestimation of the 2–5 mm day−1 rainfall class. Our results not only demonstrate the superiority of different products at different elevations but also provide suggestions for further improvement of the SRPs, especially for complex terrains.

The impact of initial conditions on convection-permitting simulations of a flood event over complex mountainous terrain

Abstract. Western Norway suffered major flooding after 4 d of intense rainfall during the last week of October 2014. While events like this are expected to become more frequent and severe under a warming climate, convection-permitting scale models are showing their skill with respect to capturing their dynamics. Nevertheless, several sources of uncertainty need to be taken into account, including the impact of initial conditions on the precipitation pattern and discharge, especially over complex, mountainous terrain. In this paper, the Weather Research and Forecasting Model Hydrological modelling system (WRF-Hydro) is applied at a convection-permitting scale, and its performance is assessed in western Norway for the aforementioned flood event. The model is calibrated and evaluated using observations and benchmarks obtained from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The calibrated WRF-Hydro model performs better than the simpler conceptual HBV model, especially in areas with complex terrain and poor observational coverage. The sensitivity of the precipitation pattern and discharge to poorly constrained elements such as spin-up time and snow conditions is then examined. The results show the following: (1) the convection-permitting WRF-Hydro simulation generally captures the precipitation pattern/amount, the peak flow volume and the timing of the flood event; (2) precipitation is not overly sensitive to spin-up time, whereas discharge is slightly more sensitive due to the influence of soil moisture, especially during the pre-peak phase; and (3) the idealized snow depth experiments show that a maximum of 0.5 m of snow is converted to runoff irrespective of the initial snow depth and that this snowmelt contributes to discharge mostly during the rainy and the peak flow periods. Although further targeted experiments are needed, this study suggests that snow cover intensifies the extreme discharge instead of acting as a sponge, which implies that future rain-on-snow events may contribute to a higher flood risk.

POWER LINE EXTRACTION AND ANALYSIS BASED ON LIDAR

Abstract. The traditional electrical power line inspection method has the disadvantages of high labor intensity, low efficiency and long cycle of re-inspection. Airborne LiDAR can quickly obtain the high-precision three-dimensional spatial information of transmission line, and the data which collected by it can make it possible to accurately detect the dangerous points.It is proposed to use the grid method to divide the data into multiple regions for the elevation histogram statistical method to obtain the power line point cloud at the complex mountainous terrain. In the non-ground point data, part of the vegetation point cloud is separated according to the point cloud dimension feature, and then the power line point and the pole point are distinguished according to the density characteristics of the point cloud so as to realize the point cloud classification of the transmission line corridor. On this basis, the power line safety distance detection is carried out on the power line points and vegetation points extracted by the classification, and the early warning analysis of the dangerous points of the transmission line tree barrier is completed. The experimental results show that the method can classify the acquired power line corridor point cloud and extract the complete power line, which effectively eliminates the hidden dangers and has certain practical significance.

Power Tower extraction method under complex terrain in mountainous area based on Laser Point Cloud data

The accurate extraction and location of pole tower point cloud in massive laser point cloud data of transmission line is the basis of airborne laser LiDAR line inspection application. In view of the current complex mountain terrain pole tower point cloud extraction depends on manual intervention, low accuracy, low efficiency and so on. An automatic extraction method of line tower point cloud based on three-dimensional grid spatial distribution feature is proposed in this paper. Firstly, the three-dimensional grid of the original point cloud of the preprocessed transmission line is divided, and the spatial distribution characteristics of the point cloud in the statistical line corridor are analyzed (point cloud density, elevation histogram, terrain elevation distribution, ground object elevation distribution). In this way, the tower point cloud is extracted, and the least square space line fitting method is used to accurately locate the tower position, so as to realize the automatic extraction and accurate location of the tower point cloud. The extraction algorithm is verified by the transmission line laser point cloud data obtained from the actual inspection of a power grid company, and the integrity of the extracted tower point cloud data is 95%, and the average positioning error of the tower position is 9cm. The experimental results show that the method has good rapidity and accuracy.