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Monitoring vegetation dynamics in terrestrial national parks (TNPs) is crucial for ensuring sustainable environmental management and mitigating the potential negative impacts of short- and long-term disturbances understanding the effect of climate change within natural and protected areas. This study aims to monitor the vegetation dynamics of TNPs in Indonesia by first categorizing them into the regions of Sumatra, Jawa, Kalimantan, Sulawesi, and Eastern Indonesia and then applying ready-to-use MODIS EVI time-series imageries (MOD13Q1) taken from 2000 to 2022 on the GEE cloud-computing platform. Specifically, this research investigates the greening and browning fraction trends using Sen's slope, considers seasonality by analyzing the maximum and minimum EVI values, and assesses anomalous years by comparing the annual time series and long-term median EVI value. The findings reveal significantly increasing greening trends in most TNPs, except Danau Sentarum, from 2000 to 2022. The seasonality analysis shows that most TNPs exhibit peak and trough greenness at the end of the rainy and dry seasons, respectively, as the vegetation response to precipitation increases and decreases. Anomalies in seasonality that is affected by climate change was detected in all of the regions. To increase TNPs resilience, suggested measures include active reforestation and implementation of Assisted Natural Regeneration, strengthen the enforcement of fundamental managerial task, and forest fire management., (© 2024. The Author(s).)

The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs)., 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 Ltd. All rights reserved.)

Montane cloud forests (MCFs) are ecosystems frequently immersed in fog and are vital for the terrestrial hydrological cycle and biodiversity hotspots. However, the potential impacts of climate change, particularly intensified droughts and typhoons, on the persistence of ecosystems remain unclear. Our study conducted cross-scale assessments using 6-year (2016-2021) ground litterfall and 21-year (2001-2021) satellite greenness data (the Enhanced Vegetation Index [EVI] and the EVI anomaly change [ΔEVI % ]), gross primary productivity anomaly change (ΔGPP % ), and meteorological variables (the standardized precipitation index [SPI] and wind speed). We found a positive correlation between summer EVI and ΔGPP % with the SPI-3 (3-month time scale), while winter litterfall showed a negative correlation. Maximum typhoon daily wind speed was negatively correlated with summer and the monthly ΔEVI % and ΔGPP % . These findings suggest vegetation damage and productivity loss were related to drought and typhoon intensities. Furthermore, our analysis highlighted that chronic seasonal droughts had more pronounced impacts on MCFs than severe typhoons, implying that high precipitation and frequent fog immersion do not necessarily mitigate the ramifications of water deficit on MCFs but might render MCFs more sensitive and vulnerable to drought. A significant negative correlation between the summer and winter ΔEVI % and ΔGPP % of the same year, suggesting disturbance severity during summer may facilitate vegetation regrowth and carbon accumulation in the subsequent winter. This finding may be attributed to the ecological resilience of MCFs, which enables them to recover from the previous summer. In the long-term, our results indicated an increase in vegetation resilience over two decades in MCFs, likely driven by rising temperatures and elevated carbon dioxide levels. However, the enhancement of resilience might be overshadowed by the potential intensified droughts and typhoons in the future, potentially causing severe damage and insufficient recovery times for MCFs, thus raising concerns about uncertainties regarding their sustained resilience., (© 2023 John Wiley & Sons Ltd.)

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region., 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 © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities., (© 2023 The Ecological Society of America.)

The study of vegetation phenology is of great significance for understanding global climate change. The Yellow River basin has a wide spatial range and a complex ecological environment. The phenological characteristics of forest and grassland need further clarification. Based on the MODIS-EVI data from 2000 to 2018, we extracted the phenology of forest and grassland in the Yellow River basin using piecewise logistic and double logistic phenological models with the corresponding curvature change extremum method and derivative method, respectively. The temporal and spatial variations of phenological parameters were analyzed. The start of growing season (SOS) was at 90-165 day of year (DOY), and gradually delayed from southeast to northwest. The increase of 100 m elevation delayed SOS 0.94 d, and the SOS of forest was earlier than that of grassland. The end of growing season (EOS) was at 270-315 DOY, which delayed from west to southeast. For every 100 m increase in altitude, the EOS advanced 0.63 d, with EOS of forest being later than that of grassland. The length of growing season (LOS) was 110-230 d, which shortened gradually from southeast to northwest. The LOS of forest was larger than that of grassland. During the study, SOS showed an advance trend from 2000 to 2018 with a rate of 4.1 d·(10 a) -1 , and the proportion of spatial advance area was 73.2%. There was an obvious advance in the central part of the basin. EOS generally showed a significant postponement trend with a rate of 2.3 d·(10 a) -1 , and the proportion of spatially delayed area was 63.4%, the phenological advance and delay of forest was less stronger than that of grassland. LOS showed a significant prolongation trend with a rate of 6.4 d·(10 a) -1 , and the proportion of spatial extension was 71.8%. The piecewise Logistic and double Logistic phenological models and the corresponding curvature extremum method and derivative method were suitable for the extraction of natural vegetation in the Yellow River Basin. The overall LOS of forest and grassland showed a prolonging trend, which was shortened with the increases of altitude. The LOS of forest was longer than that of grassland in the study area.

Background: Residential greenness offers health benefits to old people, but evidence of its association with the health of old people with disability is scarce. Moreover, due to the limited mobility of this vulnerable population, air pollutants may play an indispensable mediating role in that association, which however remains understudied., Objectives: This study aimed to investigate the association between residential greenness and all-cause mortality risk and the joint mediation effect of air pollutants among old people with disability., Methods: A total of 34,075 old people with disability were included in the Chengdu Long-term Care Insurance cohort. Participants' residential greenness exposure was measured by an enhanced vegetation index within the 500 m buffer zone (EVI 500m ). Causal mediation analysis was conducted to assess the total effect (TE) of residential greenness and the natural indirect effect (NIE) through PM 2.5 , CO, NO 2 , SO 2 , and O 3 on all-cause mortality., Results: The TE of EVI 500m on the all-cause mortality risk in overall participants showed negative, which, decreased from the 2 nd quartile (HR = 0.93, 95 % CI: 0. 91, 0.95) to the 4 th quartile (HR = 0.81, 95 % CI: 0.76, 0.85); the NIE through the five air pollutants also decreased from the 2 nd quartile (HR = 0.96, 95 % CI: 0.95, 0.98) to the 4 th quartile (HR = 0.90, 95 % CI: 0.88, 0.93), with the proportion mediated decreased from 48 % to 44 %. The stronger TE or NIE were observed in participants aged <80 years old, men, with mild-moderate disability, and having outdoor experience every week., Conclusion: Exposure to residential greenness was associated with a decreased risk of mortality, partially through the pathways of air pollutants, which varied by age, sex, degree of disability, and frequency of weekly outdoors. Our findings would provide evidence to develop aging-friendly cities., 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. Published by Elsevier B.V.)

Aerosol optical depth (AOD) provides useful information on particulate matter pollution at both regional and global levels. In this study, the long-term datasets of aerosols, meteorological parameters, and enhanced vegetation index (EVI) were used from September 2002 to December 2021 over Turkey. This study examined the spatiotemporal distribution of aerosols and their association with meteorological parameters (temperature (Temp), relative humidity (RH), wind speed (WS)), and EVI over Turkey from 2002 to 2021. Moreover, this study also performed a comparison of AOD retrieved from Aqua with other satellites (Terra, SeaWiFS, and MISR) and ground-based (AERONET) products. The higher mean seasonal AOD (> 0.3) was observed over Southeastern Anatolia Region due to the dust transport from the Saharan Desert and Arabian Peninsula. Moreover, AOD was positively correlated with Temp and WS in the east of Turkey, while negative correlations were observed in the coastal regions. The correlation between AOD and RH was also observed negative in most parts of Turkey. Furthermore, in the coastal region, the correlation between AOD and EVI was found to be positive, whereas a negative correlation was seen over less vegetative areas. The multi-seasonal AOD averages were calculated as 0.187, 0.183, 0.138, and 0.104 for the spring, summer, autumn, and winter seasons, respectively. The most important result of this study is the regional differences in AOD over Turkey. For new studies, AOD should be observed separately for coastal areas and the eastern part of Turkey., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R 2 ) is found to be highest between rainfall and latent heat flux (R 2  = 0.59) followed by rainfall and specific humidity (R 2  = 0.35), and rainfall and sensible heat flux (R 2  = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm -2 winter -1 , 0.0065 Wm -2 pre-monsoon -1 and 0.0272 Wm -2 post-monsoon -1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm -2 winter -1 , 0.00249 Wm -2 pre-monsoon -1 and 0.0037 Wm -2 post-monsoon -1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm -2 winter -1 , 0.0038 Wm -2 pre-monsoon -1 and decreasing trend at the rate of 0.0080 Wm -2 post-monsoon -1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents., Objective: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada., Methods: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level., Results: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (-7.009; 95% CI -13.130 to -0.980) and for both males (-4.544; 95% CI -8.224 to -0.895) and females (-3.513; 95% CI -6.289 to -0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature., Conclusions: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being., (©Abu Yousuf Md Abdullah, Jane Law, Christopher M Perlman, Zahid A Butt. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 28.07.2022.)

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km 2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions., (© 2022 The Ecological Society of America.)

A 21-year Enhanced Vegetation Index (EVI) time-series produced from MODIS satellite images was used to study the complex phenological cycle of the drought semi-deciduous shrub Phlomis fruticosa and additionally to identify and compare phenological events between two Mediterranean sites with different microclimates. In the more xeric Araxos site, spring leaf fall starts earlier, autumn revival occurs later, and the dry period is longer, compared with the more favorable Louros site. Accordingly, the control of climatic factors on phenological events was examined and found that the Araxos site is mostly influenced by rain related events while Louros site by both rain and temperature. Spring phenological events showed significant shifts at a rate of 1-4.9 days per year in Araxos, which were positively related to trends for decreasing spring precipitation and increasing summer temperature. Furthermore, the climatic control on the inter-annual EVI fluctuation was examined through multiple linear regression and machine learning approaches. For both sites, temperature during the previous 2-3 months and rain days of the previous 3 months were identified as the main drivers of the EVI profile. Our results emphasize the importance of focusing on a single species and small-spatial-scale information in connecting vegetation responses to the climate crisis.

Alpine grasslands play important functions in mitigating climate change and regulating water resources. However, the spatiotemporal variability of their carbon and water budgets remains unquantified. Here, 47 site-year observations of CO 2 and water vapor fluxes (ET) are analyzed at sites situated along a hydrothermal gradient across the Qinghai-Tibetan Plateau, including an alpine wetland (wettest), an alpine shrub (coldest), an alpine meadow, an alpine meadow-steppe, and an alpine steppe (driest and warmest). The results show that the benchmarks for annual net ecosystem exchange (NEE) are -79.3, -77.8, -66.7, 20.2, and 100.9 g C m -2 year -1 at the meadow, shrub, meadow-steppe, steppe, and wetland, respectively. The peak daily NEE normalized by peak leaf area index converges to 0.93 g C m -2 d -1 at the 5 sites. Except in the wetland (722.8 mm), the benchmarks of annual ET fluctuate from 511.0 mm in the steppe to 589.2 mm in the meadow. Boosted regression trees-based analysis suggests that the enhanced vegetation index (EVI) and net radiation (R n ) determine the variations of growing season monthly CO 2 fluxes and ET, respectively, although the effect is to some extent site-specific. Inter-annual variability in NEE, ecosystem respiration (RES), and ET are tightly (R 2 > 0.60) related to the inter-growing season NEE, RES, and ET, respectively. Both annual RES and annual NEE are significantly constrained by annual gross primary productivity (GPP), with 85% of the per-unit GPP contributing to RES (R 2 = 0.84) and 15% to NEE (R 2 = 0.12). Annual GPP significantly correlates with annual ET alone at the drier sites of the meadow-steppe and the steppe, suggesting the coupling of carbon and water is moisture-dependent in alpine grasslands. Over half of the inter-annual spatial variability in GPP, RES, NEE, and ET is explained by EVI, atmospheric water vapor, topsoil water content, and bulk surface resistance (r s ), respectively. Because the spatial variations of EVI and r s are strongly regulated by atmospheric water vapor (R 2 = 0.48) and topsoil water content (R 2 = 0.54), respectively, we conclude that atmospheric water vapor and topsoil water content, rather than the expected air/soil temperatures, drive the spatiotemporal variations in CO 2 fluxes and ET across temperature-limited grasslands. These findings are critical for improving predictions of the carbon sequestration and water holding capacity of alpine grasslands., 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 © 2021. Published by Elsevier B.V.)

Background: Recent evidence suggests that neurological health could be improved with the intervention of local green space. A few studies adopted cortical thickness, as an effective biomarker for neurodegenerative disorder, to investigate the association with residential greenness. However, they relied on limited data sources, definitions or applications to assess residential greenness. Our cross-sectional study assessed individual residential greenness using an alternative measure, which provides a more realistic definition of local impact and application based on the type of area, and investigated the association with cortical thickness., Methods: The study population included 2542 subjects who participated in the medical check-up program at the Health Promotion Center of the Samsung Medical Center in Seoul, Korea, from 2008 to 2014. The cortical thickness was calculated by each of the four and global lobes from brain MRI. For greenness, we used the enhanced vegetation index (EVI) that detects canopy structural variation by adjusting background noise based on satellite imagery data. To assess individual exposure to residential greenness, we computed the maximum annual EVI before the date of a medical check-up and averaged it within 750 m from subjects' homes to represent an average walking distance. Finally, we assessed the association with cortical thickness by urban and non-urban populations using multiple linear regression adjusting for individual characteristics., Results: The average global cortical thickness and EVI were 3.05 mm (standard deviation = 0.1 mm) and 0.31 (0.1), respectively. An interquartile range increase in EVI was associated with 11 μm (95% confidence interval = 3-20) and 9 μm (1-16) increases in cortical thickness of the parietal and occipital regions among the urban population. We did not find associations in non-urban subjects., Conclusions: Our findings confirm the association between residential greenness and neurological health using alternative exposure assessments, indicating that high exposure to residential greenness can prevent neurological disorders., 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 © 2021 Elsevier B.V. All rights reserved.)

The heating degree days (HDDs) could indicate the climate impact on energy consumption and thermal environment conditions effectively during the winter season. Nevertheless, studies on the spatial-temporal changes in global HDDs and their determinants are scarce. This study used multi-source data and several methods to explore the rules of the spatial distribution of global HDDs and their interannual changes over the past 49 years and some critical determinants. The results show that global HDDs generally became larger in regions with higher latitudes and altitudes. Most global change rates of HDDs were negative ( p < 0.10) and decreased to a greater extent in areas with higher latitudes. Most global HDDs showed sustainability trends in the future. Both the HDDs and their change rates were significantly partially correlated with latitude, altitude, mean albedo, and EVI during winter, annual mean PM 2.5 concentration, and nighttime light intensity ( p = 0.000). The HDDs and their change rates could be simulated well by the machine learning method. Their RMSEs were 564.08 °C * days and 3.59 °C * days * year -1 , respectively. Our findings could support the scientific response to climate warming, the construction of living environments, sustainable development, etc.

We analyzed the impacts of drought severity on a variety of sectors in a topographically complex basin (the upper Aragón basin 2181 km 2 ) in the Central Spanish Pyrenees. Using diverse data sources including meteorological and hydrological observations, remote sensing and tree rings, we analyze the possible hydrological implications of drought occurrence and severity on water availability in various sectors, including downstream impacts on irrigation water supply for crop production. Results suggest varying responses in forest activity, secondary growth, plant phenology, and crop yield to drought impacts. Specifically, meteorological droughts have distinct impacts downstream, mainly due to water partitioning between streamflow and irrigation channels that transport water to crop producing areas. This implies that drought severity can extend beyond the physical boundaries of the basin, with impacts on crop productivity. This complex response to drought impacts makes it difficult to develop objective basin-scale operational definitions for monitoring drought severity. Moreover, given the high spatial variability in responses to drought across sectors, it is difficult to establish reliable drought thresholds from indices that are relevant across all socio-economic sectors. The anthropogenic impacts (e.g. water regulation projects, ecosystem services, land cover and land use changes) pose further challenges to assessing the response of different systems to drought severity. This study stresses the need to consider the seasonality of drought impacts and appropriate drought time scales to adequately assess and understand their complexity., 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 © 2021 Elsevier B.V. All rights reserved.)

Considerable debate exists on whether exposure to vegetation cover is associated with better mental health outcomes. Past studies could not accurately capture people's exposure to surrounding vegetation and heavily relied on non-spatial models, where the spatial autocorrelation and latent covariates could not be adjusted. Therefore, a suite of five different vegetation measures was used to separately analyze the association between vegetation cover and the number of psychotic and non-psychotic disorder cases in the neighborhoods of Toronto, Canada. Three satellite-based and two area-based vegetation measures were used to analyze these associations using Poisson lognormal models under a Bayesian framework. Healthy vegetation cover was found to be negatively associated with both psychotic and non-psychotic disorders. Results suggest that the satellite-based indices, which can measure both the density and health of vegetation cover and are also adjusted for urban and environmental perturbations, could be better alternatives to simple ratio- and area-based measures for understanding the effect of vegetation on mental health. A strong dominance of spatially structured latent covariates was found in the models, highlighting the importance of adopting a spatial approach. This study can provide critical guidelines for selecting appropriate vegetation measures and developing spatial models for future population-based epidemiological research.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change., (© 2020 by the Ecological Society of America.)

Knowledge of the dynamics of dryland vegetation in recent years is essential for combating desertification. Here, we aimed to characterize nonlinear changes in dryland vegetation greenness over East Inner Mongolia, an ecotone of forest-grassland-cropland in northern China, with time series of Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GEOV2 leaf area index (LAI) values during 2000 to 2016. Changes in the growing season EVI and LAI were detected with the polynomial change fitting method. This method characterizes nonlinear changes in time series by polynomial fitting with the highest polynomial order of three, and simultaneously provides an estimation of monotonic trends over the time series by linear fitting. The relative contribution of climatic factors (precipitation and temperature) to changes in the EVI and LAI were analyzed using linear regression. In general, we observed similar patterns of change in the EVI and LAI. Nonlinear changes in the EVI were detected for about 21% of the region, and for the LAI, the percentage of nonlinear changes was about 16%. The major types of nonlinear changes include decrease-increase, decrease-increase-decrease, and increase-decrease-increase changes. For the overall monotonic trends, very small percentages of decrease (less than 1%) and widespread increases in the EVI and LAI were detected. Furthermore, large areas where the effects of climate variation on vegetation changes were not significant were observed for all major types of change in the grasslands and rainfed croplands. Changes with an increase-decrease-increase process had large percentages of non-significant effects of climate. The further analysis of increase-decrease-increase changes in different regions suggest that the increasing phases were likely to be mainly driven by human activities, and droughts induced the decreasing phase. In particular, some increase-decrease changes were observed around the large patch of bare areas. This may be an early signal of degradation, to which more attention needs to be paid to combat desertification.

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R ≥ 2 0.76 ; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation., 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., (© 2019 The Authors.)

Airborne pollen might be transported over thousands of kilometres, which has important ecological, evolutionary and clinical consequences. The long-distance transport (LDT) of birch (Betula sp.) pollen has been described in detail for northern Europe. However, a comprehensive analysis of this transport from other European regions is lacking. This study focused on the post-seasonal LDT of birch pollen to Poland (central Europe), with special attention paid to determining potential source areas of pollen and describing the causal mechanism favouring LDT episodes. Pollen monitoring (1997-2016) was conducted in Poznań and Rzeszów (500 km away from each other) using volumetric traps. The LDT episodes were characterized by analysing the (1) bi-hourly backward air mass trajectories using the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT); (2) sea level pressure (SLP) and 500 hPa geopotential height (z500) anomalies; and (3) patterns of the Enhanced Vegetation Index to determine the birch flowering time along the moving air mass trajectories. The potential locations of birch populations within broadleaved forests were estimated with GLOBCOVER data. Finally, the movement of pollen emitted from potential source areas was simulated using the HYSPLIT dispersion model. LDT episodes were mainly recorded in the first fortnight of May. The main source areas of pollen to Poland were western Russia, Belarus and to a lesser extent the eastern Baltic republics and the Scandinavian Peninsula. In most cases, a high-pressure centre located over Scandinavia and an elevated z500 over Germany-Denmark-Sweden favoured pollen transport. On average, the post-seasonal LDT episodes of birch pollen to Poland occur almost every year (Poznań) or every second year (Rzeszów). The episodes are highly variable in time; thus, the pollen concentration may unexpectedly cause allergy symptoms in sensitized patients. In some cases, these episodes may be extremely severe, thereby prolonging and strengthening the exposure to birch pollen allergens., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Global increase in drought occurrences threatens the stability of terrestrial ecosystem functioning. Evergreen broadleaf forests (EBFs) keep leaves throughout the year, and therefore could experience higher drought risks than other biomes. However, the recent temporal variability of global vegetation productivity or land carbon sink is mainly driven by non-evergreen ecosystems, such as semiarid grasslands, croplands, and boreal forests. Thus, we hypothesize that EBFs have higher stability than other biomes under the increasingly extreme droughts. Here we use long-term Standardized Precipitation and Evaporation Index (SPEI) data and satellite-derived Enhanced Vegetation Index (EVI) products to quantify the temporal stability (ratio of mean annual EVI to its SD), resistance (ability to maintain its original levels during droughts), and resilience (rate of EVI recovering to pre-drought levels) at biome and global scales. We identified significantly increasing trends of annual drought severity (SPEI range: -0.08 to -1.80), area (areal fraction range: 2%-19%), and duration (month range: 7.9-9.1) in the EBF biome over 2000-2014. However, EBFs showed the highest resistance of EVI to droughts, but no significant differences in resilience of EVI to droughts were found among biomes (forests, grasslands, savannas, and shrublands). Global resistance and resilience of EVI to droughts were largely affected by temperature and solar radiation. These findings suggest that EBFs have higher stability than other biomes despite the greater drought exposure. Thus, the conservation of EBFs is critical for stabilizing global vegetation productivity and land carbon sink under more-intense climate extremes in the future., (© 2019 John Wiley & Sons Ltd.)

Weather extremes and extreme climate events, like late spring frosts, are expected to increase in frequency and duration during the next decades. Although spring phenology of European beech is well adapted to escape freeze damages on longer time scales, the effects of occasional late spring frosts (LSF) are among the main climatic damages to these forests to such an extent that they limit beech distribution and elevation range, especially at its southern margin. The aim of this work was to evaluate the short-term effects of two consecutive LSF events occurred in 2016 and 2017 in Italy on the beech forest vegetation activity. Remotely sensed land surface temperature (LST) data were used to detect the pixels where LSF occurred, while enhanced vegetation index (EVI) data were used to quantify LSF effects by computing a spring vegetation activity anomaly index (sAI). In 2016 and 2017, the LSF covered, respectively, about 29% and 32% of the total Italian beech-dominated area. The two LSF widely differed in their spatial patterns and their effects. In 2016, the pixels belonging to the sAI classes with the highest spring anomalies were also those where prolonged LSF occur, while, in 2017, the pixels belonging to the highest sAI classes were those that underwent the shorter (but probably more intense) LSF events. Under scenarios of increased frequency risk of repeated LSF, the proposed methodology may represent an automatic and low-cost tool both for monitoring and predicting European beech growth patterns.

Ecosystems can undergo abrupt transitions between alternative stable states when the driver crosses a critical threshold. Dynamical systems theory shows that when ecosystems approach the point of loss of stability associated with these transitions, they take a long time to recover from perturbations, a phenomenon known as critical slowing down. This generic feature of dynamical systems can offer early warning signals of abrupt transitions. However, these signals are qualitative and cannot quantify the thresholds of drivers at which transition may occur. Here, we propose a method to estimate critical thresholds from spatial data. We show that two spatial metrics, spatial variance and autocorrelation of ecosystem state variable, computed along driver gradients can be used to estimate critical thresholds. First, we investigate cellular-automaton models of ecosystem dynamics that show a transition from a high-density state to a bare state. Our models show that critical thresholds can be estimated as the ecosystem state and the driver values at which spatial variance and spatial autocorrelation of the ecosystem state are maximum. Next, to demonstrate the application of the method, we choose remotely sensed vegetation data (Enhanced Vegetation Index, EVI) from regions in central Africa and northeast Australia that exhibit alternative states in woody cover. We draw transects (8 × 90 km) that span alternative stable states along rainfall gradients. Our analyses of spatial variance and autocorrelation of EVI along transects yield estimates of critical thresholds. These estimates match reasonably well with those obtained by an independent method that uses large-scale (250 × 200 km) spatial data sets. Given the generality of the principles that underlie our method, our method can be applied to a variety of ecosystems that exhibit alternative stable states., (© 2019 by the Ecological Society of America.)

Cities are natural laboratories for studying vegetation responses to global environmental changes because of their climate, atmospheric, and biogeochemical conditions. However, few holistic studies have been conducted on the impact of urbanization on vegetation growth. We decomposed the overall impacts of urbanization on vegetation growth into direct (replacement of original land surfaces by impervious built-up) and indirect (urban environments) components, using a conceptual framework and remotely sensed data for 377 metropolitan statistical areas (MSAs) in the conterminous United States (CONUS) in 2001, 2006, and 2011. Results showed that urban pixels are often greener than expected given the amount of paved surface they contain. The vegetation growth enhancement due to indirect effects occurred in 88.4%, 90.8%, and 92.9% of urban bins in 2001, 2006, and 2011, respectively. By defining offset value as the ratio of the absolute indirect and direct impact, we obtained that growth enhancement due to indirect effects compensated for about 29.2%, 29.5%, and 31.0% of the reduced productivity due to loss of vegetated surface area on average in 2001, 2006, and 2011, respectively. Vegetation growth responses to urbanization showed little temporal variation but large regional differences with higher offset value in the western CONUS than in the eastern CONUS. Our study highlights the prevalence of vegetation growth enhancement in urban environments and the necessity of differentiating various impacts of urbanization on vegetation growth, and calls for tailored field experiments to understand the relative contributions of various driving forces to vegetation growth and predict vegetation responses to future global change using cities as harbingers., (© 2018 John Wiley & Sons Ltd.)

Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVI TGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVI TGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions., (© 2017 by the Ecological Society of America.)

Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility., (© 2017 by the Ecological Society of America.)

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM 2.5 ) is currently quite limited in China. By introducing NO₂ and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM 2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO₂ and EVI, where cross-validation R² increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM 2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM 2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM 2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO₂ and EVI in GWR models could more effectively estimate PM 2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China., Competing Interests: The authors declare no conflict of interest.

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0-5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions., (© The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Temporal variability in primary productivity can change habitat quality for consumer species by affecting the energy levels available as food resources. However, it remains unclear how habitat-quality fluctuations may determine the dynamics of spatially structured populations, where the effects of habitat size, quality and isolation have been customarily assessed assuming static habitats. We present the first empirical evaluation on the effects of stochastic fluctuations in primary productivity--a major outcome of ecosystem functions--on the metapopulation dynamics of a primary consumer. A unique 13-year dataset from an herbivore rodent was used to test the hypothesis that inter-annual variations in primary productivity determine spatiotemporal habitat occupancy patterns and colonization and extinction processes. Inter-annual variability in productivity and in the growing season phenology significantly influenced habitat colonization patterns and occupancy dynamics. These effects lead to changes in connectivity to other potentially occupied habitat patches, which then feed back into occupancy dynamics. According to the results, the dynamics of primary productivity accounted for more than 50% of the variation in occupancy probability, depending on patch size and landscape configuration. Evidence connecting primary productivity dynamics and spatiotemporal population processes has broad implications for metapopulation persistence in fluctuating and changing environments., (© 2016 The Authors.)

Urbanization is one of the major environmental challenges facing the world today. One of its particularly pressing effects is alterations to local and regional climate through, for example, the Urban Heat Island. Such changes in conditions are likely to have an impact on the phenology of urban vegetation, which will have knock-on implications for the role that urban green infrastructure can play in delivering multiple ecosystem services. Here, in a human-dominated region, we undertake an explicit comparison of vegetation phenology between urban and rural zones. Using satellite-derived MODIS-EVI data from the first decade of the 20th century, we extract metrics of vegetation phenology (date of start of growing season, date of end of growing season, and length of season) for Britain's 15 largest cities and their rural surrounds. On average, urban areas experienced a growing season 8.8 days longer than surrounding rural zones. As would be expected, there was a significant decline in growing season length with latitude (by 3.4 and 2.4 days/degree latitude in rural and urban areas respectively). Although there is considerable variability in how phenology in urban and rural areas differs across our study cities, we found no evidence that built urban form influences the start, end, or length of the growing season. However, the difference in the length of the growing season between rural and urban areas was significantly negatively associated with the mean disposable household income for a city. Vegetation in urban areas deliver many ecosystem services such as temperature mitigation, pollution removal, carbon uptake and storage, the provision of amenity value for humans and habitat for biodiversity. Given the rapid pace of urbanization and ongoing climate change, understanding how vegetation phenology will alter in the future is important if we wish to be able to manage urban greenspaces effectively.

As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)