Vegetation Types Research Articles

Analysis of Surface Temperature Modified by Atypical Mobility in Mexican Coastal Cities with Warm Climates

This study takes a unique approach, using satellite remote sensing to analyze the land surface temperature (LST) in seven coastal cities located at latitudes between 18° and 22° and longitudes between 106° and 91°. The methodology consists of obtaining the LST on two selected days, one in July 2019 and one in July 2020, to determine how the temperature was modified by the atypical mobility caused by Coronavirus Disease 2019 (COVID-19) and social distancing. Using these data, we determine the representative surface temperature that tended to rise (RHST) and the representative surface temperature that tended to decrease (RLST), depending on the affected area. This approach allows us to comprehensively compare how mobility modified the four variables studied: territorial extension, population, types of soil (paving), and vegetation. We concluded that, among the factors analyzed, the types of paving and vegetation were those that generated a more significant decrease in temperature; the variables of territorial extension and the number of inhabitants had a smaller impact. This study paves the way for a discussion of the significant influence of mobility on the behavior of the LST.

Long-term variations in ecosystem water use efficiency in the Tibetan Plateau: Vegetation types, attribution methods and main drivers

Ecosystem water use efficiency (WUE) is a key indicator for understanding the response of carbon–water processes to environmental changes. However, as affected by uncertainty of WUE estimation and attribution, the individual contributions of environmental drivers to WUE changes and the underlying mechanisms remain unclear on the Tibetan Plateau (TP). Here, a theory-based analytical WUE model was modified by introducing an optimal stomatal behavior model, and used to estimate monthly WUE and their drivers during 1982–2018 for main vegetation types on the Tibetan Plateau (TP). Rationality of three mainstream analytical attribution methods—analytical-based partial derivative method (APDM), regression-based partial derivative method (NPDM), and climate elasticity method (CEM)—was examined. Furtherly, the contributions of various environmental drivers to WUE variation trend were estimated for various vegetation types. Results indicate that: (1) The modified model performed well in estimating WUE against with observed WUE at five eddy-covariance (EC) flux stations, which decreased the uncertainty of WUE estimation. WUE increased significantly in the TP with a slope of 0.49 × 10-2 g C kg−1 H2O yr−1 during 1982–2018 with forests increased most rapidly; (2) Only APDM captured both the variation and magnitude of WUE trends, and both NPDM and CEM failed to reproduce the magnitude of WUE trends, though CEM was better than NPDM benefited from its nonlinear structure. Through comparing these methods, we found that the nonlinear direct effects of these forcing variables on WUE change could be higher than their indirect effects caused by their interactions. (3) There are some differences in the main drivers of WUE trends for various vegetation types. Increase in WUE was mainly driven by the increase in leaf area index (LAI) for both steppes and meadows, while decreased air water vapor pressure deficit (VPD) and elevated CO2 showed higher positive effects for broad-leaf forests. The distinct response and extremely scarce EC observations of forests suggest that more attention should be paid to forests in the TP. This study could help in selection of attribution methods and understanding the mechanisms of carbon–water coupling of alpine ecosystems under a changing climate.

Do greenspaces really reduce heat health impacts? Evidence for different vegetation types and distance-based greenspace exposure

BackgroundWhile vegetation type, population density and proximity to greenspaces have been linked to human health, what type and location of greenspace matter most have remained unclear. In this context, there are question marks over investment-style metrics. ObjectivesThis paper aims at establishing what vegetation type may matter most in modifying heat-mortality associations, and what the optimal buffer distances of total and specific types of greenspace exposure associated with reduced heat-related mortality risks are. MethodsWe conducted small-area analyses using daily mortality data for 286 Territory Planning Units (TPUs) across Hong Kong and 1 × 1 km gridded air temperature data for the summer months (2005–2018). Using a case time series design, we examined effect modifications of total and specific types of greenspaces, as well as population-weighted exposure at varying buffer distances (200–4000 m). We tested the significance of effect modifications by comparing relative risks (RRs) between the lowest and highest quartiles of each greenspace exposure metric; and explored the strength of effect modifications by calculating the ratio of RRs. ResultsForests, unlike grasslands, showed significant effect modifications on heat-mortality associations, with RRs rising from 0.98 (95 %CI: 0.92,1.05) to 1.06 (1.03, 1.10) for the highest to lowest quartiles (p-value = 0.037) The optimal distances associated with the most apparent effects were around 1 km for population-weighted exposure, with the ratio of RRs being 1.424 (1.038,1.954) for NDVI, 1.191 (1.004,1.413) for total greenspace, and 1.227 (1.024,1.470) for forests. A marked difference was observed in terms of the paired area-level and optimal distance-based exposure to total greenspace and forests under extreme heat (p-values < 0.05). DiscussionOur findings suggest that greenspace, particularly nearby forests, may significantly mitigate heat-related mortality risks.

Effects of Organic Soil Amendments on Antimicrobial-Resistant Bacteria in Urban Agriculture Environments

Biological soil amendments of animal origin (BSAAOs) are widely used in urban agriculture to improve soil quality. Although BSAAO use is regulated due to risks for introducing foodborne pathogens, effects on antimicrobial-resistant (AMR) bacteria are not well established. Here, we aimed to explore the impacts of BSAAOs on levels of resident AMR bacteria in leafy vegetable production environments (i.e., kale, lettuce, chard, cabbage) across urban farms and community gardens in the greater Washington D.C. area (n = 7 sites). Leaf tissue (LT), root zone soil (RZS; amended soil in crop beds), and bulk soil (BS; site perimeter) were collected and analyzed for concentrations of total heterotrophic bacteria (THB), ampicillin (Amp) or tetracycline (Tet) resistant THB, and coliforms. As expected, amended plots harbored significantly higher concentrations of THB than bulk soil (P < 0.001). The increases in total bacteria associated with reduced fractions of Tet-resistant bacteria (P = 0.008), as well as case-specific trends for reduced fractions of Amp-resistant bacteria and coliforms. Site-to-site variation in concentrations of AMR bacteria in soil and vegetable samples reflected differences in land history and crop management, while within-site variation was associated with specific amendment sources, as well as vegetable type and cultivar. Representative isolates of the AMR bacteria and coliforms were further screened for multidrug resistance (MDR) phenotypes, and a high frequency was observed for the former. In amended soils, as the soil pH (range 6.56–7.80) positively correlated with the fraction of Tet-resistant bacteria (rho = 0.529; P < 0.001), crop management strategies targeting pH may have applications to control related risks. Overall, our findings demonstrate that soil amendments promote soil bacteria concentrations and have important implications for limiting the spread of AMR bacteria, at least in the urban landscape.

Divergent rainfall infiltration patterns on the Chinese Loess Plateau between growing and non-growing seasons after long-term revegetation

Rainfall infiltration into hillslopes on the Chinese Loess Plateau (CLP) undergoes noticeable changes during the growing season following long-term revegetation. However, infiltration patterns during the non-growing season have been understudied. In this study, we monitored soil moisture over three years (May 2016-December 2018) at 10-minute intervals, spanning five soil depths (10, 20, 40, 60, and 100 cm). Monitoring was conducted in both a forestland catchment and an adjacent grassland catchment on the CLP, with two monitoring sites per catchment: one downhill in the gully, and the other uphill on the slope. Our findings reveal pronounced seasonal variations in meteorological conditions and vegetation index. We observed divergent rainfall infiltration patterns and their governing factors between the growing (May to October) and non-growing seasons (November to April). During the growing season, rainfall facilitated a quicker response with shorter lag time (23.34 h vs. 56.84 h in the non-growing season) and noticeable soil moisture increments (up to 35.7 % cm3/cm3 vs. only up to 6.4 % cm3/cm3). The wetting depth often extended to 40–100 cm (compared to 10–40 cm in the non-growing season), with a higher wetting front velocity (17.44 mm/h vs. 6.24 mm/h) and an increased occurrence frequency of preferential flow (19.77 % vs. 9.72 %). The impact of rainfall amount and peak intensity on infiltration was more pronounced during the non-growing season, whereas, in the growing season, infiltration was additionally influenced by antecedent moisture conditions and vegetation type. Notably, in the growing season, the enhanced soil moisture recharge due to rainfall infiltration exceeded the rise in evapotranspiration, resulting in higher soil water storage. The findings of this study shed new light on seasonal infiltration dynamics on the greening CLP, contributing valuable insights into hillslope hydropedological processes post-revegetation.

Antibiotic-resistant bacteria contaminating leafy vegetables in Saudi Arabia’s eastern region

BackgroundFood-associated antibiotic-resistant bacteria can cause infections that may critically impact human health. The objectives of this study were to determine the microbial contamination level of green leafy vegetables and their antibiotic resistance pattern.MethodsSixty-three samples of leafy vegetables were collected from Dammam Central Fruit and Vegetables Market from January to June 2023. The vegetables included lettuce (Lactuca sativa), parsley (Petroselinum crispum), and watercress (Nasturtium officinale). Samples were tested by standard microbiological techniques for identification and antibiotic susceptibility testing.ResultEight types of bacteria belonging to six different genera were detected. Enterobacteriaceae family was represented by four genera: Klebsiella, Proteus, Morganella, and Enterobacter. The other two genera were Pseudomonas and Aeromonas. Enterobacter cloacae was the most abundant organism, followed by Pseudomonas putida and Aeromonas sobria. On the other hand, Morganella morganii, Aeromonas hydrophila, and Proteus mirabilis were the least abundant. The three vegetable types had different levels of bacterial contamination. All isolated organisms were sensitive to penicillin, cephalosporin, aminoglycoside, and fluoroquinolone. However, Klebsiella oxytoca, M. morganii, and K. pneumonia showed resistance to ampicillin. A. hydrophila, Morganella morganii, and E. cloacae showed resistance to amoxicillin. M. morganii and E. cloacae were found to be resistant to cefalotin. Moreover, A. hydrophila, M. morganii, and E. cloacae were resistant to cefoxitin. Again, A. hydrophila was found to be resistant to imipenem. Only M. morganii was resistant to Ciprofloxacin. Two isolates, P. mirabilis and M. morganii were resistant to tigecycline. Another two, M. morganii and P. mirabilis were resistant to Nitrofurantoin. Only M. morganii was found to be resistant to trimethoprim.ConclusionThis study aligns with the broad consensus in the literature about the significance of bacterial contamination in vegetables and the public health implications. The unique focus on antibiotic resistance patterns adds an essential dimension to the existing body of knowledge.

Soil Quality Assessment and Influencing Factors of Different Land Use Types in Red Bed Desertification Regions: A Case Study of Nanxiong, China

Soil environmental issues in the red bed region are increasingly conspicuous, underscoring the critical importance of assessing soil quality for the region’s sustainable development and ecosystem security. This study examines six distinct land use types of soils—agricultural land (AL), woodland (WL), shrubland (SL), grassland (GL), bare rock land (BRL), and red bed erosion land (REL)—in the Nanxiong Basin of northern Guangdong Province. This area typifies red bed desertification in South China. Principal component analysis (PCA) was employed to establish a minimum data set (MDS) for calculating the soil quality index (SQI), evaluating soil quality, analyzing influencing factors, and providing suggestions for ecological restoration in desertification areas. The study findings indicate that a minimal data set comprising soil organic matter (SOM), pH, available phosphorus (AP), exchangeable calcium (Ca2+), and available copper (A-Cu) is most suitable for evaluating soil quality in the red bed desertification areas of the humid region in South China. Additionally, we emphasize that exchangeable salt ions and available trace elements should be pivotal considerations in assessing soil quality within desertification areas. Regarding comprehensive soil quality indicators across various land use types, the red bed erosion soils exhibited the lowest quality, followed by those in bare rock areas and forest land. Within the minimal data set, Ca2+ and pH contributed the most to overall soil quality, underscoring the significance of parent rock mineral composition in the red bed desertification areas. Moreover, the combined effects of SOM, A-Cu, and AP on soil quality indicate that anthropogenic land management and use, including fertilization methods and vegetation types, are crucial factors influencing soil quality. Our research holds significant implications for the scientific assessment, application, and enhancement of soil quality in desertification areas.

Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model’s higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.

What Are the Variation Patterns of Vegetation and Its Influencing Factors in China from 2000–2020 from the Partition Perspective?

China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use.

Divergent response of energy exchange to heatwaves from flux tower observations among various vegetation types

Abstract The increasing frequency of European heatwaves and the associated impacts on ecosystems have raised widespread concern during the last two decades. The partitioning of surface energy between latent and sensible heat fluxes plays a pivotal role in regulating heat and water exchange between the land surface and the atmosphere. However, the responses of surface energy partitioning during heatwave events and the contributions of changes in energy partitioning to heatwave development have been underexplored. Here, we investigated the responses of surface energy exchange to temperature extremes during four devastating European heatwaves (2003, 2010, 2018, and 2022) based on long‒term observations from 31 flux towers. Our results demonstrated that the divergent responses of surface energy exchange to heatwaves were modulated by vegetation type and background climate in Europe. Forests maintained similar latent heat fluxes as the climatological mean but largely increased sensible heat under heat‒stressed conditions. While grasslands and croplands tended to increase sensible heat by suppressing latent heat during heatwaves, especially under water‒stressed conditions. Furthermore, the changes in surface energy partitioning strengthened positive land‒atmosphere feedbacks during the heatwave period, leading to unprecedented temperature extremes. This study highlights the importance of surface energy partitioning in land‒atmosphere interactions and heatwave developments.

Evaluating pasture cover density mapping: a comparative analysis of Sentinel-2 and Spot-5 multispectral sensor images

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

The importance of spatial scale and vegetation complexity in woody species diversity and its relationship with remotely sensed variables

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases.

The effects of various degrees of meteorological drought on forest vegetation in ecologically Fragile Karst Areas of Guangxi, China

ABSTRACT Drought presents a major challenge to the management of rocky desertification and ecological restoration in the delicate karst ecosystems of Guangxi. In this study, the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC) and net primary productivity (NPP) were selected as vegetation remote sensing parameters, and the spatial response characteristics of different types of vegetation in karst areas of Guangxi Province to light, moderate, severe and extreme drought were analyzed to provide scientific basis for the evaluation of the impact of drought on vegetation in karst areas. The results are as follows: (1) NDVI, FVC and NPP showed a fluctuating increasing trend from 2000 to 2022, and the increasing rates were 0.058, 6.90%, and 43.3gC.m-2 per decade respectively. During this period, the number of light, moderate and severe drought days showed a decreasing trend, but the number of extreme drought days tended to increase. (2) The negative correlation of NDVI, FVC and NPP and drought increased from moderate to extreme drought, and from light to extreme drought, the negative correlation between NDVI and FVC and drought decreased, while that of NPP increased. (3) Light and moderate droughts had obvious negative impact on Chinese fir and broad-leaved forest, whereas severe and extreme droughts had obvious negative effect on eucalyptus and bamboo forest.

Stability Detection of Canopy RGB Images for Different Underlying Surfaces Based on SVM

This study aims to investigate the impact of different environmental conditions on the stability of RGB images in ecological sites and the anti-interference properties of vegetation images on different underlying surfaces. Three vegetation types including sugarcane, forest, and karst (mainly shrub and grass) are used to segment green vegetation using machine learning, and the RGB vegetation indices are calculated using color channel data. Then, The effect of weather, season, and time period on different types of vegetation indices are studied, which provide technical references for quantitative application of RGB image data. The results indicate the following: ① For the vegetation with high canopy density, the SVM machine learning segmentation algorithm used in this study is more applicable, as the RGB image segmentation accuracy of sugarcane and forest is significantly higher than karst. For different weather conditions, the segmentation accuracy of sugarcane and forest RGB images on sunny or cloudy days is higher than that on rainy or foggy days, but the effect on sparse vegetation in karst is not obvious. Additionally, the segmentation accuracy of different vegetation types has a small increase with NLM filter processing. ② Change in weather, season, and time can affect the stability of the image index. For different weather conditions, the vegetation indices of sugarcane and forest images on sunny or cloudy days are the more stable (ExGR, outlier proportion between 3.25% and 5.63%), while on rainy and foggy days they are less stable (ExR, outlier proportion between 17.60% and 21.59%). For different seasons, the stability of the sugarcane and karst image index obtained in spring is better (ExG, outlier proportion between 3.32% and 6.88%), while the stability of the forest image index obtained in summer is better (ExR, outlier proportion = 10.32%). For different times within a day, the sugarcane image index obtained in the morning is more stable (ExGR, outlier proportion = 4.62%), while the stability of the forest image index obtained in the afternoon is better (ExGR, outlier proportion = 9.24%). ③ The stability of the sugarcane image index is more affected by weather and season. For forest, the influence of the weather is more obvious than the season. But, for karst, the effect of season on the vegetation index is greater than that of weather.

Intraspecific trait variation of carrion beetle species and communities across elevations

Abstract Filtering processes across environmental gradients can structure patterns of trait variation within communities. The community‐weighted mean (CWM) is a metric that is commonly used to indicate the directionality of such filtering processes and the optimal adaptive strategy of taxa within community. Proximity to the CWM indicates higher fitness, and deviations from this optimal value result in changes in the relative abundances of coexisting species. We investigated patterns of intraspecific trait variation in four coexisting carrion beetle species (Coleoptera: Silphidae) across elevational gradients. The study was conducted in temperate forest ecosystems with distinct natural vegetation zones ranging from 950 m to 1700 m above sea level. Of the 12 traits measured, we found that intraspecific variation ranged from 34% (body length) to 100% (ratio of elytra length, head length and head width to body length) and accounted for a larger proportion of variation than interspecific variation in 7 traits. For most traits, trait range, which indicates the niche breadth of species at a given elevation, was positively correlated with relative abundance. The CWMs of traits associated with long‐distance dispersal decreased with elevation, whereas those associated with microhabitat use showed the opposite trend. Soil temperature influenced tibia length after controlling for the effects of species identity, but soil water content had no effect on trait variation. Patterns of variation in body width and thorax width of two beetle species supported the CWM‐optimality hypothesis; however, patterns of trait variation in body width and thorax length of another species showed an opposite trend. Our study identifies several traits that can be highly variable within carrion beetle species. Such high levels of intraspecific trait variation may enable populations to adapt across a wide range of elevations and vegetation types.

Constructing a Bayesian belief network to provide insights into the dynamic drivers of ecosystem service relationships

Vegetation restoration has induced complex spatiotemporal changes in ecosystem service (ES) relationships. However, dynamic analysis on the driving mechanisms underlying ES relationships remains insufficient, constraining the comprehension on sustainable vegetation restoration. In this study, a comprehensive index (ESI) representing the coordination among four ESs (carbon sequestration, water yield, baseflow regulation, and soil conservation) was quantified in Yan’an City, a typical vegetation restoration area. Using the Bayesian belief network, a social-ecological framework was constructed to explore changes in driving factors of ESI during 1990 to 2020. The results revealed that: (1) the coordination of ES relationships showed a significant enhancement over the past 30 years, which primarily attributed to areas with the highest values of water yield. Areas with low values of baseflow regulation posed a risk to the ESI. (2) The combined contribution of FVC and precipitation was consistently close to 70 %, emerging as the dominant driving factors for the ESI. But in the last decade, contributions from human activities have increased significantly. (3) Scenario reasoning highlighted that expanding vegetation cover alone would exhibit threshold effects on the ESI improvement. Additionally, the results revealed a promising prospect of enhancing baseflow and maintaining the ESI through human actions. This study provides an example of using Bayesian belief network to analyze social-ecological interactions, enhancing the understanding of changes in ES relationships under vegetation restoration.

The response of terrestrial vegetation carbon use efficiency to global environmental changes

Terrestrial vegetation carbon use efficiency (CUE) is a key measure for assessing carbon transfer from the atmosphere to terrestrial biomass, crucial for understanding carbon cycling and allocation in ecosystems. CUE provides valuable insights into how terrestrial ecosystems respond to environmental changes. In this study, we utilized satellite datasets (MODIS and GLASS), MsTMIP models, and TRENDY models to analyze the spatiotemporal distribution characteristics of global vegetation CUE. We found that the average CUE for global land vegetation is 0.44 ± 0.06, with a slight annual increase and significant spatial heterogeneity, characterized by latitude gradients and vegetation types. High-latitude regions demonstrated higher CUE values compared to low-latitude regions. Further employing an integrated attribution approach, we identified the response mechanisms of vegetation CUE to global changes. The comprehensive response of vegetation CUE to climate change, land use change, atmospheric CO2, and nitrogen deposition was found to fluctuate and increase, with a model-averaged CUE increase of approximately 0.01. Land use change was identified as the largest contributor to the annual trend of overall global CUE (48.8%), while climate change was the main factor influencing the interannual variability (IAV) of global CUE (91.7%). Regarding global distribution, the IAV of vegetation CUE is mainly influenced by climate change. CUE annual trends in more regions were influenced by climate change, with 65% and 73% of the ensemble mean of the MsTMIP and TRENDY models, respectively. The results of the MsTMIP and TRENDY models consistently show that, globally, land use change affects about a quarter of the total annual trend of CUE. Land use change affected CUE annual trends to a greater extent than climate change. In addition, the vegetation type most affected by climate change is the deciduous needleleaf forests, and the CUE annual trend of cropland is most affected by land use change. Our findings reveal global patterns and drivers of CUE variability, highlighting the significant impact of climate change and land use change.

Measurement of Centroid Distance in Determining Stunting Clusters

This study aims to develop a vegetable freshness classification model using Convolutional Neural Networks (CNN). The methodology used includes collecting vegetable image datasets, image preprocessing, training CNN models, and evaluating classification accuracy. The dataset used consists of various types of vegetables with varying levels of freshness. The results of the study show that the developed CNN model is able to classify vegetable freshness with high accuracy, so it can assist in the automation process of determining vegetable freshness in the food industry. Model evaluation is carried out using accuracy metrics and mean squared error (MSE).

Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

ABSTRACT Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Scale of effect of landscape patterns on resource selection by bobcats (Lynx rufus) in a multi-use rangeland system

ContextThere is a growing appreciation that wildlife behavioral responses to environmental conditions are scale-dependent and that identifying the scale where the effect of an environmental variable on a behavior is the strongest (i.e., scale of effect) can reveal how animals perceive and respond to their environment. In South Texas, brush management often optimizes agricultural and wildlife management objectives through the precise interspersion of vegetation types creating novel environments which likely affect animal behavior at multiple scales. There is a lack of understanding of how and at what scales this management regime and associated landscape patterns influence wildlife.ObjectivesOur objective was to examine the scale at which landscape patterns had the strongest effect on wildlife behavior. Bobcats (Lynx rufus) our model species, are one of the largest obligated carnivores in the system, and have strong associations with vegetation structure and prey density, two aspects likely to influenced by landscape patterns. We conducted a multiscale resource selection analysis to identify the characteristic scale where landscape patterns had the strongest effect on resource selection.MethodsWe examined resource selection within the home range for 9 bobcats monitored from 2021 to 2022 by fitting resource selection functions which included variables representing landcover, water, energy infrastructure, and landscape metrics (edge density, patch density, and contagion). We fit models using landscape metrics calculated at 10 different scales and compared model performance to identify the scale of effect of landscape metrics on resource selection.ResultsThe scale of effect of landscape metrics occurred at finer scales. The characteristic scale for edge density and patch density was 30 m (the finest scale examined), and the characteristic scale for contagion occurred at 100 m. Bobcats avoided locations with high woody patch density and selected for greater woody edge density and contagion. Bobcats selected areas closer to woody vegetation and water bodies while avoiding herbaceous cover and energy development infrastructure.ConclusionsA key step in understanding the effect of human development and associated landscape patterns on animal behavior is the identifying the scale of effect. We found support for our hypothesis that resource selection would be most strongly affected by landscape configuration at finer scales. Our study demonstrates the importance of cross-scale comparisons when examining the effects of landscape attributes on animal behavior.