Estimation of mediators in the associations between campus green spaces and students' anxiety: a case study in Nanjing.

Introduction: Although a number of scholars have examined the theoretical pathways between green space (GS) and mental health, few have focused on how campus greenness affects the mental health of Chinese youth. Methods: Herein, two objective indicators, campus and individual 300-m normalized vegetation index (NDVI) data, were used as independent variables. A questionnaire was used to collect the self-rated anxiety level of students on campuses in Nanjing. Then, we chose "subjective perception of campus GS", "physical activity", "social cohesion", "nature relatedness" and "usage pattern" as mediating variables to explore the pathways between the campus greenery and college student' anxiety level through correlation analysis, linear regression, and mediation effect test. Results: Results showed the campus-wide NDVI and individual students' 300-m range NDVI had significant negative correlations with anxiety (p = 0.045, p = 0.023). Campus perception, nature relatedness and the frequency of using GS are the pathways through which campus GSs influence student anxiety. Discussion: Our findings emphasised the importance of subjective perceptions of greenspaces, which provided a direction that can be deepened in future research.

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

The protective role of green spaces in mitigating myopia prevalence.

Near-sightedness, or myopia, is becoming more prevalent worldwide, but its etiopathogenesis is not fully understood. This review examines the potential protective role of green spaces in reducing myopia prevalence among children and adolescents, based on recent epidemiological studies from various countries. The studies consistently used the Normalized Difference Vegetation Index (NDVI) to quantify green space exposure. The analysis reveals a significant inverse relationship between exposure to green space and the risk of developing myopia, across multiple studies. For example, a 0.1 increase in NDVI within various buffer zones around schools was associated with a 6.3-8.7% reduction in myopia prevalence. Higher residential greenness within a 100-meter buffer around homes was linked to a 38% reduction in the risk of developing myopia among preschool children. The protective effect was observed across different age groups, from preschoolers to high school students. Urban planning factors, such as the size, connectivity, and aggregation of green spaces, also influenced myopia risk. These findings suggest that increasing access to green spaces in urban environments may be an effective strategy for myopia prevention, with important implications for public health and urban planning policies.

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region.

Human activities related to land use and land cover (LULC) conversion have been the primary factor driving changes to mangrove distribution over recent decades. In order to quantify the anthropogenic influences associated with LULC changes on mangroves in the Vietnamese Southern Coast (VSC), we investigated the variations and trends in mangrove distribution between 1988 and 2023. We used a time-series of Landsat spectral indices from Google Earth Engine and applied hot spot analysis and machine learning algorithms to analyse mangrove variations and LULC classification, respectively. Our findings revealed that over the past 36 years, approximately half of the mangrove area has been lost due to LULC conversions. The most significant losses in mangrove cover occurred during the 1998-2011 period, with a decline of 46.79% in total area (an average of 3.6% per annum). The rate of mangrove deforestation more than halved to 17.49% (1.5% per annum) in the period between from 2011 to 2023. We attribute the reduction in mangrove loss to conservation efforts and natural regeneration processes. The emerging hot spot analysis indicated that the most significant restoration of new mangrove areas occurred between 1988 and 1998, totalling 1795 ha (1.4%), while the highest rate of mangrove deforestation was observed between 1998 and 2011, amounting to 2249 ha (2.0%). The primary causes of these variations in mangrove distribution were the conversion of mangrove areas to shrimp farming (38.91%), followed by other agricultural land use (5.82%) and the expansion of impervious surfaces (3.34%). In contrast, a result of enhanced conservation efforts and natural regeneration was associated with a 17.91% of mangrove area gain in the 2011-2023 period. Despite the regeneration potential of mangroves, our study highlighted the ongoing need to manage and protect mangrove forests to facilitate their expansion in the VSC. The analytical approach adopted in this study is applicable to other coastal areas when assessing changes in mangroves and land use practices.

Association of fine particulate matter and residential green space with rheumatoid arthritis.

BACKGROUND: Fine particulate matter (PM2.5) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM2.5 and rheumatoid arthritis (RA) is still controversial. Additionally, evidence on the relationship of green space with RA is scarce. This study aimed to investigate the separate and combined associations of PM2.5 and green space with risk of RA. METHODS: Our study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM2.5 concentrations were determined using a land-use regression model. Residential green space was assessed using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from satellite images. We employed the Cox proportional hazard model to evaluate the relationships of PM2.5 and green space with RA. RESULTS: During the 176,894 person-years of follow-up period, 354 cases of RA were identified. Hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) for every interquartile range (IQR) increase in PM2.5 were 1.23 (95% CI: 1.02, 1.49). Compared with lower exposure to residential green space, individuals living in areas with more green space had a decreased risk of RA (HR was 0.80 (95% CI: 0.70, 0.92), 0.80 (95% CI: 0.70, 0.92), and 0.79 (95% CI: 0.70, 0.89) for 250m, 500m, and 1000m NDVI buffers, respectively). Similar results were observed for the association between EVI and RA. Furthermore, a significant multiplicative interaction was observed between PM2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM2.5 on the relationship between green space and RA was observed. CONCLUSION: Our findings indicated that living in areas with higher green space was linked to a reduced risk of RA, whereas living in areas with higher PM2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM2.5.

Dynamic primary resources, not just wild prey availability, underpin lion depredation of livestock in a savanna ecosystem.

Because it can lead to retaliatory killing, livestock depredation by large carnivores is among the foremost threats to carnivore conservation, and it severely impacts human well-being worldwide. Ongoing climate change can amplify these human-wildlife conflicts, but such issues are largely unexplored, though are becoming increasingly recognized. Here, we assessed how the availability of primary resources and wild prey interact to shape large carnivore selection for livestock rather than wild prey (i.e., via prey switching or apparent competition). Specifically, we combined remotely sensed estimates of primary resources (i.e., water availability and primary productivity), wild prey movement, and 7 years (2015-2021) of reports for livestock depredation by African lions (Panthera leo) in the Makgadikgadi Pans ecosystem, Botswana. Although livestock depredation did not vary between wet versus dry seasons, analyses at finer temporal scales revealed higher incidences of livestock depredation when primary production, water availability, and wild prey availability were lower, though the effects of wild prey availability were mediated by water availability. Increased precipitation also amplified livestock depredation events despite having no influence on wild prey availability. Our results suggest that livestock depredation is influenced by the diverse responses of livestock, wild prey, and lions to primary resource availability, a driver that is largely overlooked or oversimplified in studies of human-carnivore conflict. Our findings provide insight into tailoring potential conflict mitigation strategies to fine-scale changes in resource conditions to efficiently reduce conflict and support human livelihoods.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

Does living close to allergenic street trees affect lung function in German adults?

INTRODUCTION: Studies on greenspace and lung function in adults produced divergent results. Some of the adverse findings could be due to long-term exposure to allergenic tree pollen. We investigated whether having more birch trees or more allergenic trees around home is related to worse lung function and whether these exposures confound the association between greenspace and lung function. METHODS: The analytic sample consisted of 874 adults aged 20-44 years at baseline from the German study centers, Erfurt and Hamburg, of the ECRHS cohort study. Spirometric lung function was measured in 1991/92, 2000/01, and 2011/12. We counted trees based on tree registries and classified them into allergenic and non-allergenic. We assessed exposure to greenspace with the normalized difference vegetation index (NDVI), tree cover density, and total number of trees in a 300 m buffer around home. Linear mixed models were used. RESULTS: The forced expiratory volume in 1 s (FEV1) and the forced vital capacity (FVC) were decreased in the presence of more birch trees after adjusting for confounders and co-exposures. For every 10 additional birch trees in a 300 m buffer around home, the average change in FEV1 was -27.6 mL (95% confidence interval (CI): [-58.7, 3.5]). For FVC the average change was -28.2 mL (95% CI: [-62.0, 5.6]). No consistent associations were found for allergenic trees, total trees, tree cover density, or NDVI. Unlike other associations, those of birch trees with FEV1 and FVC were not moderated by allergic sensitization to birch pollen, history of asthma symptoms or nasal allergies including hay fever, ozone, NO2, or age. DISCUSSION: Living close to birch trees had an adverse long-term association with lung function. That tree registries were limited to street trees prevented us from answering the question of a potential confounding of greenspace effects by allergenic neighborhood trees.

Greenness Exposure and Mortality Risk in a Cardio-Oncologic Population.

BACKGROUND AND AIMS: Knowledge is lacking on the relationship between greenness and mortality in cancer survivors who experience coronary artery disease (CAD), a cardio-oncologic population. We aimed to investigate the association between residential greenness exposure and all-cause mortality in a cardio-oncologic population. METHODS: Cancer survivors undergoing percutaneous coronary intervention at the Rabin Medical Center in Israel between 2004 and 2014 were included in the study. Clinical data were collected from medical records during index hospitalization and from the Israeli National Cancer Registry. Residential greenness was estimated by the normalized difference vegetation index (NDVI), a satellite-based index derived from Landsat imagery at a 30-meter spatial resolution, with larger values indicating higher levels of vegetative density (ranging between -1 to 1). Mortality follow-up data were obtained through the end of 2021. Cox models were used to assess the hazard ratios (HRs) for all-cause mortality per 1SD increase in NDVI. RESULTS: Among 1,331 patients analyzed [mean (SD) age, 75.6 (10.2) years, 373 (28%) females], the mean (SD) NDVI within a 300-meter radius was 0.12 (0.03). During a median follow-up period of 12.0 (IQR 9.2-14.7) years, 883 (66%) participants died. After adjustment for potential confounding factors, including residential socioeconomic status, air pollution, and smoking, NDVI was inversely associated with mortality hazard [HR (95% CI) = 0.93 (0.86, 0.99); p=.042]. The association was stronger among individuals with more recently (<10 years) diagnosed cancer [HR (95% CI) = 0.89 (0.81, 0.98); p=.019]. CONCLUSION: In a cohort of cardio-oncologic patients, greenness was independently associated with lower mortality.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

Remote sensing analysis of spatiotemporal impacts of anthropogenic influence on mountain landscape ecology in Pir Chinasi national park.

Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture. It can also be due to natural factors such as flash floods, glacial lake outbursts, land sliding, and climate change such as rising temperatures, heavy rains, or drought.The study's objective was to analyze the mountain landscape ecology of Pir Chinasi National Park under anthropogenic influence and investigate the impact of anthropogenic activities on the vegetation. This study observed spatiotemporal changes in vegetation due to human activities and associated climate change for the past 25 years (1995-2020) around Pir Chinasi National Park, Muzaffrabad, Pakistan. A structured questionnaire was distributed to 200 residents to evaluate their perceptions of land use and its effects on local vegetation. The findings reveal that 60% of respondents perceived spatiotemporal pressure on the park. On the other hand, the Landsat-oriented Normalized Difference Vegetation Index (NDVI) was utilized for the less than 10% cloud-covered images of Landsat 5, 7, and 8 to investigate the vegetation degradation trends of the study area. During the entire study period, the mean maximum NDVI was approximately 0.28 in 1995, whereas the mean minimum NDVI was - 2.8 in 2010. QGIS 3.8.2 was used for the data presentation. The impact of temperature on vegetation was also investigated for the study period and increasing temperature trends were observed. The study found that 10.81% (1469.08 km2) of the area experienced substantial deterioration, while 23.57% (3202.39 km2) experienced minor degradation. The total area of degraded lands was 34.38% (or 4671.47 km2). A marginal improvement in plant cover was observed in 24.88% of the regions, while 9.69% of the regions experienced a major improvement. According to the NDVI-Rainfall relationships, the area was found to be significantly impacted by human pressures and activities (r ≤ 0.50) driving vegetation changes covering 24.67% of the total area (3352.03 km2). The area under the influence of climatic variability and change (r ≥ 0.50 ≥ 0.90) accounted for 55.84% (7587.26 km2), and the area under both climatic and human stressors (r ≥ 0.50 < 0.70) was 64%. Sustainable land management practices of conservation tillage, integrated pest management, and agroforestry help preserve soil health, water quality, and biodiversity while reducing erosion, pollution, and the degradation of natural resources. landscape restoration projects of reforestation, wetland restoration, soil erosion control, and the removal of invasive species are essential to achieve land degradation neutrality at the watershed scale.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Assessment of Fatty Acid and Oxylipin Profile of Resprouting Olive Trees Positive to Xylella fastidiosa subsp. pauca in Salento (Apulia, Italy).

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea.

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.

Changes in soil and plant carbon pools after 9 years of experimental summer warming and increased snow depth.

Climate change can have positive and negative effects on the carbon pools and budgets in soil and plant fractions, but net effects are unclear and expected to vary widely within the arctic. We report responses after nine years (2012-2021) of increased snow depth (snow fences) and summer warming (open top chambers) and the combination on soil and plant carbon pools within a tundra ecosystem in West Greenland. Data included characteristics of depth-specific soil samples, including the rhizosphere soil, as well as vegetation responses of NDVI-derived traits, plant species cover and aboveground biomass, litter and roots. Furthermore, natural vegetation growth through the study period was quantified based on time-integrated NDVI Landsat 8 satellite imagery. Our results showed that summer warming resulted in a significant and positive vegetation response driven by the deciduous low shrub Betula nana (no other vascular plant species), while snow addition alone resulted in a significant negative response for Betula. A significant positive effect of summer warming was also observed for moss biomass, possibly driven increasing shade by Betula. The aboveground effects cascaded to belowground traits. The rhizosphere soil characteristics differed from those of the bulk soil regardless of treatment. Only the rhizosphere fraction showed responses to treatment, as soil organic C stock increased in near-surface and top 20 cm with summer warming. We observed no belowground effects from snow addition. The study highlights the plant species response to treatment followed by impacts on belowground C pools, mainly driven by dead fine roots via Betula nana. We conclude that the summer warming treatment and snow addition treatment separately showed opposing effects on ecosystem C pools, with lack of interactive effects between main factors in the combination treatment. Furthermore, changes in soil C are more clearly observed in the rhizosphere soil fraction, which should receive more attention in the future.

Spatiotemporal change characteristics of NDVI and response to climate factors in the Jixi Wetland, Eastern China.

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1 month, and the response to average temperature and relative humidity was longer.

Estimation of plant pollution removal capacity based on intensive air quality measurements.

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

Adrenal and metabolic hormones demonstrate risk-reward trade-offs for African elephants foraging in human-dominated landscapes.

A key driver of the African savannah elephant population decline is the loss of habitat and associated human-elephant conflict. Elephant physiological responses to these pressures, however, are largely unknown. To address this knowledge gap, we evaluated faecal glucocorticoid metabolite (fGCM) concentrations as an indicator of adrenal activity and faecal thyroid metabolite (fT3) concentrations as an indicator of metabolic activity in relation to land use, livestock density, and human landscape modification, while controlling for the effects of seasonality and primary productivity (measured using the normalized difference vegetation index). Our best-fit model found that fGCM concentrations to be elevated during the dry season, in areas with higher human modification index values, and those with more agropastoral activities and livestock. There was also a negative relationship between primary productivity and fGCM concentrations. We found fT3 concentrations to be higher during the wet season, in agropastoral landscapes, in locations with higher human activity, and in areas with no livestock. This study highlights how elephants balance nutritional rewards and risks in foraging decisions when using human-dominated landscapes, results that can serve to better interpret elephant behaviour at the human-wildlife interface and contribute to more insightful conservation strategies.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data.

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky-Golay (S-G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S-G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.