Complex Mountainous Areas Research Articles

Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging

The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass.

Supply–Demand Matching of Engineering Construction Materials in Complex Mountainous Areas Based on Complex Environment Two-Stage Stochastic Programing

Supply–Demand Matching of Engineering Construction Materials in Complex Mountainous Areas Based on Complex Environment Two-Stage Stochastic Programing

Large eddy simulation study of 3D wind field in a complex mountainous area under different boundary conditions

Large eddy simulation study of 3D wind field in a complex mountainous area under different boundary conditions

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

The present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution.

Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data

RANS (Reynolds-averaged Navier-Stokes) has become one of the mainstream methods to determine the wind field in the wind resource assessment of the complex terrain. However, before the simulation of wind field by RANS, defining the inlet boundary condition often depends on user's experience, and lacks of theoretical guidance, which brings great uncertainties to the accuracy of simulation results. In this paper, based on the vertical multi-point wind speed data (including wind direction) obtained from the on-site measurement and the wind speed data simulated by RANS, a data fusion method called the RANS-VMM (Vertical Multi-point on-site Measurement) method is proposed. In this method, through the polar coordinate diagram (PCD) that can express the wind direction and wind profile index at the same time, the vertical multi-point measured wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations, which can reconstruct the wind field distribution with spatial variability in complex mountainous areas, thus improving the accuracy of wind resource assessment in the complex terrain. The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain, and the results indicate that the RANS-VMM method can improves the accuracy of wind resource assessment in the complex terrain.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

UAV Seismic Source Traveltime Tomography Technology in Steep Mountainous Areas

Active source seismic exploration is one of the most important high-resolution methods to obtain information about underground structures in tunnel engineering, and the seismic source is the key equipment to acquire high-quality data. In steep mountainous areas with complex geological structures, surface undulation, and drastic changes, the use of traditional seismic sources with high cost can seriously affect the efficiency and accuracy of detection. In this paper, a survey experiment of tunnel engineering is carried out in Lijiang, Yunnan Province, completing a fast, high-quality seismic data acquisition through utilizing the convenient, environmentally friendly, and economical UAV (Unmanned Aerial Vehicle) seismic source as the shot and wireless node instrument as the receiver. The experiment shows that the UAV seismic source can achieve low-cost and environmentally friendly excitation of seismic waves in complex mountainous areas, making a breakthrough in seismic source equipment. We obtain a high-precision velocity model by traveltime tomography, identifying the thickness of the overburden layer in the study area, and providing an important reference for tunnel engineering.

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced.

Enhanced flood water depth estimation from Sentinel-1A images

ABSTRACT Floods, as a natural disaster, have severe consequences for all aspects of life and society, including but not limited to damaging harvests and transportation systems, and causing epidemic diseases. Estimating flood water depths has been a topic of great interest for several decades. Finding an effective solution to this problem is crucial for accurately estimating the impact of floods and making informed decisions to mitigate their consequences. The paper presents a novel approach to estimate flood water depths in Quang Binh province, Vietnam, by utilizing an improved Otsu algorithm to classify each image pixel as either water or non-water, and then applying the FwDET interpolation algorithm to compute the flood depth of each classified water pixel. The improved algorithm can improve the accuracy of water classification when compared to the traditional Otsu method by automatically dividing images into subregions of varying sizes, which are suitable for optimal Otsu computation.The estimated results for the study area in Quang Binh province were validated using ground measurement station data and demonstrated the relatively high accuracy of water classification (ranging from 91.4% to 92%) and estimated flood water depth (85.26%). Based on the positive results obtained from the investigation of the complex mountainous area mentioned above, it can be inferred that the proposed method has the potential to be applied to estimate flood depths in various types of terrains.

Risk Assessment and Control of Geological Hazards in Towns of Complex Mountainous Areas Based on Remote Sensing and Geological Survey

Mountainous areas have become among the most developed areas of geological hazards due to special geological environmental conditions and intensive human engineering activities. Geological hazards are a main threat to urbanization, rural revitalization, and new rural construction in complex mountainous areas. It is of great strategic significance to conduct large-scale geological hazard investigation and risk assessment in urban areas, control the risk of geological hazards at the source and propose risk control measures. In this paper, we established the technical methods of geologic hazard risk assessment and control in complex mountain towns by taking Longlin Town in the mountainous region of Gansu Longnan, China as the study area, with the Quanjia bay debris flows and Panping Village landslides as the typical pilot investigation and assessment. The methods consist of six stages—risk identification, hazard disaster model investigation, risk analysis, vulnerability assessment, risk evaluation and risk management and control measures and proposals. On this basis, the results of geological hazards with different precipitation frequencies (5%, 2%, 1%) are presented. The results show that 75.23% of the regions remained at low risk levels; 24.38% of the regions increased a risk level with decreasing precipitation frequency, and 0.39% of the regions remained at extremely high risk levels under different precipitation frequency conditions. For the Quanjia bay debris flows and Panping Village landslides case, we discussed the geological hazards risk source control contents, management and control technologies, engineering and non-engineering measures of disaster prevention and control for urban disasters and specific disaster areas. This research can provide technical support and reference for disaster prevention and mitigation, and territorial spatial planning.

Precision Terrain Modeling Approach in Complex Mountainous Areas Based on Compact UAV Ka-InSAR Data

A High-Precision digital elevation model (DEM) is useful for disaster investigation and evaluation in cloudy, rainy, and complex mountainous areas. However, clouds and rain make the optical images and laser point cloud data acquisition difficult, while noise prohibits obtaining accurate surface information. Additionally, the complex elevation difference in mountainous areas increases the data processing difficulty, such as phase unwrapping and filtering. To overcome these problems, first, we introduce a new airborne multi-baseline Ka-Interferometric Synthetic Aperture Radar (InSAR) system developed by the Beijing Institute of Radio Measurement. The system affords high resolution, small volume, is lightweight, has a good top view angle, and is flexible. Thus, it reduces the flight platform’s dependence and improves the aircrafts adaptability and universality. Moreover, a multi-baseline phase unwrapping method of a two-stage programming approach (TSPA) is selected to overcome the influence of severe noise and the phase continuity assumption limitation. Additionally, an adaptive filtering method for InSAR point clouds considering coherence and optimal bending energy is proposed. This methods validity is verified using stereo satellite images, ground observation point precision checks, and geomorphic texture analysis against existing DEM results. The experimental results demonstrate that the proposed scheme has a good filtering effect on noise, vegetation, residential building areas, and bridges, significantly reducing manual intervention. Moreover, the results highlight that our method is well integrated with stereo images and has more texture details than conventional stereo mapping results, with a mean square error of elevation of 1.938m.

Assessment Technology and Application of Wind Energy Resources in Complex Mountain Areas at High Altitude

Based on the meteorological stations and wind measurement data of a high-altitude region in Liangshan Prefecture, Sichuan Province, collation, analysis, and evaluation are carried out. The results show that: the prevailing wind direction in the region is stable and the wind energy resources have a certain development value, but the turbulence intensity is high and belongs to IEC Class B, which has a certain impact on the fatigue load of wind turbines, and the safety of local wind turbines needs to be reviewed.

Investigation of hilly terrain wind characteristics considering the interference effect

Wind characteristics at complex mountainous areas are of great practical importance, which, however, has not been fully understood. The main objective of this study is to present a comprehensive assessment of hilly terrain wind characteristics using both Computational Fluid Dynamics (CFD) method and wind tunnel test. The results indicate that hilly-terrain wind characteristics exhibit pronounced site-to-site variability. The speed-up effect at mountain top and the passing terrain can be somewhat weakened by the existence of front mountain, whereas the wind speed reduction near the mountain foots is enhanced due to the shielding effect. The effect of inflow wind direction on the hilly terrain wind characteristics is also found to be evident.

Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

Abstract Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

Geo‐environment‐aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

AbstractLandslide susceptibility evaluation (LSE) is a critical issue for disaster prevention. Limited by labor cost and observation technology, landslide samples are extremely limited in dense vegetation‐covered and remote areas, making the common supervised learning model underfit with limited samples. Therefore, the reliability of analysis results in mountainous areas is low. Transfer learning can achieve reliable assessment without the need for representative samples. However, transfer learning suffers from environmental heterogeneity in regional LSE and may transfer incorrect classification knowledge of landslide features from dissimilar environments. Aiming at these challenges, we proposed a geo‐environment‐aware LSE method based on unsupervised adversarial transfer learning. The key is to consider the difference in landslide features in different geo‐environments. The study areas were first divided into multiple sub‐environments, and the similarity between the sub‐environments was calculated. Then an environment‐aware adversarial transfer model was built for fine‐grained aligning of the landslide feature with similar sub‐environments and for reducing negative transfer between dissimilar environments. The fitted classification model was employed to predict the target regions and to generate the final LSE. The experimental results indicated that the proposed method achieves reliable LSE for sample‐free regions. The accuracy of the proposed method is 7–12% better than commonly used methods such as support vector machines, random forests, and artificial neural networks. The performance of the proposed method is even close to the results of supervised learning with the presence of representative samples, and it also performs more globally and objectively in susceptibility mapping. These results reveal that the proposed method effectively transfers the knowledge of landslide susceptibility from other regions to the sample‐free region.

Exploring Terrain Indices and Slope Morphometry for Trail and Campsite Planning on Mount Irau, Malaysia

Terrain analysis and elevation profiling are widely utilized in geographical studies where spatial information system is one of the foremost tools in assessing characteristic patterns of hiking trails and plain areas suitable for a campsite. The main objective of this study is to classify the landforms of Batu Gangan Forest Reserve by conducting a morphometric analysis of the digital terrain model (DTM) with a 10 m resolution. The classification process is based on calculating the topographic position index (TPI). Using TPI, the landscape was classified into slope position and landform categories. The delivered TPI landform classes are spatially correlated with the elevation, slope, LS factor, and topographic wetness of the mountain. Site observation was carried out to identify water sources, viewpoints, and their significance as suitable campsites and potential trails. An alternative trail was proposed to reduce the visitor impacts towards the main trail. The result suggested that the plains landform only covered 3.26% or 2.03 km2 of the study area, where two potential campsites were identified. The method presented and research findings derived from the study are beneficial to support sustainable recreation resource planning, especially in a complex mountainous area. The study's findings and data will lead to proper forest use for recreational reasons consistent with conservation efforts.

Spatiotemporal Changes in the Watershed Ecosystem Services Supply and Demand Relationships in the Eastern Margin of the Qinghai-Tibetan Plateau

Clarifying the spatiotemporal changes in the supply and demand relationship of ecosystem services (ESs) is essential for optimizing ESs management. However, several studies have reported the ESs supply and demand risk in complex mountainous areas. In this study, we quantitatively analyzed the spatiotemporal variation in ESs supply, demand, and their trade-off and synergy, including water yield, soil conservation, and food provision in the Bailongjiang watershed (BLJW) in western China. The results showed that the total supply and demand of water-yield and soil-conservation services rose with a surplus from 2002 to 2018, except for food provision. A high value characterizes the water-yield and soil-conservation supply in the south, but there are low values in the east BLJW. The spatial distribution of water and food supply–demand featured a high demand in the subareas with population aggregation. Soil-conservation demand is high in the northwest and south of Wudu. The dominant spatial matching type of supply and demand in water yield was a high supply with a low demand. Soil conservation was associated with a low supply and low demand, and food provision with a high supply and increased demand. A synergy existed between water yield and soil conservation. Trade-offs existed between water yield, food provision, and soil conservation. The spatial distribution of trade-off intensity showed distinctive patterns. The supply–demand ratio of WY and SC decreased with the increasing trade-off intensity. This study comprehensively considers ES and supply–demand conflicts, thus providing a new perspective and approach for enhancing ecosystem services and high-quality regional development.

Numerical Simulation of Wind Characteristics in Complex Mountains with Focus on Terrain Boundary Transition Curve

In recent years, an increasing number of projects have been developed in complex mountainous areas. The wind environment in mountainous areas, extremely complex due to the undulating terrain and diverse landscapes, is a key factor threatening the structural safety of buildings and their appurtenances in mountainous areas. Therefore, it is important to study the wind environment in complex terrain to clarify the wind resistance of structures in mountainous areas. Computational fluid dynamics (CFD) approaches are commonly used to examine wind fields in complex terrain; however, due to the limited range of terrain considered, direct modeling using terrain elevation data can result in truncated elevation differences, affecting the accuracy of numerical simulations. To address the problem of truncated elevation differences at terrain boundaries, the parameters of the wind tunnel contraction curve are optimized regarding the wind tunnel contraction section design principle. Moreover, several transition curves are analyzed and evaluated by numerical simulation methods, and a transition curve applicable to the terrain boundary transition form is proposed. The proposed terrain transition curves are applied to model the terrain of complex mountainous ski resort areas to be used in CFD numerical simulations. Furthermore, the accuracy of the numerical simulation is verified through a comparison with the field-measured data. Results indicate that the proposed method can accurately and effectively reflect the wind environment characteristics of a ski resort area. The proposed terrain transition curve provides a theoretical basis and case support for designing the terrain model boundary transition section, which can be used as a reference for wind tunnel and numerical simulation studies in complex mountainous areas.

Surface-based temperature inversion characteristics and impact on surface air temperatures in northwestern Canada from radiosonde data between 1990 and 2016

Assumptions of linear lapse rates in regions prone to surface-based inversions (SBIs) can generate biases in the prediction of surface air temperature. Although studies of Arctic inversions are common, few regional studies of their characteristics exist in high-latitude regions with mountainous topography. To address this gap, vertical atmospheric temperature profiles for five sites in northwestern Canada were analysed using archived radiosonde data from 1990 to 2016. We present monthly, seasonal, and annual SBI characteristics including the occurrence of transient and persistent SBIs. A novel metric, surface-based inversion impact (SBIimp), was developed by combining the traditional inversion characteristics of depth, strength, and frequency, and was used to quantify the impact of SBIs on cooling the surface air temperature. SBIimp values of >5 °C yr−1 and ∼10 °C winter−1 occur locally. A weak linear relationship between sea ice coverage in the Beaufort Sea and SBIimp manifests across parts of the study area, though this relationship does not persist after detrending the datasets. Topographic analysis of areas surrounding each radiosonde location reveals highly variable SBIimp in complex mountain areas and more consistent SBIimp across areas of low relief. Our results can help interpret the role of inversions in climatic conditions maintaining cryospheric elements such as permafrost.

Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method

Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping.