Maximum Enhanced Vegetation Index Research Articles

Changes in peak greenness timing and senescence duration codetermine the responses of leaf senescence date to drought over Mongolian grassland

Leaf senescence is a key trait for vegetation in temperate regions to overcome winter temperatures, and extreme drought events have profoundly affected its timing. The effects of drought on autumn phenology have been investigated by correlating leaf senescence date (LSD) to multiple drought indices. However, leaf senescence is a dynamic process starting from peak greenness timing (PGT) and lasting several months (senescence duration, SD). How PGT and SD changes affected the LSD's response to drought is still poorly understood. Here, we investigated LSD's response to drought in the Mongolian grassland based on PGT and SD. We first extracted the LSD, PGT, and SD from the MODIS Enhanced Vegetation Index (EVI) during 2001–2020 and determined the late-season drought events (LDE) using the Standardized Precipitation Evapotranspiration Index. Subsequently, we quantified the response of LSD to LDE as the difference between drought and normal years (ΔLSD), and then quantified the contributions of changes in PGT and SD on ΔLSD. We also analyzed the effects of drought on late-season productivity. The results showed that LSD may advance or delay under LDE, but the earlier LSD (in 68.4 % of areas) was mainly distributed in warmer and drier regions. Earlier LSD under drought was more controlled by earlier PGT, while later LSD under drought was more controlled by extended SD. Changes in late-season productivity under LDE were more correlated with changes in annual maximum EVI (EVImax) than ΔLSD. These results enhanced our understanding of how LSD of temperate grassland responds to drought events and emphasized the importance of investigating the response of autumn phenology to drought from a process-based perspective.

Can economic growth and urban greenness achieve positive synergies during rapid urbanization in China?

Rapid urban expansion reflects the increasing economic growth in China. As an original conceive for urban sustainability, economic growth contributes to vegetation changes. However, the spatiotemporal heterogeneity of the relationships between these two factors remains unclear in China. After analyzing the spatiotemporal characteristics of the urban expansion in different cities in China from 2002 to 2020, we found a rapid and continuous urban expansion in China, with the mean annual expansion of 1710.96 km2 year−1 (p < 0.01). However, based on the trends of annual maximum enhanced vegetation index (EVImax) and the nighttime light (NTL), a strong incoordination status (areas with significant increasing NTL (p < 0.05) but significantly decreasing EVImax) between urban greenness and economic growthwas found during this rapid urban expansion period. Spatially, >60% of cities showed incoordination status and 38.08% of cities showed strongly incoordination status. At pixels levels, these strong incoordination status in urban areas can be fully explained by long-term average NTL digital Number (DN) gradient.Based on a piecewise linear regression model, the proportion of areas with the strong incoordination status was significantly increased by the 1.07% DN−1 (p < 0.01) when multi-year average NTL value is <50, but decreased strongly afterward, with the slope of −2.96% DN−1 (p < 0.01). The strong incoordination between urban greenness and economy can be rapidly mitigated with the rapid economic growth. This threshold can be a critical indicator for decision-makers in identifying and analyzing the status of ecological civilization in various regions of China.

Economic Growth Does Not Mitigate Its Decoupling Relationship with Urban Greenness in China

Accompanied by China’s rapid economic growth, significant urban greening has occurred in Chinese cities, in particular in the urban core areas. In contrast, rapid urbanization and economic growth also led to a high probability of vegetation degradation in urban fringe regions. However, these significant spatial differences in urban greenness associated with economic growth in Chinese cities are not well understood. This study explored the spatiotemporal characteristics of the nighttime light (NTL) and annual maximum enhanced vegetation index (EVImax) in urban areas from 2001 to 2020. A strong decoupling status between economic growth and urban greenness on the national scale was found. Overall, 49.15% of urban areas showed a decoupling status. Spatially, this percentage of urban areas with a decoupling status would significantly decrease when the long-term average NTL surpasses 51. Moreover, this significant threshold of decoupling status was found in 189 cities out of 344 (54.65%) in China. This threshold in each city showed significant spatial heterogeneity but can mostly be attributed to the gradient in the long-term average precipitation (Pmean) of each city during the period of 2001–2020. Specifically, a spatial increase in Pmean of 100 mm responded to a decrease in the threshold of 0.4 DN (p &lt; 0.01). In contrast, there was no significant correlation between the threshold and the economic growth status of each city. Our results provide valuable insights for coordinating the development of urban greening and economic growth.

Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI < 0.1) over arid/semi-arid shrublands and savannas, tropical and subtropical savannas, and temperate evergreen forests, whose LSP metrics were captured by our regional algorithm and not by the global product. Some ecosystems, such as arid/semi-arid shrublands and savannas, showed more irregular phenology with low seasonal dynamics, and the growing seasons could skip a year or occur more than once in a year depending on climate conditions. Our algorithm was more sensitive to ecosystems with low seasonal amplitudes. We found that the detectability of LSP increases as the growing season amplitude increases, regardless of vegetation cover. Evaluation of the LSP metrics using eddy covariance flux tower measurements of gross primary productivity (GPP) demonstrated the reliability and accuracy of the algorithm. These improved LSP retrievals provide a greater understanding of the vegetation phenology across diverse ecosystems, especially savanna, shrubland, and evergreen forest ecosystems that cover more than 30 % of the land globally. The LSP provides essential information for ecological and agricultural studies such as quantifying bushfire fuel accumulation and forest carbon cycling, whilst enhancing our capacity for quantifying ecological responses to climate change.

Vegetation productivity of the Lake Poyang Wetland in the post Three Gorges Dam era

三峡运行以来,气候变化与人类活动共同驱动了鄱阳湖水文情势的急剧变化,并对其湿地植被产生了显著影响,但当前研究尚未系统量化鄱阳湖湿地植被在此条件下的演变规律与趋势。因此,本研究以增强型植被指数EVI(enhanced vegetation index)的时段最高值作为表征湿地植被生产力的代用指标,定量揭示了后三峡工程时代鄱阳湖湿地植被生产力的气候态特征及其变化,包括变化的量级、显著性及阶段性。结果表明:(1)2000-2020年,鄱阳湖湿地植被EVI最高值在全年及春、秋两季分别为0.37、0.30和0.33,入湖河流三角洲及碟形洼地边滩是其生产力中心;春季EVI最高值在此区域高于秋季,而在湖心区低于秋季。(2)三峡运行后的湿地植被生产力变化在全年及春、秋两季均有极大空间异质性,在湖心深泓线附近显著增加,而在入湖河流三角洲及湿地边缘下降。春季EVI增长区域面积占比极大(84.2%),而秋季增长区面积占比较小(71.2%),即有较大面积区域EVI在秋季下降(38.8%)。(3)三峡运行后,鄱阳湖湿地植被生产力中心经历了原位增长、空间扩张以及湖心向转移3个阶段,最终完成了由支流三角洲向湖心区的转移。从总体来看,鄱阳湖湿地植被生产力在全年及春、秋季均呈增长趋势,增速分别为0.0035、0.0049和0.0028 EVI/a。本研究量化了后三峡工程时代的鄱阳湖湿地植被生产力变化,为该区域进一步的综合管理提供了数据支撑。;Since the operation of the Three Gorges Dam (TGD), the superposition of human activities and climate change has dramatically changed hydrological regimes of the Lake Poyang. These changes significantly affected the structure and function of the surrounding wetland ecosystems. However, vegetation variations in the Lake Poyang Wetland under this changing condition has not yet been systematically quantified. Hence, this study took the maximum EVI (enhanced vegetation index) as a proxy index to characterize vegetation productivity of the Lake Poyang Wetland, aiming to quantitatively reveal its changes in the post TGD era. The results show that: (1) During 2000 to 2020, the regional averaged maximum EVI of the wetland was 0.37, 0.30 and 0.33 in the whole year, spring, and autumn, respectively. River deltas of the lake tributaries and the dished depressions in the wetland were generally with higher EVI values compared to other areas, thus regarding as high productivity center of the wetland. In springs, the maximum EVI in the productivity center was higher than that in autumn, whereas it was lower than that in autumn in other areas with low EVI values such as the center of the lake. (2) Changes of vegetation productivity had great spatial heterogeneity for all the periods including the whole year, springs, and autumns. The maximum EVI values increased significantly in the central of the wetland (surrounding the thalweg), while the maximum EVI values decreased in river deltas of lake tributaries and wetland edge. The areas with increasing EVI values in spring accounted for about 84.2% of the whole wetland, while the area with increasing EVI in autumn accounted merely 71.2% of the wetland. That is, there was still a large proportion of the wetland with decreasing EVI in autumn (38.8%). (3) After TGD, the high productivity center of the wetland experienced three stages: in-situ growth, spatial expansion and towards lake center transfer, thus moved from river deltas to lake center areas. Overall, the maximum EVIs of the wetland showed an increasing trend, with a rate of 0.0035, 0.0049, and 0.0028 EVI/year for the annual, spring and autumn, respectively. The findings systematically quantified vegetation productivity changes of the Lake Poyang Wetland in the post TGD era. It also provided a scientific basis for proposing water resource management strategies to maintain the stability of wetland ecosystem.

Drought monitoring and its effects on vegetation and water extent changes using remote sensing data in Urmia Lake watershed, Iran

Abstract The assessment of drought hazards is important due to their socio-economic impacts on water resources, agriculture, and ecosystems. In this study, the effects of drought on changing water area and canopy of the Lake Urmia watershed in the northwest of Iran have been monitored and evaluated. For this purpose, the Standardized Precipitation Index (SPI) was calculated in short and medium periods (1-month and 3-month) to determine the dry-spell periods in the Lake Urmia basin. In reviewing this analysis, the annual average has been examined and evaluated. Furthermore, Moderate Resolution Imaging Spectroradiometer (MODIS) and remote sensing data were used to calculate the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Normalized Difference Water Index (NDWI), and the Temperature–Vegetation–Dryness Index (TVDI) to identify the area of water body, water level, and vegetation changes during 20 years (2000–2020). The Pearson correlation coefficient was also employed to explore the relationship between the drought and the remote sensing-derived indices. According to the results of drought analysis, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, and 2020 had experienced dry spells in the Lake Urmia basin. The NDWI changes also showed that the maximum area of the Lake Urmia happened in 2000, and its minimum was recorded in 2014. The variation of NDVI values showed that the highest values of vegetation cover were estimated to be 2,850 km2 in.2000, and its lowest value was 1,300 km2 in.2014. The maximum EVI and TDVI were calculated in 2000, while their minimum was observed in 2012 and 2014. Also, the correlation analysis showed that the SPI had the highest correlation with NDVI. Meanwhile, 1-month SPI had a higher correlation than the 3-month SPI with NDVI and EVI. As a concluding remark, NDVI and NDWI were more suitable indices to monitor the changes in vegetation and drought-related water area. The results can be used to make sound decisions regarding the rapid assessment of remote sensing-derived data and water-related indices.

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

ABSTRACT Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan’s Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R 2), Root Mean Square Error (RMSE, t/ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6–8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.

New assessment for residential greenness and the association with cortical thickness in cognitively healthy adults.

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. 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 750m 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. The average global cortical thickness and EVI were 3.05mm (standard deviation=0.1mm) and 0.31 (0.1), respectively. An interquartile range increase in EVI was associated with 11μm (95% confidence interval=3-20) and 9μm (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. 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.

Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

Wild Turkey Nest Success in Pine‐Dominated Forests of the Southeastern United States

ABSTRACTNest success is a primary component of productivity for wild turkeys (Meleagris gallopavo; turkeys) and there is concern that turkey productivity is declining across the southeastern United States. We evaluated the influence of nest site and landscape characteristics on risk of nest failure for turkeys in pine (Pinus spp.)‐dominated forests across the southeastern United States. We used Cox proportional hazard models to evaluate daily hazard of nest failure associated with nest site and landscape metrics within 500‐m and 1‐km buffers centered on nests. Of 451 nests monitored (n = 320 females) between 2014 and 2018, 76% failed, with predation as the primary cause. Daily hazard of nest failure increased by 1.2% for each day that females delayed nest incubation following the mean nesting date (29 Apr; βday = 0.010 ± 0.002 [SE]; hazard ratio [HR] = 1.01, 95% CI = 1.006–1.015). Environmental covariates associated with risk of nest failure included the maximum enhanced vegetation index (EVI) and distance to the nearest ecotone. Daily hazard increased with increasing distance away from an ecotone (βecotone = 0.16 ± 0.06; HR = 1.17, CI = 1.03–1.32) and with lower EVI around the nest (βEVI = −0.30 ± 0.06; HR = 0.74, CI = 0.65–0.83). Additional nest site or landscape covariates were included in competitive models but did not influence risk of nest failure significantly. Our study highlights the importance of considering landscape context when designing and implementing land management actions intended to enhance wild turkey reproduction. Our findings suggest that landscape metrics thought to be important to turkeys in northern agro‐forested landscapes may not be relevant to turkeys in pine ecosystems of the southeastern United States. © 2021 The Wildlife Society.

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.

Elevation-dependent effects of growing season length on carbon sequestration in Xizang Plateau grassland

Abstract Interannual variations in terrestrial carbon sequestration are jointly controlled by vegetation phenology and physiological activity. However, it remains unclear how this joint control varies with elevation in alpine ecosystems. To explore the relationships among these factors in Xizang Plateau grassland during 1981–2014, we used the remotely sensed total enhanced vegetation index (EVI) during the growing season (EVIGST), growing season length (GSL), and maximum EVI during the growing season (EVImax) as indicators of annual carbon sequestration, phenology, and photosynthetic activity, respectively. Using these indicators, we investigated the effects of phenology and photosynthetic activity on annual carbon sequestration along an elevation gradient. We found that EVIGST increased, indicating an improvement in carbon sequestration, under prolonged GSL and enhanced EVImax. The trend of a prolonged GSL increased with elevation by 0.039 day year−1 per 1000 m (p

Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0

Abstract. We present the first representation of grass pollen in a 3-D dispersion model in Australia, tested using observations from eight counting sites in Victoria. The region's population has high rates of allergic rhinitis and asthma, and this has been linked to the high incidence of grass pollen allergy. Despite this, grass pollen dispersion in the Australian atmosphere has not been studied previously, and its source strength is untested. We describe 10 pollen emission source methodologies examining the strengths of different immediate and seasonal timing functions, and the spatial distribution of the sources. The timing function assumes a smooth seasonal term, modulated by an hourly meteorological function. A simple Gaussian representation of the pollen season worked well (average r=0.54), but lacked the spatial and temporal variation that the satellite-derived enhanced vegetation index (EVI) can provide. However, poor results were obtained using the EVI gradient (average r=0.35), which provides the timing when grass turns from maximum greenness to a drying and flowering period; this is due to noise in the spatial and temporal variability from this combined spatial and seasonal term. Better results were obtained using statistical methods that combine elements of the EVI dataset, a smooth seasonal term and instantaneous variation based on historical grass pollen observations (average r=0.69). The seasonal magnitude is inferred from the maximum winter-time EVI, whereas the timing of the season peak is based on the day of the year when the EVI falls to 0.05 below its winter maximum. Measurements are vital to monitor changes in the pollen season, and the new pollen measurement sites in the Victorian network should be maintained.

Diverse Responses of Vegetation Dynamics to Snow Cover Phenology over the Boreal Region

Snow cover phenology plays an important role in vegetation dynamics over the boreal region, but the observed evidence of this interaction is limited. A comprehensive understanding of the changes in vegetation dynamics and snow cover phenology as well as the interactions between them is urgently needed. To investigate this, we calculated two indicators, the start of the growing season (SOS) and the annual maximum enhanced vegetation index (EVImax), as proxies of vegetation dynamics using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI). Snow cover duration (SCD) and snow cover end date (SCE) were also extracted from MODIS snow cover datasets. Then, we quantified the spatial-temporal changes in vegetation dynamics and snow cover phenology as well as the relationship between them over the boreal region. Our results showed that the EVImax generally demonstrated an increasing trend, but SOS varied in different regions and vegetation types from 2001 to 2014. The earlier onset of SOS was mainly concentrated in the Siberian boreal region. In the Eurasian boreal region, we observed an advance in the SCE and decrease in the SCD, while in the North American boreal region, the spatial distribution of the trends exhibited substantial heterogeneity. Our results also indicated that the snow cover phenology had significant impacts on the SOS and the EVImax, but the effects varied in different regions, vegetation types, and climate gradients. Our findings provide strong evidence of the interaction between vegetation dynamics and snow cover phenology, and snow cover should be considered when analyzing future vegetation dynamics in the boreal region.

Investigating spatiotemporal relationship between EVI of the MODIS and climate variables in northern Iran

Climate variability and fluctuations dramatically affect terrestrial ecosystems and their variations. Several studies have been conducted on the relationship between them in terms of use of vegetation indices. In this study, GIS-based spatiotemporal analyses were used to model the relationship between vegetation variations based on the EVI-MODIS and its response to land surface temperature (LST) and rainfall in Mazandaran province during the period of 2000–2016 in the north of Iran. The LST parameter was derived from the 17-year MODIS data, and rainfall parameter was achieved via meteorological station data in the region. Correlation and linear regression analyses at 0.95% confidence level (P value = 0.5) were used to study the relationship between spatiotemporal enhanced vegetation index (EVI) and two climatic parameters. The results indicated that the EVI had a rising trend over the study period. This was mostly due to the increase in paddy fields. The result also shows significant spatial correlation between EVI with LST values, which was direct during winter and inverse during summer. The tabulate area analysis showed that throughout the winter months, the spatial distribution of pixels matched the highest EVI values in pixels with a maximum temperature (20–27 °C), while during June to September, the maximum EVI values were related to areas where the LST was less than 25 °C. Although we found no significant simultaneous relationship between EVI/MODIS and rainfall in studied area, but by 1.5–2.5 months lag time in spring season, the relation between them reaches peak.

Spatio-Temporal Variation Analysis of Landscape Pattern Response to Land Use Change from 1985 to 2015 in Xuzhou City, China

Rapid urbanization has dramatically spurred economic development since the 1980s, especially in China, but has had negative impacts on natural resources since it is an irreversible process. Thus, timely monitoring and quantitative analysis of the changes in land use over time and identification of landscape pattern variation related to growth modes in different periods are essential. This study aimed to inspect spatiotemporal characteristics of landscape pattern responses to land use changes in Xuzhou, China durfing the period of 1985–2015. In this context, we propose a new spectral index, called the Normalized Difference Enhanced Urban Index (NDEUI), which combines Nighttime light from the Defense Meteorological Satellite Program/Operational Linescan System with annual maximum Enhanced Vegetation Index to reduce the detection confusion between urban areas and barren land. The NDEUI-assisted random forests algorithm was implemented to obtain the land use/land cover maps of Xuzhou in 1985, 1995, 2005, and 2015, respectively. Four different periods (1985–1995, 1995–2005, 2005–2015, and 1985–2015) were chosen for the change analysis of land use and landscape patterns. The results indicate that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has occurred in a dual-core development mode throughout the urbanization process. We quantified the patch complexity, aggregation, connectivity, and diversity of the landscape, employing a number of landscape metrics to represent the changes in landscape patterns at both the class and landscape levels. The results show that with respect to the four aspects of landscape patterns, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation in landscape patterns was examined based on 900 × 900 m sub-grids. Combined with the land use changes and spatiotemporal variations in landscape patterns, urban growth mainly occurred in a leapfrog mode along both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period of 1995 to 2005, and the edge-expansion and leapfrog modes coexisted in the period from 2005 to 2015. The high value spatiotemporal information generated using remote sensing and geographic information system in this study could assist urban planners and policymakers to better understand urban dynamics and evaluate their spatiotemporal and environmental impacts at the local level to enable sustainable urban planning in the future.

Local variability in the timing and intensity of tropical dry forest deciduousness is explained by differences in forest stand age

Tropical Dry Forest deciduousness is a behavioral response to climate conditions that determines ecosystem-level carbon uptake, energy flux, and habitat conditions. It is regulated by factors related to stand age, and landscape scale variability in deciduous phenology may affect ecosystem functioning in forests throughout the tropics. This study determines whether observed phenological differences are explainable by forest age in the southern Yucatán Peninsula in Mexico, where forest clearing for shifting cultivation has created a mosaic of forest stands of varying age. Matched-pair statistical tests compare neighboring forest pixels of different age class (12–22 years versus 22+ years) and detect significant differences in Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI)-derived metrics related to the timing and intensity of deciduousness during three dry seasons (2008–2011). In all seasons, young forests exhibit significantly more intense deciduousness, measured as total seasonal change of EVI normalized by annual maximum EVI (p < 0.001), and larger normalized EVI change during successive dry season months relative to start-of-dry-season EVI (p < 0.001), than neighboring older forests subject to similar environmental conditions.

Spatiotemporal patterns of vegetation phenology change and relationships with climate in the two transects of East China

Changes in vegetation phenology due to global climate change directly impact the dynamic balance of terrestrial carbon and nutrients and the biodiversity pattern, and send feedbacks to climate system. It is crucial to understand spatiotemporal patterns and mechanisms of vegetation phenology change and their relationship with climatic change. In this paper, we quantified the spatiotemporal patterns of start and end of growing season for seven vegetation types in Northeast China Transect (NECT) and North–South Transect of East China (NSTEC) during 2001–2013 using MODIS Enhanced Vegetation Index (EVI). The integral of EVI during growing season was also calculated as an additional phenological index. We then proceeded to analyze the relationship between the three phenological metrics and climate variables including daytime and nighttime temperature and precipitation. To study the temporal phenological trend of deciduous broadleaved forest in northeast China, we identified stands of deciduous broadleaved forest without land cover changes during 2001–2013 and derived the slopes of temporal changes in phenological metrics. Regression was applied to quantify the relationship between phenological trends and trends of climate variables.The vegetation types along the two transects in eastern China have distinctly different phenological characteristics and the strength of climate modulations on phenology varies among vegetation types. The study of temporal trend of phenological changes of the deciduous broadleaved forest in northeast China revealed that nighttime temperature is the most important driver. Increasing in nighttime temperature in spring tends to advance the start of growing season, while increasing in nighttime temperature during growing season tends to delay the growing season. Cold daytime prior to growing season also favors advanced start of growing season. On the other hand, summer nighttime temperature tends to bring down, whereas precipitation tends to enhance, the maximum EVI and hence the EVI integral as the latter is mostly determined by maximum EVI and to a less extent by length of growing season.

Drivers of Productivity Trends in Cork Oak Woodlands over the Last 15 Years

Higher biodiversity leads to more productive ecosystems which, in turn, supports more biodiversity. Ongoing global changes affect ecosystem productivity and, therefore, are expected to affect productivity-biodiversity relationships. However, the magnitude of these relationships may be affected by baseline biodiversity and its lifeforms. Cork oak (Quercus suber) woodlands are a highly biodiverse Mediterranean ecosystem managed for cork extraction; as a result of this management cork oak woodlands may have both tree and shrub canopies, just tree and just shrub canopies, and just grasslands. Trees, shrubs, and grasses may respond differently to climatic variables and their combination may, therefore, affect measurements of productivity and the resulting productivity-biodiversity relationships. Here, we asked whether the relationship between productivity and climate is affected by the responses of trees, shrubs, and grasses in cork oak woodlands in Southern Portugal. To answer this question, we linked a 15-year time series of Enhanced Vegetation Index (EVI) derived from Landsat satellites to micrometeorological data to assess the relationship between trends in EVI and climate. Between 2000 and 2013 we observed an overall decrease in EVI. However, EVI increased over cork oaks and decreased over shrublands. EVI trends were strongly positively related to changes in relative humidity and negatively related to temperature. The intra-annual EVI cycle of grasslands and sparse cork oak woodland without understorey (savannah-like ecosystem) had higher variation than the other land-cover types. These results suggest that oaks and shrubs have different responses to changes in water availability, which can be either related to oak physiology, to oaks being either more resilient or having lagged responses to changes in climate, or to the fact that shrublands start senesce earlier than oaks. Our results also suggest that in the future EVI could improve because the rate of increase in minimum EVI is greater than the rate of decrease in maximum EVI, and that this is contingent on management of the shrub understorey as it affects the rate of decrease in maximum EVI. This will be the challenge for the persistence of cork oak woodlands, their associated biodiversity and social-ecological system.

A geo-statistical approach to model Asiatic cheetah, onager, gazelle and wild sheep shared niche and distribution in Turan biosphere reserve-Iran

Presence data for four mammals in the Turan Biosphere Reserve in Iran including the Asiatic cheetah (Acinonyx jubatus venaticus), the Persian onager (Equus hemionus onager), the wild sheep (Ovis vignei), and the gazelle (Gazelle Bennettii) were used to analyze and model their potential interaction, facilitation, habitat coverage and niche dimensions. A geostatistical approach using the spatial autocorrelation between the locality points, and their relationship with habitat resources and characteristics with application of remotely sensed maximum enhanced vegetation index (EVI) and surface temperature, elevation, aspect, vegetation cover and soil moisture was used to predict herbivores species niche. The potential suitable habitat of herbivores along with environmental variables was used to model the predator species (cheetah) niche. The model results were tested using fivefold cross validation by area under the curve (AUC) values on set of independent testing data and were compared to more commonly used models of generalized linear model (GLM) and MaxEnt. The results show that cheetah's potential suitable habitat has 61% overlap with wild sheep, 36% with onager, and 30% with gazelle. Onager habitat has 64% overlap with gazelle and 60% the wild sheep. Wild sheep on the hand, shares only 37% of its habitat with gazelle. The most prey and predator interaction exists between cheetahs and wild sheep, while onagers provides facilitation for gazelles and wild sheep by potentially providing extra water sources. Among the implemented modeling techniques, spatial GLM showed better performance over GLM and MaxEnt. We suggest that conservation effort should focus more on maintaining the population of wild sheep and onagers to support other species in the habitat.