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Germany experienced extreme drought periods in 2018 and 2022, which significantly affected forests. These drought periods were natural experiments, providing valuable insights into how different tree species respond to drought. The quantification of species-specific drought responses may help to identify the most climate-change-resilient tree species, thereby informing effective forest regeneration strategies. In this study, we used remotely sensed peak-season canopy greenness as a proxy for tree vitality to estimate the drought response of four widely abundant tree species in Germany (oak, beech, spruce, and pine). We focused on two questions: (1) How were the four tree species affected by these two droughts? (2) Which environmental parameters primarily determined canopy greenness? To address these questions, we combined a recently published tree species classification map with remotely sensed canopy greenness and environmental variables related to plant available water capacity (PAWC) and atmospheric vapor pressure deficit (VPD). Our results indicate that the more isohydric species featured a greater decline in canopy greenness under these droughts compared to the more anisohydric species despite similar soil moisture conditions. Based on spatial lag models, we found that the influence of PAWC on canopy greenness increases with increasing isohydricity while the influence of VPD decreases. Our statistical analysis indicates that oak was the only species with significantly higher canopy greenness in 2022 compared to 2018. Yet, all species are likely to be susceptible to accumulated drought effects, such as insufficient recovery time and increased vulnerability to biotic pathogens, in the coming years. Our study provides critical insights into the diverse responses of different tree species to changing environments over a large environmental gradient in Central Europe and sheds light on the complex interactions between soil moisture, climate variables, and canopy greenness. These findings contribute to understanding forests' climate-change resilience and may guide forest management and conservation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Fine particulate matter (PM 2.5 ) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM 2.5 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 PM 2.5 and green space with risk of RA., Methods: Our study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM 2.5 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 PM 2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM 2.5 ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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 (LAI W ) and herbaceous LAI (LAI H ), respectively, derived from aggregate leaf area index (LAI A ), 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 (LAI A ); and (2) partitioned biomass model (LAI P ) comprising LAI H and LAI W . By integrating forage estimates with ancillary environmental variables, we found that LAI P , with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAI P yielded the lowest RMSE (5.9 TLUs) and higher R 2 (0.83), surpassing NDVI and LAI A models RMSE (9.3 TLUs) and R 2 (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 LAI P has the potential to reduce basis risk by enhancing IBLI index accuracy., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

It is of great significance to explore the dynamic variations in vegetation cover and to identify its driving factors for the restoration and sustainable development of the regional ecological environment. Based on MODIS NDVI data from 2000 to 2020 and contemporaneous meteorological, DEM, land use type, and other data, the spatiotemporal variation characteristics of vegetation in the Greater Khingan Mountains forest-steppe ecotone were deeply analyzed, and its future evolution pattern was predicted by using the methods of Sen+Mann-Kendall trend analysis and Hurst index. At the same time, the influence degree and mechanism of each detection factor and its interaction on vegetation spatial differentiation at the scale of the whole area and different physical geographic divisions were quantitatively revealed by introducing the GeoDetector model. The results showed that:① In terms of spatiotemporal variation, the spatiotemporal heterogeneity of NDVI in the Greater Khingan Mountains forest-steppe ecotone was obvious from 2000 to 2020. Temporally, NDVI fluctuated growth at a rate of 0.002 a -1 ( P < 0.05) and underwent an upward mutation in 2011. Spatially, NDVI showed a distribution pattern of "increasing from southwest to northeast," and the NDVI grade transfer was mainly "medium vegetation cover→medium-high vegetation cover" during the 21 years, and the area of vegetation improvement was much larger than that of degradation. ② In terms of trend prediction, the future variation trend of NDVI in the Greater Khingan Mountains forest-steppe ecotone was mainly continuous improvement, accounting for 37%, but was mostly weakly sustained. ③ In terms of driving mechanism, the wind speed, evaporation, and relative humidity had the most significant influence on the spatial differentiation of NDVI over the whole area. The influence of natural factors has been decreasing over the past 21 years, whereas the influence of human factors has been increasing, and the main driving factors of NDVI spatial differentiation were quite different in different vegetation, climate, soil, and geomorphic zones. The synergistic effect between each factor at different spatial scales all showed two-factor or non-linear enhancement relationships, which was significantly enhanced compared with the single-factor effect. This study contributes to clarifying the causes of ecological fragility in the forest-steppe ecotone in the northern cold region and provides scientific support for formulating differentiated protection and management plans for vegetation resources under different environmental conditions.

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., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

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.

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 (β = -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., Competing Interests: Declaration of competing interest The authors declare they have no actual or potential competing financial interest or any other conflicts of interest., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

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.

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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Greenness exposure is beneficial to human health, but its potential mechanisms through which the risk for metabolic syndrome (MetS) could be reduced have been poorly studied. We aimed to estimate the greenness-MetS association in southeast China and investigate the independent and joint mediation effects of physical activity (PA), body mass index (BMI), and air pollutants on the association., Methods: A cross-sectional study was conducted among the 38,288 adults based on the Fujian Behavior and Disease Surveillance (FBDS), established in 2018. MetS was defined as the presence of three or more of the five components: abdominal obesity, elevated triglyceride, reduced high-density lipoprotein cholesterol (HDL-C), high blood pressure, and elevated fasting glucose. The residential greenness exposure was measured as the 3-year mean values of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) within the 250, 500, and 1,000 meters (m) buffer zones around the residential address of each participant. Logistic regression models were used to estimate the greenness-MetS association. The causal mediation analysis was used to estimate the independent and joint mediation effects of PA, BMI, particulate matter with an aerodynamic diameter of 2.5 μm (PM 2.5 ), particulate matter with an aerodynamic diameter ≤ 10 μm (PM 10 ), nitrogen dioxide (NO 2 ), and sulfur dioxide (SO 2 )., Results: Each interquartile range ( IQR ) increase in greenness was associated with a decrease of 13% ( OR = 0.87 [95% CI : 0.83, 0.92] for NDVI 500m and OR = 0.87 [95% CI : 0.82, 0.91] for EVI 500m ) in MetS risk after adjusting for covariates. This association was stronger in those aged < 60 years (e.g., OR = 0.86 [95% CI : 0.81, 0.92] for NDVI 500m ), males (e.g., OR = 0.73 [95% CI : 0.67, 0.80] for NDVI 500m ), having an educational level of primary school or above ( OR = 0.81 [95% CI : 0.74, 0.89] for NDVI 500m ), married/cohabitation ( OR = 0.86 [95% CI : 0.81, 0.91] for NDVI 500m ), businessman ( OR = 0.82 [95% CI : 0.68, 0.99] for NDVI 500m ), other laborers ( OR = 0.77 [95% CI : 0.68, 0.88] for NDVI 500m ), and non-smokers ( OR = 0.77 [95% CI : 0.70, 0.85] for NDVI 500m ). The joint effect of all six mediators mediated about 48.1% and 44.6% of the total effect of NDVI 500m and EVI 500m on the MetS risk, respectively. Among them, BMI showed the strongest independent mediation effect (25.0% for NDVI 500m ), followed by NO 2 and PM 10 ., Conclusion: Exposure to residential greenness was associated with a decreased risk for MetS. PA, BMI, and the four air pollutants jointly interpreted nearly half of the mediation effects on the greenness-MetS association., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Wang, Feng, Yu, Lin, Fu, Dong, Qiu, Jin Aik, Yin, Xia, Huang, Liu, Lin, Zhang, Fang, Zhong, Jia and Yang.)

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 km 2 /year. In 2015, this area reached its maximum size of 19.7 km 2 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., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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 CO 2 emissions and vegetation growth; that is, the fertilization effect of CO 2 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Wang, Wang and Peng.)

Introduction: Greenery in the residential environment and in the hospital has been associated with improved surgical outcomes and recovery. We investigated the association between the level of residential greenness of patients with coronary disease and their heart disease-related Quality of Life (HRQoL) 1-year after a coronary artery bypass grafting (CABG) surgery., Methods: Participants in a prospective cohort study who underwent CABG surgery at seven cardiothoracic units throughout Israel during the years 2004-2007 filled in the MacNew HRQoL one day before and one year after surgery. Successful recovery was defined as ≥0.5 increase in the MacNew score between baseline and follow-up. Exposure to residential greenness in 90 m and 300 m buffers around the patient's home was assessed with Linear Spectral Unmixing analysis of Landsat 30 m imagery., Results: The cohort comprised of 861 patients (22% female) with a mean age of 65.5 years, and 59.2% classified as low-income. In the total cohort, higher residential greenness was associated with an improvement in emotional HRQoL (OR = 1.33 (95%CI: 0.99-1.79)), adjusting for demographic and socio-economic factors, living in the periphery/center, presence of diabetes, attending cardiac rehabilitation following surgery, BMI, and change in physical fitness and depression over the 1-year follow-up. Although no association was found between greenness and change in the physical or social subscales, a positive association was specifically observed among the low-income patients for the global HRQoL score, OR = 1.42 (95%CI: 0.97-2.10), as compared to the higher-income patients, p for interaction = 0.03., Conclusions: Residential greenness is associated with improvement in HRQoL 1-year after CABG surgery, but not the physical and social scales, only in low-income patients. Ensuring greenery in the living environment may act as a social intervention that supports human health and disease recovery., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s). Published by Elsevier B.V.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Balestrino, Puggioli, Malfacini, Albieri, Carrieri, Bouyer and Bellini.)

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 R 2  = 0.81; conditional R 2  = 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., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Water availability controls the functioning of dryland ecosystems, driving a patchy vegetation distribution, unequal nutrient availability, soil respiration in pulses, and limited productivity. Groundwater-dependent ecosystems (GDEs) are acknowledged to be decoupled from precipitation, since their vegetation relies on groundwater sources. Despite their relevance to enhance productivity in drylands, our understanding of how different components of GDEs interconnect (i.e., soil, vegetation, water) remains limited. We studied the GDE dominated by the deep-rooted phreatophyte Ziziphus lotus, a winter-deciduous shrub adapted to arid conditions along the Mediterranean basin. We aimed to disentangle whether the groundwater connection established by Z. lotus will foster soil biological activity and therefore soil fertility in drylands. We assessed (1) soil and vegetation dynamics over seasons (soil CO 2 efflux and plant activity), (2) the effect of the patchy distribution on soil quality (properties and nutrient availability), and soil biological activity (microbial biomass and mineralization rates) as essential elements of biogeochemical cycles, and (3) the implications for preserving GDEs and their biogeochemical processes under climate change effects. We found that soil and vegetation dynamics respond to water availability. Whereas soil biological activity promptly responded to precipitation events, vegetation functioning relies on less superficial water and responded on different time scales. Soil quality was higher under the vegetation patches, as was soil biological activity. Our findings highlight the importance of groundwater connections and phreatophytic vegetation to increase litter inputs and organic matter into the soils, which in turn enhances soil quality and decomposition processes in drylands. However, biogeochemical processes are jeopardized in GDEs by climate change effects and land degradation due to the dependence of soil activity on: (1) precipitation for activation, and (2) phreatophytic vegetation for substrate accumulation. Therefore, desertification might modify biogeochemical cycles by disrupting key ecosystem processes such as soil microbial activity, organic matter mineralization, and plant productivity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

Recent studies have examined how the environment can influence obesity in young people. The research findings are conflicting: in some studies, green spaces have shown a protective association with obesity and urbanization has turned out to worsen this condition, while other studies contradicted these results. The aim of the study was to examine the relationships between greenness, urbanization, and weight status among Italian adolescents. Student data (11-13 years old) on weight and height, physical activity (PA), and demographic characteristics were extracted from the 2018 Health Behaviour in School-aged Children (HBSC) survey in Piedmont, Northwest of Italy. Data on Normalized Difference Vegetation Index (NDVI) and urbanization were obtained from satellite images and the National Institute of Statistics (ISTAT). A multilevel regression model was used to assess the association between NDVI, urbanization, and obesity, controlling for PA. Students living in greener areas reported a lower likelihood of being obese [OR = 0.11, 95% CI 0.02-0.56, p = 0.007], while students living in areas with a higher level of urbanization showed a significantly increased risk of obesity [OR = 2.3, 95% CI:1.14-4.6, p = 0.02]. Living surrounded by higher amounts of greenness and lower levels of urbanization may positively influence health status through lower risk of obesity among youth.

Across the United States, cities are creating sustainability and climate action plans (CAPs) that call to increase local vegetation. These greening initiatives have the potential to not only benefit the environment but also human health. In epidemiologic literature, greenness has a protective effect on mortality through various direct and indirect pathways. We aimed to assess how an increase in greenness could decrease mortality in the largest urban areas in the United States. We conducted a nationwide quantitative health impact assessment to estimate the predicted reduction in mortality associated with an increase in greenness across two decades (2000, 2010, and 2019). Using a recently published exposure-response function, Landsat 30 m 16-day satellite imagery from April to September, and publicly available county-level mortality data from the CDC, we calculated the age-adjusted reduction in all-cause mortality for those 65 years and older within 35 of the most populated metropolitan areas. We estimated that between 34,000 and 38,000 all-cause deaths could have been reduced in 2000, 2010, and 2019 with a local increase in green vegetation by 0.1 unit across the most populated metropolitan areas. We found that overall greenness increased across time with a 2.86% increase from 2000 to 2010 to 11.11% from 2010 to 2019. These results can be used to support CAPs by providing a quantitative assessment to the impact local greening initiatives can have on mortality. Urban planners and local governments can use these findings to calculate the co-benefits of local CAPs through a public health lens and support policy development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Brochu, Jimenez, James, Kinney and Lane.)

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability., Competing Interests: SP was employed by company BASF. FL was employed by company MLM Medical Labs. MM was employed by company Bayer Crop Science. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Prince, Anower, Motes, Hernandez, Liao, Putman, Mattson, Seethepalli, Shah, Komp, Mehta, York, Young and Monteros.)

Background: The Normalized Difference Vegetation Index (NDVI) is a measure of greenness widely used in environmental health research. High spatial resolution NDVI has become increasingly available; however, the implications of its use in exposure assessment are not well understood., Objective: To quantify the impact of NDVI spatial resolution on greenness exposure misclassification., Methods: Greenness exposure was assessed for 31,328 children in the Greater Boston Area in 2016 using NDVI from MODIS (250 m 2 ), Landsat 8 (30 m 2 ), Sentinel-2 (10 m 2 ), and the National Agricultural Imagery Program (NAIP, 1 m 2 ). We compared continuous and categorical greenness estimates for multiple buffer sizes under a reliability assessment framework. Exposure misclassification was evaluated using NAIP data as reference., Results: Greenness estimates were greater for coarser resolution NDVI, but exposure distributions were similar. Continuous estimates showed poor agreement and high consistency, while agreement in categorical estimates ranged from poor to strong. Exposure misclassification was higher with greater differences in resolution, smaller buffers, and greater number of exposure quantiles. The proportion of participants changing greenness quantiles was higher for MODIS (11-60%), followed by Landsat 8 (6-44%), and Sentinel-2 (5-33%)., Significance: Greenness exposure assessment is sensitive to spatial resolution of NDVI, aggregation area, and number of exposure quantiles. Greenness exposure decisions should ponder relevant pathways for specific health outcomes and operational considerations., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Haemosporidians are among the most common parasites of birds and often negatively impact host fitness. A multitude of biotic and abiotic factors influence these associations, but the magnitude of these factors can differ by spatial scales (i.e., local, regional and global). Consequently, to better understand global and regional drivers of avian-haemosporidian associations, it is key to investigate these associations at smaller (local) spatial scales. Thus, here, we explore the effect of abiotic variables (e.g., temperature, forest structure, and anthropogenic disturbances) on haemosporidian prevalence and host-parasite networks on a horizontal spatial scale, comparing four fragmented forests and five localities within a continuous forest in Papua New Guinea. Additionally, we investigate if prevalence and host-parasite networks differ between the canopy and the understory (vertical stratification) in one forest patch. We found that the majority of Haemosporidian infections were caused by the genus Haemoproteus and that avian-haemosporidian networks were more specialized in continuous forests. At the community level, only forest greenness was negatively associated with Haemoproteus infections, while the effects of abiotic variables on parasite prevalence differed between bird species. Haemoproteus prevalence levels were significantly higher in the canopy, and an opposite trend was observed for Plasmodium . This implies that birds experience distinct parasite pressures depending on the stratum they inhabit, likely driven by vector community differences. These three-dimensional spatial analyses of avian-haemosporidians at horizontal and vertical scales suggest that the effect of abiotic variables on haemosporidian infections are species specific, so that factors influencing community-level infections are primarily driven by host community composition., Competing Interests: All the authors declare there is no competing interest related to the material of this manuscript., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

The NDVI (normalized difference vegetation index) was used as the vegetation coverage index. Based on the NDVI and weather data from 2000 to 2020, the characteristics of the spatiotemporal evolution and the driving mechanism of vegetation were investigated by using correlation analysis, the Theil-Sen estimator, the Mann-Kendall method, and multivariate residual trend analysis. The results showed that the growing season average NDVI in the Yellow River basin was a fluctuating upward trend of 0.005 a -1 from 2000 to 2020. Areas with significantly improved vegetation in the basin were mainly distributed in the Qinling Mountains, the Northern Shaanxi Plateau, and the Lvliang Mountains in the midstream. The average value of the partial correlation coefficient between the growing season average NDVI and rainfall in the Yellow River basin was 0.57, and the average value of the partial correlation coefficient between the growing season average NDVI and temperature was 0.49. The impact of rainfall on vegetation was higher than that of temperature. The areas where human activities significantly improved vegetation growth were mainly distributed in the northern Shaanxi Plateau, the Lvliang Mountains, and southern Ningxia. The areas where human activities inhibited vegetation growth were mainly distributed in cities with strong human activities such as Yinchuan, Baotou, Xi'an, Luoyang, Zhengzhou, and Taiyuan. Human activities and climate change contributed to 72% and 28% of the vegetation change in the Yellow River basin. Driven by human activities and climate change, the area where vegetation growth has improved in the Yellow River basin accounted for 96.4% of the basin area, of which the contribution rate of human activities greater than 80% of the area accounted for 34.3%, which was mainly distributed in the middle and southeast of the basin. The area with a contribution rate of climate change greater than 80% accounted for 4.2%, which was mainly distributed in the Sichuan-Tibet Plateau and Longzhong Loess Plateau in the basin. The results of this research can provide scientific support for the ecological protection and high-quality development of the Yellow River basin.

In order to explore the responses of different vegetation types to climatic change in the Chinese Loess Plateau (CLP), we analzyed the changes of different vegetation types and their relationships with meteorological factors using trend analysis, Hurst index, and geographical detector model based on normalized difference vegetation index (NDVI). The results showed that NDVI of different vegetation types from 2002 to 2019 was dominated by a growing trend and codirectional moderate persistence. The NDVI of crops in the built-up and adjacent areas decreased significantly. Except for grassland or meadow that was affected by mixed pixels, the spatial variation of NDVI was significant in the growing season (from April to October). The mean NDVI of different vegetation types followed an oder: coniferous forest > broadleaved forest > scrub > meadow > grassland > crop > steppe > desert. The interactions between meteorological factors were synergistic and non-linear enhancement in the CLP. Moreover, the interaction was more prominent under steppe and desert where habitat was fragile. The synergistic effect of precipitation and temperature had a great influence on all vegetation types. Water vapor, relative humidity, sunshine duration, atmospheric pressure, and wind speed had different explanatory powers on NDVI through indirectly affec-ting hydrothermal conditions.

Land use land cover (LULC) change has become a major concern for biodiversity, ecosystem alteration, and modifying the climatic pattern especially land surface temperature (LST). The present study assessed past and predicted future LULC and LST change in the Swabi District of Pakistan. LULC maps were generated from satellite data for years 1987, 2002, and 2017 using supervised classification. Mean LST and its areal change were estimated for different LULC classes from thermal bands of satellite images. LULC and LST were projected for the year 2047 using the integrated weighted evidence-cellular automata (WE-CA) model and a regression equation developed in this study, respectively. LULC change revealed an increase of > 5% in the built-up while a decrease in the agricultural area by ~ 9%. There was an increase of ~ 63% area in the LST class ≥ 27 °C which may create urban heat island (UHI). Simulation results indicated that the built-up area will further be increased by ~ 3% until 2047. Area associated with LST class > 30 °C indicated a further increase of ~ 38% till 2047 with reference to year 2017. Findings of this study suggested proper utilization of LULC in order to mitigate the creation of UHIs associated with urbanization and built-up areas., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R 2 ) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R 2 , root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

Wheat accounts for more than 50% of Australia's total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet's high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well ( R 2 = 0.86, RMSE = 0.18 t ha -1 ). RF models for individual paddocks in VIC ( R 2 = 0.89, RMSE = 0.15 t ha -1 ) and NSW ( R 2 = 0.87, RMSE = 0.07 t ha -1 ) performed well, but moderate performance was seen for SA ( R 2 = 0.45, RMSE = 0.25 t ha -1 ). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded 'big data' for regional as well as local-scale yield prediction.

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories, and traffic in a green viewpoint. The literature review confirms that this reconsideration for green space has a positive effect on the air quality of large cities and to reduce the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summer seasons of 2013-2019 were used to extract changes in vegetation by calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the optimized soil adjusted vegetation index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy, and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran, has been increased by 64, 81, and 53% by NDVI, EVI, and OSAVI, respectively, during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52, and -0.53, respectively., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000-2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves-Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann-Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran's Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%., Competing Interests: Conflicts of interestThe authors declare that there is no conflict of interest., (© Islamic Azad University (IAU) 2022.)

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa ( Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Biswas, Andrade, Acharya, de Souza, Lopez, de Assis, Shirbhate, Singh, Munoz and Rios.)

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The mining industry is a significant asset to the development of countries. Ghana, Africa's second-largest gold producer, has benefited from gold mining as the sector generates about 90% of the country's total exports. Just like all industries, mining is associated with benefits and risks to indigenes and the host environment. Small-scale miners are mostly accused in Ghana of being environmentally disruptive, due to their modes of operations. As a result, this paper seeks to assess the environmental impacts of large-scale gold mining with the Nzema Mines in Ellembelle as a case study. The study employs a double-phase mixed-method approach-a case study approach, consisting of site visitation, key informant interviews, questionnaires, and literature reviews, and the Normalized Difference Vegetation Index (NDVI) analysis method. The NDVI analysis shows that agricultural land reduced by -0.98%, while the bare area increases by 5.21% between the 2008 and 2015 periods. Our results show that forest reserves and bare area were reduced by -4.99% and -29%, respectively, while residential areas increased by 28.17% between 2015 and 2020. Vegetation, land, air, and water quality are highly threatened by large-scale mining in the area. Weak enforcement of mining policies, ineffective stakeholder institution collaborations, and limited community participation in decision-making processes were also noticed during the study. The authors conclude by giving recommendations to help enhance sustainable mining and ensure environmental sustainability in the district and beyond.

Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing. A novel scheme of the Group Method of Data Handling (GMDH) known as the generalized structure of GMDH (GSGGMDH) is developed to overcome this classical approach's limitation. A simple time series based scenario with exogenous variables including precipitation and Normalized Difference Vegetation Index (NDVI) was introduced for runoff forecasting. MODIS data included MOD13Q1 product was employed and a JavaScript code was developed to preprocess collected data in the Google Earth Engine (GEE) environment. Using different seasonal and non-seasonal lags of all input variables, the developed GSGMDH found the most optimum input combination for each station in terms of simplicity and accuracy, simultaneously (average values; SI = 0.554, RMSRE = 1.55, MAE = 5.076). The precipitation values are modelled with the CanEsm2 climate change model. To apply NDVI for runoff forecasting, a simple spatial-temporal GSGMDH based model was developed (average values; SI = 0.27; RMSRE = 8.27, MAE = 0.08). The forecasting results indicated that the months in which the maximum runoff occurred have changed, and these months have increased compared to the historic period. In the historical period, the frequency of maximum runoff was in April and March. Still, for the two forecasting periods (i.e. 2020-2039 and 2040-2059), the months in which the maximum runoff has occurred have changed, and their amount has been reduced and added to other months, especially February and August., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

It is important to understand the response of vegetation to climate change in Tibetan Pla-teau (TP), an ecological barrier for China and Asia. The spatiotemporal variation of the normalized difference vegetation index (NDVI) of vegetation growing season were analyzed based on the gro-wing season NDVI retrieved from MOD09A1. The relationship between NDVI and climate factors was analyzed by combining the data of meteorological stations in TP from 2001 to 2018. The results showed that NDVI in the growing season showed a slow upward trend during the study period. There was substantial interannual variation of NDVI in different climate regions. The fluctuation magnitude of NDVI value was plateau humid climate region>semi-humid climate region>semi-arid climate region>arid climate region. The proportion of area with increasing and decreasing NDVI in humid climate region, semi-humid climate region, arid climate region, semi-arid climate region on TP were 1.4% and 1.9%, 4.9% and 1.5%, 16.4% and 0.8%, 7.0% and 2.0%, respectively. The areas of increasing NDVI in arid and semi-arid climate region was significantly larger than humid and semi-humid region. Temperature was the leading factor affecting the change of NDVI in humid and semi-humid region. The impact of precipitation on NDVI was significantly stronger than that of other climate factors in arid region. The impact of air temperature in growing season on NDVI was stronger than that of precipitation and relative humidity.

Disadvantaged groups worldwide, such as low-income and racially/ethnically minoritized people, experience worse health outcomes than more privileged groups, including wealthier and white people. Such health disparities are a major public health issue in several countries around the world. In this systematic review, we examine whether green space shows stronger associations with physical health for disadvantaged groups than for privileged groups. We hypothesize that disadvantaged groups have stronger protective effects from green space because of their greater dependency on proximate green space, as they tend to lack access to other health-promoting resources. We use the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and search five databases (CINAHL, Cochrane, PubMed, Scopus, and Web of Science) to look for articles that examine whether socioeconomic status (SES) or race/ethnicity modify the green space-health associations. Based on this search, we identify 90 articles meeting our inclusion criteria. We find lower-SES people show more beneficial effects than affluent people, particularly when concerning public green spaces/parks rather than green land covers/greenness. Studies in Europe show stronger protective effects for lower-SES people versus higher-SES people than do studies in North America. We find no notable differences in the protective effects of green space between racial/ethnic groups. Collectively, these results suggest green space might be a tool to advance health equity and provide ways forward for urban planners, parks managers, and public health professionals to address health disparities.

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05-0.18 and 1.20-4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2-4 and 2-6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wang, Liu, Hao, Wang, Xing, Luo, Zhou, He and Gai.)

Introduction/aims: Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover., Methods: NDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method., Results: For the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas., Conclusions: LSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study determines whether surrounding greenness is associated with the incidence of type 2 diabetes Mellitus (T2DM) in Taiwan. A retrospective cohort study determines the relationship between surrounding greenness and the incidence of T2DM during the study period of 2001-2012 using data from the National Health Insurance Research Database. The satellite-derived normalized difference vegetation index (NDVI) from the global MODIS database in the NASA Earth Observing System is used to assess greenness. Cox proportional hazard models are used to determine the relationship between exposure to surrounding greenness and the incidence of T2DM, with adjustment for potential confounders. A total of 429,504 subjects, including 40,479 subjects who developed T2DM, were identified during the study period. There is an inverse relationship between exposure to surrounding greenness and the incidence of T2DM after adjustment for individual-level covariates, comorbidities, and the region-level covariates (adjusted HR = 0.81, 95% CI: 0.79-0.82). For the general population of Taiwan, greater exposure to surrounding greenness is associated with a lower incidence of T2DM., Competing Interests: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Environmental factors that drive vegetation change in the Three River Headwater Region (TRHR) on Qinghai-Tibetan Plateau are largely unknown. In particular, the response of alpine grasslands in the TRHR to changing climate and ecological compensations is still poorly understood. Here, we present data on vegetation trends of the TRHR from 1982 to 2015 by employing multiple high-resolution satellite data to determine the mean annual normalized difference vegetation index (NDVI). In addition, spatio-temporal changes in climate were monitored by long-term climate data collection and by using the distributed modeling system. It emerged that: 1) there was a weak increasing trend, albeit not significant, in overall TRHR NDVI, ranging between 0.23 and 0.27; whereas, grassland NDVI ranged between 0.43 and 0.50, and displayed a significant (r 2 adj  = 0.46; P = 0.004) linear increase with year; 2) annual average temperature was below 0 °C and increased linearly (r 2 adj  = 0.60; P = 0.01) at a rate of 0.06 °C/yr from 2000 to 2015, which was almost four times faster than the rate of global warming; and 3) average rainfall was 493 mm/yr, with no significant yearly trend. In conclusion, climate warming enhanced vegetation growth and recovery in the TRHR since 2000; whereas, rainfall did not show a trend. However, vegetation changes on the spatial scale demonstrated zoning and segmentation effects. Consequently, for restoration of degraded lands in the TRHR, effective one-to-one ecological conservation projects, which are particular to an eco-fragile area, should be implemented. In addition, these results are important for regional planning of livestock stocking rates and animal husbandry systems, which can have great impact on the livelihood of the people in the area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Carabid communities are influenced by landscape features. Chinese steppes are subject to increasing desertification processes that are changing land-cover characteristics with negative impacts on insect communities. Despite those warnings, how land-cover characteristics influence carabid communities in steppe ecosystems remains unknown. The aim of this study is to investigate how landscape characteristics drive carabid abundance in different steppes (desert, typical, and meadow steppes) at different spatial scales. Carabid abundances were estimated using pitfall traps. Various landscape indices were derived from Landsat 8 Operational Land Imager (OLI) images. Indices expressing moisture and productivity were, in general, those with the highest correlations. Different indices capture landscape aspects that influence carabid abundance at different scales, in which the patchiness of desert vegetation plays a major role. Carabid abundance correlations with landscape characteristics rely on the type of grassland, on the vegetation index, and on the scale considered. Proper scales and indices are steppe type-specific, highlighting the need of considering various scales and indices to explain species abundances from remotely sensed data.

To understand the dynamics of temperate forest in Northeast Asia and its response to climate change under the scenario of global change, we examined the temporal and spatial changes of normalized difference vegetation index (NDVI) and their correlation with temperature and precipitation of Changbai Mountain Nature Reserve in the growing season during 2001 and 2018, based on the remote sensing database of MODIS with a resolution of 250 m, land surface temperature data with a resolution of 1 km and meteorological data in the studied and surrounding area. The results showed that, in the growing season of 2001-2018, the averaged NDVI value of the study area was 0.711. Vegetation coverage was relatively high, increasing with a rate of 0.0025·a -1 . The temperature showed an extremely significantly increasing trend (0.032 ℃·a -1 ), the rate of which was higher than that at global level. Precipitation also showed a significantly increasing trend (5.54 mm·a -1 ) with increased interannual variation. Spatially, NDVI generally was higher in the northwest and decreased with elevation. During the study period, the area with increased NDVI accounted for 46.2%, mainly concentrated in the north and south central high-altitude areas, while 53.8% of total area remained unchanged or slightly decrease. NDVI of the study area was mainly affected by temperature. At the annual scale, NDVI and land surface temperature were positively correlated, with 90.2% presented positive correlation and 43.6% significantly correlated. At the monthly scale, the impact of temperature on NDVI was more significant at the beginning and the end of growing season.

The "resource availability hypothesis" predicts occurrence of larger rodents in more productive habitats. This prediction was tested in a dataset of 1,301 rodent species. We used adult body mass as a measure of body size and normalized difference vegetation index (NDVI) as a measure of habitat productivity. We utilized a cross-species approach to investigate the association between these variables. This was done at both the order level (Rodentia) and at narrower taxonomic scales. We applied phylogenetic generalized least squares (PGLS) to correct for phylogenetic relationships. The relationship between body mas and NDVI was also investigated across rodent assemblages. We controlled for spatial autocorrelation using generalized least squares (GLS) analysis. The cross-species approach found extremely low support for the resource availability hypothesis. This was reflected by a weak positive association between body mass and NDVI at the order level. We find a positive association in only a minority of rodent subtaxa. The best fit GLS model detected no significant association between body mass and NDVI across assemblages. Thus, our results do not support the view that resource availability plays a major role in explaining geographic variation in rodent body size., (© The Author(s) (2019). Published by Oxford University Press on behalf of Editorial Office, Current Zoology.)

This study aims to examine the spatio-temporal urban expansion pattern and its impacts on green space variation as well as thermal behavior in Doon valley over the last two decades during 2000 and 2019. Landsat 5 and Landsat 8 images of February and May month of two study years 2000 and 2019 were used for the analysis. The land use change analysis revealed notable outgrowth of urbanization with 184% increase in Doon valley during 2000-2019. To examine the effects of locational factors on urban growth, relative Shannon entropy analysis was carried out based on two factors, i.e., distances from city center and roads. It was seen that all the roads and city center have witnessed consistent and higher urban spread in its surroundings with high relative entropy value more than 0.9. Further analysis shows that there was considerable loss of agriculture crop lands and fallow lands along the major roads and around city center. Forest area was mostly affected along the road towards Mussorie hill station (road 2) because of its hilly surroundings whereas in Subhash Nagar area (road 4), fallow land and cultivated land were mainly replaced by the development activities. Analysis was also carried out to assess the spatial-temporal distribution of land surface temperature (LST) and its changing dynamics with land covers. It revealed that LST has increased in all the land use types with overall increase of 1.86 °C and 8.62 °C in the months February and May, respectively, during the study period. It is also found that normalized difference vegetation index (NDVI) and LST are negatively correlated with R 2 0.46 and 0.28 for the months February and May, respectively. However, the correlation between NDVI and LST was found highly significant with P value less than 0.01. Therefore, spatial and temporal changes of different land use types especially rapid urbanization at the cost of green spaces with rampant anthropogenic activities is one of the main factor for LST increase in the study area. Moreover, this temperature rise with ever-increasing anthropogenic activities is not a healthy indication for the hilly region like Doon Valley which may adversely affect the ecosystem stability and its resources as well. The study may be used as reference for future ecological and urban management studies and policies.

Asymmetric warming has been increasingly discussed recently, yet knowledge of this difference in warming between daytime and nighttime is still limited. Most studies of how climate warming influences the terrestrial ecosystem often ignore this asymmetric effect. We investigated the change in temperature between daytime and nighttime and analyzed the relationships between normalized difference vegetation index and the temperature in the daytime (T max ) and the nighttime (T min ) from 1982 to 2015 in temperate China. Results showed a faster increase in T min (0.46 °C dec -1 , p < 0.01) during the nighttime than in T max (0.42 °C dec -1 , p < 0.01) during the daytime, which indicated an asymmetric warming rate. The asymmetric warming during the daytime and nighttime was closely related to variations in precipitation and solar radiation. The increasing T min and T max were most pronounced over a large portion of the entire temperate China, and their warming trends displayed a non-uniform spatial distribution. The area with daytime warming was larger than that with nighttime warming, approximately accounting for 99.53% and 96.22% of temperate China, respectively. The area with warming enhancing vegetation greenness was larger during the day (71.16% of temperate China, p < 0.05) than at night (61.60% of temperate China, p < 0.05), and vice versa, which presented asymmetric warming effects on China's temperate vegetation. We also found clear differences in the responses of the normalized difference vegetation index among different vegetation biomes to this asymmetric warming. Averagely, T max was significantly related to the NDVI of shrub, desert, broadleaf forest, needleleaf forest, and swamp (p < 0.01). However, this similar relationship appeared only between T min and desert vegetation (p < 0.01). Our findings emphasized the crucial role of asymmetric warming between the daytime maxima and nighttime minima in climate change research.

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation., (© 2019 Published by Elsevier Inc.)