Terrain Variables Research Articles

Spatiotemporal analysis of landscape dynamics and their use as input for the design of a habitat suitability index: A case study of Oryctolagus cuniculus in a Mediterranean region

The connectivity of landscapes is increasingly threatened by urban sprawl, which leads to the fragmentation of habitats and thus endangers the species that live there. To address this situation, landscape dynamics in the literature have mostly been analyzed using predefined land cover classes, which were then not used to assess habitat suitability, but instead relied on physical variables. As a bridge between both aspects, this study aimed to model the spatiotemporal patterns of landscape fragmentation by analyzing the landscape metrics associated with four land cover groups classified according to their naturalness. Landscape metrics were then combined with a range of terrain, climate and proximity variables to develop a Habitat Suitability Index (HSI) for terrestrial animal species. The proposed methodology was tested using a case study in the Mediterranean region of the Valencian Community (Spain): landscape dynamics were assessed over the period 1992–2015, while the HSI was tested with the European rabbit (Oryctolagus cuniculus) as the target species. The results obtained in terms of landscape dynamics showed a growing presence of artificial surfaces at the expense of fewer transformed areas, which became progressively fragmented over time. Moreover, the results of the developed HSI were highly concordant with observations of rabbit abundance in the region, based in particular on variables related to the temperature in the area and the patch shape in the preferred habitats of this species. These outputs can be used to support the implementation of habitat restoration strategies aimed at increasing ecological connectivity through measures such as wildlife crossings.

Break Out of the Sea Surface: Genetic Algorithm based Search Path Planning Model for Locating and Searching Submersible Lost in Deep Sea

Deep-sea tourism and exploration, which require regulatory approval due to safety concerns, are burgeoning entertainment activities. Our study introduces a probabilistic model for locating missing submersibles to facilitate these approvals. The model predicts the submersible's location based on Newton's second law, analyzing three potential malfunction scenarios. It incorporates dynamics to account for variations in water flow and terrain, using a normal distribution to handle random location fluctuations. To address the complexities of deep-sea search and rescue, we propose a method that integrates detailed equipment selection, suited to the extreme conditions. Furthermore, we develop a discrete grid-based model for search path planning. This model simplifies path planning into a sorting optimization problem by connecting grid centers and calculating the probability integral for each grid. Our research also discusses the scalability of our positioning and search models, crucial for adapting to various deep-sea environments. We demonstrate the efficacy of our model through simulations and calculations, proving its potential to enhance safety and efficiency in deep-sea tourism projects. This comprehensive approach ensures that our probabilistic model not only predicts the submersible's location but also optimizes the search process, addressing both regulatory and practical challenges in the field of underwater exploration.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

Opportunity to integrate machine management data, soil, terrain and climatic variables to estimate tree harvester and forwarder performance

Opportunity to integrate machine management data, soil, terrain and climatic variables to estimate tree harvester and forwarder performance

Enhancing Terrain Analysis from Digital Elevation Models Using 2-D Kalman Filtering Technique

Three-dimensional spatial information, particularly elevation, is crucial for understanding terrain characteristics essential for meaningful development, often expressed as a Digital Elevation Model (DEM). To achieve reliable and accurate DEM values for terrain analysis, modeling uncertainties is necessary. The primary objective of this study is to determine improved terrain variables from the Digital Elevation Model of the study area. The recursive 2-D Kalman filtering technique was applied four times at different orientations to 121 elevation values extracted from a 30-meter resolution ALOS DEM of the study area using QGIS Desktop 3.22.7 software of an area covering approximately 10.80 Hectares using QGIS, the process involved 144 iterations. MATLAB was used for the computations. The terrain variables (elevation, first partial derivatives along the X and Y axes) of the central point of the DEM were obtained as a linear combination of the four filtering results. The final estimated values for the central point were 26.5589m for elevation, 0.0002m and 0.0011m for partial derivatives along the X and Y directions, with standard errors of ±0.0001m, ±0.0005m, and ±0.0007m, respectively. A 3-D plot of the terrain surface of the study area using Surfer10 software showed that the recursive 2-D Kalman filtering significantly improved the quality of the terrain surface when applied over the DEM. Therefore, the adopted recursive 2-D Kalman filter is well-suited for terrain surface modeling using grid DEMs. Its use is encouraged for determining improved values of terrain topographic variables, leading to more accurate terrain interpretation. In addition, when compared with ground survey data confirmed the technique's efficiency in reducing DEM noise. These results are promising as they are necessary information for flood route modelling, land use allocation and enhance functionality of the urban space domain of the study area.

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

In this study, we introduce a new model for bipedal locomotion that enhances the classical spring-loaded inverted pendulum (SLIP) model. Our proposed model incorporates a damping term in the leg spring, a linear actuator serially interconnected to the leg, and a rotary actuator affixed to the hip. The distinct feature of this new model is its ability to overcome the non-integrability challenge inherent in the conventional SLIP models through the application of partial feedback linearization. By leveraging these actuators, our model enhances the stability and robustness of the locomotion mechanism, particularly when navigating across varied terrain profiles. To validate the effectiveness and practicality of this model, we conducted detailed simulation studies, benchmarking its performance against other recent models outlined in the literature. Our findings suggest that the redundancy in actuation introduced by our model significantly facilitates both open-loop and closed-loop walking gait, showcasing promising potential for the future of bipedal locomotion, especially for bio-inspired robotics applications in outdoor and rough terrains.

Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data

Accurate aboveground biomass (AGB) estimations over large areas are essential for assessing carbon stocks and forest resources. This study evaluated machine learning approaches for AGB modeling in Pakistan's mountainous region of Diamir district using freely available Sentinel-1 and Sentinel-2 data and 171 field-measured AGB training points. Random Forest, Gradient Tree Boosting, CatBoost, LightGBM, and XGBoost algorithms were implemented and optimized. Models were developed using individual and combined datasets. Sentinel-2 optical data outperformed Sentinel-1 radar data, but the fusion of both sensors achieved the highest accuracy (R2 > 0.7, RMSE = 105.64 Mg/ha, MAE = 85.34 Mg/ha). Tree canopy height was the most informative predictor for this data, besides terrain variables and radar textures. The machine learning models significantly improved AGB estimates compared to traditional regression techniques, and gradient boosters outperformed Random Forest. This research demonstrates the potential of multi-sensor remote sensing data and advanced algorithms for forest biomass mapping in complex terrain, with modeling accuracies reaching root mean squared errors below 90 Mg/ha. The framework provides an effective solution for monitoring biomass using freely available satellite data. Further refinements include integrating higher-resolution optical data and additional field samples for better validation. This study contributes to remote sensing capabilities for assessing vegetation carbon stocks and dynamics.

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains (Adv. Theory Simul. 7/2024)

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains (Adv. Theory Simul. 7/2024)

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa)

AbstractAimSeamounts are conspicuous geological features with an important ecological role and can be considered vulnerable marine ecosystems (VMEs). Since many deep‐sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold‐water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data are scarce.LocationSeamounts around the Cabo Verde archipelago (NW Africa).MethodsWe used species presence–absence data obtained from remotely operated vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100‐m‐resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (generalized additive model and random forest) were employed and single‐model predictions were combined into a final weighted‐average ensemble model. Model performance was validated using different metrics through cross‐validation.ResultsTerrain orientation, at broad scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic–topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.Main ConclusionsOur study shows how data‐poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.

EUNet: Edge-UNet for Accurate Building Extraction and Edge Emphasis in Gaofen-7 Images

Deep learning is currently the mainstream approach for building extraction tasks in remote-sensing imagery, capable of automatically learning features of buildings in imagery and yielding satisfactory extraction results. However, due to the diverse sizes, irregular layouts, and complex spatial relationships of buildings, extracted buildings often suffer from incompleteness and boundary issues. Gaofen-7 (GF-7), as a high-resolution stereo mapping satellite, provides well-rectified images from its rear-view imagery, which helps mitigate occlusions in highly varied terrain, thereby offering rich information for building extraction. To improve the integrity of the edges of the building extraction results, this paper proposes a dual-task network (Edge-UNet, EUnet) based on UNet, incorporating an edge extraction branch to emphasize edge information while predicting building targets. We evaluate this method using a self-made GF-7 Building Dataset, the Wuhan University (WHU) Building Dataset, and the Massachusetts Buildings Dataset. Comparative analysis with other mainstream semantic segmentation networks reveals significantly higher F1 scores for the extraction results of our method. Our method exhibits superior completeness and accuracy in building edge extraction compared to unmodified algorithms, demonstrating robust performance.

A tree's view of the terrain: downscaling bioclimate variables to high resolution using a novel multi‐level species distribution model

Fine‐scale spatial climate variation fosters biodiversity and buffers it from climate change, but ecological studies are constrained by the limited accessibility of relevant fine‐scale climate data. In this paper we introduce a novel form of species distribution model that uses species occurrences to predict high‐resolution climate variation. This new category of ‘bioclimate' data, representing micro‐scale climate as experienced by one or more species of interest, is a useful complement to microclimate data from existing approaches. The modeling method, called BISHOP for ‘bioclimate inference from species' high‐resolution occurrence patterns,' uses data on species occurrences, coarse‐scale climate, and fine‐scale physiography (e.g. terrain, soil, vegetation) to triangulate fine‐scale bioclimate patterns. It works by pairing a climate‐downscaling function predicting a latent bioclimate variable, with a niche function predicting species occurrences from bioclimate. BISHOP infers how physiography affects bioclimate, estimates how these effects vary geographically, and produces high‐resolution (10 m) maps of bioclimate over large regions. It also predicts species distributions. After introducing this approach, we apply it in an empirical study focused on topography and trees. Using data on 216 North American tree species, we document the biogeographic patterns that enable BISHOP, estimate how four terrain variables (northness, eastness, windward exposure, and elevational position) each influence three climate variables, and use these results to produce downscaled maps of tree‐specific bioclimate. Model validation demonstrates that inferred bioclimate outperforms macroclimate in predicting distributions of separate species not used during inference, confirming its ecological relevance. Our results show that nearby bioclimates can differ by 5°C in temperature and twofold in moisture, with equator‐facing, east‐facing, windward‐facing, and locally elevated sites exhibiting hotter, drier bioclimates on average. But these effects vary greatly across climate zones, revealing that topographically similar landscapes can differ strongly in their bioclimate variation. These results have important implications for micrometeorology, biodiversity, and climate resilience.

The impact of local wind and spatial conditions on geometry blade of wind turbine

AbstractThe research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field.

Blow flies (Diptera: Calliphoridae) of the Baja California Peninsula

The blow fly fauna of the Baja California peninsula, Mexico has been sparsely documented. This study incorporates and lists recent and historical records of blow flies from collecting trips throughout the peninsula, visits to major southern California museums for historical collection records, and a literature search. Seven genera and 16 species are reported from the two peninsular Mexican States, Baja California, and Baja California Sur. Three species are recorded that are not included in the 2020 Catalog of Mexican Calliphoridae by Jaume-Schinkel, & Ibanez-Bernal (2020), they are: Calliphora livida, Calliphora terraenovae, and Calliphora vomitoria. One blow fly species, Cochliomyia hominivorax (the primary Screw-Worm Fly) is not included here but is found in museum collections. Once present in Baja, it is considered eradicated from continental North America as far south as the Panama Canal by the U.S. Department of Agriculture (USDA) however, it is occasionally reintroduced (Skoda et al. 2018) and is still present in some Caribbean islands. It is noteworthy that all calliphorid species from this mountainous desert peninsula are also found in the State of California, USA which supports 11 genera and 47 species in this family (Whitworth 2006) primarily because of its more varied climate and terrain. The Baja peninsula can be considered a southern extension of the State of California, with reduced species representation due to reduced climate and terrain variation, so no separate identification keys are necessary. Forensic entomologists and taxonomists can use the blow fly keys for North America by Whitworth, 2006, and 2017 for this region. The latest key by Jones, Whitworth, and Marshall (2019) is revolutionary and much welcome in taxonomy in that it is an online computer-aided key that has actual macro-photos of species identification characters and is intended to be used with a computer screen next to your microscope for identifications in place of printed material.

Dynamic Hazard Assessment of Rainfall-Induced Landslides Using Gradient Boosting Decision Tree with Google Earth Engine in Three Gorges Reservoir Area, China

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE’s powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant “8–31” 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies.

Mapping fault geomorphology with drone-based lidar

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.

Specific Physical Training of Future Officers for Actions in Mountain Environments

Abstract The mountain environment implies a certain degree of difficulty even in peacetime, in civilian sector activities, even more so from a military point of view, especially in conducting operations in this environment. The varied terrain, sudden weather changes, oxygen depletion with increasing altitude and the lack of suitable travel lanes are just some of the challenges that future officer mountain hunters have to face and overcome. Preparation for this stage must start early with an emphasis on physical and mental endurance, the latter being a basic pillar for any military person, but even more so for a mountain hunter. Sustained physical effort in difficult terrain and exposure to bad weather combined with strong winds requires a trained psyche, ready to carry on even when the physique might give way. Endurance training also plays an important role and contributes to the ability of mountain hunters to traverse large areas of terrain in a relatively short time using weapon-specific methods and techniques. Being a mountain hunter puts a strain on your body and mind. One thing we can improve as mountain hunters is certainly the level of physical training specific to this weapon. Given the specificity of mountain hunter actions in terms of both ski and alpine training, training with a focus on the lower body is a starting point in the physical preparation needed to carry out actions in the mountain environment.

Assessing Trail Running Biomechanics: A Comparative Analysis of the Reliability of StrydTM and GARMINRP Wearable Devices.

This study investigated biomechanical assessments in trail running, comparing two wearable devices-Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMINRP and StrydTM, with strong correlations and minimal variability. However, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMINRP and StrydTM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running.

Optimizing bathymetric position index (BPI) calculation: An analysis of parameters and recommendations for the selection of their optimal values

The present research paper addresses a critical gap in existing literature concerning the absence of a standardized methodology for parameter selection in the computation of the Bathymetric Position Index (BPI) values. The BPI is a measure of where a georeferenced location, with a defined depth, is relative to the neighbouring seascape, and it plays a significant role in characterizing benthic terrain for modelling and classification. Arguably, the two most important parameters when calculating the BPI are the size and the shape of the neighbourhood of analysis. With regards to the radius parameter, which defines the size of the neighbourhood, the optimal radius value for calculating the BPI must be carefully chosen, considering both the size of the target morphology and the scale factor, which is equal to the radius in map units multiplied by the cell size. It is recommended that the optimal radius value should closely match the size of the target morphology. Tests were performed using an annular neighbourhood shape and they have revealed that the outer radius is the most influential factor in the BPI calculation. Further experimentations and comparisons between circular and annular shapes have indicated that the use of different shapes has no significant impact on the results. The study has found no substantial correlation between the BPI values and other examined terrain variables, such as depth, slope, and curvature. This lack of correlation may be attributed to the BPI values accounting for the specific neighbourhood size, while for the studied variables the default window size was used, which is a considerably smaller scale than the ones used in most BPI calculations. In conclusion, this research highlights the importance of parameter selection in BPI calculations and provides valuable insights into the optimal radius choice and the negligible impact of neighbourhood shape. The findings also shed light on the unique nature of BPI values and their relationship with other geospatial variables.

Nature-Inspired Wind Farm Layout Optimization: Harnessing Smart Patterns for Sustainable Energy

This research investigates the transformative realm of wind farm layout optimization, with a specific focus on harnessing the efficiency of bio-inspired patterns. The positioning of wind turbine is play a vital role in the maximizing the energy output of a wind farm. If a wind turbine is placed in the wake region of the upstream turbine, the energy produced by the downstream turbine is reduced. Hence, it is imperative to place turbines in such a way that effect of the wake is minimum on a performance of turbines. The conventional grid-based approaches, commonly employed in wind farm layouts, face limitations in capturing the inherent complexity of wind flow dynamics, especially in varied terrains. In contrast, bio-inspired layouts, inspired by patterns observed in natural ecosystem, offer the promising results over the conventional Grid based approach. Our investigation involves the development and implementation of a novel bio-inspired wind farm layout positioning pattern. In the proposed approach, various nature inspired pattern like honeycomb and sunflower seeds pattern is explored for the turbine positioning. Further, the modeling of wind behavior includes both uniform wind speeds and variable wind speeds originating from all directions. The modified passing vehicle search (mPVS) optimization algorithm is used for optimizing the wind turbine placement. The results are obtained for the different wind scenario and compare it with the available results in the literature. The anticipated outcomes of this research include a deeper understanding of the potential benefits and challenges associated with bio-inspired wind farm layouts. The findings aim to contribute valuable insights into optimizing wind turbine placements, maximizing energy capture, and fostering sustainable practices in the wind energy sector. Results shows that the turbine positioning in the proposed bio-inspired pattern produce the higher power output (6%) compared to the conventional grid based approach. Hence, it can be concluded that the approach present in this study can assist wind farm designers and developers in optimally placing turbines for better performance.