High Vegetation Density Research Articles

Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America.

Rapid warming and increasing disturbances in high-latitude regions have caused extensive vegetation shifts and uncertainty in future carbon budgets. Better predictions of vegetation dynamics and functions require characterizing resilience, which indicates the capability of an ecosystem to recover from perturbations. Here, using temporal autocorrelation of remotely sensed greenness, we quantify time-varying vegetation resilience during 2000-2019 across northwestern North American Arctic-boreal ecosystems. We find that vegetation resilience significantly decreased in southern boreal forests, including forests showing greening trends, while it increased in most of the Arctic tundra. Warm and dry areas with high elevation and dense vegetation cover were among the hotspots of reduced resilience. Resilience further declined both before and after forest losses and fires, especially in southern boreal forests. These findings indicate that warming and disturbance have been altering vegetation resilience, potentially undermining the expected long-term increase of high-latitude carbon uptake under future climate.

Detecting small-scale landslides along electrical lines using robust satellite-based techniques

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

The novel triangular spectral indices for characterizing winter wheat drought

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized Difference Vegetation Index (NDVI) is used for characterizing crop growth in almost all spectral drought indices, with the spectral saturation problem of NDVI for closed crop canopy. When vegetation cover is high, NDVI values tend to saturate, which makes them insensitive to further changes in crop health. Therefore, the NDVI saturation phenomenon may lead to an underestimation of the extent of crop drought, as it is not effective in identifying subtle changes in crops under high-density vegetation conditions. Hence, we propose three novel triangular spectral indices for characterizing winter wheat drought using three features including LAI, Land Surface Water Index (LSWI) and LST. For validating the proposed spectral indices, we compared the agreement between these indices with measured Relative Soil Moisture (RSM) and Volumetric Water Content (VWC) of soil in agricultural meteorological station and present popular indices including Crop Water Stress Index (CWSI), Temperature-Vegetation Drought Index (TVDI), and Vegetation Health Index (VHI). The results revealed that our proposed indices including Euclidean distance Crop Health Index (ECHI), Difference Crop Health Index (DCHI) and Perpendicular Water Stress Index (PWSI) outperformed the popular CWSI, TVDI and VHI, with stronger correlations with measured RSM and VWC in agricultural meteorological station. Secondly, there are spatial consistencies for characterizing winter wheat drought between proposed ECHI, DCHI and PWSI with popular CWSI, TVDI and VHI. In addition, our proposed ECHI, DCHI and PWSI have achieved good performance of drought monitoring both in irrigated and rainfed croplands. All these results suggest that our proposed indices have great potential in crop drought monitoring.

Utilization of Google Earth to Identify Building Density

Land-use change is now common due to human activities, including economic, social, and cultural activities. Areas transforming for economic, social, and cultural purposes experience changes in land use, leading to a transition from green open spaces to buildings and resulting in hot daily surface temperatures due to a lack of vegetation. The purpose of this study is to examine the changes in built and non-built land in the Kartasura District. This study examines the relationship between surface temperature, vegetation, and building levels by using NDVI, NDBI, and ESG analysis, which is processed using the Google Earth Engine programming language. This study uses a qualitative descriptive method with a spatial approach, one of the approaches in the field of geography. The spatial approach in this study is more emphasized on spatial process analysis. The results indicate that there is a correlation between high surface temperature and low vegetation density, as well as high building density.

Laboratory study on effect of vegetation in reducing wave overtopping under wind effect

Understanding and mitigating wave overtopping are crucial for hinterland safety, yet research primarily focuses on designing coastal structures, with limited attention to structural materials. As global warming intensifies coastal hazards, there is a growing need for resilient infrastructures. A hybrid infrastructure incorporating vegetation alongside concrete may offer a viable solution. In this study, we evaluated the effect of vegetation on wave overtopping reduction under strong winds through hydraulic model experiments simulating extreme storm conditions. The experiments were conducted under the simultaneous action of wind, waves, and vegetation using a two-dimensional wind tunnel flume and vegetation. A higher vegetation density was found to exponentially reduce the overtopping rate. Moreover, dense vegetation was demonstrated to be equivalent to raising the height of protective structures by at least 1.1 m. Furthermore, the increase in overtopping rate by the effects of wind can be compensated by the reduction effects of vegetation. Based on these investigations, an equation was proposed to predict the wave overtopping rate that considers the effects of both wind and vegetation. The results highlight the potential applicability of vegetated coastal protection in future coastal disaster prevention against extreme events with stronger winds and waves that may be induced by climate change.

Modeling wave dynamics with coastal vegetation using a smoothed particle hydrodynamics porous flow model

Coastal vegetation serves as an effective nature-based solution for protecting shorelines from erosion and storm surges. A detailed understanding of the dynamic interactions between waves and vegetation is crucial for quantifying the mitigation potential of coastal vegetation during extreme climatic events. This study investigates wave dynamic interactions with coastal vegetation by developing a numerical model using the weakly compressible Smoothed Particle Hydrodynamics (WCSPH) method. The model simulates the effects of vegetation by incorporating vegetation porosity, drag, and inertia forces into the momentum equations. By integrating porosity information at specific background points, the SPH interpolation method effectively treats the interfaces between fluid and vegetation regions, allowing versatility in modeling various vegetation layouts without explicitly simulating their solid structures, which is computationally prohibitive. The numerical model is validated using laboratory-based measurements and analytical models for both submerged and emergent scenarios involving cylindrical and strip-type vegetation. The model is then utilized to investigate the effects of vegetation as both a natural line of defense and a retrofitting solution in front of a sea dike, to quantify the performance of nature-based solutions for enhancing climate resilience in natural and hybrid coastal protection infrastructures. The results reveal that canopy density significantly impacts solitary wave run-up and energy dissipation. For the configurations tested in this study, high-density vegetation reduces normalized wave run-up by 52% compared to simulations without vegetation, while low-density vegetation achieves a 17% reduction. These findings demonstrate the potential of vegetation, particularly at higher densities, to mitigate solitary wave energy and reduce run-up hazards. Furthermore, simulation results indicate that using vegetation as a retrofitting solution effectively mitigates overtopping rates, achieving a 37% reduction with minimal forest density (∅ = 0.023). These results underscore the effectiveness of nature-based interventions in enhancing coastal protection and resilience.

Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition

AbstractAimMountain ecosystems are hotspots of biodiversity due to their high variation in climate and habitats. Yet, above average rates of climate change and enhanced forest disturbance regimes alter local climatic conditions and vegetation structure, which should impact biodiversity. We here investigated the impact of vegetation and elevation as well as their interactions on bird communities to improve our ability to predict climate change effects on bird communities.LocationEuropean Alps, Germany.MethodsWe studied patterns and drivers of bird communities at 213 plots along gradients in vegetation density and elevation using autonomous sound recorders. Bird species were identified from soundscapes by Convolutional Neural Networks (BirdNET) and taxonomists.ResultsBird diversity and community metrics were moderately to strongly correlated for data based on either identification by BirdNET or taxonomists (Pearson's r = .47–.94), and ecological findings were overall similar for both datasets. Vegetation density 1–2 m and >2 m above ground strongly affected bird diversity and community composition and mediated effects of elevation. Community composition changed with elevation more strongly in habitats with low than high vegetation density >2 m. Species numbers decreased with elevation in habitats with low vegetation density 1–2 m and >2 m above ground, but increased in habitats with high vegetation density. Overall, functional and phylogenetic diversity increased with elevation indicating lower habitat filtering, but patterns were also mediated by vegetation density.Main ConclusionsOur results indicate that bird communities in the German Alps are determined by strong interactive effects of elevation and vegetation, underlining the importance to consider variation in vegetation in studies of biodiversity patterns along elevational gradients and under climate change. Combining remote sensing data and biodiversity monitoring based on autonomous sampling and AI‐based species identification opens new avenues for bird monitoring and research in remote areas.

Coastal Dune Vegetation Dynamism and Anthropogenic-Induced Transitions in the Mexican Caribbean during the Last Decade.

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011-2020 period in the Sian Ka'an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann-Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism.

Analysis of the spatiotemporal trends and influencing factors of Hyphantria cunea in China

In recent years, the situation of the Hyphantria cunea (Drury) (Lepidoptera: Erebidae), infestation in China has been serious and has a tendency to continue to spread. A comprehensive analysis was carried out to examine the spatial distribution trends and influencing factors of H. cunea. This analysis involved integrating administrative division and boundary data, distribution data of H. cunea, and environmental variables for 2021. GeoDetector and gravity analysis techniques were employed for data processing and interpretation. The results show that H. cunea exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern China. During these years, the focal point of the infestation was in Shandong Province with a spread towards the northeast. Conditions such as high vegetation density in eastern China provided favorable situations for growth and development of H. cunea. In China, the spatial distribution of the moth is primarily influenced by two critical factors: precipitation during the driest month and elevation. These play a pivotal role in determining the spread of the species. Based on these results, suggestions are provided for a multifaceted approach to prevention and control of H. cunea infestation.

Thermal scanners versus spotlighting: New opportunities for monitoring threatened small endotherms

AbstractThreatened species monitoring is challenging for small, cryptic endotherms that are most effectively detected at night. Low detectability is a challenge for monitoring programmes, resulting in low statistical power and sparse or zero‐inflated datasets. To advance conservation management programmes, efforts to address this are required. In recent years thermal scanners have emerged as an effective tool for detecting small endotherms, but the diversity of available thermal tools, focal habitats and target species mean that their applicability in many key scenarios remain untested. We directly compared vehicle‐mounted thermal surveys with vehicle‐based spotlighting targeting small endotherms in Australian native grasslands. Our targets included both common species that occur at high densities, and species that are notoriously difficult to detect with spotlights, which may occur at very low densities. We completed paired surveys of roosting grassland birds, and nocturnally active small mammals at 22 sites, once using thermal scanners and once using spotlights. Ultimately, distance sampling was conducted across 136 km of transects. Thermal scanners facilitated greater detection distances and higher total detections for small endotherms when compared with spotlighting. Species of greatest conservation concern, the Plains‐wanderer (Pedionomus torquatus—Pedionomidae) and Fat‐tailed Dunnart (Sminthopsis crassicaudata—Dasyuridae) were only detected using thermal scanners. Detection distances generated for thermal scanners were reduced by higher vegetation density; however, thermal scanners continued to outperform spotlights under this scenario. Observers also detected more stationary animals and fewer birds were flushed upon detection using thermal scanners when compared with spotlighting. Thermal scanners have the potential to improve the quality of monitoring datasets by increasing detection probabilities for small endotherms. We recommend the adoption of thermal scanners as a best‐practice tool for monitoring small endotherms in open grassland habitats at night, offering new opportunities to monitor endotherms where monitoring has historically been challenging, inadequate or impossible.

The impact of cross-regional social and ecological interactions on ecosystem service synergies

Increasing socioecological systems (SESs) sustainability requires establishing a reasonable cross-regional social and ecological interaction. In this study, we examine how cross-regional ecological and social interactions affect synergistic effects. Using InVEST and correlation analysis with data from 2010 through 2020, we assessed ESs (i.e., water retention-WR, nutrient retention-NR, and carbon storage-CS) in the Beijing-Tianjin-Hebei (BTH) region. A small watershed, a river network, and settlement development capacity are used to delineate ecological and social interactions units. Based on a Bayesian network model that considers population, economy, and spatial agglomeration patterns between social units, we assessed the potential for achieving a synergistic improvement of ESs and the driving forces behind them. The results show that ESs in the BTH region compete, only a small percentage (6.38%) shows synergetic improvement across CS, WR, and NR. It is beneficial for upstream watersheds to retain water and nutrients, but to maintain carbon storage they may sacrifice water retention. Upstream areas with less development and higher vegetation density have better ecosystem integrity of up- and down-stream watersheds, and can be enhanced with minimal human impact, as social interactions and settlement spatial structures influence ES synergies. There is a higher risk for ecological issues in downstream areas, but greater awareness and collaboration can lead to better ES synergies.

Stereo Vision for Plant Detection in Dense Scenes.

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.

Analysis of NDVI and Plant Vegetation Diversity in the Traditional Zone, Mount Halimun Salak National Park, Bogor

Mount Halimun Salak National Park (MHSNP) traditional zone is a zone which is highly utilized by the local community to fulfill their daily needs by utilizing non-timber forest resources and implementing an agroforestry system with the main commodity is poh-pohan (Pilea melastomoides). Forest monitoring in the MHSNP traditional zone is very important to do as part of sustainable forest management and the realization of supporting Indonesia's Forestry And Other Land Use (FOLU) Net Sink 2030 program. This study aims to analyze the density of vegetation based on NDVI values; besides, analyze the level of plant species diversity and stand structure in the traditional zone of MHSNP. Furthermore, the data collection of plant species diversity at the area based on NDVI value of vegetation density. NDVI values ​​are obtained into three classes that have different land conditions. The class 1 value range from 0.147 to 0.273 has a low vegetation density. Class 2 from 0.273 to 0.319 has medium vegetation density. Meanwhile, class 3 has high vegetation density with a value of 0.319 to 0.433. Diversity of plant species has 60 different species from a total of three classes. The density of seedlings is lower than saplings and at the level of poles, trees is decreasing that is indicates a balanced stand structure. The low seedling level is caused by inhibition of seedling growth due to cleaning by the local community in preparation for planting understory since the community prefers to plant understory which harvest faster woody plant seedling.

Direct and indirect effects of linear non-cultivated habitats on epigaeic macroarthropod assemblages

Non-cultivated habitats are indispensable ecological spaces within agricultural landscapes that support biodiversity and associated ecosystem services on farmland, thereby contributing to sustainable agriculture. The abundance of taxonomic groups within these habitats serves as a reliable indicator of the diversity levels in epigaeic macroarthropod communities, while the functional groups offer insights into the trophic dynamics of macroarthropods. In recent decades, the impact of non-cultivated habitats on biodiversity within agricultural contexts has received increasing attention. However, few studies have focused on the pathways and factors that influence epigaeic macroarthropod assemblages in different non-cultivated habitats. In this study, Changtu County, a typical agricultural county in the Northeastern Black Soil Region of China, was selected as the study area. The functional and taxonomic groups of epigaeic macroarthropod abundance in different non-cultivated habitats were compared. The structural equation model (SEM) was used to evaluate the direct and indirect effects of non-cultivated habitat type, soil nutrients and vegetation structure reflecting major differences in non-cultivated habitat types on epigaeic macroarthropod assemblages. Our results showed that: (1) linear semi-natural non-cultivated habitats around fields can effectively enhance landscape connectivity. Among these habitats, woodlands (WL), grasslands (GL), and ditches (CD) with high herbaceous vegetation density are usually more favorable for biodiversity conservation in agricultural ecosystems. (2) While dirt roads (DR) and paved roads (PR) may affect the mobility and foraging ability of epigaeic macroarthropods, improving herbaceous vegetation structures along road margins (e.g., planting flowering plants, establishing vegetation buffer zones) can effectively enhance field biodiversity. (3) The characteristics of herbaceous vegetation community are the most important factors affecting the functional and taxonomic groups of epigaeic macroarthropods in different non-cultivated habitats, especially the herbaceous vegetation height (PAH) and species abundance (PAB). In addition, for epigaeic macroarthropods taxonomic groups, non-cultivated habitats affect them not only directly through herbaceous vegetation, but also indirectly through soil organic carbon (SOC). Our results emphasize that in the planning of agricultural landscapes, it is feasible to achieve a harmonious synergy between ecosystem stability and agricultural production through strategic allocation and alteration of herbaceous vegetation structure and soil conditions in different types of non-cultivated habitats.

SPATIAL-TEMPORAL ANALYSIS BETWEEN LANDCOVER CHANGE AND URBAN SURFACE TEMPERATURE OF BEKASI CITY, INDONESIA

Unregulated urban growth can result in a rise in urban population density, leading to the expansion of developed land into suburban regions. The urbanization of Bekasi City inevitably results in the conversion of vegetated land and green open spaces into built-up areas. In addition, there has been a notable rise in the exceptionally high average surface temperature of 12.66 °C during the past 25 years. It is vital to investigate the correlation between landcover change factors and surface temperature, considering these two significant occurrences. This study conducted a spatial-temporal analysis of the relationship between landcover and urban surface temperature in the years 1993, 1998, 2004, 2009, 2018, and 2023. The Random Forest classification approach was employed to acquire comprehensive landcover information, while the remote sensing/image satellite approach was utilized to obtain surface temperature data. The temperature is determined using the thermal channel of satellite photography. The research findings indicate a robust correlation between alterations in land cover, specifically high-density buildings, medium/low-density buildings, and high-density vegetation, and variations in the surface temperature of an urban area. Hence, it is imperative to closely monitor the expansion of land cover to uphold the stability of surface temperature in urban areas.

Analisis Perubahan Kerapatan Vegetasi DAS Air Dingin Akibat Migrasi Penduduk Pasca Gempa Padang 30 September 2009

The aim of this research is to determine changes in vegetation density due to population migration in the Air Cool watershed using the Normalized Difference Vegetation Index (NDVI) method. This research is descriptive research using a quantitative approach. Determining the level of vegetation density in the Air Winter watershed is carried out using the vegetation index score classification by Marwoto &amp; Ginting (2009) which consists of Non-Vegetation, Low Vegetation, Medium Vegetation and High Vegetation density classes. The location of this research is in the Air Cold Watershed, Lubuk Minturun Village, Koto Tangah District, Padang City. Based on the results of the research conducted, several things can be concluded as follows. The results of the analysis of vegetation density levels obtained from image data for 2016 and 2022 consist of Non Vegetation density classes covering 102.86 ha and 12.46 ha, Low Vegetation 730.71 ha and 1279.74 ha, Medium Vegetation 399.76 ha and 5301.17 ha and High Vegetation 11559.82 ha and 6199.63 ha. This change in Vegetation Density was then also followed by an increase in population by 2493 people in Lubuk Minturun Village and 3856 people from Balai Gadang Village, as well as a reduction in the number of people living in areas close to the coastline. This significant population movement of course triggers land clearing for settlements and other land clearing activities so that the Vegetation Density changes along with population growth in an area.

Effects of vegetation density on flow, mass exchange and sediment transport in lateral cavities

Large-Eddy Simulations (LES) were used to investigate the hydrodynamics and mass transfer between the flow in the main channel and a vegetated lateral cavity. Fourteen vegetation densities (0 to 10.65 %) were tested, revealing two distinct hydrodynamic patterns. For cavities with low vegetation density (a < 3.99 %), one primary gyre in contact with the interface between main channel and cavity with high velocity was formed; the thickness of the mixing layer grew longitudinally along the interface, and regions with high vorticity and turbulence kinetic energy appeared at the interface and inside the cavity. For cavities with high vegetation density (a > 3.99 %), two gyres in contact with the interface with low velocity were formed, the thickness of the mixing layer did not grow, and the vorticity and turbulence kinetic energy were low inside the cavity. The mass transport presented the same threshold value as the hydrodynamics (a = 3.99 %). For cavities with low vegetation density, a fast mass transfer occurred through the interface between the main channel and cavity and inside the cavity, while the opposite was observed for cavities with high vegetation density. Finally, the modelled hydrodynamics was used to infer possible sediment deposition patterns and flow resistance.

The Effect of Vegetation Density on The Total Suspended Solid (TSS) Concentration in The Aquatic Area of Pomalaa District

Analysis of the influence of vegetation density levels on the concentration of Total Suspended Solids (TSS) in aquatic areas is crucial in the Pomalaa District, an area characterized by extensive mining activities. This research aims to examine changes in vegetation density conditions and analyze the influence of vegetation density levels on TSS concentrations in the aquatic region. Changes in vegetation density conditions are analyzed through multi-temporal remote sensing image processing using the NDVI method. The TSS concentration is analyzed using the gravimetric method. Meanwhile, the influence of vegetation density level on TSS concentration is analyzed using the Pearson correlation coefficient equation. The results of the research show that there has been an increase in the area with very low levels of vegetation density and a decrease in the area with high levels of vegetation density in the period 2014 to 2023. The calculated results of TSS concentrations at the research location are 78 mg/l to 111 mg/l. The correlation analysis shows that the broader the area with very low vegetation density, the higher the TSS concentration in the aquatic environment. Additionally, the larger the area with high vegetation density, the lower the TSS concentration value of the water at the research locations.

Steady Flow Over a Finite Patch of Submerged Flexible Vegetation

AbstractAn immersed boundary‐finite element with soft‐body dynamics has been implemented to study steady flow over a finite patch of submerged flexible aquatic vegetation. The flow structure interaction model can resolve the flow interactions with flexible vegetation, and hence the reconfiguration of vegetation blades to ambient flow. Flow dynamics strongly depend on two dimensionless parameters, namely vegetation density and Cauchy number (defined as the ratio of the fluid drag force to the elastic force). Five different flow patterns have been identified based on vegetation density and Cauchy number, including the limited reach, swaying, “monami” A, “monami” B with slow moving interfacial wave, and prone. The “monami” B pattern occurred at high vegetation density and is different from “monami” A, in which the passage of Kelvin‐Helmholtz billows strongly affects the vegetation interface. With soft‐body dynamics, blade‐to‐blade interactions can also be resolved. At high vegetation density, the hydrodynamic interactions play an important role in blade‐to‐blade interactions, where adjacent vegetation blades interact via the interstitial fluid pressure. At low vegetation density, direct contacts among vegetation blades play important roles in preventing unphysical penetration of vegetation blades.

Environmental characterization and cartographic modeling of wild plant habitats at the northern coastal zone of Egypt

Assessing natural vegetation through conventional methods faces considerable constraints, such as limited geographical scope, reduced precision, a lack of historical data, high expenses and time demands. The study aims to use environmental and spectral data to identify and map natural vegetation and plant species along the Mediterranean coast of Egypt. This involves employing spatial analyses and cartographic modeling techniques, marking an initial effort in this endeavor. To fulfill this aim, a total number of 70 wild plant habitats were surveyed and sampled for further laboratory identification of plant species. Multispectral and thermal bands of Landsat imagery were processed to generate land cover map, as well as calculate Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to be integrated with the naturally grown plants in a cartographic model to predict the wild plant habitats. Coastal wild plant habitats were sparsely located and associated with a vegetation density ranging from 0.096 to 0.280 with an average of 0.167. Besides, the LST of these habitats fluctuated from 30.559 to 38.652 with an average of 34.361°C. Although the wild plant habitats at the Northern coastal region of Egypt are similar in environmental and climatic conditions, there are variability in NDVI and LST of each single habitat (pure or mixed). On the other hand, the lowest LST were associated with Erodium laciniatum/Rumex pictus and Erodium laciniatum habitats reporting 30.559 and 30.741°C, respectively. However, Lotus halophilus mixed with Ifloga spicata habitats reported the highest LST (38.652°C). The high vegetation density is mainly characterized by low LST which indicates the mixed wild plant habitats. The developed cartographic model showed a narrow strip along the Mediterranean region as highly suitable habitats for wild plant growth. The model can be used to map pure and mixed habitats of various wild plants with an accuracy exceeding 90%. The model was applied for mapping Arthrocnemum macrostachyum showing that the middle coastal region is most suitable for its growth. It’s recommended to integrate remotely sensed data with spatial analyses for the environmental analyses of natural plants. Present findings support researchers and scientists interested in environmental, botanic, and medical studies.