Large Patch Size Research Articles

Enhancing Real-Time Vision Systems: Integrating Dynamic Vision Sensors with Vision Transformers to Increase Computational Efficiency

Abstract. Optimizing and understanding vision systems' computational accuracy is crucial as they become increasingly integrated into daily life. Dynamic Vision Sensors (DVS) and Vision Transformers (ViTs) lead computer vision technology with efficient object recognition and image processing. However, DVS data's high computational complexity poses a problem for its real-time implementations. Merging these technologies can enhance vision system performance in dynamic environments and optimize real-time DVS processing. In our work, we use a ViT architecture to classify the DVS 128 dataset and compare our results with existing works using SNNs. We analyze how our method affects accuracy and loss, experimenting with different DVS-to-ViT input patch sizes. Our results show that the large patch size of 32x32 pixels has better accuracy and smaller loss than the 4x4 pixel patches as epochs increase. Our method also achieved a high 98.4% accuracy and low 0.22 loss within five epochs, significantly outperforming previous works averaging 93.13% accuracy over more epochs. These results highlight ViTs' large potential for real-time DVS data classification in applications that require high accuracy, like autonomous vehicles and surveillance systems.

The effect of habitat fragmentation on Malay tapir abundances in Thailand’s protected areas

Habitat loss and fragmentation in tropical regions are major threats to the persistence of endangered Malay tapir (Tapirus indicus). The Malay tapir distribution is largely constrained to fragmented habitats inside protected areas. However, it is unclear how the spatial patterns of habitat fragmentation affect its relative abundance. Here, we investigated the effects of habitat fragmentation on Malay tapir relative abundance in Thailand. We first quantified the spatial patterns of habitat fragmentation within nine of Thailand’s protected areas. Second, we assessed the relationship of fragmentation metrics and relative abundance of Malay tapirs. Third, we identified the relative importance of the fragmentation metrics in explaining relative abundance. We found that tapir abundance remained unexpectedly high in the Southern forest complex despite the fact that tapir habitats were significantly more fragmented there than in the protected area in the western forest complex (p < 0.05). Additionally, we found a significantly negative relation with clumpiness index (R2 = 0.51, p < 0.05). This suggests that other factors may also be influencing their populations, so that the Southern protected areas provide preferred habitat with higher relative proportions of moist evergreen forest, large habitat patch size, precipitation, and elevation. It highlights the importance of interconnected habitat for tapirs, and the benefit of conservation efforts in small, less recognized protected areas.

Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions

ABSTRACT Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limited research has addressed the challenges of FCH extraction in plain regions with sparse and fragmented forest distributions. In this study, we proposed innovative processing approaches to extract FCH from ICESat-2 photons and GEDI footprints in the plain regions of Anhui Province, China. Specifically, we proposed a sectional photon denoising method for processing ICESat-2 data and a geolocation error correction method for processing GEDI data. Airborne LiDAR data were used to validate the extracted FCH products across typical plain regions. The results demonstrated the effectiveness of the proposed methods in improving FCH extraction accuracy. Evaluation indicated that the directly extracted FCH products from ATL08 and GEDI L2A had Pearson’s correlation coefficients (r) of 0.6 and 0.93, respectively. After processing with the proposed methods, the 2019 FCH products from ICESat-2 exhibited r of 0.82 and relative root mean square error (rRMSE) of 31.11% based on 3,217 ICESat-2 segments, and the products from GEDI showed r of 0.96 and rRMSE of 18.35% based on 4,862 GEDI footprints. Further application of these methods to extract FCH products from GEDI and ICESat-2 for the years 2020, 2021, and 2022 indicated their promise for addressing sparse vegetation coverage in plain regions.

Bird Habitat Suitability Analysis of The New City Centre Gede Bage Bandung Using Landscape Metrics

Abstract One focus of landscape ecology studies is the study of landscape patterns. Understanding the landscape patterns can help to find suitable habitat for the development or restoration of wildlife habitats, which can partly be expressed quantitatively with measurable or computable indicators by Geographic Information System, i.e. landscape metrics. This paper aims to analyze suitable habitat for bird habitat in The New City Centre in Gede Bage Bandung using statistical relationships between the occurrence of birds and the landscape pattern. Our study focus on habitat for Javan Pond Heron (A. speciosa) and Cattle Egret (B. ibis), two bird species which has been lived since 1970 in Ranca Bayawak Village inside The New City Centre Gede Bage Bandung. This study using landscape metrics software FRAGSTAT 4.2, parameter characterising landscape pattern were determined for the UTM 4 km x 4 km cells covering the whole of The New City Centre Gede Bage Bandung. The characteristic bird habitat define based on three types of landscape pattern : built-up areas, green spaces and water bodies. The study results based on statistical calculations found green spaces landscape metrics especially paddy field, forest and green open spaces are suitable for bird habitat, the type of patch that has the second largest patch size from total area. From statistical calculations also known built-up areas landscape metrics has the highest probability of fragmentation and the highest edge effect as a result of The New City Centre Gede Bage Bandung development.

Assessing four decades of fire behavior dynamics in the Cerrado biome (1985 to 2022)

BackgroundFire significantly transforms ecology and landscapes worldwide, impacting carbon cycling, species interactions, and ecosystem functions. In the Brazilian Cerrado, a fire-dependent savanna, the interaction between fire, society, and the environment is evident. Given that wildfires significantly contribute to greenhouse gas emissions, our study aimed to analyze four decades of burned area data to understand changes in fire dynamics, using Collection 2 of annual MapBiomas Fire maps (1985 to 2022). Our study examined spatiotemporal patterns, fire recurrence, fire distribution across land uses, temporal changes in fire scar size, burned area variations across ecoregions, and their correlation with farming areas.ResultsFrom 1985 to 2022, fire impacted 40% (792,204 km2) of the Cerrado biome, with 63% burning more than once. Natural vegetation was the most affected, primarily due to human-driven ignition during the dry season. A noticeable trend of later peaks in fire activity, concentrated towards the end of the dry season, along with an increase in patch size over time, characterized a clear shift in the Cerrado fire regime. Recently, the MATOPIBA region and the northern biome exhibited significant fire clusters, with burned areas rising alongside farming expansion. The ecoregion-based analysis identified fire hotspots, with the "Bananal" ecoregion, the largest wetland area in the biome, exhibiting increased fire recurrence and larger patch size over time.ConclusionsOur four-decade analysis of fire dynamics in the Cerrado revealed human-induced changes in the fire regime, originally shifting from July to September to a new fire season from August to October. This shift poses several environmental threats given their overlap with the driest months of the year. This study improved our understanding of changes in fire patterns and their impacts on each ecoregion and land use. Wetlands experienced the highest relative burned area, highlighting their ecological importance and increased vulnerability. In the southern Cerrado, where farming is established and natural vegetation more fragmented, fire events tend to decrease; while in the north, with recent farming expansion, fire susceptibility rises. Conservation-oriented strategies, like the Brazilian Integrated Fire Management (MIF), are crucial for mitigating impacts while enhancing the Cerrado’s resilience to climate change.

Chemical heterogeneity size effects at nanoscale on interface thermal resistance of solid–liquid polymer interface via molecular dynamics simulations

The shrinking size of integrated chips poses thermal management challenges. Understanding the size effect of chemical heterogeneity on solid–liquid interfacial thermal transfer is essential for heterogeneous chip design, yet the underlying mechanisms remain lacking. The present work used the liquid n-alkanes as the thermal interface material between solid platinum substrates. To characterize chemical heterogeneity, periodic solid surface patterns composed of patches with alternating solid–liquid affinities were constructed. By using non-equilibrium molecular dynamics simulations, we investigated the size effect of chemically heterogeneous patterns on interfacial thermal resistance (ITR) at the nanoscale. At larger heterogeneity sizes, i.e., larger patch sizes, most alkane molecules directly in contact weak interaction patches cannot interact with strong interaction patches due to long atomic distances. In the case of alkanes in contact a cold substrate, alkanes in contact weak interaction patches transferred thermal energy to the substrate at a lower rate than those in contact strong interaction patches. The different rates resulted in the higher temperature of alkanes in contact weak interaction patches than those in contact strong interaction patches and, therefore, a larger disparity between temperature jump at the strong interaction areas and that at the weak interaction areas. The non-uniformity of temperature jump distribution increased ITR when compared to the heterogeneous surface system characterized by a smaller patch size with a more uniform temperature distribution in the plane perpendicular to the heat flux direction. In addition, the classical parallel thermal resistance model predicted ITR accurately for the heterogeneous surface systems with small size patches but overestimated overall thermal resistance.

Neighbour effects on plant biomass and its allocation for forbs growing in heterogeneous soils

Abstract Focal plants are considerably affected by their neighbouring plants, especially when growing in heterogeneous soils. A previous study on grasses demonstrated that soil heterogeneity and species composition affected plant biomass and above- and belowground allocation patterns. We now tested whether these findings were similar for forbs. Three forb species (i.e. Spartina anglica, Limonium bicolor and Suaeda glauca) were grown in pots with three levels of soil heterogeneity, created by alternatively filling resource-rich and resource-poor substrates using small, medium or large patch sizes. Species compositions were created by growing these forbs either in monocultures or in mixtures. Results showed that patch size × species composition significantly impacted shoot biomass, root biomass and total biomass of forbs at different scales. Specifically, at the pot scale, shoot biomass, root biomass and total biomass increased with increasing patch size. At the substrate scale, shoot biomass and total biomass were higher at the large patch size than at the medium patch size, both in resource-rich and resource-poor substrates. Finally, at the community scale, monocultures had more shoot biomass, root biomass and total biomass than those in the two- or three-species mixtures. These results differ from earlier findings on the responses of grasses, where shoot biomass and total biomass decreased with patch size, and more shoot biomass and total biomass were found in resource-rich than resource-poor substrates. To further elucidate the effects of soil heterogeneity on the interactions between neighbour plants, we advise to conduct longer-term experiments featuring a variety of functional groups.

Apple pest and pathogen reduction in landscapes with large patch size and small area of orchards: a national-scale analysis

ContextThe composition and configuration of habitats in agricultural landscapes may determine crop damage resulting from pests or pathogens either by directly affecting their population dynamics or through indirect effects on their natural enemies.ObjectivesThe aim of this study was to assess the impact of landscape composition and configuration on the occurrence and damage caused by the codling moth and apple scab in apple orchards.MethodsUsing monitoring data at the French national scale, we examined how the proportion of landscape area grown with orchards, the mean patch area of orchards, the share of organic orchards and the proportion of woodlands and grasslands affected the occurrence and damage of these two pests from 2015 to 2019 in approximately sixty apple orchards each year.ResultsLandscapes with a higher proportion of orchards supported a higher occurrence of apple scab and earlier colonisation of codling moths. In addition, we found that codling moth damage decreased with increasing orchard patch area in the landscape. The proportion of seminatural habitats or organic farming in the landscape never significantly explained pest occurrence or damage.ConclusionsOur results clearly highlight the importance of considering the amount and spatial arrangement of the pests’ and pathogens’ host crops to understand their infestation levels. Our study calls for the territorial management of orchard distribution to limit pesticide use in apple orchards.

From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-resolution

Image super-resolution (SR) serves as a fundamental tool for the processing and transmission of multimedia data. Recently, Transformer-based models have achieved competitive performances in image SR. They divide images into fixed-size patches and apply self-attention on these patches to model long-range dependencies among pixels. However, this architecture design is originated for high-level vision tasks, which lacks design guideline from SR knowledge. In this paper, we aim to design a new attention block whose insights are from the interpretation of Local Attribution Map (LAM) for SR networks. Specifically, LAM presents a hierarchical importance map where the most important pixels are located in a fine area of a patch and some less important pixels are spread in a coarse area of the whole image. To access pixels in the coarse area, instead of using a very large patch size, we propose a lightweight Global Pixel Access (GPA) module that applies cross-attention with the most similar patch in an image. In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a spatial convolution is applied to process the finest details. In addition, a Cascaded Patch Division (CPD) strategy is proposed to enhance perceptual quality of recovered images. Extensive experiments suggest that our method outperforms state-of-the-art lightweight SR methods by a large margin. Code is available at https://github.com/passerer/HPINet.

Role of Landscape and Land-Use Transformation on Nonpoint Source Pollution and Runoff Distribution in the Dongsheng Basin, China

Non-point source pollution (NSP) and runoff intensities and distribution are primarily affected by landscape structure and composition. Multiple causalities hinder our ability to determine significant variables that influence NSP. Therefore, we developed an approach that integrates the Soil and Water Assessment Tool (SWAT), random forest regression model, redundancy analysis, and correlation coefficient to assess the role of landscape structure on runoff and NSP in the Dongsheng basin. We used R to calculate landscape metrics and the SWAT to simulate NSP loads from 1990 to 2019. redundancy analysis (RDA), random forest, and Pearson correlation were used to analyze the relationships among landscape metrics and NSP variables. The largest patch index (LPI) shows a significant negative correlation with NSP, with an R2 of −0.58 for TP and TN and −0.62 for sediment load. The findings indicate that landscapes with larger patch sizes, a high number of patches, and aggregation of patches largely influence pollution distribution. Overall, the results suggest that the role of landscape patterns on NSP outweighs that of runoff. Moreover, the findings infer that the aggregation and connectivity of forest patches contribute to the decline in NSP load and vice versa for cropland cover. Thus, for sustainable watershed management, it is crucial to encourage unfragmented landscapes, especially pollutant-intercepting landcovers such as forests.

Effects of Variation in Tamarix chinensis Plantations on Soil Microbial Community Composition in the Middle Yellow River Floodplain

Floodplains have important ecological and hydrological functions in terrestrial ecosystems, experience severe soil erosion, and are vulnerable to losing soil fertility. Tamarix chinensis Lour. plantation is the main vegetation restoration measure for maintaining soil quality in floodplains. Soil microorganisms are essential for driving biogeochemical cycling processes. However, the effects of sampling location and shrub patch size on soil microbial community composition remain unclear. In this study, we characterized changes in microbial structure, as well as the factors driving them, in inside- and outside-canopy soils of three patch sizes (small, medium, large) of T. chinensis plants in the middle Yellow River floodplain. Compared with the outside-canopy soils, inside-canopy had higher microbial phospholipid fatty acids (PLFAs), including fungi, bacteria, Gram-positive bacteria (GP), Gram-negative bacteria (GN), and arbuscular mycorrhizal fungi. The ratio of fungi to bacteria and GP to GN gradually decreased as shrub patch size increased. Differences between inside-canopy and outside-canopy soils in soil nutrients (organic matter, total nitrogen, and available phosphorus) and soil salt content increased by 59.73%, 40.75%, 34.41%, and 110.08% from small to large shrub patch size. Changes in microbial community composition were mainly driven by variation in soil organic matter, which accounted for 61.90% of the variation in inside-canopy soils. Resource islands could alter microbial community structure, and this effect was stronger when shrub patch size was large. The results indicated that T. chinensis plantations enhanced the soil nutrient contents (organic matter, total nitrogen, and available phosphorus) and elevated soil microbial biomass and changed microbial community composition; T. chinensis plantations might thus provide a suitable approach for restoring degraded floodplain ecosystems.

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique.

Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

Three grassland bird species’ responses to fire and habitat structure in southern Illinois, USA suggest broad benefits of grassland size and plant diversity

Grassland birds are the most rapidly declining bird guild in North America, largely due to extensive habitat loss and fragmentation. Because many grassland bird species have different habitat preferences, managing grasslands to provide habitat for a range of species can be a challenge. We used four years of data from southern Illinois, USA grasslands to estimate the influence of prescribed fire and habitat structure on nest survival, nest density, and abundance of three grassland bird species with different habitat preferences: Dickcissel (<em>Spiza americana</em>), Field Sparrow (<em>Spizella pusilla</em>), and Common Yellowthroat (<em>Geothlypis trichas</em>). We found that Dickcissels exhibited the strongest response to prescribed fire, as nest density and nest survival both increased after previously undisturbed grasslands were burned. Fire may have also benefitted Common Yellowthroats and Field Sparrows by reducing woody cover and increasing bare ground, both of which were characteristics associated with nest survival for these birds. Dickcissel abundance was positively related to plant diversity within a grassland and agriculture in the surrounding landscape (within 400 m of a grassland patch), and negatively related to edge-interior ratio. Field Sparrows demonstrated a positive association with woody cover and proximity to forests. Common Yellowthroats were associated with tall vegetation and agriculture in the surrounding landscape. Both Field Sparrows and Common Yellowthroats associated positively with habitat characteristics that reduced nest survival, suggesting potential adaptive mismatches. Our results suggest that periodic prescribed fire, increased plant diversity, and larger patch size may simultaneously benefit a broad variety of grassland bird species with different habitat preferences.

Carbon storages and sequestration potentials in remnant forests of different patch sizes in northern Ethiopia: an implication for climate change mitigation

Abstract Background Forests provide various ecosystem services. They are natural capitals that enhance nature to regulate itself via carbon sinks. However, anthropogenic and natural factors have altered their CO2 sequestration and carbon storage potentials. This study is aimed for examining the effect of patch size and biomass extraction on carbon stocks in northern Ethiopia. A total of 61 sample plots measuring 20 m × 20 m size each (0.04 ha) had been systematically assigned on patches classified into three size categories. However, the numbers of plots taken per patch were different with their sizes. Moreover, stump density has been computed at each plot to estimate the difference in the level of disturbance among patches. Carbon stocks had been estimated via models previously developed. One-way ANOVA was used to examine a variation in carbon stocks and sequestration potentials. Besides, a linear regression analysis was discretely done to examine the relationship between patch sizes, disturbance level, and carbon stocks. Results The overall aboveground biomass (ton ha−1) for the studied patches was 2059.13. There was a statistically significant variation in carbon stocks (ton ha−1) among patch size categories. The mean levels of disturbance ranges from 10.83% ± 1.30 to 30.8% ± 4.04. However, statistically significant difference in the level of disturbance was observed between large and small patch size categories, respectively (p &lt; 0.05). Besides, a regression analysis confirmed a significant and negative relationship between patch size and patch disturbances (R2 = 0.65, p &lt; 0.05). However, significant positive relation between carbon stocks (ton ha−1) and patch size (R2 = 0.53, p &lt; 0.05) had observed. Conclusions In general, patch size and biomass extinction significantly influenced carbon stocks and CO2 sequestration potentials of forests. Consequently, with the pressing need to mitigate the effects of rising atmospheric CO2, maximizing carbon storage in the forest ecosystem is increasingly considered a viable management strategy. Therefore, disturbed land restoration, increasing forest patch size, sustainable management, and conservation of the existing remnant forest patch is needed to enhance carbon stocks and CO2 sequestration potentials.

The outsized role of California’s largest wildfires in changing forest burn patterns and coarsening ecosystem scale

Although recent large wildfires in California forests are well publicized in media and scientific literature, their cumulative effects on forest structure and implications for forest resilience remain poorly understood. In this study, we evaluated spatial patterns of burn severity for 18 exceptionally large fires and compared their cumulative impacts to the hundreds of smaller fires that have burned across California forests in recent decades. We used a burn severity atlas for over 1,800 fires that burned in predominantly conifer forests between 1985 and 2020 and calculated landscape metrics to evaluate spatiotemporal patterns of unburned refugia, low-moderate-severity, and high-severity post-fire effects. Total annual area burned, mean annual fire size, and total annual core area burned at high severity all significantly increased across the study period. Exceptionally large fires (i.e., the top 1% by size) were responsible for 58% and 42% of the cumulative area burned at high and low-moderate severities, respectively, across the study period. With their larger patch sizes, our results suggest that exceptionally large fires coarsen the landscape pattern of California’s forests, reducing their fine-scale heterogeneity which supports much of their biodiversity as well as wildfire and climate resilience. Thus far, most modern post-fire management has focused on restoring forest cover and minimizing ecotype conversion in large, high-severity patches. These large fires, however, have also provided extensive areas of low-moderate severity burns where managers could leverage the wildfire’s initial “treatment” with follow-up fuel reduction treatments to help restore finer-scale forest heterogeneity and fire resilience.

A Joint Bayesian Optimization for the Classification of Fine Spatial Resolution Remotely Sensed Imagery Using Object-Based Convolutional Neural Networks

In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban settings. As a result, combining deep learning and object-based image analysis (OBIA) has become a new avenue in remote sensing studies. This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. The proposed classification model achieved the best accuracy, with 0.96 OA, 0.95 Kappa, and 0.96 mIoU in the training area and 0.97 OA, 0.96 Kappa, and 0.97 mIoU in the test area, outperforming several benchmark methods including Patch CNN, Center OCNN, Random OCNN, and Decision Fusion. The analysis of CNN variants within the proposed classification workflow showed that the HybridSN model achieved the best results compared to 2D and 3D CNNs. The 3D CNN layers and combining 3D and 2D CNN layers (HybridSN) yielded slightly better accuracies than the 2D CNN layers regarding geometric fidelity, object boundary extraction, and separation of adjacent objects. The Bayesian optimization could find comparable optimal MRS parameters for the training and test areas, with excellent quality measured by AFI (0.046, −0.037) and QR (0.945, 0.932). In the proposed model, higher accuracies could be obtained with larger patch sizes (e.g., 9 × 9 compared to 3 × 3). Moreover, the proposed model is computationally efficient, with the longest training being fewer than 25 s considering all the subprocesses and a single training epoch. As a result, the proposed model can be used for urban and environmental applications that rely on VHR satellite images and require information about land use.

Factors affecting functional diversity of grassland vegetations

The functional diversity of local plant communities is considered to be an important driver of ecosystem resistance and resilience. Various landscape characteristics can influence local functional diversity, but their relative importance is poorly understood. We used a spatially explicit grassland model (TRANSPOP) to simulate competition between 19 functional plant strategies as defined by Grime's C-S-R framework, each with different affinities for levels of Nutrient availability (N) and Disturbance (D). First, we explore the effects of N and D levels on strategy preference. Subsequently, we studied functional diversity in patchy landscapes with various levels of heterogeneity, patch size, connectivity and environmental dynamics (with patches differing in N and D). We evaluated the relative importance of these factors in determining the functional diversity of permanent grasslands of the temperate climatic zone.Simulated N and D optima for occurrence of strategies matched those of Grime. Perpendicular N and D gradients were twice as diverse as correlated configurations. The results show that heterogeneity in N and D was the most important factor determining strategy diversity, followed by environmental dynamics, connectivity and patch size. Grasslands with large heterogeneity and patch size have a high functional diversity, whereas high values for dynamics and connectivity had, in general, a negative impact on functional diversity. The frequent occurrence of significant interactions among characteristics suggests that the optimal spatial design of landscapes with respect to functional diversity is context dependent.Contrary to expectations, connectivity generally had a negative effect on the diversity of plant strategies. This was attributed to the fact that connectivity intensifies competition between strategies. High connectivity and environmental dynamics, low patch size and low heterogeneity intensifies interspecies competition and causes rapid species loss, which strongly reduces resistance and resilience. This shows that high species diversity in itself does not protect against diversity loss, but that factors contributing to the maintenance of high diversity reduce the risk of species extinction and enable resistance and resilience.

Predictors of Mammalian Diversity in the New York Metropolitan Area

Urbanization can have profound consequences for mammalian biodiversity and is thought to contribute to patterns of species richness and community composition. Large cities can be particularly challenging environments for mammals because these habitats are often impacted by anthropogenic perturbations, including high human population density, fragmented habitats, and extensive human development. In this study, we investigated mammalian species richness, Shannon–Wiener diversity, and evenness in the most densely populated region in the United States: the New York metropolitan area. Specifically, we deployed camera traps from 2015 to 2019 to investigate six drivers of mammalian diversity across 31 greenspaces: (1) human population density, (2) patch size, (3) habitat type, (4) surrounding land cover, (5) geographical barriers to dispersal, and (6) habitat heterogeneity. We found that mammal community composition is largely influenced by a multitude of anthropogenic factors. Specifically, mammal species richness was higher in greenspaces with larger patch sizes and lower in greenspaces surrounded by more development. Moreover, Shannon–Wiener diversity and evenness were higher in urban natural landscapes than human-altered landscapes. In a subset of data that only included carnivores, we found that carnivore Shannon–Wiener diversity was higher in urban natural habitats and in sites with lower human population densities. Finally, we found that geographical barriers to dispersal contributed to both patterns of mammalian diversity and patterns of carnivore diversity: mammal taxa richness, Shannon–Wiener diversity, and evenness were all significantly higher on the continent (Bronx/Westchester) than on Long Island. These results suggest that preserving urban greenspaces is important for maintaining both mammalian and carnivore biodiversity and that management of mammals in cities should concentrate on maintaining large, connected, natural greenspaces.

Landslide Segmentation with Deep Learning: Evaluating Model Generalization in Rainfall-Induced Landslides in Brazil

Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models to segment landslides in areas that differ from the ones used to train the models. In this study, we evaluated three different locations to assess the generalization capacity of these models in areas with similar and different environmental aspects. The model training consisted of three distinct datasets created with RapidEye satellite images, Normalized Vegetation Index (NDVI), and a digital elevation model (DEM). Here, we show that larger patch sizes (128 × 128 and 256 × 256 pixels) favor the detection of landslides in areas similar to the training area, while models trained with smaller patch sizes (32 × 32 and 64 × 64 pixels) are better for landslide detection in areas with different environmental aspects. In addition, we found that the NDVI layer helped to balance the model’s results and that morphological post-processing operations are efficient for improving the segmentation precision results. Our research highlights the potential of deep learning models for segmenting landslides in different areas and is a starting point for more sophisticated investigations that evaluate model generalization in images from various sensors and resolutions.

基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究 DOI: 10.5846/stxb202109142585 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41771101）；中国科学院"一带一路"生态水文学团队项目（Y929731） Spatial pattern of shrub in southeast Alxa Desert based on high resolution unmanned aerial vehicle images Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱区灌丛植被空间格局受多种物理和生态过程影响，能够指示生态系统的状态。研究通过量化灌丛斑块大小的空间分布来评估阿拉善高原东南部覆沙荒漠植被生态系统的状态，采用点格局分析法分析灌木种群的相互关系，以阐明不同灌木种在斑块格局形成中的作用，并结合土壤条件及下垫面粗糙度等指标验证评估的准确性，探讨灌丛空间格局差异的内在机理。结果表明，研究区样方2灌丛斑块大小符合截尾幂律分布，其他样方符合对数正态分布，前者的空间结构及生境条件均优于后者，说明植被空间格局可以准确表征生态系统状态。在局地尺度上灌木种内和种间呈现不同的相互关系，以竞争关系为主导是导致斑块破碎化的主要驱动机制。小灌木（如猫头刺）的种内互利关系有利于促进多样化斑块形态的形成，而大灌木（如沙冬青和蒙古扁桃）种间的互利作用则有利于形成异质性更强的复杂空间格局。基于灌丛斑块的空间格局评估生态系统状态，可为保护和恢复生态脆弱区受损植被提供重要的借鉴。 Abstract:The vegetation spatial pattern in drylands, which is dictated by various physical and ecological processes, could be interpreted as an indicator of the ecosystem state. According to the statistical analysis of shrub patch size distribution, we estimated the ecosystem state of desert vegetation in the southeast of the Alxa Plateau. The interactions among shrub populations were shown by point pattern analysis, to illustrate that different species might act as various ingredients in patchiness. Soil properties and roughness of underlying surface were surveyed to support the estimation. Ultimately, the internal mechanism of the differentiation of shrub spatial pattern was discussed. The results show that the truncated power law is the best fitting model for the patch sizes distribution in quadrat 2, while the lognormal distribution is the best for others. Assessment of ecosystem states is verified by the comparation of habitat conditions, which shows that quadrat 2 is better too. The interaction at small scales between shrubs varies with different species, competition dominated relationship is the main reason for the narrowing down of patch size distribution. The positive relationship within species of small patch size, such as Oxytropis aciphylla, is conducive to the formation of diversified patches, whereas species of large patch size with facilitation at small scales, such as Ammopiptanthus mongolicus and Prunus mongolica, give rise to a complex pattern. Sound management should consider to comprise assessment of ecosystem states through shrub spatial patterns, which will provide indications of conservation and restoration of vulnerable vegetation. 参考文献 相似文献 引证文献