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African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

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Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering area a total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest area degradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

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Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 µS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

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The southwest alpine canyon area is a typical ecologically fragile area. Understanding the characteristics of vegetation change here and its influencing factors can provide a theoretical basis for formulating countermeasures for ecological environment construction in the southwest alpine canyon area and has practical significance for realizing the harmonious and unified development of the regional economy, environment, and ecology. Based on the data set of NDVI, socio-economic factors, and natural factors from 2000 to 2019, the spatial and temporal variation and stability characteristics of NDVI in the southwest alpine canyon area were analyzed by using the methods of unary linear regression, Hurst index, geographic detector model, and coefficient of variation, and the influencing factors of the spatial differentiation of NDVI were also discussed. The results showed that:① in terms of spatial distribution, the vegetation was high in the southeast and low in the northwest. The area covered by medium and high vegetation accounted for 71.71%, and the vegetation cover was at a relatively high level. From the perspective of time, the area of vegetation showing an improvement trend accounted for 85.90%, and the recovery effect was obvious, and the future vegetation change trend will continue to be improved. ② Elevation, vegetation type, and soil type were the main factors affecting the spatial differentiation of NDVI, and the q value was no less than 0.40. Temperature and rainfall were secondary factors, with q values of 0.274 and 0.225, respectively. The dual-factor interaction enhanced the single factor influence, which was manifested as the dual-factor enhancement and nonlinear enhancement relationship. The highest q value was 0.714 for the combination of altitude ∩ vegetation, followed by 0.688 for the combination of altitude ∩ soil. ③ The overall stability of NDVI during the study period was good, and the proportion of the regional area with low fluctuating changes and slightly low fluctuating changes was 89.95%. The area with moderate fluctuation accounted for 10.05%, concentrated in the relatively fragile ecological environment with factors such as high altitude, low temperature, little rainfall, barren soil, and sparse vegetation. Vegetation change is the result of a combination of multiple factors, so it is necessary to adapt to local conditions and adopt different strategies to restore the ecological environment of the southwest alpine canyon area.

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Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

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This paper presents geospatial datasets, figures, and tables illustrating i) the location and total area of fish farms under cultivation; and ii) the spatiotemporal dynamics of reed cover in Hungarian fishponds generated from the published study of Sharma et al., [1]. Preliminary data for fish farm locations were obtained from the Institute of Agricultural Economics (AKI), followed by significant refinement based on high-resolution Google Earth Pro-imagery. The fishpond area dataset was validated against the values reported in annual statistical reports on aquaculture. In order to map reed vegetation freely available Sentinel-2 imagery (between 2017 and 2021) was accessed from the Copernicus Open Access Hub [2] and emergent macrophyte cover was classified using the NDVI-based threshold values [1]. Scientists, policymakers, and fish farmers can all benefit from such geospatial datasets. It could be used to monitor the extent of fishponds in Hungary and to design farm-level reed management plans to optimize the provision of ecological and production services.

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Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000-2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 "high" incidence states and the District of Columbia for 2000-2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a "u-shaped" association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed "n-shaped" relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.

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Based on the datasets of normalized difference vegetation index (NDVI), temperature, precipitation, and solar radiation and the methods of trend, partial correlation, and residual analyses, this study explored the spatiotemporal variation in NDVI and its response to climate change from 1982 to 2019 in eastern coastal areas of China. Then, the effects of climate change and non-climatic factors (e.g., human activities) on NDVI trends were analyzed. The results showed that:① the NDVI trend varied greatly in different regions, stages, and seasons. On average, the growing season NDVI increased faster during 1982-2000 (stage I) than that during 2001-2019 (stage Ⅱ) in the study area. Moreover, NDVI in spring showed a more rapid increase than that in other seasons in both stages. ② For a given stage, the relationships between NDVI and each climatic factor varied in different seasons. For a given season, the major climatic factors associated with NDVI change were different between the two stages. The relationships between NDVI and each climatic factor showed great spatial differences in the study period. In general, the increase in growing season NDVI in the study area from 1982 to 2019 was closely related to the rapid warming. The increase in precipitation and solar radiation in stage Ⅱ also played a positive role. ③ In the past 38 years, climate change played a greater role in the change in growing season NDVI than non-climatic factors, including human activities. Whereas non-climatic factors dominated the increase in growing season NDVI during stage I, climate change played a major role during stage Ⅱ. We suggest that more attention should be paid to the impacts of various factors on vegetation cover variation during different periods to promote the understanding of terrestrial ecosystem changes.

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BACKGROUND: Gestational diabetes mellitus (GDM) is associated with reduced gut microbiota richness that was also reported to differ significantly between those living in rural compared to urban environments. Therefore, our aim was to examine the associations between greenness and maternal blood glucose levels and GDM, with microbiome diversity as a possible mediator in these associations. METHODS: Pregnant women were recruited between January 2016 and October 2017. Residential greenness was evaluated as mean Normalized Difference Vegetation Index (NDVI) within 100, 300 and 500 m buffers surrounding each maternal residential address. Maternal glucose levels were measured at 24-28 weeks of gestation and GDM was diagnosed. We estimated the associations between greenness and glucose levels and GDM using generalized linear models, adjusting for socioeconomic status and season at last menstrual period. Using causal mediation analysis, the mediation effects of four different indices of microbiome alpha diversity in first trimester stool and saliva samples were assessed. RESULTS: Of 269 pregnant women, 27 participants (10.04%) were diagnosed with GDM. Although not statistically significant, adjusted exposure to medium tertile levels of mean NDVI at 300 m buffer had lower odds of GDM (OR = 0.45, 95% CI: 0.16, 1.26, p = 0.13) and decreased change in mean glucose levels (ß = -6.28, 95% CI: 14.91, 2.24, p = 0.15) compared to the lowest tertile levels of mean NDVI. Mixed results were observed at 100 and 500 m buffers, and when comparing highest tertile levels to lowest. No mediation effect of first trimester microbiome on the association between residential greenness and GDM was observed, and a small, possibly incidental, mediation effect on glucose levels was observed. CONCLUSION: Our study suggests possible associations between residential greenness and glucose intolerance and risk of GDM, though without sufficient evidence. Microbiome in the first trimester, while involved in GDM etiology, is not a mediator in these associations. Future studies in larger populations should further examine these associations.

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The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index-NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

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Using the MOD13A3 NDVI time series from 2000 to 2020, climate date from 1999 to 2020, and land use type data in 2000 and 2020, the spatio-temporal variation in vegetation cover and the driving mechanisms of climate change and human activities to vegetation variation were analyzed based on Theil-Sen Median analysis, the Mann-Kendall significance test, the multi-collinearity test, residual analysis, and relative analysis. The results showed that the vegetation cover exhibited a fluctuating and increasing trend with a magnitude of 0.0016 a-1 in southwest China from 2000 to 2020. The increasing trend of vegetation cover was mostly significant in the Guangxi Hills and Yunnan-Guizhou Plateau and slightly significant in the Tibet Plateau. The vegetation cover had increased in the context of climate change and human activities, with an increasing rate of 0.0010 a-1 and 0.0006 a-1, respectively. The vegetation improvement was mostly dominated by the combination effects of climate change and human activities. The vegetation improvement was dominated by climate change, and the relative role of climate change reached 61.86%. What is more, the vegetation degradation was dominated by human activities, and the relative role of human activities reached 58.39%. Vegetation cover was positively related to minimum temperature, precipitation, maximum temperature, potential evapotranspiration rate, and relative humidity and negatively related to mean temperature, atmosphere pressure, sunshine duration, warmth index, and humidity index. As a whole, the minimum temperature, sunshine duration, and precipitation were the dominant climate factors affecting the vegetation variation in southwest China. Furthermore, the land use and land cover change were significantly related to vegetation variation in southwest China. The implementation of ecological afforestation projects could be beneficial to regional vegetation improvement, whereas the vegetation degradation was mostly conducted by the built-up land expansion.

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Economic and industrial development results in worldwide population concentration in cities, leading to increases in impervious surfaces. Thus, the surface temperatures increase and cities are exposed to the urban heat island effect. This study analyzed the changes in the urban heat island effect in the 30 years (from 1990 to 2021) in the central district of Bartin. In this sense, there were two primary goals. Firstly, land use/land cover change, land surface temperature (LST), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) were analyzed by using remote sensing methods between 1990 and 2021. Secondly, a linear regression analysis was conducted to determine the factors associated with LST, NDVI, and NDBI. The study results revealed increases in urban surfaces and the average land surface temperature values in the past 30 years and showed a decline in the vegetation. Regression analysis results indicated a strong negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. It was also found a robust negative relationship between NDBI and NDVI. In light of the findings, it was stated that the amount of open and green areas should be increased in order to prevent the negative effects of the urban heat island in the central district of Bartin. For this purpose, it has been proposed to encourage green roof systems throughout the city, to create city parks and to create a green belt system. In addition, as a result of the study, the importance of preventing forest destruction caused by over settlement in the Mountains, which is one of the rare habitats of the world with different plant species, was emphasized. In this sense, legal sanctions should be employed to protect those areas and prevent construction.

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The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages: a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.

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Vegetation is an essential component of the earth's surface system and its dynamics is a clear indicator of global climate change. However, the vegetation trends of most studies were based on time-unvarying methods, cannot accurately detect the long-term nonlinear characteristics of vegetation changes. Here, the ensemble empirical mode decomposition and the Breaks for Additive Seasonal and Trend algorithm were applied to reconstruct the the normalized difference vegetation index (NDVI) data and diagnose spatiotemporal evolution and abrupt changes of long-term vegetation trends in China during 1982-2018. Residual analysis was used to separate the influence of climate and human activities on NDVI variations, and the effect of specific human drivers on vegetation growth was obtained. The results suggest that based on the time-varying analysis, high vegetation browning was masked by overall vegetation greening. Vegetation growth in China experienced an abrupt change in the 1990s and 2000s, accounting for 50% and 33.6% of the whole China respectively. Of the area before the breakpoint, 45.4% showed a trend of vegetation decrease, which was concentrated mainly in east China, while 43% of the area after the breakpoint also showed vegetation degradation, mainly in northwest China. Climate was an important driving force for vegetation change in China. It played a positive role in south China, but had a negative effect in northwest China. The impact of human activities on vegetation growthchanged from an initial negative influence to a positive one. In terms of human activities, an inverted-U-shaped relation was detected between CO2 emissions and vegetation growth; that is, the fertilization effect of CO2 had a certain threshold. Once that threshold was exceeded, it would hinder vegetation growth. Population density had a slight constraint on vegetation growth, and the implementation of ecological restoration projects (e.g., the Grain for Green Program) can promote vegetation growth to a certain extent.

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Quantifying street-level greenery has been the subject of interest for researchers as it has several implications for community residents. Green View Index (GVI) is a widely used parameter to compute the greenery along the streets. However, it does not account for the health of the greenery. The new Enhanced Green View Index (EGVI) that we propose computes the amount of greenery along the streets along with the health of the greenery. • The new indicator computes street-level greenery; • Considers the health of vegetation while calculating greenery; and • Helps to study the impact of street-level greenery on community residents precisely.

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Mark-release-recapture (MRR) trials have been conducted in Northern Italy to evaluate the capacity of radio-substerilized Aedes albopictus males to survive, disperse, and engage in mating in the field. Two MRR sessions with the human landing collection method (HLC) were conducted with the simultaneous release of irradiated males marked with four different pigment colors. The survival and dispersal rates seem to be influenced more by environmental factors such as barriers, shading, and vegetation rather than weather parameters. In this study, we confirmed a positive linear relationship between the sterile adult male's daily survival rate and the relative humidity previously reported in similar experimental conditions and a different dispersal capacity of the released A. albopictus males in low- (NDVI index <0.4) and high (NDVI index >0.4)-vegetated areas. Consistent with previous studies, A. albopictus males have their maximal dispersion in the first days after release, while in the following days the males become more stationary. The similar field performances obtained with marked and unmarked radio-sterilized and untreated A. albopictus males on similar environments confirm the negligible effects of irradiation and marking procedures on the quality of the males released. The similar sterile to wild (S/W) male ratio measured in high- and low-vegetation areas in the release sites indicates a similar distribution pattern for the wild and the released sterile males. According to the MRR data collected, the Lincoln index estimated different A. albopictus mean population densities in the study areas equal to 7,000 and 3,000 male/ha, respectively.

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The forage maturation hypothesis (FMH) assumes that herbivores cope with the trade-off between digestibility and biomass in forage by selecting vegetation at intermediate growth. The green wave hypothesis (GWH) extends the FMH to suggest how spatiotemporal heterogeneity in plant quality shapes migratory movements of herbivores. Growing empirical support for these hypotheses mainly comes from studies in vast landscapes with large-scale habitat heterogeneity. It is unclear, however, to what extent ungulates surf green waves in human-altered landscapes with small-scale heterogeneity in terms of land use and topography. We used plant phenological proxies derived from Sentinel 2 satellite data to analyze the habitat selection of 93 collared red deer (Cervus elaphus) in montane and alpine habitats. Using a step selection analysis, we investigated how plant phenology, that is, the instantaneous rate of green-up (IRG) and normalized difference vegetation index (NDVI), and a set of variables describing topography and human presence influenced red deer resource selection in open habitats. We learned that red deer selected areas with high biomass at green-up and avoided habitats with possible exposure to human activity. Additionally, landscape structure and topography strongly influenced spatial behavior of red deer. We further compared cumulative access to high-quality forage across migrant strategies and found migrants gained better access than residents. Many migratory individuals surfed the green wave, and their surfing behavior, however, became less pronounced with decreasing distance to settlements. Within the constraints of topography and human land use, red deer track spring green-up on a fine spatiotemporal scale and follow the green wave across landscapes in migration movements. Thus, they benefit from high-quality forage even in human-dominated landscapes with small-scale heterogeneity and vegetation emerging in a heterogenic, dynamic mosaic.

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Studies on the dynamic variation in vegetation cover and detecting its influencing factors are highly valuable for monitoring regional ecological environment quality and evaluating forestry restoration project effects. In this study, on the basis of the MODIS normalized difference vegetation index (NDVI), in situ climate data, digital elevation model, population density, nighttime lights using Theil-Sen Median analysis, Mann-Kendall significance test, stability analysis, and geographical detector model, the spatiotemporal variation and stability of vegetation cover in the context of multi-spatiotemporal scales were analyzed, and the dominant influencing factors that affect the spatial differentiation of vegetation cover were further detected. The results showed that the vegetation cover showed a fluctuant increasing trend, and the changing trend exhibited obvious spatial heterogeneity with the increasing rate being higher in the middle and lower in the east and west portion of the Yangtze River basin from 2000 to 2020. At the sub-basin scale, except for that in the Taihu Lake basin, the vegetation cover in all sub-basin units exhibited an increasing trend during the study period. The areas with an increasing trend accounted for 84.09% of the study area, in which the areas with extremely significant increases and significant increases accounted for 53.67%, which were mainly distributed in the Wujiang River basin, Yibin-yichang, Jialing River basin, Han River basin, and Dongting Lake basin. The vegetation cover showed lower stability in the upper reaches of the Jinsha-shigu River basin and Taihu Lake basin and higher stability in other sub-basin units of the study area. Elevation was an important factor affecting the vegetation variation in all sub-basin areas. Climatic factors presented the highest impact on vegetation variation in the upper reaches of the Jinsha-shigu River basin, and human activities exhibited the greatest impact on vegetation variation in the Wujiang River basin, lower reaches of Hukou basin, and Taihu Lake basin. The interaction of the two influencing factors on vegetation variation showed mutual and non-linear enhancement, and the interaction between elevation and wind speed presented the highest value, with an explanatory power of 68%. The ecological exploration results showed that human activities combined with topographic factors and climate factors, except for slope and relative humidity, significantly differed in the explanatory power of vegetation variation in the Yangtze River basin. These results can provide a basis for formulating comprehensive vegetation resource management in the Yangtze River basin that takes into account regional climate, topography, and human activities.

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Plants in their life cycle go through a series of life processes. These phenological changes are influenced by different climatic conditions. Abiotic factors like temperature, precipitation, and photoperiodism affect the onset and offset of particular phenophase in the plant periodic cycle. In this study, we tested the influence of precipitation on the forest phenology at two sites of Dudhwa National Park (DNP), Uttar Pradesh and Simlipal National Park (SNP), Odisha, India. DNP and SNP receive an annual average rainfall of 1093.5 mm and 1500 mm, respectively, of which most rainfall (~ 90%) occurs during June-September. Normalized Difference Vegetation Index (NDVI) was measured for 2 years 2015 and 2018, with 2015 being a drought year and 2018 being a normal rainfall year. NDVI was analyzed at different temporal scales of months, season, and years using the t test (Welch's two-tailed) and General Linear Mixed Model (GLMM). Effect of drought (2015) and normal (2018) rainfall year was not significant at both the sites, whereas season, year*season interaction, season*rainfall interaction, and year*season*rainfall interaction were found significant at DNP (P < 0.05, ICC = 0.68, marginal R2 = 0.81; conditional R2 = 0.94). At SNP, rainfall, year, season, and their interaction were non-significant, whereas several months showed a significant effect on the NDVI values for both sites. Winter and monsoon season in DNP, and post-monsoon season in SNP, showed a significant effect on the NDVI patterns. Thus, the effect of precipitation stress in the deciduous forests was evident at small intervals of observation. Tree phenology compensated for differences when observed from a higher temporal scale of a year. There existed a mechanism in trees to tide over adverse conditions and maintain the phenology over longer intervals of time. The resilience and vulnerability of such forest ecosystems against abiotic factors and extreme events would be instrumental in climate change adaptation strategies. Tree phenology can be used as an indicator of forest health and resilience.

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Recently, attention has been drawn to the need to integrate sex/gender more comprehensively into environmental health research. Considering theoretical approaches, we define sex/gender as a multidimensional concept based on intersectionality. However, operationalizing sex/gender through multiple covariates requires the usage of statistical methods that are suitable for handling such complex data. We therefore applied two different decision tree approaches: classification and regression trees (CART) and conditional inference trees (CIT). We explored the relevance of multiple sex/gender covariates for the exposure to green spaces, measured both subjectively and objectively. Data from 3742 participants from the Cooperative Health Research in the Region of Augsburg (KORA) study were analyzed within the INGER (Integrating gender into environmental health research) project. We observed that the participants' financial situation and discrimination experience was relevant for their access to high quality public green spaces, while the urban/rural context was most relevant for the general greenness in the residential environment. None of the covariates operationalizing the individual sex/gender self-concept were relevant for differences in exposure to green spaces. Results were largely consistent for both CART and CIT. Most importantly we showed that decision tree analyses are useful for exploring the relevance of multiple sex/gender dimensions and their interactions for environmental exposures. Further investigations in larger urban areas with less access to public green spaces and with a study population more heterogeneous with respect to age and social disparities may add more information about the relevance of multiple sex/gender dimensions for the exposure to green spaces.

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