A satellite-derived dataset on vegetation phenology across Central Asia from 2001 to 2023.

Satellite-observed land surface phenology (LSP) data have helped us better understand terrestrial ecosystem dynamics at large scales. However, uncertainties remain in comprehending LSP variations in Central Asian drylands. In this article, an LSP dataset covering Central Asia (45-100°E, 33-57°N) is introduced. This LSP dataset was produced based on Moderate Resolution Imaging Spectroradiometer (MODIS) 0.05-degree daily reflectance and land cover data. The phenological dynamics of drylands were tracked using the seasonal profiles of near-infrared reflectance of vegetation (NIRv). NIRv time series processing involved the following steps: identifying low-quality observations, smoothing the NIRv time series, and retrieving LSP metrics. In the smoothing step, a median filter was first applied to reduce spikes, after which the stationary wavelet transform (SWT) was used to smooth the NIRv time series. The SWT was performed using the Biorthogonal 1.1 wavelet at a decomposition level of 5. Seven LSP metrics were provided in this dataset, and they were categorized into the following three groups: (1) timing of key phenological events, (2) NIRv values essential for the detection of the phenological events throughout the growing season, and (3) NIRv value linked to vegetation growth state during the growing season. This LSP dataset is useful for investigating dryland ecosystem dynamics in response to climate variations and human activities across Central Asia.

After effects of historical grassland on soil organic carbon content and plant growth in croplands in southern Germany determined using satellite data.

Numerous studies have reported that grasslands harbor higher soil organic carbon (SOC) stocks compared with arable land; however, the relevant carbon dynamics and sink persistence remain unclear. Herein, arable fields characterized by historical grassland zones (h_GL; grassland use decades ago) and permanent arable land zones (h_CL) were examined. The h_GL zones were determined using historical maps. The change in land use from grassland to cropland occurred 30-50 years ago. In eight arable fields, SOC and total nitrogen (TN) stocks in the topsoil were analyzed at a high spatial resolution. Additionally, remote sensing via satellites was employed to determine the biomass yield at a high spatial resolution using the normalized difference vegetation index (NDVI). In all the fields, the mean SOC content of the h_GL zones (1.81 %, n = 97 measuring points) was higher than the mean SOC content of the h_CL zones (1.52 %, n = 220). Furthermore, the mean relative NDVI was higher in the h_GL zones than in the h_CL zones. SOC and NDVI were positively correlated (up to r = 0.79), as well as TN and NDVI (up to r = 0.72). To evaluate the first dataset, zonal soil samples were collected from the h_GL and h_CL zones from 14 arable fields to determine the SOC and TN content. The mean SOC content of the h_GL zones was 1.92 % and that of the h_CL zones was 1.39 %-a difference of absolute SOC stocks in the topsoil of 23.8 t ha-1 (bulk density: 1.5 g cm-3). The work combines the knowledge of historical soil maps, remote sensing applications and georeferenced soil sampling and shows that SOC stocks in grassland have a high persistence and can have positive impact on yields even decades after a land use change. Historical land use proved to be a major factor for spatial SOC variability at the study site.

Multivariate analysis of the summer herbaceous vegetation and environmental factors of the sub-tropical region.

Understanding the distribution of the plant species of an unexplored area is the utmost need of the present-day. In order to collect vegetation data, Quadrat method was used having size of 1 m2. The composite soil samples from each site were tested for various edaphic properties. PC-ORD v.5 was used for the classification of the vegetation while CANOCO v.5.1 was used for ordination of the data and to find out the complex relationship between plants and environment. Survey was conducted during summer season and a total of 216 herbaceous species were recorded from forty different sites of District Kohat, Pakistan. Cluster Analysis (CA) and Two-Way Cluster Analysis (TWCA) classified the vegetation of forty sites into six major plant groups i.e., 1. Paspalum paspalodes, Alternanthera sessilis, Typha domingensis, 2. Cynodon dactylon, Parthenium hysterophorus, Brachiaria ramosa, 3. Cynodon dactylon, Eragrostis minor, Cymbopogon jwarancusa, 4. Cymbopogon jwarancusa, Aristida adscensionis, Boerhavia procumbens, 5. Cymbopogon jwarancusa, Aristida adscensionis, Pennisetum orientale and 6. Heteropogon contortus, Bothriochloa ischaemum, Chrysopogon serrulatus. They were named after the dominant species based on their Importance Value (IV). The detrended correspondence analysis (DCA) analysis further confirmed the vegetation classification. Canonical correspondence analysis (CCA) indicated that the species distribution in the area was strongly affected by various environmental factors including status, soil characteristics, topography and altitude.

Characterisation of urban aerosol size distribution by radiocarbon and PIXE analyses in a middle-European urban environment for source identification: a pilot study.

This study, conducted in Debrecen, Hungary, aimed to analyse atmospheric particulate matter (APM or PM) through radiocarbon and PIXE analyses during the winter smog (23-25 January) and spring (15-18 May) seasons. The information presented in this pilot study aims to provide insight into the importance of utilising detailed characteristics of the mass size distributions of fossil carbon (ff) and contemporary carbon (fC) content. Additionally, it seeks to compare these characteristics with the size distributions of various elements to enable even more accurate PM source identification. In winter, APM concentrations were 86.27 µg/m3 (total), 17.07 µg/m3 (fC) and 10.4 µg/m3 (ff). In spring, these values changed to 29.5 µg/m3, 2.64 µg/m3 and 7.01 µg/m3, respectively. Notably, differences in mass size distribution patterns were observed between the two seasons, suggesting varied sources for contemporary carbon. Biomass burning emerged as a crucial source during the smog period, supported by similar MMAD (Mass Median Aerodynamic Diameter) values and a strong correlation (r = 0.95, p < 0.01) between potassium and fC. In spring, a significant change in the concentration and distribution of fC occurred, with a broad, coarse mode and a less prominent accumulation mode. Ff was found to have similar distributions as PM, with nearly the same MMADs, during both periods. Finally, a comprehensive comparison of modal characteristics identified specific sources for the various components, including biomass burning, vehicle exhaust, coal and oil combustion, vehicle non-exhaust, road dust, tyre abrasion, mineral dust and biogenic emission. This study showcases how using radiocarbon and PIXE analysis in size distribution data can enhance our understanding of the sources of PM and their effects on different size fractions of PM.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16 days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

Exploring main soil drivers of vegetation succession in abandoned croplands of Minqin Oasis, China.

Background: The Minqin Oasis, which is located in Wuwei City, Gansu Province, China, faces a very serious land desertification problem, with about 94.5% of its total area desertified. Accordingly, it is crucial to implement ecological restoration policies such as cropland abandonment in this region. In abandoned croplands, abiotic factors such as soil properties may become more important than biotic factors in driving vegetation succession. However, the connections between soil properties and vegetation succession remain unclear. To fill this knowledge gap, this study investigated these connections to explore major factors that affected vegetation succession, which is meaningful to designing management measures to restore these degraded ecosystems. Methods: This study investigated seven 1-29-year-old abandoned croplands using the "space for time" method in Minqin Oasis. Vegetation succession was classified into different stages using a canonical correlation analysis (CCA) and two-way indicator species analysis (Twinspan). The link between soil properties and vegetation succession was analyzed using CCA. The primary factors shaping community patterns of vegetation succession were chosen by the "Forward selection" in CCA. The responses of dominant species to soil properties were analyzed using generalized additive models (GAMs). Results: Dominant species turnover occurred obviously after cropland abandonment. Vegetation succession can be classified into three stages (i.e., early, intermediate, and late successional stages) with markedly different community composition and diversity. The main drivers of vegetation succession among soil properties were soil salinity and saturated soil water content and they had led to different responses of the dominant species in early and late successional stages. During the development of vegetation succession, community composition became simpler, and species diversity decreased significantly, which was a type of regressive succession. Therefore, measures should be adopted to manage these degraded, abandoned croplands.

Pollinator activity and flowering in agricultural weeds in Sweden.

The extent to which weeds in arable land are useful to pollinators depends in part on the temporal pattern of flowering and insect flight activity. We compiled citizen science data on 54 bees and hoverflies typical of agricultural areas in southern Sweden, as well as 24 flowering weed species classified as pollinator-friendly in the sense that they provide nectar and/or pollen to pollinators. The flight periods of the bees and hoverflies varied greatly, but there were also some consistent differences between the four groups studied. The first group to fly were the early flying solitary bees (7 species), followed by the social bees (18 species). In contrast, other solitary bees (11 species) and hoverflies (22 species) flew later in the summer. Solitary bees had the shortest flight periods, while social bees and hoverflies had longer flight periods. Flowering of weed species also varied greatly between species, with weeds classified as winter annuals (e.g., germinating in autumn) starting early together with germination generalists (species that can germinate in both autumn and spring). Summer annuals (spring germinators) and perennials started flowering about a month later. Germination generalists had a much longer flowering period than the others. Weekly pollinator records were in most cases significantly explained by weed records. Apart from early flying solitary bees, all models showed strong positive relationships. The overall best explanatory variable was the total number of weeds, with a weight assigned to each species based on its potential as a nectar/pollen source. This suggests that agricultural weeds in Sweden provide a continuous potential supply of nectar and pollen throughout the flight season of most pollinators.

Increased crossing of thermal stress thresholds of vegetation under global warming.

Temperature extremes exert a significant influence on terrestrial ecosystems, but the precise levels at which these extremes trigger adverse shifts in vegetation productivity have remained elusive. In this study, we have derived two critical thresholds, using standard deviations (SDs) of growing-season temperature and satellite-based vegetation productivity as key indicators. Our findings reveal that, on average, vegetation productivity experiences rapid suppression when confronted with temperature anomalies exceeding 1.45 SD above the mean temperature during 2001-2018. Furthermore, at temperatures exceeding 2.98 SD above the mean, we observe the maximum level of suppression, particularly in response to the most extreme high-temperature events. When Earth System Models are driven by a future medium emission scenario, they project that mean temperatures will routinely surpass both of these critical thresholds by approximately the years 2050 and 2070, respectively. However, it is important to note that the timing of these threshold crossings exhibits spatial variation and will appear much earlier in tropical regions. Our finding highlights that restricting global warming to just 1.5°C can increase safe areas for vegetation growth by 13% compared to allowing warming to reach 2°C above preindustrial levels. This mitigation strategy helps avoid exposure to detrimental extreme temperatures that breach these thresholds. Our study underscores the pivotal role of climate mitigation policies in fostering the sustainable development of terrestrial ecosystems in a warming world.

Enhancing plant resilience: arbuscular mycorrhizal fungi's role in alleviating drought stress in vegetation concrete.

Introduction: Drought stress usually inhibits plant growth, which may increase the difficulty of greening slopes. Methods: In this study, we systematically investigated the effects of arbuscular mycorrhizal (AM) fungi on the growth and drought tolerance of two plant species, Festuca elata and Cassia glauca, in a vegetation concrete environment by exogenously inoculating AM fungi and setting three drought levels: well water, moderate drought and severe drought. The results showed that plant growth was significantly inhibited under drought stress; however, AM fungi inoculation significantly promoted plant height, root length, and above- and belowground biomass in these two plant species. Results: Compared with, those in the CK treatment, the greatest increases in the net photosynthesis rate, stomatal conductance and transpiration rate in the AM treatment group were 36.72%, 210.08%, and 66.41%, respectively. Moreover, inoculation with AM fungi increased plant superoxide dismutase and catalase activities by 4.70-150.73% and 9.10-95.70%, respectively, and reduced leaf malondialdehyde content by 2.79-55.01%, which alleviated the damage caused by oxidative stress. These effects alleviated the damage caused by oxidative stress and increased the content of soluble sugars and soluble proteins in plant leaves by 1.52-65.44% and 4.67-97.54%, respectively, which further increased the drought adaptability of plants. However, inoculation with AM fungi had different effects on different plants. Conclusion: In summary, this study demonstrated that the inoculation of AM fungi in vegetation concrete environments can significantly increase plant growth and drought tolerance. The plants that formed a symbiotic structure with AM fungi had a larger root uptake area, greater water uptake capacity, and greater photosynthesis and gas exchange efficiency. In addition, AM fungi inoculation further increased the drought adaptability of the plants by increasing their antioxidant enzyme activity and regulating their metabolite content. These findings are highly important for promoting plant growth and increasing drought tolerance under drought conditions, especially for potential practical applications in areas such as slope protection, and provide useful references for future ecological engineering and sustainable development.

Influence of climate variability and land cover dynamics on the spatio-temporal NDVI patterns in western hydrological regions of Bangladesh.

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area.

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Exploring Unusual Cardiac Complications: Chorda Tendinea Rupture and Pulmonary Valve Vegetation in Infective Endocarditis-A Comprehensive Review.

Infective endocarditis (IE) is a severe infection of the endocardium, frequently involving heart valves, and is associated with significant morbidity and mortality. At the same time, traditional complications of IE, such as valvular dysfunction and embolic events, are well-documented, and uncommon cardiac manifestations, such as chorda tendinea rupture and pulmonary valve vegetation, present unique diagnostic and management challenges. This comprehensive review explores the pathophysiology, clinical presentation, diagnostic strategies, and management approaches for IE's chorda tendinea rupture and pulmonary valve vegetation. Through a detailed examination of the literature and discussion of clinical scenarios, we highlight the importance of recognizing these rare complications and discuss the implications for clinical practice. Additionally, we identify knowledge gaps and propose areas for future research to enhance further our understanding and management of these unusual cardiac complications in IE. This review aims to provide clinicians with valuable insights to improve patient care and outcomes in the challenging setting of infective endocarditis.

Towards accurate monitoring of water content in woody tissue across tropical forests and other biomes.

Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content to assess drought stress and tree mortality risks. While Frequency Domain Reflectometry offers a viable method for monitoring stem water content by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked to wood properties and species variability, impeding its wider usage. We sampled tropical forest trees and palms in eastern Amazônia, to evaluate how sensor output differences are controlled by wood density, temperature and taxonomic identity. Three individuals per species were felled and cut into segments (total n = 262), within a diverse dataset comprising five dicotyledonous tree-and three monocotyledonous palm species on a wide range of wood densities. Water content was estimated gravimetrically for each segment using a temporally explicit wet-up/dry-down approach, and the relationship with the dielectric permittivity was examined. Woody tissue density had no significant impact on the calibration, but species identity and temperature significantly affected sensor readings. The temperature artefact was quantitatively important at large temperature differences which may have led to significant bias of daily and seasonal water content dynamics in previous studies. We established the first tropical tree and palm calibration equation that performed well for estimating water content. Notably, we demonstrated that the sensitivity remained consistent across species, enabling the creation of a simplified one-slope calibration for accurate, species-independent measurements of relative water content. Our one-slope calibration serves as a general, and species-independent standard calibration for assessing relative water content in woody tissue, offering a valuable tool for quantifying drought responses and stress in trees and forest ecosystems.

Plant fire-adaptive traits mediate long-term fire recurrence impact on the potential supply capacity of ecosystem services and their resilience.

Fire-induced changes in vegetation composition due to fire-regime intensification are leading to alterations in ecosystem services that might threaten their future sustainability. Fire recurrence, in particular, could be a key driver shaping ecosystem service resilience in fire-prone ecosystems. This study evaluates the impact of fire recurrence, over twenty-four years, on the potential supply capacity of ten regulating, provisioning, and cultural services selected as critical services by stakeholders and experts. We assessed fire effects in four fire-prone landscapes dominated by species with different functional-traits response to fire (i.e., obligate seeder vs resprouter species). Trends in the potential supply capacity linked to fire recurrence were estimated by applying a supervised classification of Land Use and Land Cover (LULC) classes performed using Landsat imagery, associated to an ecosystem service capacity matrix adapted to the local socio-ecological context. In landscapes dominated by seeders, fire recurrence broke off the potential supply capacity of services traditionally associated to mature forest cover (i.e., the predicted probability of a decrease in the potential supply capacity of climate regulation, timber, wood fuel, mushroom production, tourism, landscape aesthetic, and cultural heritage occurred with high fire recurrence). In landscapes dominated by resprouter species, the effect of fire recurrence was partially buffered in the short-term after fire and no substantial differences in trends of change were found (i.e., equal predicted probability in the potential supply capacity of ecosystem services regardless of fire recurrence). We detected two new opportunities for ecosystems service supply associated to fire recurrence: livestock and honey production, especially in sites dominated by seeders. These findings provide valuable information aiming at recovering post-fire ecosystem service potential supply to partially counterbalance the loss in the socio-ecological system. When the main post-fire restoration goal is preserving ecosystem service resilience in fire-prone ecosystems, establishing management strategies focused on promoting resprouter species could aid mitigating the fire-driven loss of their supply capacity.

The impact of compound drought and heatwave events from 1982 to 2022 on the phenology of Central Asian grasslands.

In the context of global warming, the occurrence and severity of extreme events like atmospheric drought (AD) and warm spell duration index (WSDI) have increased, causing significant impacts on terrestrial ecosystems in Central Asia's arid regions. Previous research has focused on single extreme events such as AD and WSDI, but the effect of compound hot and dry events (CHWE) on grassland phenology in the arid regions of Central Asia remains unclear. This study utilized structural equation modeling (SEM) and the Pettitt breakpoint test to quantify the direct and indirect responses of grassland phenology (start of season - SOS, length of season - LOS, and end of season - EOS) to AD, WSDI, and CHWE. Furthermore, this research investigated the threshold of grassland phenology response to compound hot and dry events. The research findings indicate a significant increasing trend in AD, WSDI, and CHWE in the arid regions of Central Asia from 1982 to 2022 (0.51 day/year, P < 0.01; 0.25 day/year, P < 0.01; 0.26 day/year, P < 0.01). SOS in the arid regions of Central Asia showed a significant advancement trend, while EOS exhibited a significant advance. LOS demonstrated an increasing trend (-0.23 day/year, P < 0.01; -0.12 day/year, P < 0.01; 0.56 day/year). The temperature primarily governs the variation in SOS. While higher temperatures promote an earlier SOS, they also offset the delaying effect of CHWE on SOS. AD, temperature, and CHWE have negative impacts on EOS, whereas WSDI has a positive effect on EOS. AD exhibits the strongest negative effect on EOS, with an increase in AD leading to an earlier EOS. Temperature and WSDI are positively correlated with LOS, indicating that higher temperatures and increased WSDI contribute to a longer LOS. The threshold values for the response of SOS, EOS, and LOS to CHWE are 16.14, 18.49, and 16.61 days, respectively. When CHWE exceeds these critical thresholds, there are significant changes in the response of SOS, EOS, and LOS to CHWE. These findings deepen our understanding of the mechanisms by which extreme climate events influence grassland phenology dynamics in Central Asia. They can contribute to better protection and management of grassland ecosystems and help in addressing the impacts of global warming and climate change in practice.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations.

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

[Characteristics and drivers of vegetation temporal dynamics in Hunan Province of China during 2002-2020]. / 湖南省2002­2020å¹´æ¤è¢«åŠ¨æ€æ¼”å˜ç‰¹å¾åŠå½±å“å› å­.

Understanding the influences of climate change and human activities on vegetation change is the foundation for effective ecosystem management. Based on the 250 m MODIS-NDVI data from 2002 to 2020, we employed Theil-Sen Median trend analysis and the Mann-Kendall test to quantify vegetation change in Hunan Province. By combining with meteorological, nighttime light index, land cover and other data, residual analysis and correlation analysis, we examined the impacts of human activities and climate change on vegetation dynamics at both the pixel level and the county level. The results showed that the normalized difference vegetation index (NDVI) in Hunan Province exhibited a spatial pattern of "overall improvement with localized degradation" during 2002-2020. Approximately 64.9% of the study area experienced significant vegetation improvement, mainly occurring in the western and central-southern parts of Hunan Province. 1.4% of the study area experienced significant vegetation degradation, mostly in the newly developed urban areas and the farmland in the Dongting Lake Plain. Human activities and climate change jointly promoted vegetation improvement in 67.9% of the study area. Human activities and climate contributed to 96% and 4% of the NDVI change, respectively. At the county level, human activities contributed to over 80% of the NDVI change in each district or county. The impacts of human activities on vegetation change exhibited significant spatial heterogeneity. Urban expansion led to vegetation degradation in the newly developed areas, while vegetation growth appeared in the old developed urban areas. The ecological restoration projects promoted vegetation restoration in the western part of Hunan Province. This study could help us better understand the spatiotemporal variations of vegetation and their responses to climate change and human activities, which would offer scientific basis for effective ecological restoration policy.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.