Mean Enhanced Vegetation Index Research Articles

The use of Enhanced Vegetation Index for assessing access to different types of green space in epidemiological studies.

Exposure to green space can protect against poor health through a variety of mechanisms. However, there is heterogeneity in methodological approaches to exposure assessments which makes creating effective policy recommendations challenging. Critically evaluate the use of a satellite-derived exposure metric, the Enhanced Vegetation Index (EVI), for assessing access to different types of green space in epidemiological studies. We used Landsat 5-8 (30 m resolution) to calculate average EVI for a 300 m radius surrounding 1.4 million households in Wales, UK for 2018. We calculated two additional measures using topographic vector data to represent access to green spaces within 300 m of household locations. The two topographic vector-based measures were total green space area stratified by type and average private garden size. We used linear regression models to test whether EVI could discriminate between publicly accessible and private green space and Pearson correlation to test associations between EVI and green space types. Mean EVI for a 300 m radius surrounding households in Wales was 0.28 (IQR = 0.12). Total green space area and average private garden size were significantly positively associated with corresponding EVI measures (β = < 0.0001, 95% CI: 0.0000, 0.0000; β = 0.0001, 95% CI: 0.0001, 0.0001 respectively). In urban areas, as average garden size increases by 1 m2, EVI increases by 0.0002. Therefore, in urban areas, to see a 0.1 unit increase in EVI index score, garden size would need to increase by 500 m2. The very small β values represent no 'measurable real-world' associations. When stratified by type, we observed no strong associations between greenspace and EVI. It is a widely implemented assumption in epidiological studies that an increase in EVI is equivalent to an increase in greenness and/or green space. We used linear regression models to test associations between EVI and potential sources of green reflectance at a neighbourhood level using satellite imagery from 2018. We compared EVI measures with a 'gold standard' vector-based dataset that defines publicly accessible and private green spaces. We found that EVI should be interpreted with care as a greater EVI score does not necessarily mean greater access to publicly available green spaces in the hyperlocal environment.

Association between residential greenness and depression symptoms in Chinese community-dwelling older adults

BackgroundStudies of residential greenness and depression symptoms among community-dwelling older adults in China are limited. However, understanding the role of greenness in depression symptoms among older adults can inform depression prevention and interventions. ObjectiveThis study explored the relationship between residential greenness and depression symptoms among community-dwelling older adults in China. MethodsA cluster random sampling method was used to survey 7512 community-dwelling adults aged 60 and above from three towns in Shanghai. Depression symptoms were assessed using the Geriatric Depression Scale (GDS30). Residential greenness was measured using the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). Long-term greenspace exposure was defined as the mean NDVI and EVI in the three years prior to the baseline survey. Controlling for the covariates, the relationship between greenness and depression symptoms was assessed using binomial logistic regression and mixed-effects linear regression. Interaction analysis was conducted to explore which covariates potentially alter the association. We also assessed the mediating role of physical activity. ResultsThe prevalence of depression symptoms among the participants was 13.72%. Higher residential greenness was associated with lower odds of depression symptoms, after adjusting for covariates. In the logistic regression analysis, the odds of depression symptoms decreased with increasing NDVI and EVI. In linear regression analysis, GDS30 scores decreased with increasing NDVI and EVI. Interaction analyses revealed that higher NDVI and EVI were more protective against depression among male individuals and older adults living with others than among female individuals and older adults living alone. Additionally, physical activity had a masking effect on residential greenness and depression symptoms. ConclusionHigher residential greenness is associated with lower odds of depression symptoms in community-dwelling Chinese older adults. Increasing urban and neighborhood green spaces may contribute to the prevention and intervention of depression symptoms in community-dwelling older adults.

Satellite-derived forest canopy greenness shows differential drought vulnerability of secondary forests compared to primary forests in Peru

Understanding tropical secondary forest canopy greenness and responses to climatic conditions is important for climate change mitigation, particularly in the tropics where secondary forest growth is a substantial carbon sink and a promoted natural climate solution. We here test three hypotheses: (a) forest canopy greenness is higher in younger, secondary forests than in older, primary or mature forests, (b) secondary forests are more vulnerable to climatic pressures and (c) there are significant differences between forest types regarding primary–secondary canopy greenness and their differential responses to drought anomalies. To explore these relationships, we monitored wet and dry seasonal greenness from 2001 to 2020, estimated through the enhanced vegetation index (EVI), of Peruvian tropical dry, montane and lowland secondary forests and compared it to nearby primary forests. We developed predictive models of seasonal EVI using remotely sensed variables, including land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), ratio of ET and PET (ETn), and the standard precipitation index (SPI). Overall, there was a higher change in annual and seasonal EVI for secondary forests compared to primary forests. However, primary forests maintained relatively stable EVI levels during the wet season despite drought anomalies. When decoupling forest type canopy greenness and drought response, primary forest greenness in dry and lowland ecosystems were temporally more stable. Secondary montane had a lower increase in greenness when drought anomalies held during different seasons. Stepwise multiple linear regression models indicated that LST and ETn, a plant water use index, were the most significant factors to predict greening fluctuations in dry and montane forest types. ET and SPI mostly drove wet season mean EVI across all forest types. Predictors of dry season mean EVI varied, but mostly including water availability. Our results suggest that tropical secondary forests are more productive overall yet more vulnerable to prolonged drought.

Responses of vegetation to changes in terrestrial water storage and temperature in global mountainous regions

As an important component of terrestrial ecosystem, vegetation acts as a sensitive recorder of changes in hydroclimatic conditions. Long-term time series of remote sensing-based vegetation indices and their influencing environmental driving factors, such as human activities and climate change, have been widely discussed in the literature. Globally, however, little is known about the hydroclimatic processes controlling vegetation changes in mountainous regions, which are conceived as more sensitive to climate change than other landscapes. The present study aims to quantify the respective roles of two dominant hydroclimatic factors, namely, TWS (i.e., terrestrial water storage) and Tair (i.e., temperature), in the spatio-temporal changes of mountainous vegetation over global six contrasting climate zones (i.e., tropical, arid, subtropical, temperate, sub-frigid, and frigid zones) during the period 2003–2016 based on EVI (i.e., enhanced vegetation index), TWS, Tair, and elevation data. Results indicate that the mean EVI shows a larger increasing trend (+0.85 %/decade, p-value < 0.01) and a larger decreasing trend in TWS (−85 mm/decade, p-value < 0.01) across the global mountainous regions than other global regions combined together (+0.61 %/decade, p-value < 0.01), particularly over high latitudes. With the increasing latitudes, the positive effect of temperature more dominates mountainous vegetation growth than moisture, as evidenced by the increasing trends of EVI with warming. However, in certain low-latitude mountainous regions (e.g., East Africa, South Asia, the western Tibetan Plateau, Brazil Plateau, and the southern Rocky Mountains), mountainous vegetation may face degradation due to water deficit induced by increased snowmelt, especially among the high-elevation ecosystems. The water availability controls vegetation activities more than Tair in the mid- and low-latitude regions, including the tropical, arid, and subtropical climate zones. These findings indicate that the potential shifts in mountainous vegetation may occur under the notable interactions with hydroclimatic factors, as the high-latitudes are experiencing ongoing warming and the mid- and low-latitudes are getting dryer.

Time to Move On: The Role of Greenness in Africa and Temperatures at a Mediterranean Stopover Site in Migration Decision of Long-Distance Migratory Passerines

Many migratory species have advanced their migration timing as a response to advanced breeding conditions. While data on arrival timing to breeding grounds in Europe is plentiful, information from the African departure sites are scarce. Here we investigated changes in arrival timing of four long-distance migratory passerines to a stopover site in Israel and potential links to Enhanced Vegetation Index (EVI) at the species-specific African pre-departure sites and local temperatures at the stopover site. We found that Lesser whitethroat (Curruca curruca) and Eastern Bonelli’s warbler (Phylloscopus orientalis) advanced arrival to the stopover site. The arrival timing of Thrush nightingale (Luscinia luscinia) and Olive-tree warbler (Hippolais olivetorum) did not change and was associated with mean EVI at the pre-departure site in Africa during the pre-migratory period. Additionally, temperatures at the stopover site affected the arrival timing of Lesser whitethroat only. This is probably because this species breed at higher northern latitudes and fine-tune their migration timing to match local conditions. Our results show that spring migration can be influenced by exogenous cues such as weather condition and food availability, and the level of response is species-specific. Moreover, some species show flexibility and fine-tuned migration speed in response to local conditions en route. While flexibility seems advantageous, dependence on multiple sites with varying conditions may ultimately limit advanced arrival to the breeding ground and result in mismatch with optimal conditions.

Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

Assessment of impacts to the sequence of the tropical cyclone Nisarga and monsoon events in shoreline changes and vegetation damage in the coastal zone of Maharashtra, India

The tropical cyclones impact both the eastern and western coasts of India, causing severe socio-environmental problems. This study analyzed shoreline changes and vegetation degradation caused by cyclone Nisarga and monsoon events in Maharashtra coastal zone and Mumbai region, India. In this study, the shoreline change was studied using the Net Shoreline Movement (NSM) statistical technique embedded in the digital shoreline analysis system (DSAS) tool. The effects of the cyclone on the vegetation were mapped using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and the rainfall distribution from Global Precipitation Measurement (GPM) data. The correlation between rainfall data and vegetation loss was analyzed using geographically weighted regression. The results also show that 90% of the events were concentrated in the 80–300 mm classes, being classified as sudden increases. This cyclone caused erosion in 56.32% of the shoreline; the highest erosion level was observed along the coastal zone of Maharashtra (near Mumbai city). Cyclone Nisarga has also impacted the vegetation loss most prominently in the region, with mean EVI in pre-cyclone equal to 0.4 and post-cyclone equal to 0.2. These eco-physical studies using geospatial technology are needed to understand the behavior of changes in shoreline and vegetation and can also help coastal managers plan for resilient coastal systems after the passage of tropical cyclones.

A Long-Term Spatiotemporal Analysis of Vegetation Greenness over the Himalayan Region Using Google Earth Engine

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.

GIS-based hazard and vulnerability assessment of a torrential watershed

Torrents are ephemeral streams which usually carry heavy bedload and flash flows. The frequent changes in its course caused by meandering and migration lead to extensive damage to environment in valley and alluvial land. In the present study, application programming interface (API) was developed to analyze temporal Landsat data in Google Earth Engine(GEE) to understand land use/land cover and vegetation characteristics in the Tangri (Dangri) River, India, and Indian Remote Sensing Satellites data were used for hazard and vulnerability assessment. It is observed that because of various conservation activities as well as due to watershed inhabitants’ intervention, the areas under cropland, orchard/plantation and grass/scrub have increased. The monthly mean enhanced vegetation index (EVI) has also increased in the watershed. Although, the average annual soil loss is high, i.e., 40.4 t/ha/yr. It is highest for mountain physiographic unit (67.6 t/ha/yr.) and scrub forest (61.5 t/ha/yr.). Based on flood inundation modeling, it is estimated that for two-, five-, ten-, twenty-five-, fifty- and hundred-year return periods, 1.42–16.14% areas of watershed are likely to get inundated. Torrents’ vulnerability analysis was carried out using analytical hierarchy process (AHP) to derive composite vulnerability index by aggregating weights of various parameters and sub-parameters. The alternatives for the treatment of vulnerable sections in torrential regime were evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and ELimination Et Choice Translating REality (ELECTRE) models. The methodology demonstrated has potential to analyze torrential systems and also for their proficient planning, monitoring and management.

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

AbstractThe Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

Increasing Summer Rainfall and Asymmetrical Diurnal and Seasonal Warming Enhanced Vegetation Greenness in Temperate Deciduous Forests and Grasslands of Northern China

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.

Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the United States.

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.

Differences of Regulative Flexibility between Hydrological Isolated and Connected Lakes in a Large Floodplain: Insight from Inundation Dynamics and Landscape Heterogeneity

The inundation areas of floodplains are crucial to wetland ecosystems, especially in supporting biodiversity. Accurately identifying the spatial and temporal patterns of inundation areas is important for understanding floodplain ecosystem processes. Here, lakes in the Yangtze River Floodplain were divided into two types according to hydrological conditions: the natural connected lakes (Dongting Lake and Poyang Lake) with natural water level fluctuations and the isolated lakes (lakes in Jianghan Plain) with stable water levels. We established a method to identify inundation areas using multi-sources remote sensing data based on the Google Earth Engine. The dynamics of inundation areas were determined, and the relative indices were calculated in common year (2017) and a drought year (2018). The differences between the connected lakes and the isolated lakes were analyzed, and impacts of hydrological fluctuations on inundation area and habitat quality were evaluated. The results show that lakes with natural hydrological fluctuations have a greater regulative flexibility, with both patch density (PD) and submerged elasticity index (SEI) values higher than that of isolated lakes. The trend of the vegetation index in the connected lakes and in the isolated lakes is also different. The mean EVI in Dongting Lake and Poyang Lake showed a U-shaped trend which is similar to the shape of the trend of PD. The trend of mean enhanced vegetation index (EVI) in the isolated lakes is the opposite and has a lower range of variation over a year. This study provides new indicators and rapid methods for habitat quality assessment in floodplains, as well as presenting scientific information useful for improving wetland management in the middle and lower Yangtze River.

Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo

Rainforests in the equatorial tropical regions are facing severe changes in strength and distribution due to natural and anthropogenic activities. Climate change causes major issues in vegetation characteristics whereas logging and developmental activities destruct the natural characteristics of terrain and vegetation. In the present research, MODIS vegetation cover type and vegetation indices (Enhanced Vegetation Index –EVI) data were interpreted to assess the decade (2008–2017) variation in vegetation types and strength characteristics in the Baram River Basin (BRB) in Borneo. International Geosphere-Biosphere Programme (IGBP) based classification identified ten land cover types in the BRB and the mean EVI in most periods varies between −0.30 and greater than 0.80. Results showed spatial and temporal variation in the area covered by individual land cover during the period of analysis and the fluctuation in mean EVI values indicates variation in strength characteristics of land cover types in the region. Statistical analysis showed a significant correlation (r > 0.50) of mean EVI values between consecutive years of analysis. Though the IGBP classification scheme could not categorise the most dominant land use activities in the BRB i.e. logging and oil palm plantations, the findings of the present research validate the use of MODIS data in equatorial tropical rainforest regions for characterising the regional scale vegetation dynamics.

Effect of hydrological variation on vegetation dynamics for wintering waterfowl in China’s Poyang Lake Wetland

Poyang Lake wetland of China, with designated national nature reserves, is one of the most important habitats for waterfowl in the world. During the wintering season, it is home to various rare and endangered species with abundant plant resources. However, extensive disturbances from both global climate change and human activities, such as the construction of hydroelectric dams, have exerted severe impacts on the hydrological regime and the vegetation dynamics in this region. To evaluate the suitability of the habitat for wintering waterfowl, it is necessary to monitor the spatio-temporal changes of vegetation over the protected wetland, and explore the potential environmental driving factors of the changes during the past decades. In this study, the time-series MODIS Enhanced Vegetation Index (EVI) data from 2000 to 2018, were used to analyze the seasonal dynamics and interannual tendencies of vegetation coverage over the wintering period from October to January. The results showed that the largest monthly mean EVI generally occurred in November, and was related to the quantity of wintering waterfowl in Poyang Lake wetland. The mean EVI in November had a lagged response to both temperature and accumulated precipitation. Long-term trend analysis revealed that the average EVI of the reserve increased significantly in the winters since the new millennium. Over the past 18 years, the hydrological regime, particularly the water area, is recognized as the dominant environmental factor determining the interannual variation of EVI. From 2010 onwards there was also an increasing influence of temperature. These analyses would help in the creation of appropriate management strategies to protect the habitat of wintering waterfowl in Poyang Lake wetland.

Ground-Based MAX-DOAS Observations of CHOCHO and HCHO in Beijing and Baoding, China

Glyoxal (CHOCHO) and formaldehyde (HCHO) trace gases were successfully retrieved from a multi-axis differential optical absorption spectroscopy (MAX-DOAS) system in Beijing (39.95°N, 116.32°E) and Baoding (39.15°N, 115.40°E), China. The measurements of these trace gases span the period from May 2017 to April 2018. Higher levels of trace gases were observed in Beijing most likely due to increased transport and industrial activities compared to Baoding. Different time scales were analyzed from seasonal to daily levels. Seasonal variation categorized by wintertime maximum and summertime minimum was observed for CHOCHO, while for HCHO maximum values were recorded during summer at both observation points. Variations in the diurnal cycle of trace gases were examined. The results are consistent with strong links to photo-oxidations of VOCs for HCHO production, whereas the CHOCHO diurnal variation can be related to anthropogenic effects in the evening. Weekends didn’t have any significant effect on both HCHO and CHOCHO. We investigated the temperature dependency of HCHO and CHOCHO. HCHO shows positive correlation with air temperature, which strengthened the argument that HCHO production is linked to photo-oxidation of VOCs. CHOCHO is anti-correlated with air temperature. This suggests that photolysis is a major sink for CHOCHO in Beijing and Baoding. We also investigated the relationship between CHOCHO and HCHO VCDs with enhanced vegetation index (EVI) data obtained from MODIS, which represents a direct relation with biogenic emissions. The positive correlations were observed among monthly mean HCHO VCDs and monthly mean EVI at both monitoring stations. The strong correlation of HCHO with EVI found, suggests that oxidation of isoprene and HCHO production is strongly related, while negative correlation was observed among CHOCHO VCDs and EVI.

Increased spatial heterogeneity in vegetation greenness due to vegetation greening in mainland China

Spatial heterogeneity in vegetation greenness (VG) can influence earth surface process and resource ecology. However, its long-term change and driving forces remain poorly understood. In this study, MODIS enhanced vegetation index (EVI) data was used to examine the change in spatial heterogeneity in VG and its relationships with vegetation greening in mainland China during 2000–2017. Moving window standard deviation and range of growing season mean EVI (GSEVI) were used as proxies for spatial heterogeneity in VG. It was found that moving window standard deviation of GSEVI increased significantly over 33.8% to 53.7% of mainland China during 2000–2017, while it decreased significantly over less than 5% of mainland China. The results of moving window range of GSEVI were similar to moving window standard deviation of GSEVI. These may be explained by: (1) increased standard deviation and range of GSEVI accompanied by increased GSEVI value; and (2) faster greening speed in dense than in sparse vegetated areas. Additionally, the increased spatial heterogeneity in VG means the increased difference in VG between arid and humid regions, and between urban cores (UCs) and rural areas. These may primarily be attributed to slower greening speed in arid than in humid regions, and in UCs than in rural areas. Thus some benefits from vegetation greening may be much less in arid than in humid regions, and in UCs than in rural areas. Overall, this study analyzed an interesting phenomenon that vegetation greening may increase spatial heterogeneity in VG, which has a series of implications for environment and human activities.

Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China

The interactions between climate change and vegetation have a significant impact on the dynamics of the global carbon cycle. Based on the observed meteorological data from 1961 to 2013 and the temperature and precipitation data simulated by various climate models (simulations phase 5 of the Climate Model Intercomparison Project dataset), this paper analyzes the temperature and precipitation changes of the Yangtze River Basin (YRB) and finds that they are a similar trend, that is, the temperature presents a significant upward trend (R2 = 0.49, p &lt; 0.01), and the variation trend of precipitation is not significant (R2 = 0.01). Specifically, based on observed meteorological data, the annual mean temperature increased significantly and the area of increasing temperature accounted for 99.94% of the total region (p &lt; 0.05); however, there was no significant change in annual precipitation. Ecological indicators (normalized difference vegetation index (NDVI); enhanced vegetation index (EVI); leaf area index (LAI); gross primary production (GPP); and net primary production (NPP)) of the YRB showed an increasing trend, and annual NDVI, annual EVI, LAI, annual total GPP and annual total NPP increased at respective rates of 0.002 yr−1, 0.001 yr−1, 0.07 m2m−2decade−1, 9 TgCyr−1yr−1, and 6 TgCyr−1yr−1, respectively. Correlation analysis between temperature/precipitation and NDVI/EVI/LAI/GPP/NPP was used to determine the relationships between climatic parameters and ecological indicators. Specifically, the temperature is significantly positively correlated with annual NDVI (R2 = 0.37, p &lt; 0.05), with annual mean LAI (R2 = 0.35, p &lt; 0.05) and with annual GPP (R2 = 0.37, p &lt; 0.05). In addition, there is a moderate positive correlation between mean EVI and mean growing season air temperature (R2 = 0.24); annual mean air temperature is a moderate positive correlation with annual NPP (R2 = 0.28). Our findings confirm that temperature is more closely related to ecological factors than precipitation over the YRB in these decades.

Sensitivity of mangrove range limits to climate variability

AbstractAimCorrelative distribution models have been used to identify potential climatic controls of mangrove range limits, but there is still uncertainty about the relative importance of these factors across different regions. To provide insights into the strength of climatic control of different mangrove range limits, we tested whether temporal variability in mangrove abundance increases near range limits and whether this variability is correlated with climatic factors thought to control large‐scale mangrove distributions.LocationNorth and South America.Time period1984–2011.Major taxa studiedAvicennia germinans, Avicennia schuaeriana, Rhizophora mangle, Laguncularia racemosa.MethodsWe characterized temporal variability in the enhanced vegetation index (EVI) at mangrove range limits using Landsat satellite imagery collected between 1984–2011. We characterized greening trends at each range limit, examined variability in EVI along latitudinal gradients near each range limit, and assessed correlations between changes in EVI and temperature and precipitation.ResultsSpatial variability in mean EVI was generally correlated with temperature and precipitation, but the relationships were region specific. Greening trends were most pronounced at range limits in eastern North America. In these regions variability in EVI increased toward the range limit and was sensitive to climatic factors. In contrast, EVI at range limits on the Pacific coast of North America and both coasts of South America was relatively stable and less sensitive to climatic variability.Main conclusionsOur results suggest that range limits in eastern North America are strongly controlled by climate factors. Mangrove expansion in response to future warming is expected to be rapid in regions that are highly sensitive to climate variability (e.g. eastern North America), but the response in other range limits (e.g. South America) is likely to be more complex and modulated by additional factors such as dispersal limitation, habitat constraints, and/or changing climatic means rather than just extremes.

How to deal with ground truthing affected by human-induced habitat change?: Identifying high-quality habitats for the Critically Endangered Red Siskin.

Species distribution models (SDM) can be valuable for identifying key habitats for conservation management of threatened taxa, but anthropogenic habitat change can undermine SDM accuracy. We used data for the Red Siskin (Spinus cucullatus), a critically endangered bird and ground truthing to examine anthropogenic habitat change as a source of SDM inaccuracy. We aimed to estimate: (1) the Red Siskin's historic distribution in Venezuela; (2) the portion of this historic distribution lost to vegetation degradation; and (3) the location of key habitats or areas with both, a high probability of historic occurrence and a low probability of vegetation degradation. We ground‐truthed 191 locations and used expert opinion as well as landscape characteristics to classify species' habitat suitability as excellent, good, acceptable, or poor. We fit a Random Forest model (RF) and Enhanced Vegetation Index (EVI) time series to evaluate the accuracy and precision of the expert categorization of habitat suitability. We estimated the probability of historic occurrence by fitting a MaxLike model using 88 presence records (1960–2013) and data on forest cover and aridity index. Of the entire study area, 23% (20,696 km2) had a historic probability of Red Siskin occurrence over 0.743. Furthermore, 85% of ground‐truthed locations had substantial reductions in mean EVI, resulting in key habitats totaling just 976 km2, in small blocks in the western and central regions. Decline in Area of Occupancy over 15 years was between 40% and 95%, corresponding to an extinction risk category between Vulnerable and Critically Endangered. Relating key habitats with other landscape features revealed significant risks and opportunities for proposed conservation interventions, including the fact that ongoing vegetation degradation could limit the establishment of reintroduced populations in eastern areas, while the conservation of remaining key habitats on private lands could be improved with biodiversity‐friendly agri‐ and silviculture programs.