Terrain Roughness Research Articles

A new attempt for modeling erosion risks using remote sensing-based mapping and the index of land susceptibility to wind erosion

Digital soil maps at proper scales can support sustainable land-use planning over large areas; however, applying this approach in erosion studies is still limited. Therefore, in this work, we adopted remote sensing-based mapping and fuzzy logic techniques to develop wind erosion risk maps with a spatial resolution of 30 m. These procedures were then conducted in a newly-reclaimed area in Matruh Governorate, the Egyptian western desert. Sixty surface soil samples (0–30 cm) were collected and analyzed for soil erodibility-related properties (sand, silt, clay, organic matter, and CaCO3). The relationships of these properties with the reflectance data of Landsat 8 multispectral bands were explored. Then, the spatial predictions were performed using the stepwise multiple linear regression models. The results showed that band reflectance in the shortwave infrared spectrum had higher correlations with soil properties than in the visible and near-infrared regions. The regression models achieved reliable precision and acceptable prediction abilities for all soil properties, except for the silt content. For validation datasets (30 % of the total data), the best-fitted models had coeffect of determination (R2) and residual prediction deviation of 0.61–0.92 and 1.42–2.67, respectively. The fuzzy memberships revealed various contributions of the five drivers (climate erosivity, soil erodibility, soil crust, vegetation, and terrain roughness) to potential soil loss. The success rate curves indicated the different performances of the applied fuzzy overlay operators (AND, OR, Sum, Product, and Gamma 0.9). The best-predictive model with an overall success rate of 85.7 % was obtained by overlaying the five fuzzified layers under the fuzzy algebraic Sum operator. This model delineated five hazard classes over the study area, including severe (79.5 %), high (12.6 %), moderate (5.7 %), slight (1.8 %), and very slight (0.4 %). The developed approach would improve the insight into large-scale monitoring of wind erosion status in desert ecosystems.

Morphodynamics and sediment connectivity index in an unmanaged, debris-flow prone catchment: a through time perspective

Torrential processes are among the main actors responsible for sediment production and mobility in mountain catchments. For this reason, the understanding of preferential pathways for sediment routing has become a priority in hazard assessment and mitigation. In this context, the sediment Connectivity Index (IC) enables to analyse the existing linkage between sediment sources and the selected target (channel network or catchment outlet). The IC is a grid-based index that allows fast computation of sediment connectivity based on landscape information derived from a single Digital Terrain Model (DTM). The index computation is based on the log-ratio between an upslope and a downslope component, including information about drainage area, slope, terrain roughness, and distance to the analysis target (e.g. outlet). The output is a map that highlights the degree of structural connectivity of sediment pathways over analysed catchments. Until now, these maps are however rarely used to help defining debris-flow hazard maps, notably due to a lack of guidelines to interpret the IC spatial distribution. This paper proposes an exploitation procedure along profiles to extract more information from the analysis of mapped IC values. The methodology relies on the analysis of the IC and its component variables along the main channel profile, integrated with information about sediment budgeting derived from Difference of DEMs (DoD). The study of connectivity was applied in the unmanaged sub-catchment (without torrent control works) of the Rio Soial (Autonomous Province of Trento – NE Italy) to understanding the geomorphic evolution of the area after five debris flows (in ten years) and the related changes of sediment connectivity. Using a recent DTM as validation, we demonstrated how an IC analysis over the older DTM can help predicting geomorphic changes and associated hazards. The results show an IC aptitude to capture geomorphic trajectories, anticipate debris flow deposits in a specific channel location, and depict preferential routing pathways..

Visuelle Kommunikation von Fahrradrouten mittels kartographischer Symbolisierung

ZusammenfassungMit zunehmender Förderung nachhaltiger Mobilitätsformen im Rahmen der Verkehrswende stellt das Fahrrad eine umweltfreundliche Alternative zum motorisierten Individualverkehr dar - insbesondere zur Bewältigung innerstädtischer Wege. Im Gegensatz zu Autofahrenden hängt der empfundene Fahrkomfort und das Sicherheitsempfinden von Radfahrenden jedoch stark von Routenmerkmalen wie der Oberflächenbeschaffenheit oder der Verkehrsinfrastruktur ab. Aktuell verfügbare Systeme zur Fahrradnavigation berücksichtigen diese für die Routenwahl von Radfahrenden relevanten Faktoren jedoch kaum und die Visualisierung beschränkt sich vielfach auf eine einfache Hervorhebung der empfohlenen Route. In dieser Arbeit wird daher untersucht, inwieweit sich verschiedene kartographische Darstellungsvarianten für Fahrradrouten zur visuellen Kommunikation der Routenmerkmale Art des Untergrunds, Untergrundrauigkeit, Geländeneigung und Fahrtunterbrechungen, als angemessen erweisen. Im Rahmen einer Nutzerbefragung wird die Effektivität, Attraktivität, Eignung und Entbehrlichkeit einer Legende der verschiedenen Darstellungsvarianten für die unterschiedlichen Routenmerkmale überprüft. Die Ergebnisse der Umfrage zeigen auf, dass viele der vorgeschlagenen Visualisierungsvarianten angemessen für die visuelle Kommunikation von Fahrradrouten sind. Dies betrifft insbesondere Farbdarstellungen sowie Darstellungen mit Symbolen oder Signaturen. Hinsichtlich der getesteten Fahrradroutenmerkmale hängt die angemessenste Darstellung jedoch stark von der zu kommunizierenden Information ab. Die Erkenntnisse dieser Studie sollen zur Entwicklung von speziell auf die Bedürfnisse der Radfahrenden zugeschnittenen Routenvisualisierungen beitragen und somit Entwicklern von Fahrradnavigationssystemen bei Designentscheidungen unterstützen.

Evaluation of flood susceptibility prediction based on a resampling method using machine learning

AbstractThe largest recorded flood loss occurred in the study area in 2013. This study aims to examine resampling methods (i.e. cross-validation (CV), bootstrap, and random subsampling) to improve the performance of seven basic machine learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest (RF), Boosted Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, and Flexible Discriminant Analysis, and found the factors causing flooding and the strongest correlation between variables. The model is evaluated using Area Under the Curve, Correlation, True Skill Statistics, and Deviance. This methodology was applied in Kendari City, an urban area that faced destructive floods. The evaluation results show that CV-RF has a good performance in predicting flood susceptibility in this area with values, AUC = 0.99, COR = 0.97, TSS = 0.90, and deviance = 0.05. A total of 89.44 km2 or equivalent to 32.54% of the total area is a flood-prone area with a dominant area of lowland morphology. Among the 17 parameters that cause flooding, this area is strongly influenced by the vegetation density index and the Terrain Roughness Index (TRI) in the 28 models. The strongest correlation occurs between the TRI and the Sediment Transport Index (STI) = 0.77, which means that flooding in this area is strongly influenced by elements of violence.

Assessment of soil quality in a heavily fragmented micro-landscape induced by gully erosion

Soil quality degradation induced by erosion significantly inhibits sustainable development worldwide. For assessment of soil quality variations in an area with a heavily fragmented micro-landscape induced by gully erosion, 16 soil quality indicators were tested in laboratory settings and selected by principal component analysis (PCA). Meanwhile, soil quality prediction was conducted by the random forest (RF) model with its quality indicators derived from a 3-dimensional structure of the landscape (resolution, 0.01 m) obtained with an unmanned aerial vehicle (UAV). During RF modelling, 80 % of the Soil Quality Indices (SQIs) estimated by PCA were randomly selected as training data, and the remaining was used to validate the prediction result. The optimal SQIs were shown to include Mnd, bulk density, silt content, and cation exchange capacity (CEC). Additionally, the PCA-calculated SQI ranging from 0.33 to 0.85 decreased with decreasing elevation in the gully erosional area. Moreover, the spatial soil quality predicted by RF with a satisfied accuracy (R2 = 0.83 ∼ 0.86; RMSE = 0.03 ∼ 0.04) was comparable to PCA-calculated SQI. Overall, the spatial variation of soil quality in the gully was attributed to elevation (13.4 ∼ 24.1 %), slope gradient (8.0 ∼ 13.4 %), relief amplitude (9.8 ∼ 12.9 %), and terrain roughness index (10.3 ∼ 11.9 %). This study confirmed the excellent performance of RF for SQI prediction, and also indicated that ultra-high-resolution (0.01 m) terrain obtained by unmanned aerial vehicle (UAV) was a competent tool for soil quality assessment in areas with complicated microtopography and limited availability for soil sampling.

Short-term physicochemical and biological impacts on soil after forest logging in Mediterranean broadleaf forests: 15 years of field studies summarized by a data synthesis under the meta-analytic framework

Abstract Sustainable Forest Management (SFM) is a key objective of the European Forest Strategy. One of the most important aspects to be considered to achieve this fundamental goal is the implementation of Sustainable Forest Operations (SFOs). This study aims to investigate how and to what extent forest logging and silvicultural treatments affect soil physicochemical properties in the context of Mediterranean forestry by summarizing the results of 55 field studies, performed within the development of Master’s and PhD theses, carried out in Italy over the last 15 years. In order to define reliable and scalable conclusions, and going beyond the typical case-study approach, we applied a data synthesis under the meta-analytic framework summarizing the results of the studies analyzed. The results show that disturbance triggered by the silvicultural treatments (meant as an alteration of canopy cover as a consequence of the removal of some trees) is much lower than that caused by forest logging, in particular passage of forest vehicles and woody loads. Furthermore, as expected, a clear relationship was observed between the impacts on soil physicochemical properties and biological impacts. Based on our results we recommend that ground-based extraction on slopes steeper than 20 per cent should be limited, extending the applications of skyline systems or winch-assisted extraction. Interestingly, different conditions of terrain roughness showed variable effects on the magnitude of impacts, suggesting the need for future dedicated studies on this topic. The application of machineries specifically developed for the forestry sector showed better performance than lower levels of mechanizations, confirming what was reported in previous literature on the topic. However, the major finding from this study clearly shows that the way in which extraction is carried out is the major factor causing soil disturbance.

High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

Holocene evolution of parabolic dunes, White River Badlands, South Dakota, USA, revealed by high-resolution mapping

AbstractThe White River Badlands (WRB) of South Dakota record eolian activity spanning the late Pleistocene through the latest Holocene (21 ka to modern), reflecting the effects of the last glacial period and Holocene climate fluctuations (Holocene Thermal Maximum, Medieval Climate Anomaly, and Little Ice Age). The WRB dune fields are important paleoclimate indicators in an area of the Great Plains with few climate proxies. The goal of this study is to use 1 m/pixel-resolution digital elevation models from drone imagery to distinguish Early to Middle Holocene parabolic dunes from Late Holocene parabolic dunes. Results indicate that relative ages of dunes are distinguished by slope and roughness (terrain ruggedness index). Morphological differences are attributed to postdepositional wind erosion, soil formation, and mass wasting. Early to Middle Holocene and Late Holocene paleowind directions, 324°± 13.1° (N = 7) and 323° ± 3.0° (N = 19), respectively, are similar to the modern wind regime. Results suggest significant landscape resilience to wind erosion, which resulted in preservation of a mosaic of Early and Late Holocene parabolic dunes. Quantification of dune characteristics will help refine the chronology of eolian activity in the WRB, provide insight into drought-driven landscape evolution, and integrate WRB eolian activity in a regional paleoenvironmental context.

Contribution of land use, soil properties and topographic features for providing of ecosystem services

The aim of this study was to assess the ecosystem services including water yield (WY), sediment retention (SR), and sediment yield (SY) using the integrated valuation of ecosystem services and tradeoffs (InVEST) and their relationships were analyzed concerning land use, soil properties (clay, silt, sand, total porosity and bulk density) and topographic features (elevation, slope, aspect, TRI: terrain roughness index, and TPI: topographic position index) across the Shoor River basin in southwestern Iran. The results showed that the higher values of WY and SR were seen in the northern-eastern parts of the region while the SY was higher in western-southern parts. The average of WY, SR, and SY was estimated at 63 mm, 13.3 t ha−1 y−1 and 2.7 t ha−1 y−1, respectively. The highest WY (2828 m3 ha−1), SR (21 t ha−1 y−1), and SY (4.6 t ha−1 y−1) occurred in built-up areas, forests, and rangelands, respectively. The lowest rate of WY (348 m3 ha−1) and SR (2.2 t ha−1 y−1) were also found in agricultural lands but SY (0.9 t ha−1 y−1) in the forest lands. WY and SR had a positive and significant correlation with total porosity, sand fraction and all the topographic features except aspect (0.55 < R < 0.94, P-value <0.05), while they showed an inverse relationship with clay, silt and bulk density (−0.81 < R < -0.40, P-value <0.05). There was a weak to moderate correlation of the SY with all the topographic features and soil properties. Hot spot analysis also illustrated that all three ecosystem services are directly related to changes in topographic features and inversely related to soil properties across the basin. In general, the topographic features and soil properties had a higher influence on the WY and SR than on the SY. Moreover, the topographic features had more effect on the provision of all three ecosystem services than soil properties in the study area.

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide‐prone areas in construction projects. More studies on landslide susceptibility using machine learning have emerged in recent years, but there is a need to study ways to draw up high‐precision evaluation maps. In this article, a 60 km oil pipeline in China’s Kunming was selected as the research area. The data of 141 landslide points in the research area were obtained through field work and data collection. Meanwhile, lithology, elevation, aspect, slope, stream power index, topographic wetness index, average annual rainfall from 2017 to 2021, distance to roads, and terrain roughness were selected as causal factors of landslide susceptibility in the research area. First, the information value method was used to quantify the impact of conditional factors on landslides. Genetic algorithm (GA), particle swarm optimization (PSO), and bat algorithm (BA) were then used for parameter tuning, and the support vector machine (SVM) was used to analyze landslide susceptibility in the research area. Finally, the receiver operating characteristic (ROC) curve was used to test the model performance after parameter tuning with GA, PSO, and BA. The results show that the area‐under‐the‐curve (AUC) values obtained through SVM, GA‐SVM, PSO‐SVM, and BA‐SVM are 81.1%, 86.2%, 89%, and 91.8%, respectively. SVM had the best performance after parameter tuning with the BA algorithm.

Interference effects mapping on the static wind loading of a tall building

Wind-induced loading on buildings may be altered due to presence of upwind structures. These variations are quantified by the interference factors (IFs), which is determined experimentally for each case analyzed, since obtaining this quantification in codes and standards is still impractical due to the complexity of the multi-parameters involved. To understand the influence of some of these parameters on the interference effects, contour plots and power spectra density were presented for IFs corresponding to aerodynamic coefficients of the along-wind and cross-wind force, as well as for torsion. Wind tunnel tests were conducted applying the synchronous pressure measurement technique. Interference arrangements with one and two upstream interfering buildings were investigated for different wind angles, relative positions and terrain roughness. Results indicate that the shielding effect is predominant in most of the studied cases. Nevertheless, the amplification effects are generally present in specific configurations and are usually caused by channeling and buffeting effects, as well as vortex shedding.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

Impact of urban parameterization and integration of WUDAPT on the severe convection

Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.

Overlooked species diversity in the hyper‐arid Sahara Desert unveiled by dryland‐adapted lizards

AbstractAimHyper‐arid sandy and rocky fields rank among the least biologically diverse habitats of the desert biome, yet knowledge of local biodiversity patterns is also extremely poor. In the Sahara Desert, palaeoclimate oscillations affected the extent of hyper‐arid habitats, but it is unclear how these dynamics determined the evolution and distribution of local specialists. Herein, we assessed cryptic diversity, diversification patterns and spatial connectivity within a Sahara‐widespread group of dryland‐adapted lizards.LocationSahara‐Sahel ecoregions.TaxonAcanthodactylus scutellatus species group.MethodsInter‐ and intraspecific phylogenetic structure, divergence times, spatial genetic patterns and cryptic diversity were assessed using nuclear and mitochondrial loci. The effects of topography and land cover on phylogeographic structure and diversity were tested with generalized linear models. Interspecific hybridization was evaluated using 11 microsatellites across the group's major sympatry zone, predicted based on ecological niche models.ResultsSpecies of Acanthodactylus scutellatus group exhibit Late Miocene origins, followed by extensive intraspecific divergence throughout the Pliocene. The northern Sahara worked as a major diversification hotspot, harbouring a patchwork of small‐ranged, divergent lineages. These lineages are parapatric or sympatric and present concordant nuclear and mitochondrial differentiation, suggesting species status. Genetic connectivity increases in southern latitudes, with wide‐ranging lineages spanning from the Red Sea to the Atlantic coast. Within these potential corridors, mountain outskirts and sand fields in the Sahara interior seemingly acted as origins for recent population expansions. Genetic diversity and connectivity are favoured by terrain roughness and soft‐sand cover respectively. Three species inhabit the Atlantic Sahara sympatry zone without evidence of gene flow.Main conclusionsOverlooked species‐level diversity within a major specialist group of Sahara drylands exposes the recurrent knowledge shortfalls present in hyper‐arid desert environments. Humidity and sandy habitat shifts triggered potential successions of population isolation and re‐connectivity, which favoured cladogenesis in northern desert regions and population expansions across southern east–west corridors.

A simplified GIS and google-earth-based approach for lineaments and terrain attributes mapping in a basement complex terrain

In this study, we use an integrated geologic mapping technique for remote mapping of lineaments and geologic terrain. Our workflow is based on geographic information system tools and consists of stream network delineation, lineaments mapping, terrain roughness index calculation, and interpretation of structural fabrics from google earth aerial photographs. The case study area, the Idanre Hills in southwestern Nigeria, has a protracted history and is characterized by steep-sided outcrops of a granitic batholith and migmatite-gneiss. Lineaments are widespread and dense around the batholith, occurring in areas of high elevation, and slope gradient. Terrain roughness indices are high at the outcrops and lineament sites. Streams in the area exhibit variable flow and partly align with the lineaments. The high roughness indices observed have tectonic connotations and are related to the occurrence of lineaments, strain domains, and high degree of rock weathering. Importantly, our method is effective in remote mapping of lineaments and terrain attributes within the study area and has wider applications in other basement complex terrains.

Predicting Linearised Wind Resource Grids using Neural Networks

Modelling the flow over terrain is a key element of wind resource assessments within the wind energy industry. Existing flow modelling methods range from fast, low fidelity analytical models to time-consuming and computationally expensive high-fidelity Computational Fluid Dynamics (CFD) software. In this work, a Grid-Kernel Neural Network approach has been developed and used to create surrogate models to emulate the WAsP wind resource software, by calculating the changes in wind speed and direction due to the orography and roughness of terrain. This data-driven approach has proven to be successful in predicting the orographic speed and direction changes at multiple heights above ground. At 100 m above ground, the mean absolute error values were 1.6% speedup and 0.4° for the orographic speed and direction changes, respectively. Although the WAsP model is a linear, potential flow solver, the findings here can be counted as a first step towards creating a fully data-driven CFD wind resource model.

Multi-resolution topographic analysis in hexagonal Discrete Global Grid Systems

• We did multi-resolution topographic analysis in hexagonal grid systems. • Five algorithms to compute slope gradient and aspect were proposed and compared. • Slope gradient showed strong positive relationships between various algorithms. • Grid resolution and terrain roughness influenced topographic parameters. Discrete Global Grid Systems (DGGS) have been increasingly adopted as a standard framework for multi-source geospatial data. Previous research largely studied the mathematical foundation of discrete global grids, developed open-source libraries, and explored their application as data integration platforms. This study investigated the multi-resolution terrain analysis in a pure hexagonal DGGS environment, including descriptive statistics, topographic parameters, and topographic indices. Experiments across multiple grid resolutions were carried out in three study areas with different terrain roughness in Alberta, Canada. Five algorithms were proposed to calculate both the slope gradient and terrain aspect. A cell-based pair-wise comparison showed a strong positive correlation between the gradient values as calculated from five algorithms. The grid resolutions as well as the terrain roughness had a clear effect on the computed slope gradient and topographic indices. This research aims to enhance the analytical functionality of hexagonal DGGS to better support decision-making in real world problems.

The urban morphology classification under local climate zone scheme based on the improved method - A case study of Changsha, China

Generating local climate zone (LCZ) maps has been a new way for understanding urban morphology recently. So far, LCZ mapping has two major methods. WUDAPT workflow is widely used because of time-saving mapping process, while GIS stream is more complex and has obstacles such as lack of localized LCZ parameters, difficulty of parameter collection and unclear mapping specifications. Based on the original LCZ scheme, the study aims to fill the research gap by adopting GIS-based rules to identify urban morphology using multi-source dataset. Firstly, the study adjusts the LCZ types and LCZ parameters according to the current situation of Changsha. Secondly, a comprehensive set of LCZ parameters are calculated and evaluated, including Building Height (BH), Building Surface Fraction (BSF), Floor Area Ratio (FAR), Sky View Factor (SVF), Street Canyon Ratio (SCR), Impervious Surface Fraction (ISF), Pervious Surface Fraction (PSF), Tree Information (TH), Terrain Roughness (TR) and Albedo (AL). Thirdly, each 250 m × 250 m cell is classified according to the LCZ parameter threshold. The result indicates a higher OA (84.40%) of GIS stream comparing WUDAPT workflow. This study provides a clear depiction of improved GIS stream, which contributes to the development of LCZ mapping and also provides support for climate-sensitive urban development.

How Rough Is the Path? Terrain Traversability Estimation for Local and Global Path Planning

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method.

A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain

This study proposes a novel probabilistic power curve model for wind turbine and combines it with a hybrid wind farm model to quantify the accuracy and uncertainty of power prediction of wind farm over complex terrain with low computational cost. The proposed probabilistic power curve model for an active stall-regulated turbine is expressed by the beta distribution to estimate the uncertainty of power output at a certain wind speed. The predicted mean value and standard deviation of power output by the proposed model show favorable agreement with the measurement, while the conventional deterministic model cannot estimate the uncertainty of power output from wind turbines at all. The hybrid wind farm flow model is then presented, in which the effects of local terrain and surface roughness on the wind speed, wind direction and turbulence intensity are taken into account by the CFD simulation, and the wind turbine wakes are represented by an advanced wake model. The predicted wind speed and turbulence intensity show good agreement with those measured in a wind farm over complex terrain in the north of Japan. Finally, the proposed probabilistic power curve model is combined with the hybrid farm flow model to estimate the mean value and standard deviation of wind farm power production and is validated by the field measurement. The weighted mean absolute percentage error in mean value is reduced from 18.1% to 7.2% with consideration of wake effects and that in standard deviation is reduced from 100% to 15.6% by using the proposed probabilistic power curve model.