Number Of Patches Research Articles

Projected Spatiotemporal Evolution of Urban Form Using the SLEUTH Model with Urban Master Plan Scenarios

Urban growth, a pivotal characteristic of economic development, brings many environmental and ecological challenges. Modeling urban growth is essential for understanding its spatial dynamics and projecting future trends, providing insights for effective urban planning and sustainable development. This study aims to assess the spatiotemporal patterns of urban growth and morphological evolution in mainland Shanghai from 2016 to 2060 using the SLEUTH model under multiple growth scenarios based on the Shanghai Urban Master Plan (2017–2035). A comprehensive set of urban growth metrics and quadrant analysis were employed to quantify the magnitude, rate, intensity, and direction of urban growth, as well as morphological evolution, over time. We found that (1) significant urban growth was observed across most scenarios, with the exception of stringent land protection. The most substantial growth occurred prior to 2045 with an obvious north–south disparity, where southern regions demonstrated more pronounced increases in urban land area and urbanization rates. (2) The spatiotemporal patterns of the rate and intensity of urban growth exhibited similar characteristics. The spatial pattern followed a “concave shape” pattern and displayed anisotropic behavior, with the high values for these indicators primarily observed before 2025. (3) The urban form followed a diffusion–coalescence process, with patch areas dominated by the infilling mode and patch numbers dominated by the edge-expansion mode. This resulted in significant alternating urban growth models in the infilling, edge-expansion, and leapfrog modes over time, influenced by varying protection intensities. These findings provide valuable insights for forward-looking urban planning, land use optimization, and the support of sustainable urban development.

Effectiveness Trade-Off Between Green Spaces and Built-Up Land: Evaluating Trade-Off Efficiency and Its Drivers in an Expanding City

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km2 grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.

How Does Landscape Structure Affect Dung Beetle Assemblages in Amazon Cities?

The growth of cities is one of the main direct and indirect factors responsible for the loss of native vegetation cover. Urbanization directly affects the biological communities inhabiting forest remnants inserted in cities, compromising the maintenance of urban and natural ecosystems. By understanding the effects of landscape transformation due to urbanization, we can have insights regarding the distribution of land uses that allow a proper maintenance of the urban ecosystems. This work assessed the effects of landscape structure variables (forest cover, agricultural area, edge density, and number of forest patches) on dung beetle assemblages and functional groups (i.e., diet and resource removal strategy) sampled in 38 sites located along an urban-rural gradient of six cities belonging to the metropolitan area of Manaus in Central Amazonia. Losses of forest cover were the most determining factor, negatively affecting species richness, abundance, and body size. The increases in agriculture cover negatively affected dung beetle abundance, while edge density positively affected their abundance. The number of forest patches positively affected dung beetle abundances-except for dweller species-and negatively affected the body size of diet-generalist species. These results demonstrate that changes in ecological diversity caused by urbanization are driven mostly by forest cover loss, although forest configuration is important for dung beetle abundance. This study contributes to the understanding of how changes in the amount and distribution of forest cover in tropical cities affect the taxonomic diversity of dung beetle assemblages.

Priority areas and implementation of ecological corridor through forest restoration to safeguard biodiversity

Ecological Corridors (ECs) are proposed as cost-effective solutions to improve ecological connectivity in fragmented landscapes. Planning the implementation of ECs must take into account landscape features as they affect the viability of the endeavor and the ECs associated costs. A novel set of geoprocessing tools were used to assess (i) economic viability; (ii) socioeconomic cost-effectiveness; and (iii) to determine priority targets for ECs establishment in a highly fragmented region of Atlantic Forest. Landscape features (e.g. size, edge density, number of patches), land use and cover, and Enhanced Vegetation Index data were obtained from remote sensing. Composition and configuration landscape metrics were estimated by geostatistical methods. Study area was represented by 35,344 forest fragments, of which 94% were composed of fragments smaller than 10 hectares in size, leading to a landscape characterized by low connectivity. We identified 13 priority fragments for protection and propose five ecological corridors. These five ECs connect six priority fragments, with a total of 283.93 ha of area to restore, on an estimated cost of nearly US$550.000,00. Conserving the biodiversity of one of the most threatened and fragmented tropical rainforest regions could be promoted at modest costs, through the protection of key fragments and their reconnection in the landscape.

Multiscale Effects of Xylella fastidiosa on Landscape Services

The spread of Xylella fastidiosa since 2013 in the Mediterranean olive groves of the Apulia region has modified the landscape. The aims of this research are focused on the analysis of its effects on the following: (1) Landscape multifunctionality supported by olive groves in terms of landscape service provision; (2) The functional relations among the main Mediterranean land covers in terms of landscape service supply and demand. (3) The landscape fragmentation at different spatial scales. The landscape has completely changed, mainly in those land covers that, in the past, acted as stabilizing factors (croplands and olive groves), which has been replaced by grasslands in 2021. The main effects of Xylella fastidiosa were on the multifunctionality of olive grove landscape in terms of food production, water regulation, carbon sequestration, and pollination, as well as on landscape cultural value. Ecosystem service supply is mainly related to olive groves, tree covers, shrublands, and wetlands. The province of Lecce showed the highest fragmentation, as demonstrated by the number of patches, the mean patch area, and the DIVISION metric, while the province of Brindisi was the least fragmented, with a DIVISION metric similar in 2011 and in 2021. The multiscale assessment of “olive groves” fragmentation has helped in better analyzing the effect of its spatial configuration on the provision of landscape services and in identifying the right spatial scale for each landscape service provision. It is essential to analyze landscape service flow to enlarge the understanding of the ways in which their supply is maintained through a landscape regeneration policy toward the socio-economic–ecological recovery.

Human and natural factors affect habitat quality in ecologically fragile areas: evidence from Songnen Plain, China.

Habitat quality (HQ) has been progressively degrading worldwide in recent decades due to rapid climate change and intensive human activities. These changes not only threaten biodiversity and ecosystem functions, but also impact socio-economic development. Therefore, a few studies have focused on the dynamics of HQ and its natural and anthropogenic drivers. However, many contributions have failed to reveal how these factors interact to impact HQ, especially in ecologically fragile areas. We estimated HQ in the Songnen Plain of Northeast China, an ecologically fragile area, from 2000 to 2020 using the InVEST model and explored the response of HQ to the interactions of natural factors (topography, climate, NDVI) and anthropogenic factors (nighttime light index, population density) influencing HQ using Structural Equation Modelling (SEM). The results showed that 1) HQ decreased constantly from 2000 to 2018, and then increased slightly from 2018 to 2020. 2) In terms of spatial distribution, HQ appeared to be highly heterogeneous with a pattern of 'high HQ in the east - low HQ in the center - high HQ in the west' at each time point. The high-HQ areas were significantly clustered in the eastern parts with dense forests, while the low-HQ areas in the central parts were dominated by a large number of man-made patches of agriculture and towns or cities. 3) The spatial patterns of HQ are mainly affected by the interactions of factors including the natural environment and human disturbance. Natural factors had a greater impact on HQ than human disturbance, and human disturbance factors had significant negative impact among all these factors at 4 time points. Furthermore, the intensity of the impact of various influencing factors on habitat quality, as well as the positive or negative effects of these drivers on habitat quality, changed over time. The most important influencing factor was temperature in 2000 and topography in 2010, 2018, and 2020. This study can provide important suggestions for future ecological protection and restoration in similar ecologically fragile areas.

Lacustrine Wetlands Landscape Simulation and Multi-Scenario Prediction Based on the Patch-Generating Land-Use Simulation Model: A Case Study on Shengjin Lake Reserve, China

Landscape simulation and prediction are crucial for understanding the dynamic evolution and future trends of wetlands. However, only a few existing studies have focused on the applicability and limitations of commonly used land-use/cover change (LUCC) simulation models in lake wetland landscapes. Taking Shengjin Lake Reserve in China as the study area, we firstly analyzed landscape variations during 2010–2020 using multisource remote sensing images. Then, the patch-generating land-use simulation (PLUS) model was employed to simulate wetland landscapes in 2020, the accuracy and limitation of which in simulating lacustrine wetlands were also explored. Lastly, the changing trends of wetland landscapes in 2030 under different development scenarios were predicted. The results show that the landscape of Shengjin Lake Reserve has changed significantly during 2010–2020, with increases in mudflats, reservoirs/ponds, woodlands, and built-up land, and there has been decreases in lakes, grass beaches, and croplands. The PLUS model demonstrated an ideal simulation accuracy for Shengjin Lake Reserve, with the overall accuracy exceeding 80%, kappa coefficient greater than 0.75, and figure of merit (FOM) coefficient of 0.35, indicating that the model can capture the dynamic changes in wetland landscapes accurately. The simulation accuracy can be effectively improved with the adjacent initial year, shorter time interval, and the primary driver factors. Under the natural development scenario, the number of patches in the Shengjin Lake Reserve increased sharply, and landscape fragmentation intensified. Under the urban development scenario, the expansion of built-up land increased, and the average patch area increased. In the ecological protection scenario, the Shannon diversity index and Shannon evenness index of the landscape improved significantly, and the natural wetlands such as grass beaches and lakes can be protected effectively. Our study confirms the applicability of the PLUS model in simulating and predicting lacustrine wetlands landscapes, and the conclusions provide a scientific basis for formulating reasonable development strategies to realize wetland resource conservation and management.

Forecasting Suitable Habitats of the Clouded Leopard (Neofelis nebulosa) in Asia: Insights into the Present and Future Climate Projections Within and Beyond Extant Boundaries.

Terrestrial carnivores, such as the clouded leopard (Neofelis nebulosa), are 'vulnerable' and experiencing significant population declines in mainland Asia. Considering the assessed threats, the IUCN has repetitively revised the range of this felid and now characterized it into four distinct categories (extant, possibly extant, possibly extinct, and extinct). Although several ecological works have been accomplished on this enigmatic carnivore, the overall view of its habitat suitability, fragmentation, and corridor connectivity in both present and historical ranges is lacking. Thus, achieving this ecological information under present and future climate scenarios is crucial. The model identified merely 44,033 sq. km falling within the extant range (representing 31.66%) and 20,034 sq. km (8.13%) in the possibly extant range. Fascinatingly, within the historical ranges, an additional 15,264 sq. km (6.58%) has been identified as suitable habitat in the possibly extinct range and 14,022 sq. km (2.38%) in the extinct range. Notably, a total of 25,614 sq. km of suitable habitat is found within designated protected areas across the entire range. Nevertheless, climate change is expected to drive habitat loss of up to 41% (overall IUCN range) for N. nebulosa in both present and historical extent, with habitat patches becoming increasingly fragmented. This is reflected in a projected decline in the number of viable habitat patches (NP) by up to 23.29% in the future. This study also identified 18 transboundary biological corridors for N. nebulosa, with Southeast Asian countries expected to experience the most significant declines in corridor connectivity. In contrast, the South Asian countries (Bhutan, Nepal, and India) are projected to maintain relatively higher connectivity in the future. Nevertheless, a substantial decline in overall mean corridor connectivity is projected in the near future due to the impacts of climate change. This study underscores the urgent need for a coordinated and multifaceted conservation strategy for N. nebulosa, focusing on mitigating habitat loss and fragmentation. Practical measures must be implemented to protect the species' shrinking range, considering its declining corridor networks and heightened vulnerability to inbreeding depression. Moreover, the assessment of habitat suitability both within and beyond the extant range, alongside corridor connectivity measures, provides valuable insights into potential translocation and reintroduction sites for this species. These findings provide a critical foundation for developing a strategic conservation plan tailored to the specific needs of this felid species across South and Southeast Asia, ensuring enhanced climate resilience and mitigating associated threats.

Assessing climatic change impacts on mangrove structural dynamics on the northern coasts of the Persian Gulf

Abstract. Monitoring long-term trends in structural changes within mangrove ecosystems is essential for understanding their response to climate change and devising effective adaptation strategies in coastal regions. This study examines changes in mangrove patchiness within the Hara Biosphere Reserve (HBR) along the northern coasts of the Persian Gulf (PG) over a 31-year period (1986-2017), focusing on variations in rainfall patterns and drought occurrences. Employing a 35-year time series of monthly Standardized Precipitation Index (SPI) values alongside satellite imagery analysis, trends in both the number of patches (NP) and the Largest Patch Index (LPI) were assessed at the mangrove stand level. The analysis reveals a significant correlation between structural changes in mangroves and drought events. Pre-1998, characterized by wetter conditions, witnessed a decrease in both NP and LPI, indicating patch expansion and habitat extension. Conversely, post-1998, during drought periods, both indices increased, indicating habitat degradation due to heightened drought intensity. Pre-1998 structural changes in the HBR signify habitat expansion, with increased patch extent and core areas reflecting enhanced structural integrity. However, post-1998, a concerning trend of habitat degradation emerged, with increased NP and LPI attributed to intensified droughts. These findings highlight a transitional period marked by favourable conditions for mangrove growth followed by habitat degradation linked to increased drought intensity. This underscores the vulnerability of mangrove ecosystems to climate change impacts, particularly exacerbated droughts, necessitating urgent efforts for conservation and management to preserve biodiversity and ecosystem services in coastal regions.

Pore-scale study on shear rheology of wet granular materials

We study pore-scale rheological phenomena in two-dimensional sheared wet granular materials. Simulations use a coupled cascaded lattice Boltzmann and discrete element method, to model the liquid–gas multiphase flows and multiple-solid-particle dynamics, respectively. The wet granular material is prepared by first filling a rectangular domain with solid particles and then partially filling the pores between the particles with the liquid phase. The material is then sheared based on standard Couette flow configuration, i.e., with lid-driven velocities U and -U on the top and bottom walls, respectively. The simulations show that the apparent viscosity of the system attains a minimum when the material is wet but not fully saturated, i.e., at a saturation of ∼0.10. Such an observation is coherent both for materials composed of monodisperse and polydisperse particles. Interestingly, this observation coincides with the experimental finding of the decrease in sliding friction on sand by adding a small amount of water. The underlying mechanism is elucidated based on the pore-scale study of liquid patch dynamics. It is shown that, with increasing liquid saturation, the rheology of the wet granular materials is affected by two competing effects: (i) a larger number of liquid patches appear leading to fluidization of the system and (ii) larger patches are formed, clogging the flow. The minimum apparent viscosity saturation of ∼0.10 coincides with the maximum of the product of the two factors: the number of liquid patches and ratio between the system height and largest patch height.

Nonlinear Radiative Response to Patterned Global Warming due to Convection Aggregation and Nonlinear Tropical Dynamics

Abstract Climate responses to global warming exhibit large dependence on the spatial pattern of sea surface temperature (SST) changes. A Green’s function (GF) approach—predicting the global climate response to a complex SST pattern change as the linear superposition of the response to finite-area SST perturbations evaluated in isolation—has been used to systematically evaluate and understand the top of atmosphere (TOA) radiation response to patterned warming. However, in general, the linear GF approach fails for TOA radiation reconstruction under future global warming scenarios in coupled models. Here, we show that the linear superposition of global mean TOA radiation responses to large SST warming perturbations at multiple patches (the GF approach prediction) overestimates the actual response to simultaneous multipatch perturbation. This is because the linear superposition overestimates tropical large-scale convection aggregation strengthening upon localized heating that enhances longwave radiative cooling, which is further explained by the overestimation of circulation response and associated horizontal water vapor transport. The nonadditivity of TOA radiation response is caused by the nonadditivity of convection aggregation, ultimately rooted in nonlinear tropical dynamics. We also demonstrate that the prediction error of the GF approach grows with decreasing patch size (equivalently, increasing patch number). We conclude that using the GF approach to predict future climate change could overestimate longwave radiative cooling and underestimate (effective) climate sensitivity. Numerical experiments may be used to identify specific perturbation patterns where the errors are smaller than the signal. Our research also highlights that an increase in the degree of large-scale convection aggregation has a nonlinear and negative feedback for mean warming.

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes

Background and Objective:Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details. Methods:To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time. Results:VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at https://github.com/marziehoghbaie/VLFATRollout/.

Analysis of the Spatial Pattern and Driving Forces of Linpan in Western Sichuan

Abstract. This study aims to explore the landscape pattern and its driving forces of Linpan in western Sichuan, aiming to gain a deeper understanding of the spatial distribution of this unique rural settlement form. The landscape indices and redundancy analysis method were employed to investigate the landscape pattern and driving forces of Linpan in the western Sichuan region. By extracting the spatial information of Linpan in the study area, quantitative analysis of their landscape pattern was conducted using landscape indices. The results reveal certain characteristics of the landscape pattern of Linpan in the study area, where Longquanyi exhibits the highest degree of landscape fragmentation, with the highest patch density and smaller average patch size, yet the highest landscape diversity, while Wenjiang shows the lowest diversity, and Xinjin exhibits a more dispersed distribution of patches, and Dujiangyan has the highest number of patches. Using redundancy analysis, this study reveals the influence of natural and social driving forces on the landscape pattern of Linpan in Western Sichuan. The results indicate that urbanization rate, patch density, mean patch size, and average elevation are significant factors influencing the distribution and morphology of Linpan. These factors affect the spatial layout and connectivity of Linpan, driving changes in the land use structure, and profoundly impacting the landscape pattern of Linpan. To achieve the ecological sustainability, it is essential to comprehensively consider the impacts of both natural and human factors, scientifically plan and manage Linpan, and promote their harmonious coexistence with the surrounding environment.

Quantitative Evaluation of Soil Erosion in Loess Hilly Area of Western Henan Based on Sampling Approach

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha−1⋅a−1, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements.

Effects of Bt-cotton cultivation on Helicoverpa armigera activity-density in agricultural landscapes in northwestern China.

Cotton bollworm, Helicoverpa armigera (Hübner), threatens many crops. Bacillus thuringiensis (Bt) cotton has been planted to control this severe pest in northern Xinjiang, China. In 2021 and 2022, we monitored the activity densities of H. armigera males using sex pheromone traps in Bt cotton and non-Bt maize fields. We assessed how much of the population variation of cotton bollworm in the fields within the Bt cotton planting area could be explained by (i) landscape composition [including the proportion of Bt cotton among total area of host crops (cotton, maize, wheat and vegetables)], (ii) landscape configuration (patch density - i.e. the number of patches within a given area) across 0.5-2.0 km scales, or (iii) the population density of the previous pest generation. Cotton bollworm activity-density exhibited two distinct peaks annually (mid- to late May and mid-July each year), with the number of males caught during the second peak significantly and positively correlated with the first peak's numbers. The suppressive effect of the proportion of Bt cotton in the landscape on bollworms was more pronounced at larger scales, and patch density had a significant positive effect on bollworm activity density. These findings support (i) the promotion of Bt cotton in northwestern China has reduced and suppressed the occurrence of cotton bollworms at the landscape scale and (ii) the importance of controlling spring populations for effective summer outbreak management, and (iii) that cotton bollworm control should be coordinated at a large scale across multiple crop fields. © 2024 Society of Chemical Industry.

A piezoelectric acoustic black hole absorber for rail vibration reduction and energy harvesting: Semi-analytical model

Theoretically, vibration energy can be converted and harvested during the process of vibration reduction, thus enabling a self-powered supply for sensor nodes. However, little research was reported on rail absorbers that combine vibration reduction and energy harvesting. Accordingly, this study aims to design an absorber based on the acoustic black hole (ABH) effect, termed the P-ABH absorber, that can simultaneously achieve broadband vibration reduction and energy harvesting. Firstly, the P-ABH absorber was preliminarily designed, and the semi-analytical model of the track with the P-ABH absorber was established by the Rayleigh-Ritz method. Secondly, the semi-analytical model was verified by the 3D finite element method, and the vibration reduction and energy harvesting performance were initially evaluated. The dimensions of the ABH structure, along with the sizes of the piezoelectric patches, were then determined. Finally, the vibration reduction and energy harvesting performances of the P-ABH absorbers fully deployed between each fastener were evaluated. The results demonstrated that the P-ABH absorber exhibited a wider vibration reduction bandwidth than the uniform piezoelectric absorber and the piezoelectric patches, with an increase in energy harvesting power by 27.47 % and 115.26 %, respectively. The vibration reduction and energy harvesting performance were significantly affected by the number of piezoelectric patches but little affected by the thickness and total length. When the P-ABH absorbers were fully attached to the rail, the overall mean square velocities at the three resonant frequencies, which required emphatic control, were reduced by 36.77 %, 98.37 %, and 3.52 %, respectively. Meanwhile, the energy harvesting performance was excellent. The P-ABH absorber presents a promising solution for improving the sustainability and cost-effectiveness of track systems.

Climate gradient‐driven intraspecific aggregation propensity linked to interpatch modulation in grassland communities

AbstractThe response of vegetation to climate change on a large scale should be studied at the community level rather than the species level. This necessitates a focused exploration of emerging spatial patterns. Here, we surveyed 264 sites in the Inner Mongolia typical steppe, using the “needling” method to investigate 39,600 clumps formed through the coexistence relationships of dominant species. We found that the effects of slow climate change on grassland communities can be categorized into two general trends: (1) a monotone relationship, characterized by changes in the number of dominant species, compositional diversity, and optimal patch area, and (2) a unimodal relationship, reflected in variations in the number of patches and interspecific associations. The two distinct trends, connected by optimal patch area, concurrently support both the habitat amount hypothesis and the intermediate disturbance hypothesis. These findings suggest that climate change indirectly influences the area and amount of vegetation patches by regulating the arrangement of clumps. Moreover, they indicate that it is the distribution, rather than the number, of species that serves as the front line for plant communities adapting to climate change.

Automating field‐based floral surveys with machine learning

Abstract The abundance and diversity of flowering plant species are important indicators of pollinator habitat quality, but traditional field‐based surveying techniques are time‐intensive. Therefore, they are often biased due to under‐sampling and are difficult to scale. Aerial photography was collected across 10 sites located in and around Rouge National Urban Park, Toronto, Canada using a consumer‐grade drone. A convolutional neural network (CNN) was trained to semantically segment, or identify and categorize, pixel clusters which represent flowers in the collected aerial imagery. Specifically, flowers of the dominant taxa found in the depauperate fall flowering plant community were surveyed. This included yellow flowering Solidago spp., white Symphyotrichum ericoides/lanceolatum and purple Symphyotrichum novae‐angliae. The CNN was trained using 930 m2 of manually annotated data, ~1% of the mapped landscape. The trained CNN was tested on 20% of the manually annotated data concealed during training. In addition, it was externally validated by comparing the predicted drone‐derived floral abundance metrics (i.e. floral area (m2) and the number of floral patches) to the field‐based count of floral units estimated for 34 4 m2 plots. The CNN returned accurate multiclassification when evaluated against the testing data. It obtained a precision score of 0.769, a recall of 0.849, and an F1 score of 0.807. The automated floral abundance counting yielded estimates that were strongly correlated with field‐based manual counting. In addition, flower segmentation using the trained CNN was time‐efficient. On average, it took roughly the same amount of time to segment the flowers occurring in an entire drone scene as it took to complete the abundance count of a single quadrat. However, the training process, particularly manual data annotation, was the most time‐consuming component of the study. Practical implication: Overall, the analysis provided valuable insights into automated flower classification and abundance estimation using drone imagery and machine learning. The results demonstrate that these tools can be used to provide accurate and scalable estimates of pollinator habitat quality. Further research should consider diverse wildflower systems to develop the generalizability of the methods.

Monitoring urban growth and landscape fragmentation in Kaduna, Nigeria, using remote sensing approach

This study assessed urban growth in Kaduna using remote sensing indices, landscape metrics, and Landsat images (2003, 2013, and 2023). Land use/land cover was carried out using the decision tree algorithm, while the transition matrix was computed using ArcGIS 10.8. Landscape fragmentation was assessed using Fragstat 4.3. The results revealed that between 2003 and 2023, bare land, built-up areas, and water bodies increased by 596 km2, 121.04 km2, and 0.22 km2, respectively, while cultivated land and tree cover decreased by 525.54 km2 and 191.91 km2, respectively. For the rate of change, bare lands, built-up areas, and water bodies increased by 29.81 km2, 6.05 km2, and 0.01 km2 annually, respectively, while cultivated lands and water bodies decreased by 26.28 km2 and 9.60 km2, respectively. For the land-use transition, 112.71 km2 of the built-up areas remained persistent. Cultivated lands, tree cover, and water bodies transitioned to built-up areas, losing 85.43 km2, 13.91 km2, and 0.91 km2, respectively. Landscape fragmentation analysis revealed that the number of patches increased from 405 in 2003 to 614 in 2023. Patch density, largest patch index, total edge, edge density, area-weighted mean shape index, and fractal dimension index decreased between 2003 and 2013 but increased between 2013 and 2023, indicating urban sprawl. However, the area-weighted mean contiguity index consistently increased between 2003 and 2023. The study concluded that urbanization is the leading driver of landscape fragmentation. This study recommends that development should be controlled to reduce encroachment on agricultural lands and habitats, thereby reducing landscape fragmentation and land degradation.

THE ROLE AND IMPACT OF VEGETATION ON THE URBAN FABRIC. CASE OF GUELMA CITY

In cities, land use changes caused by various human activities can affect how natural ecosystems function. In this context, it is increasingly important for cities to consider the role of vegetation in preserving a sustainable environment. A diachronic analysis of landscape changes was applied to assess the presence and distribution of vegetation to determine whether it was evolving or regressing. This phenomenon was studied within the current administrative boundaries of the city of Guelma (Algeria). For this purpose, as an approach based on landscape metrics, several Landsat TM and ETM+ remote sensing satellite images were used throughout the period 1987-2019. A set of landscape indices, including NP, AREA_MN, LPI, PLAND, AI and LSI, were calculated to map land cover, the mechanism of land cover and vegetation change, and their impact on the urban ecosystem. The geo-statistical procedure was carried out using a geographic information system Qgis combined by statistical software using Fragstats to calculate various landscape metrics at class level for the analysis of fragmentation . The results of the landscape metric analysis show that the decrease in average area and the increase in the number of green patches are important indicators of land degradation, meaning that the mechanism of landscape degradation and transformation is progressive. This underlines the need to give particular attention to land use and land cover in the region to ensure the sustainable allocation of natural resources.