Patch Size Research Articles

The importance of heuweltjie patch isolation, size, and quality for arthropods in the Succulent Karoo, South Africa

AbstractA characteristic feature of Earth's drylands is the patchy nature of the vegetation, often referred to as a two‐phase mosaic landscape, comprised of a homogenous matrix containing distinctive vegetated patches. The latter are considered vital for ecosystem functioning as they provide refuge to biota from unsuitable conditions. Ground‐living (epigeic) and foliage arthropods contribute to dryland biodiversity patterns and processes, but little is known of how their richness, abundance, and composition varies between patches and the matrix in these systems. Throughout the Succulent Karoo, South Africa, such patches (earthen mounds referred to as heuweltjies) are hotspots for both floral and faunal diversity. We investigate how epigeic and foliage‐dwelling arthropod species richness, abundance, and community composition respond to heuweltjie patch characteristics, particularly isolation (distance to nearest neighbor, average distance to nearest neighbor, patch density), size (area), and quality (average plant height, dead plant cover, leaf litter cover, termite frass) during a severe drought. Patch isolation and quality were significantly correlated with arthropod richness, abundance, and community composition. More specifically, average proximity of sampled heuweltjies to other neighboring heuweltjies, termite frass, and vegetation structure (height, leaf litter and dead plant cover) were key determinants for epigeic and foliage‐dwelling arthropods' species richness, abundance, and community composition. The uniqueness of these mounds as landscape features increases niche and microhabitat availability for arthropods. This emphasizes their importance as promoters of landscape heterogeneity and highlight heuweltjies as key to the spatial distribution of arthropod assemblages in the Succulent Karoo.

Pansharpening with multi-CAE: impact of patch size and overlapping pixels on spectral and spatial distortion

Pansharpening with multi-CAE: impact of patch size and overlapping pixels on spectral and spatial distortion

Scale effect of landscape characteristics on undergrowth vegetation variance with different ecological traits

Herbaceous understory vegetation plays a significant role in temperate forested ecosystems, with its diversity strongly dependent on both local and landscape conditions. However, the contribution of these factors across different spatial scales (e.g., scale effect) remains inadequately understood. The objective of this study was to determine the most important landscape and local characteristics influencing the species richness of the herb layer. We surveyed and sampled 53 sites in a mountainous forest within the Baihuashan National Nature Reserve of Beijing. Using generalized linear models, we investigated the simultaneous effect of local (e.g. tree density, average tree height, and cover of tree, shrub, and ground vegetation) and landscape (e.g. Area-weighted Mean Patch Size, Area-weighted Mean Euclidean Nearest Neighbor Distance, Area-weighted Mean Fractal Dimension Index, forest area, and shrubbery area) variables on the richness of species in the understory herb layer. Variation partitioning was used to examine the individual contribution of local and landscape variables. Our results identified 366 plant species from 95 families across 53 sampling sites. Significant differences in species richness were found among life forms, seed dispersal types, and habitat preferences (P < 0.001). Parsimonious models indicated that combining local and landscape factors at a 2 km scale explained the most variation in phanerophytes (R2 = 0.658). Locally, average tree height significantly influenced the species richness of biotic dispersal, phanerophytes, and geophytes. At the landscape level, variables such as the Area-weighted Mean Fractal Dimension Index and farmland area significantly impacted plant community traits across different scales. Understanding these effects is crucial for developing effective sustainable forest ecosystem management strategies and for establishing robust ecological conservation policies.

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.

Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and Machine Learning Approach for Pore Recognition on Pipes Surfaces.

This study investigates the methods for controlling porosity in thermal pipes manufactured using selective laser melting (SLM) technology. Experiments conducted include water permeability tests and surface roughness measurements, which are complemented by SEM image ML-based analysis for pore recognition. The results elucidate the impact of SLM printing parameters on water permeability. Specifically, an increase in hatch and point distances leads to a linear rise in permeability, while higher laser power diminishes permeability. Using machine learning (ML) techniques, precise pore identification on SEM images depicting surface microstructures of the samples is achieved. The average percentage of the surface area containing detected pores for microstructure samples printed with laser parameters (laser power (W) _ hatch distance (µm) _ point distance (µm)) 175_ 80_80 was found to be 5.2%, while for 225_120_120, it was 4.2%, and for 275_160_160, it was 3.8%. Pore recognition was conducted using the Haar feature-based method, and the optimal patch size was determined to be 36 pixels on monochrome images of microstructures with a magnification of 33×, which were acquired using a Leica S9 D microscope.

Fire refugia patch dynamics differ between prescribed fires and wildfires in longleaf pine savanna

Increasing wildfire activity has spurred an increasing push for the application of prescribed fire to reduce wildfire risk while simultaneously acting as a surrogate for fire’s historical role as a fundamental ecosystem process. However, prescribed fires are often ignited using uniform ignitions to maintain operational control and there are concerns that they may not be able to replicate the landscape heterogeneity, particularly patterns in unburned patches, generated by historical fires. Fire refugia, unburned areas within fire perimeters, play an integral role in determining post-fire recovery and community structure. We assess patterns in fire refugia across 443 large (>200 ha) wildfire and prescribed fire perimeters using remotely sensed fire severity data in longleaf pine (Pinus palustris) savannas of the Florida Panhandle. Contrary to concerns, large prescribed fires had a significantly greater proportion of unburned area than wildfires, driven by larger refugia patch sizes. Drier conditions promoted smaller and more numerous fire refugia patches. Our study demonstrates differences in wildfires versus prescribed fire outcomes on landscape structure, with implications for future longleaf pine savanna management.

Semantic Web Techniques for Extracting and Analyzing of Cropland Abandonment in Hilly Areas

Due to rising rural labor costs, farmland abandonment is common in China's hilly areas. Timely, accurate extraction of its spatiotemporal changes is crucial for sustainable farmland use. This study presents a novel approach for extracting abandoned cropland using NDVI time series from Sentinel-2 satellite images, exploiting the difference in vegetation phenology between abandoned and cultivated croplands. Dynamic Time Warping (DTW) algorithm was used to determine the similarity between NDVI time-series curves of different cropland types. The similarity metrics were used to find the optimal NDVI threshold for abandoned cropland through F1-score evaluation. The method's overall accuracy is 92.4%, higher than comparison methods at 84.60% and 75.6%. The approach captures year-round vegetation changes, expands time dimension data, and improves accuracy. Spatiotemporal analysis revealed decreased patch size, increased shape complexity, and more dispersed distribution of abandoned cropland over time. Major factors were proximity to settlements, roads, water bodies.

Fusing Multispectral and LiDAR Data for CNN-Based Semantic Segmentation in Semi-Arid Mediterranean Environments: Land Cover Classification and Analysis

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map.

Projecting the long‐term effects of large‐scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome

AbstractDecades of human activities and fire suppression have adversely affected longleaf pine (Pinus palustris) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long‐term, large‐scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer‐scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long‐term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.

Insights of ecological resilience in Caatinga assemblages – landscape configuration drives Chrysomeloidea (Coleoptera) diversity in a seasonally dry tropical forest

Seasonally Dry Tropical Forests (SDTFs) have long suffered from chronic, intensive and extensive landscape transformations due to human activities, challenging biodiversity conservation in this biome. The Caatinga is the largest Neotropical SDTF, serving as an excellent model to understand how land use changes modulate ecological communities. In this study, we assessed the effects of landscape configuration and composition on the abundance and diversity of Chrysomeloidea in a region of Caatinga. We sampled beetles in 10 landscape units, in which we measured landscape configuration (patch size, edge density) and composition (landscape diversity, patch richness). Hill numbers (q0, q1, q2) were used to analyze the diversities of Chrysomeloidea. A total of 484 individuals belonging to 82 species were collected. Landscape configuration affected Chrysomeloidea assemblages with a positive correlation between edge density and Chrysomeloidea diversity. We provide a new perspective regarding the spatial distribution of Chrysomeloidea in Caatinga and conclude that landscape configuration, but not composition, directly affects Chrysomeloidea assemblages.

Effect of fluid patch clustering on the P-wave velocity-saturation relation: a critical saturation model

Quantitative analysis of the relationship between seismic wave velocities and fluid saturation in porous media is of great significance for any fluid injection and extraction operation in subsurface rock formations. However, seismic velocities are not only dependent on the amount of saturation, but also on the distribution of fluid patches and their size. The patch size variation during changes in saturation is oftentimes ignored in modeling studies, even though it is natural to assume that with increasing saturation, fluid patches will form larger and, at some critical saturation, percolating clusters. To capture the evolution of patch size with saturation implied in the velocity-saturation relations, we are inspired by percolation theory. By incorporating the connectivity of water-filled patches in the continuous random medium model, we develop a critical saturation model. We apply this critical saturation model to examine recently reported experimental measurements, specifically analyzing the patch size changes. For measurements of drainage or imbibition processes in four sandstone samples, we indeed find a clear indication of growing patch size with water saturation. The predictions of the critical saturation model are in reasonable agreement with observations. Our approach improves the accuracy of the interpretation of the velocity-saturation relations in partially saturated rocks and forms a basis for exploring its underlying mechanisms.

A New Paradigm for Assessing Detailed Dynamics of Forest Landscape Fragmentation

There is an urgent need for a thorough assessment of forest landscape fragmentation to inform forest protection and restoration, and reforestation policies. However, there is currently a lack of an effective comprehensive index for forest landscape fragmentation, and detailed knowledge of the forest landscape fragmentation dynamics remains insufficient. Here, taking Putian City of Fujian Province in Southeastern China as a case, we employed a forest fragmentation comprehensive index (FFCI) to capture key features of forest landscape fragmentation, such as patch size, number, and distribution. Then, bivariate spatial autocorrelation analysis was employed to identify the spatial associations between the static forest landscape fragmentation (FFCI) and the dynamic forest landscape fragmentation (ΔFFCI), and the spatial coupling modes among the three individual components of FFCI (mean patch area, MPA; aggregation index, AI; patch density, PD) were identified to explore the detail process of forest landscape fragmentation. Finally, the random forest model was applied to observe the impact factors of forest landscape fragmentation dynamics. The findings showed that forest landscapes with different degrees of fragmentation exhibited more noticeable changes at both ends (i.e., either high or lower-level fragmentation), with the intermediate level remaining consistent from 2000 to 2020. Around 18.3% of forest landscapes experienced a decrease in fragmentation, particularly in the northern part of the study area, while approximately 81.7% of forest landscapes exhibited an increasing trend in fragmentation. The bivariate spatial autocorrelation analysis indicated that the proportion of Low–High-type grids was the highest at 17.3%, followed by the High–High type at 7.0%. We also identified eight forest landscape fragmentation modes, which indicate the most significant forest landscape fragmentation pattern is a decrease in MPA and an increase in PD. Moreover, the anthropogenic factors (e.g., population density and night light intensity) were found to dominate the FFCI dynamics during 2000–2020. This study offers an efficient research paradigm for the dynamics of forest landscape fragmentation. The outcomes are conducive to an in-depth comprehension of the detailed dynamic information of forest landscape fragmentation, and supply a scientific foundation for enhancing the overall ecological service function of the forest.

The role of agroforestry in solving the agricultural landscapes vulnerabilities in the Po Plain district

AbstractThe current trend of biodiversity deterioration in rural systems is a complex issue that operates across multiple spatial scales. Agroforestry practices have the potential to positively contribute towards addressing these trends by shaping the structure of agricultural landscapes and their underlying ecological functions. This study aims to test a multi-scale analytical approach to understand and account for these processes. Specifically, the study seeks to assess the contributions that agroforestry practices at the farm scale can make towards supporting biodiversity, in response to the wider-scale landscape eco-mosaic structural and functional challenges and requirements (both at the local and extra-local landscape systems). To achieve this, a series of landscape ecology analyses are conducted on an agroforestry-based rice farm located in the western Po Plain region of Northern Italy. These analyses examine various landscape structural traits (such as matrix composition, patch size, shape complexity, and diversity indices) and functional traits (including connectivity and bionomic indices), with different levels of detail for each scale of analysis. This allows for the evaluation of the current ecological status of both the extra-local and local scale landscape systems (including drivers of vulnerability and resilience) and the assessment of the farm's current contributions to biodiversity support. Based on these findings, strategic agroforestry interventions are identified at the farm scale to enhance its capacity to address the wider-scale ecological gaps. Two design scenarios are assessed, wherein functional ecological traits such as landscape diversity, connectivity, and ecological stability are improved. The results confirm the role of farm scale agroforestry management as a buffering tool, demonstrating how it contributes to the restoration of broader-scale landscape vulnerabilities. The applied approach provides cost-effective assessments of biodiversity-related ecological processes, with the accuracy of the findings dependent on the comprehensive multi-scale analysis conducted.

Quantifying Forest Cover Loss during the COVID-19 Pandemic in the Lubumbashi Charcoal Production Basin (DR Congo) through Remote Sensing and Landscape Analysis

In the context of the Lubumbashi Charcoal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and dissection observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges.

Research on the Inversion of Chlorophyll-a Concentration in the Hong Kong Coastal Area Based on Convolutional Neural Networks

Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations in optically complex coastal waters presents a significant challenge for traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged the power of five machine learning models, including a convolutional neural network (CNN), to enhance the inversion process in the coastal waters near Hong Kong. The CNN model demonstrated superior performance with on-site data validation, outperforming the other four models (R2 = 0.810, RMSE = 1.165 μg/L, MRE = 35.578%). The CNN model was employed to estimate Chl-a concentrations from images captured over the study area in April and October 2022, resulting in the creation of a thematic map illustrating the spatial distribution of Chl-a levels. The map indicated high Chl-a concentrations in the northeast and southwest areas of Hong Kong Island and low Chl-a concentrations in the southeast facing the open sea. Analysis of patch size effects on CNN model accuracy indicated that 7 × 7 and 9 × 9 patches yielded the most optimal results across the tested sizes. Shapley additive explanations were employed to provide post-hoc interpretations for the best-performing CNN model, highlighting that features B6, B12, and B8 were the most important during the inversion process. This study can serve as a reference for developing machine learning models to invert water quality parameters.

A customised vision transformer for accurate detection and classification of Java Plum leaf disease

Vision Transformer (ViT) has recently attracted significant attention for its performance in image classification. However, studies have yet to explore its potential in detecting and classifying plant leaf disease. Most existing research on diseased plant leaf detection has focused on non-transformer convolutional neural networks (CNN). Moreover, the studies that applied ViT narrowly experimented using hyperparameters such as image size, patch size, learning rate, attention head, epoch, and batch size. However, these hyperparameters significantly contribute to the model performance. Recognising the gap, this study applied ViT to Java Plum disease detection using optimised hyperparameters. To harness the performance of ViT, this study presents an experiment on Java Plum leaf disease detection. Java Plum leaf diseases significantly threaten agricultural productivity by negatively impacting yield and quality. Timely detection and diagnosis are essential for successful crop management. The primary dataset collected in Bangladesh includes six classes, ‘Bacterial Spot’, ‘Brown Blight’, ‘Powdery Mildew’, and ‘Sooty Mold’, ‘healthy’, and ‘dry’. This experiment contributes to a thorough understanding of Java Plum leaf diseases. Following rigorous testing and refinement, our model demonstrated a significant accuracy rate of 97.51%. This achievement demonstrates the possibilities of using deep-learning tools in agriculture and inspires further research and application in this field. Our research offers a foundational model to ensure crop quality by precise detection, instilling confidence in the global Java Plum market.

Modeling wave attenuation through vegetation patches: The overlooked role of spatial heterogeneity

Coastal wetlands serve as vital buffers against extreme hazards due to the wave-dissipating capacity of vegetation. While most studies assume a spatial-uniform vegetation cover when modeling wave attenuation through wetlands, they are far from homogeneous in reality due to life-stage dependent growths, die-offs, and zonation of different vegetation species. In the present study, the XBeach model was applied to explore the role of vegetation spatial heterogeneity on wave attenuations. Random distributed vegetation maps with the same coverage but diverse patch sizes are generated to replicate the complex patterns of real wetland vegetations. We focus on the attenuation of solitary waves and wave groups, representing tsunami and storm waves, respectively. Different wave heights, vegetation densities, and land slopes are considered. For solitary wave cases, it is found that smaller patch sizes lead to higher wave attenuation rates, lower run-up, and less inundation extents. For wave group cases, it is found that the attenuation rates are dependent not only on patch sizes but also wave frequencies; while the higher frequency wave components are attenuated at similar magnitudes by different patch sizes, the infragravity waves are much less attenuated by larger patch sizes due to the penetrating effects through the major gaps between patches. Both cases revealed that under the same vegetation coverage, smaller patch sizes outperform their larger counterparts in terms of wave attenuation capacity. Out study highlights the importance of vegetation spatial heterogeneity, which are seldom considered in evaluating the wetlands' capacity of attenuating extreme waves.

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.