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Increasing urbanization has affected vegetation cover (VC) in various regions worldwide. However, barely any attention has been paid to Pakistan, which is facing rapid urbanization. The study utilized MODIS Land Cover (MDC12Q1, 500 m resolution) and Enhanced Vegetation Index (MOD13A3, 1000 m resolution) data to examine urbanization trends and their effects on vegetation cover in Pakistan from 2005 to 2019. The difference between urban EVI and rural EVI (ΔEVI) was used as a representation of the impact of urbanization on VC in Pakistan. Results revealed an increment of 4.27% in the average urban area of the nine (9) cities from 2005 (321.19 km2) to 2019 (334.91 km2). Pakistan’s annual ΔEVI was negative (− 0.05695), suggesting that the urban EVI is lower than the rural EVI. However, the ΔEVI in six (6) cities decreased significantly (p < 0.05) from 2005 to 2019. The average annual effect of the urbanization area on the VC was 461.5 km2 for Pakistan. The seasonal spatial distribution and trends are nearly stable. These results indicated that the impacts of urbanization on VC should be considered sustainable development in Pakistan, which could also be evident in other developing countries. [ABSTRACT FROM AUTHOR]

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, ‘creeping’ events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A ‘fair’ to ‘moderate’ concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context. [ABSTRACT FROM AUTHOR]

Abstract 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.

Increased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area's vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings. [ABSTRACT FROM AUTHOR]

Canopy layer urban heat island (CLUHI) is closely related to about 4.7 billion urban residents’ well-being. Compared with sparse observation stations, remote sensing can obtain spatially continuous temperature, which is conducive to intra-annual variation and associated factors of CLUHI over large regions. Such research is scarce, especially from apparent temperature perspective. Thus, the intra-annual spatio-temporal variations and twelve determinants of CLUHI intensities (CLUHIIs) were investigated for 917 urban region agglomerations in China’s five ecological regions, using remotely sensed near-surface air temperature, apparent temperature and other multi-source data, and several spatial analysis and mathematical statistic methods. The findings revealed that annual CLUHIIs by mean temperatures were most between 0.5 and 1.0 ℃, higher in the summer and spring, and larger in North China than South China in the summer. The annual CLUHIIs by maximum temperatures were negative in several cities, especially in rainless West China. North China generally had stronger CLUHIIs by minimum temperatures in all seasons compared to South China. The CLUHIIs by mean apparent temperatures were larger in the summer (most between 0.5 and 2.0 ℃) and varied across ecological regions. Significant positive correlations generally existed among CLUHIIs by various air temperature and land surface temperature indices (p

Numerous studies have examined the potential impacts of urban expansion on vegetation growth. However, the impacts of urbanization on vegetation growth (Urbi) across urban-rural gradients and its driving forces remain poorly understood. In this study, we used satellite observations of MODIS Enhanced Vegetation Index (EVI) from 2001 to 2019 to quantify the spatial–temporal dynamics of indirect urbanization impacts on vegetation growth and analysed their driving forces in 60 prefectural cities across China. The relationship between the Urbi and urban intensity (β) was S-shaped, with a turning point where the indirect impact changed from negative to positive. The left-moving trend of the turning point in the southeastern cities indicated the expansion of areas with positive Urbi during the last two decades. We further investigated the spatially heterogeneous effects of two natural and three anthropogenic driving factors on the Urbi across the urban-rural gradient in different climate zones. The results showed that in temperate cities, as urban intensity increased, the presence of turning points was probably attributed to population increase. In cold cities, the change in the Urbi was most likely induced by the enlarging population and increasing economic level from rural to urban areas. However, in arid cities, it was mainly determined by water supply and the cool island effect in urban cores rather than socioeconomic factors.

Although considerable effort has been deployed to understand the impact of climate variability and vegetation change on runoff in major basins across Africa, such studies are scarce in the Gulf of Guinea Basin (GGB). This study combines the Budyko framework and elasticity concept along with geospatial data to fill this research gap in 44 nested sub‐basins in the GGB. Annual rainfall from 1982 to 2021 show significant decreasing and increasing trends in the northern and southern parts of the GGB, respectively. Annual potential evapotranspiration (PET) also shows significant increasing trends with higher magnitudes observed in the northern parts of the GGB. Changing trends in climate variables corroborates with shift to arid and wetter conditions in the north and south, respectively. From 2000 to 2020 vegetation cover estimated using enhanced vegetation index (EVI) shows significant increasing trends in all sub‐basins including those experiencing a decline in annual rainfall. Vegetation composition measured using vegetation continuous fields (VCFs) from 2000 to 2020 show an increase in tree canopy cover (TC), a decline in short vegetation cover and marginal changes in bare ground cover (BG). Elasticity coefficients show that a 10% increase in annual rainfall and PET may lead to a 33% increase and 24% decline in runoff, respectively. On the other hand, a 10% increase in EVI may lead to a 4% decline in runoff while a 10% increase in TC, SV and BG may reduce runoff by 4% and increase runoff by 3% and 2%, respectively. Even though changes are marginal, decomposing vegetation into different parameters using EVI and VCFs may lead to different hydrological effects on runoff which is one of the novelties of this study that may be used for implementing nature‐based solutions. The study also demonstrates that freely available geospatial data together with analytical methods are a promising approach for understanding the impact of climate variability and vegetation change on hydrology in data‐scarce regions. [ABSTRACT FROM AUTHOR]

Utilizing path analysis, we examined the interconnectedness among six meteorological variables. Among these, three pertain to energy conditions—air temperature, net solar radiation, and reference evapotranspiration (ET0)—while the others are associated with hydrological conditions: precipitation, relative humidity, and water deficiency. These variables were assessed across five distinct temporal delay levels to understand their influences on the normalized difference vegetation Index (NDVI) and enhanced vegetation index (EVI) within grassland areas situated in the state of Espírito Santo, southeastern Brazil. The images underwent processing using analytical algorithms and a geographic information system (GIS). The direct and indirect impacts of these variables on the NDVI and EVI exhibited remarkable similarity across varying temporal delays and geographic regions. Meteorological variables explained over 50% of the observed variation in both indices, occasionally even reaching levels of 70%. Temperature and relative humidity primarily exerted direct effects on the indices. Conversely, precipitation exhibited indirect effects on the indices, often in conjunction with other hydrological variables. ET0 demonstrated a direct effect on the vegetation indices, particularly after a delay of 32 days. Solar radiation and water deficiency displayed direct effects up to the 32-day mark, implying that vegetation responds more promptly to these variables. The proposed methodology enabled a consistent and stable assessment of the direct and indirect effects of meteorological variables on vegetation indices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Carbon Monoxide (CO) is an important urban pollutant with a relation to transition economies based on emission intensities. In this study, Sentinel-5, MODerate resolution Imaging Spectroradiometer (MODIS), and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them. From the assessed correlations for 932 Iranian cities, it occurred that the assessed indices were all correlated. The highest CO levels were 0.031 in the spring of 2019 and 2020, whereas in 2021 it was equal to 0.030 in both the spring and winter, respectively. In 2019 and 2020 the maximum values of the Enhanced Vegetation Index (EVI) in the spring were 0.181 and 0.183. Exceptionally high Absorbing Aerosol Index (AAI) values of – 0.834 and – 1.0, along with Urban Index (UI) of 0.102 and 0.092, were correlated with recorded spikes in CO level, despite that these seasons’ EVI values were not so abnormal. It was forecasted that in 2030 rises in the CO level by 13.2% in the winter and by 17.5% in the fall are expected, with the simultaneous increase of AAI by 204.5% and 980.2%, and Aerosol Optical Depth (AOD) by 27% and 5% in the winter and spring, respectively.

Introduction: Vegetation provides valuable ecosystem services to various terrestrial ecosystems, such as carbon sequestration, fodder supply, and water resource regulation. It is predicted that the fluctuation and changes of climate in the future will have a great impact on terrestrial ecosystems, including the destruction of rangelands and desertification, increased drying of trees and reduction of carbon sequestration. Drought is a complex and climatic phenomenon that occurs frequently or intermittently in any type of climate and its progress is slow, so that its effects may appear gradually. Due to the high sensitivity of vegetation to climate fluctuations especially in arid and semi-arid regions, droughts can affect vegetation faster and disrupt its natural growth. Therefore, knowing the severity, duration and place covered by the drought phenomenon could reduce the damages. As the global air temperature is continuously increasing under the influence of climate change, the spatio-temporal pattern of rainfall and land use is expected to change significantly in the coming decades. Therefore, it is necessary to develop more accurate and effective drought monitoring tools. In this case, remote sensing plays an important role in drought studies since it ptovides unique data from the earth's surface. Satellite data provide the possibility to identify the damaged vegetation in different types of uses and assess the severity of the damage. Using satellite data to monitor plant activities in a large area has many advantages compared to studies based on traditional methods and is currently a hot topic of pioneering research in the field of drought diagnosis. The purpose of this research is to evaluate the dynamics of vegetation cover and the effect of climate change on it in Eshtehard Karaj township. It is hoped that its results will be used by managers and planners of natural resources for sustainable management of vegetation. Materials and methods: This research was done in two parts. In the first part, the effect of drought on vegetation was investigated using Standardised Precipitation Evapotranspiration Index (SPEI) and Enhanced vegetation index (EVI), and in the next part, the vulnerability of vegetation was calculated using EVI index. In the second part, SPEI of 3, 6, 9 and 12 months were calculated in MATLAB software for 8 meteorological stations. It should be noted that SPEI has positive and negative values, in which the more negative values indicate the severity of drought. After calculating the SPEI index in the scale of 3, 6, 9 and 12 months for all 8 weather stations, drought intensity maps were obtained using the Inverse Distance Weighted (IDW) method. Then, the ready-made EVI, the MOD13Q1 product of the MODIS sensor was used from the beginning of 2001 to the end of 2022. First, the average EVI index for the entire county of Eshtehard was extracted on a monthly basis and the month with the highest average EVI was selected as the target month. Pearson correlation and coefficient of determination (R2) were calculated between SPEI maps of 3, 6, 9 and 12 months and EVI of the selected month in Earth Trend Modeler (ETM) TerrSet software. Then the time scale of SPEI with the highest R2 was extracted. In the following, the intensity of EVI changes of the selected month was calculated in terms of SPEI changes (slope of changes) using linear analysis in ETM tool of TerrSet software. In the second part, the probability of vegetation vulnerability was calculated using the probability of vegetation vulnerability index (PVVI). For this reason, vegetation condition index (VCI) was first calculated using EVI index. After calculating the VCI index for the selected month of each year in the period from 2001 to 2022, this index was classified into five classes. Then the probability percentage of each class was calculated. Finally, the PVVI index was calculated by multiplying the weight of each class by the probability of its occurrence. Results: The monthly average EVI index showed that the months of April and October have the highest and lowest average value of EVI in the studied area, respectively. The month of April was chosen as the target month. According to the R2, the relationship between SPEI in different time scales and EVI of the chosen month, in 68.9% of the studied area, three-month SPEI has the strongest relationship with vegetation compared to 6, 9, 12-month SPEI. Most of the mountainous areas in the south of Eshtehard city are in this class. Also, parts of the northwest are included in this class. SPEI of six months in 17.3% of the study area has the strongest relationship with vegetation and SPEI ratio in other time scales. Examining the results of this section showed that the general spatial pattern of vegetation vulnerability probability obtained from the probability of vegetation vulnerability index (PVVI) is to an acceptable extent similar to the pattern of the intensity map of vegetation changes compared to drought. Therefore, the middle belt from west to east is in the probability of vegetation vulnerability map in the high and very high vulnerability class (26.4% and 14.4% of the study area, respectively). In general, 13% and 20.6% of the studied area are placed in the classes of low and very low probability of vulnerability, respectively. These areas are mostly concentrated in the northern belt from east to west, as well as south and southwest of the studied area. Discussion and conclusion: The results of evaluating the probability of vegetation vulnerability in Eshtehard city showed that the general spatial pattern of the probability of vegetation vulnerability obtained from the PVVI index is to an acceptable extent similar to the pattern of the map of intensity of changes in vegetation compared to drought. Therefore, it can be said that the PVVI index can determine areas with high vulnerability of vegetation for better drought management. [ABSTRACT FROM AUTHOR]

In Mongolia, where nomadic pastoralism is still practiced by around one-third of the population, increasing livestock numbers, socio-economic constraints and climate change raise concerns over rangeland health. Little empirical evidence explains what triggers camp moves of pastoralists in the Dzungarian Gobi in Mongolia, which factors influence grazing mobility around camps, and how altitudinal migration benefits small livestock. We combined GPS tracking data of 19 small livestock herds monitored from September 2018 to April 2020 with remotely sensed climate and environmental data. We used general linear-mixed models to analyse variables influencing camp use duration and daily mobility patterns. To understand the importance of the altitudinal migration, we compared climatic conditions along the elevation gradient and looked at seasonal body weight changes of small livestock. We found that available plant biomass and season best explained camp use duration. Daily walking distance and maximum distance from camp increased with camp use duration. Pasture time increased with increasing biomass and rising temperatures. We conclude that herders in the Dzungarian Gobi have optimized pasture use by reacting to changes in biomass availability at landscape and local scale, and by embracing altitudinal migration. Flexibility in grazing mobility seems to have enabled local herder communities to practise sustainable pasture use. Maintaining this mobility will most likely be the best strategy to deal with environmental change under the current climate change scenarios. [ABSTRACT FROM AUTHOR]

• We studied the urban–rural gradient of urban vegetation phenology changes (△UVP) and its drivers across 1500+ cities in China. • Along the urban–rural gradient, the SOS advance and EOS delay increase first, decrease later, and finally steady. • The △UVP is contributed more by climate change than by urbanization level for urban subzones. A comprehensive investigation on the long-term changes of urban vegetation phenology (△UVP) is critical for a better understanding of changes in urban ecosystems. However, the urban–rural gradient of the △UVP across cities with diverse climate zones and city sizes, as well as the associated drivers, remains largely unclear. Here, using multi-source remote sensing data from 2001 to 2019, we investigated the urban–rural gradient (including six urban–rural subzones, i.e., the old urban core, new urban core, new urban area, suburban area, exurban area, and rural area) of the △UVP across 1500 + cities in China and analyzed the impacts of urbanization level and climate change on the △UVP. First, over the past two decades, the △UVP during spring (i.e., the changes of the start of growing season, termed △SOS) is generally negative (advanced SOS; 73 % ± 4 %) and that during autumn (i.e., changes of the end of growing season, termed △EOS) is mainly positive (delayed EOS; 63 % ± 3 %) in all urban–rural subzones. The spatial distribution of the △UVP based on enhanced vegetation index (EVI) data is relatively consistent with that based on solar-induced chlorophyll fluorescence (SIF) data. Along the urban–rural gradient, both the SOS advance and EOS delay exhibit an increasing first, decreasing later, and finally steady curve, with the maximum occurring in the new urban area at − 12.4 ± 18.5 days (for △SOS) and 3.8 ± 9.9 days (for △EOS). Second, the SOS changes from an advance to a delay from the middle temperate to southern subtropical zones, while the EOS changes from a delay to an advance. The magnitudes of both △SOS and △EOS show a general trend of increasing first and then decreasing with decreasing latitude. Third, the SOS advance and EOS delay generally decrease with city size for the old urban core, increase initially and decrease later with city size for the three suburban subzones, while they show little deviations with city size for the new urban core and new urban area. Finally, in the three urban subzones, the mean △UVP across all the cities is contributed more by climate change (mean: △SOS: 73 %; △EOS: 78 %) than by urbanization level (mean: △SOS: 27 %; △EOS: 22 %). Our findings can improve the understanding of urban vegetation changes in the context of rapid urbanization and climate change. [ABSTRACT FROM AUTHOR]

Extracting crop phenophases from satellite remote sensing data is crucial for managing agricultural activities and estimating crop yield over large scales. The traditional Vegetation Indices (VIs), such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), primarily indicate changes in vegetation greenness, which characterize crop phenophases well in vegetative growth period but may be difficult to associate with phenophases in reproductive growth period that are more related with physiology status. In this study, we investigated the potential of the satellite Solar-Induced chlorophyll Fluorescence (SIF) on extracting the crop phenophases in reproductive growth period, i.e., milk-ripe phase and maturity phase, using single-season cropland in mid-temperate zone in China as a test bed. We found that SIF outperformed EVI and NDVI in extracting milk-ripe phase and maturity phase of maize, rice and wheat using double logistic method. In particular, SIF-derived maturity phase were closer to field-observed phenophases (Mean Bias = 0.73 days) with higher R2 (0.87) than that from EVI and NDVI. At a regional scale, the milk-ripe phase and maturity phase were observed from August to mid-September, and from mid-September to mid-October, respectively, which varied with crop types and in spatial distribution. Out of all crops in mid-temperate zone in China, 65% experienced a delay in the milk-ripe phase, whereas 77% exhibited a delay in the maturity phase from 2001 to 2019. In addition to the adjustment of human managements under climate warming, we further found that crop phenophases in reproductive growth period exhibited the strongest correlation with preseason water-related environmental variables, particularly vapor pressure deficit and total precipitation. This work highlighted the potential of satellite SIF in identifying the crop-specific milk-ripe and maturity phases, and improving our understanding for spatio-temporal variations of crop phenophases in reproductive growth period as well as their responses to preseason environmental variables, which will in turn promote SIF applications in agriculture. [ABSTRACT FROM AUTHOR]

Predictive distribution models are widely used in species conservation planning. However, identifying specific sites for undertaking conservation action such as reintroduction pose a practical challenge for practitioners. We demonstrate that the potential habitat distribution models intersected with existing land use and land cover (LULC) map helped in identifying the specific conservation areas with greater confidence. We used a case of Magnolia punduana Hk. f & Th. (Magnoliaceae)—a threatened and rare tree species distributed in the Khasi, Jaintia and Garo Hills of northeast India at an elevation range of 800–1600 m asl. We modeled the distribution of potential habitats of the species using maximum entropy (Maxent) software, moderate resolution imaging spectroradiometer (MODIS) imageries, and ASTER-based digital elevation data. Amongst the environmental variables, elevation and the EVI for the months of May, June and July had a collective contribution of > 70%, indicating their dominant role in determining the potential habitat. The predicted potential habitat of the species covered ~ 2.88% area of the state in the southern part of the Khasi and Jaintia hills with a few patches in Garo hills. However, intersection of the potential distribution area map with the existing forest cover map of the study area showed ~ 3853 ha of the state, has high suitability. The ground-truthing of the high suitability regions revealed that they are similar to the species' original habitat and may support conservation-related activities such as habitat protection, restoration, and species (re)introduction. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 quickly spread around the globe, with staggering infection and mortality rates. Given the reported benefits of greenness to human health, we hypothesized that greener neighbourhoods would suffer less from COVID-19 infection and severity than not. We modelled COVID-19 infection and severity in South Africa by fitting Generalized Linear Mixed Effects models to various socio-demographic and greenness data (EVI, i.e., Enhanced Vegetation Index, and tree and grass covers) collected from January 2020 to June 2022 at local municipalities, Wards, and districts scales. We found that income level showed a significant negative association with COVID-19 prevalence at the Ward level, suggesting that poorer neighbourhoods are more susceptible to COVID-19 infection. We also found that EVI and tree cover consistently showed a significant negative correlation with COVID-19 infection rate, irrespective of the scale of analysis, whereas grass cover showed a similar pattern but only at the Ward scale. Finally, we found that neither EVI, grass cover, nor tree cover showed a significant association with COVID-19 severity. Overall, greener neighbourhoods show greater resilience to the spread of COVID-19, suggesting that exposure to greenness may boost natural killer cells in the human body, thus reinvigorating our defence system against infections. Our results re-enforced the need to establish and preserve greens paces particularly in the developing world, given their ability to buffer the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology. [ABSTRACT FROM AUTHOR]

Under the background of global warming, seasonal drought has become frequent and intensified in many parts of the world in recent years. Drought is one of the most widespread and severe natural disasters, and poses a serious threat to normal sugarcane growth and yield. However, a deep understanding of sugarcane responses to drought stress remains limited, especially at a large spatial scale. In this work, we used the traditional vegetation index (enhanced vegetation index, EVI) and newly downscaled satellite solar-induced chlorophyll fluorescence (SIF) to investigate the impacts of drought on sugarcane in a major sugarcane-planting region of China (Chongzuo City, Southwest China). The results showed that Chongzuo City experienced an extremely severe drought event during the critical growth periods of sugarcane from August to November 2009. During the early stage of the 2009 drought, sugarcane SIF exhibited a quick negative response with a reduction of approximately 2.5% from the multiyear mean in late August 2009, while EVI was not able to capture the drought stress until late September 2009. Compared with EVI, sugarcane SIF shows more pronounced responses to drought stress during the later stage of drought, especially after late September 2009. SIF anomalies can closely capture the spatial and temporal dynamics of drought stress on sugarcane during this drought event. We also found that sugarcane SIF can provide earlier and much more pronounced physiological responses (as indicated by fluorescence yield) than structural responses (as indicated by the fraction of photosynthetically active radiation) to drought stress. Our results suggest that the satellite SIF has a great potential for sugarcane drought monitoring in a timely manner at a large spatial scale. These results are important for developing early warning models for sugarcane drought monitoring, and provide reliable information for developing measures to relieve the negative impacts of drought on sugarcane yield and regional economics. [ABSTRACT FROM AUTHOR]

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Utilizing path analysis, we examined the interconnectedness among six meteorological variables. Among these, three pertain to energy conditions—air temperature, net solar radiation, and reference evapotranspiration (ET0)—while the others are associated with hydrological conditions: precipitation, relative humidity, and water deficiency. These variables were assessed across five distinct temporal delay levels to understand their influences on the normalized difference vegetation Index (NDVI) and enhanced vegetation index (EVI) within grassland areas situated in the state of Espírito Santo, southeastern Brazil. The images underwent processing using analytical algorithms and a geographic information system (GIS). The direct and indirect impacts of these variables on the NDVI and EVI exhibited remarkable similarity across varying temporal delays and geographic regions. Meteorological variables explained over 50% of the observed variation in both indices, occasionally even reaching levels of 70%. Temperature and relative humidity primarily exerted direct effects on the indices. Conversely, precipitation exhibited indirect effects on the indices, often in conjunction with other hydrological variables. ET0 demonstrated a direct effect on the vegetation indices, particularly after a delay of 32 days. Solar radiation and water deficiency displayed direct effects up to the 32-day mark, implying that vegetation responds more promptly to these variables. The proposed methodology enabled a consistent and stable assessment of the direct and indirect effects of meteorological variables on vegetation indices.

Extreme climate anomalies are expected to become more frequent under climate change, and rare extreme events, such as the 2021 western North American heat wave, provide an opportunity for comparative empirical analysis of ecosystem resilience. This study evaluates anomalies in a remotely sensed enhanced vegetation index (EVI) in the aftermath of the record-setting western North American heat wave in 2021, with temperatures approaching 50 °C in coastal and interior regions of the Pacific Northwest. The results show that the forest ecosystems most affected were not necessarily those that experienced the highest absolute temperature values. Instead, the greatest reductions in greenness were observed across northern coastal temperate rainforests. Most affected were the cooler, very wet, hyper-maritime ecosystems that are normally buffered from large temperature fluctuation by a strong oceanic influence. In contrast, moisture-limited forests of the interior plateau of British Columbia, where most of the all-time record temperatures occurred, generally showed normal or even increased productivity during and after the heat wave. A putative explanation for this heat resistance of interior forests was normal or above average precipitation leading up to the heat event, allowing for transpirational cooling. Nevertheless, the data suggest that the largest protected coastal temperate rainforest in the world, with 6.4 million hectares, is comparatively more vulnerable to extreme heat waves, which are expected to become more frequent under climate warming, than other ecosystems of the Pacific Northwest. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Chinese mangroves have been recovered in area over the past two decades from previous declining trend, and about half of existing mangroves are still in their young growth stage. This provides a unique opportunity to assess mangrove conservation by examining the growth dynamics of young mangroves over different conservation periods. However, we are currently short of effective assessment tools for spatially explicit quantification of mangrove conservation effects. To fill up this gap, we proposed a novel remote sensing approach using readily available unmanned aerial vehicle (UAV) and Landsat enhanced vegetation index (EVI) data to assess the spatial evolution of aboveground biomass (AGB) of young mangroves. With the space‐for‐time hypothesis, the approach implemented with an empirical EVI‐height‐AGB equation was tested in four subtropical estuarine mangroves in the southeastern coast of China. The results indicated: (a) the UAV‐based Structure from Motion (SfM) technology served as an effective and low‐cost means for capturing the spatial heterogeneity of mangrove canopy heights; (b) a maximum stand age of 15 years could be used to define the young growth stage of mangroves, for which the EVI‐height relationships could be described by exponential equations without suffering significant spectral saturation effects; (c) mangrove forests had overall faster annual AGB accumulation during the young growth stage over the post‐2000 versus pre‐2000 conservation period. This study is one of the first attempts to develop a remote sensing approach for quantifying spatially explicit AGB accumulation rates of young mangroves. It highlights the practicability and advantage of the UAV‐SfM technology and confirms that stronger conservation efforts promote mangrove AGB accumulation over the past two decades. The developed EVI‐height‐AGB framework fueled with readily available UAV and Landsat data provides a unique tool for assessing mangrove conservation effects from landscape to regional scales.

We applied a robust framework for agricultural drought identification in the State of Espírito Santo, Brazil, by employing the Vegetation Condition Index (VCI) based on data obtained through the Enhanced Vegetation Index (EVI). By doing so, we analyzed the interrelationships between the VCI and anomalies in the Land Surface Temperature (LST), along with connections between the VCI and data considering water deficits in vulnerable areas. When it came to image processing, we focused on the use of analytics and GIS algorithms, while the Scott–Knott method elucidated the statistical analyses. Consequently, we identified drought areas followed by periods susceptible to their occurrence, indicating 2016 as the driest year. The North macroregion presented the lowest average values regarding VCI values in the most vulnerable periods, followed by the Central one. We also call attention to the highest LST averages observed in 2015 and 2016, as strong El Niño events marked the same timeframe periods. The methodological approach was efficient for the identification, analysis, and characterization of agricultural drought occurrences, enabling mitigation actions, as well as the management of the exploitation and protection of water resources. Moreover, further research should be conducted by incorporating other indices to enhance the understanding of agricultural drought and its effects on vegetation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Controversy surrounds the reported dry season greening of the Central Amazon forests based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). As the solar zenith angle decreases during the dry season, it affects the sub-pixel shade content and artificially increases Near-infrared (NIR) reflectance and EVI. MODIS' coarse resolution also creates a challenge for cloud and terrain filtering. To reduce these artifacts and then validate MODIS seasonal spectral patterns we use 16 years of 1 km resolution MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction) images, corrected to a nadir view and 45° solar zenith angle, together with an improved cloud filter. Then we show that the 30 m Landsat-8 Operational Land Imager (OLI) surface reflectance over two Landsat scenes provides independent evidence supporting the MODIS-MAIAC seasonality for EVI, NIR, and GCC (an additional important vegetation index, green chromatic coordinate). Our empirical method for controlling for sun-sensor geometry effects in Landsat scenes encompasses the use of seasonally distinct images that have similar solar zenith angles and cloud-free pixels on flat uplands having the same phase angle. We extended this validation to nine Amazon sub-basins comprising ∼546 Landsat-8 images. Our study shows that the dry-season green-up pattern observed by MODIS is corroborated by Landsat-8, and is independent of satellite data artifacts. To investigate the mechanisms driving these seasonal changes we further used Central Amazon tower-mounted RGB cameras providing a 4-year record at the Amazon Tall Tower (ATTO, 2°8′36″S, 59°0′2″W) and a 7-year record at the Manaus k34 tower (2°36′33″ S, 60°12′33″W) to obtain monthly upper canopy green leaf cover (a proxy for Leaf Area Index - LAI) and monthly leaf age class abundances (based on the age since leaf flushing, by crown). These were compared to seasonal patterns of GCC and EVI in small MODIS-MAIAC windows centered on each tower. MODIS-MAIAC GCC was positively correlated with newly flushed leaves (R2 = 0.76 and 0.44 at ATTO and k34, respectively). EVI correlated strongly with the abundance of mature leaves (R2 = 0.82 and 0.80) but was poorly correlated with LAI (R2 = 0.20 and 0.41, respectively). Therefore, seasonal spectral patterns in the Central Amazon are likely controlled by leaf age variation, not quantity of leaf area. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to analyze the contribution of land capital to the growth of emissions and income per capita in the long run. We collect new satellite data from the Earth Observatory to obtain estimates of the Enhanced Vegetation Index at the country level for the period 2000–2015. We use these data and the World Bank wealth estimates of natural capital to calibrate and empirically test an extension of the Green Solow model with land degradation and land capital investment. We show that the model is consistent with the cross-country variation in growth rates of carbon emissions per capita and find that there is convergence at the global level, with the contribution of land capital investment to the growth of emissions being negative and significant in all specifications. [ABSTRACT FROM AUTHOR]

Global warming is causing some regions to experience frequent and severe drought, with important impacts on montane forest vegetation. In this study, the Qilian Mountains is on the northeastern margin of the Tibetan Plateau which was divided into three study areas, the eastern (HaXi), middle (XiShui) and western (QiFeng) parts. This work focused on interannual trend comparison of tree-ring width (TRW) and enhanced vegetation index (EVI), their relationship characterization from 2000 to 2020, and spatial and temporal pattern portrayal of response to climate factors. The results showed that: (1) the overall interannual variation of TRW and EVI showed a stable increasing trend, and the trend of TRW and EVI gradually became consistent with the increase in drought stress (from the eastern region to the western region and from high elevation to low elevation) (p < 0.01); (2) a significant positive relation was observed between TRW and EVI at the same sampling sites, and the synchrony of the positive correlation gradually increased with the increase of drought stress (p < 0.01); and (3) compared to TRW, EVI is significantly more sensitive with climatic variations, and the dominant climate factors affecting both TRW and EVI dynamics are gradually identical with the increase of drought stress. [ABSTRACT FROM AUTHOR]

Carbon Monoxide (CO) is an important urban pollutant with a relation to transition economies based on emission intensities. In this study, Sentinel-5, MODerate resolution Imaging Spectroradiometer (MODIS), and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them. From the assessed correlations for 932 Iranian cities, it occurred that the assessed indices were all correlated. The highest CO levels were 0.031 in the spring of 2019 and 2020, whereas in 2021 it was equal to 0.030 in both the spring and winter, respectively. In 2019 and 2020 the maximum values of the Enhanced Vegetation Index (EVI) in the spring were 0.181 and 0.183. Exceptionally high Absorbing Aerosol Index (AAI) values of – 0.834 and – 1.0, along with Urban Index (UI) of 0.102 and 0.092, were correlated with recorded spikes in CO level, despite that these seasons' EVI values were not so abnormal. It was forecasted that in 2030 rises in the CO level by 13.2% in the winter and by 17.5% in the fall are expected, with the simultaneous increase of AAI by 204.5% and 980.2%, and Aerosol Optical Depth (AOD) by 27% and 5% in the winter and spring, respectively. [ABSTRACT FROM AUTHOR]

Many freshwater ecosystems worldwide are threatened by increasing water turbidity and extensive submerged aquatic vegetation (SAV) loss, with potential consequences on aquatic communities. Such changes in water quality and habitat features affect the relative abundance of species in the community and/or the composition of ecological guilds altering the trophic network. Here, we estimated the relationship between fish species and both SAV abundance and water quality in Lake St. Pierre (Québec, Canada), a shallow fluvial lake of the St. Lawrence River. To explain the association between the fish community and environmental variables, we performed multiple linear regressions on fish abundance, species richness, and Shannon diversity calculated at 133 stations, along a gradient of turbidity, temperature, and SAV abundance. In addition, we estimated the relationship between dominant fish species abundance and environmental variables by using canonical correspondence analysis (CCA). Species richness and abundance were positively related to SAV. Turbidity was negatively related to fish abundance, but had an unexpected positive effect on diversity (Shannon and species richness). By quantifying the association between fish species and habitat features, this study contributes to a better understanding of mechanisms structuring fish communities in changing environments. [ABSTRACT FROM AUTHOR]

Climate change-related acute or long-term drought stress can weaken forest ecosystems and result in widespread bark beetle infestations. Eurasian spruce bark beetle (Ips typographus L.) infestations have been occurring in Norway spruce [Picea abies (L.) Karst.]-dominated forests in central Europe including the Czechia. These infestations appear regularly, especially in homogeneous spruce stands, and the impact varies with the climate-induced water stress conditions. The removal of infected trees before the beetles leave the bark is an important step in forest pest management. Early identification of susceptible trees to infestations is also very important but quite challenging since stressed tree-tops show no sign of discolouration in the visible spectrum. We investigated if individual spectral bandwidths or developed spectral vegetation indices (SVIs), can be used to differentiate non-attacked trees, assumed to be healthy, from trees susceptible to attacks in the later stages of a growing season. And, how the temporal-scale patterns of individual bands and developed SVIs of susceptible trees to attacks, driven by changes in spectral characteristics of trees, behave differently than those patterns observed for healthy trees. The multispectral imagery from the PlanetScope satellite coupled with field data were used to statistically test the competency of the individual band and/or developed SVIs to differentiate two designated classes of healthy and susceptible trees. We found significant differences between SVIs of the susceptible and healthy spruce forests using the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI). The accuracy for both indices ranged from 0.7 to 0.78; the highest among all examined indices. The results indicated that the spectral differences between the healthy and susceptible trees were present at the beginning of the growing season before the attacks. The existing spectral differences, likely caused by water-stress stimuli such as droughts, may be a key to detecting forests susceptible to early infestations. Our introduced methodology can also be applied in future research, using new generations of the PlanetScope imagery, to assess forests susceptibility to bark beetle infestations early in the growing season.

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems, including vegetation destruction. However, the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague, especially in the severely damaged areas (including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Maoxian, Anzhou, Dujiangyan, Pingwu and Pengzhou). Here, we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition (EEMD) to analyze the time series characteristics of the Enhanced Vegetation Index (EVI), and explored the driving effects of climate, land use types, nighttime light, water system, slope, and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression (GTWR) model. The results indicated that the post-seismic vegetation recovery rate increased rapidly (acceleration > 0) but slowed down after 2013. And the areas of best vegetation recovery (EVI increments > 0.1) were distributed in the north of the study area, the Minjiang River Basin, and front fault and central fault of the Longmenshan Fault Zone. While the areas with the worst vegetation recovery (EVI increments < −0.1) were concentrated in the southern high-altitude areas and the Chengdu Plain. Additionally, a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery (regression coefficients > 0), but the impacts weakened after the earthquake, possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature. The impact of cultivated land on vegetation recovery was mostly positive (regression coefficients > 0), which may be related to the implementation of the Grain for Green Project. The nighttime light inhibited vegetation recovery (regression coefficients < 0), which could be closely associated with urbanization. The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends, and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated. [ABSTRACT FROM AUTHOR]

Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology.

Under the background of global warming, seasonal drought has become frequent and intensified in many parts of the world in recent years. Drought is one of the most widespread and severe natural disasters, and poses a serious threat to normal sugarcane growth and yield. However, a deep understanding of sugarcane responses to drought stress remains limited, especially at a large spatial scale. In this work, we used the traditional vegetation index (enhanced vegetation index, EVI) and newly downscaled satellite solar-induced chlorophyll fluorescence (SIF) to investigate the impacts of drought on sugarcane in a major sugarcane-planting region of China (Chongzuo City, Southwest China). The results showed that Chongzuo City experienced an extremely severe drought event during the critical growth periods of sugarcane from August to November 2009. During the early stage of the 2009 drought, sugarcane SIF exhibited a quick negative response with a reduction of approximately 2.5% from the multiyear mean in late August 2009, while EVI was not able to capture the drought stress until late September 2009. Compared with EVI, sugarcane SIF shows more pronounced responses to drought stress during the later stage of drought, especially after late September 2009. SIF anomalies can closely capture the spatial and temporal dynamics of drought stress on sugarcane during this drought event. We also found that sugarcane SIF can provide earlier and much more pronounced physiological responses (as indicated by fluorescence yield) than structural responses (as indicated by the fraction of photosynthetically active radiation) to drought stress. Our results suggest that the satellite SIF has a great potential for sugarcane drought monitoring in a timely manner at a large spatial scale. These results are important for developing early warning models for sugarcane drought monitoring, and provide reliable information for developing measures to relieve the negative impacts of drought on sugarcane yield and regional economics.

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 (EVITGS ) 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 EVITGS , 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.

Due to climate change and human activities, drought has become one of the most complex natural disasters in the Huang-Huai-Hai River basin, China. The purpose of the current research is to analyze the response of vegetation to drought in the Huang-Huai-Hai River basin by calculating the lag time correlation coefficient between the enhanced vegetation index (EVI) and drought indices. First, the water storage deficit index (WSDI) calculated from the total water storage anomalies (TWSA) was used as the hydrological drought index. The meteorological drought index we used was the self-calibrating Palmer drought severity index (scPDSI) provided by the Climate Research Unit (CRU). Then, the relationship between hydrological and meteorological drought was obtained based on the wavelet coherent change. Finally, the response of different vegetation types to drought was analyzed based on the lag time correlation coefficient. The results showed that: the average time of hydrological drought lagged meteorological drought in the three first-level basins of the Huang-Huai-Hai River basin, respectively, was 50 days (Yellow River basin), 43 days (Huaihe River basin), and 35 days (Haihe River basin); the lag time correlation coefficient of the different time scales (1–12 months) can reflect the sensitivity of vegetation to drought in different vegetation types and regions. The forest was the least sensitive to drought in the Huang-Huai-Hai River basin, followed by cropland, while grassland was the most sensitive to drought. It was discovered that hydrological drought had a huge influence on vegetation rather than meteorological drought by comparing the maximum lag time correlation coefficients of EVI with drought indices (scPDSI and SPEI). This study stresses the importance of taking into account the effects of various types of drought on the vegetation in the Huang-Huai-Hai River basin. This will help us better understand how drought affects vegetation.

The resistance and recovery rate of forest ecosystems to droughts vary with the severity of the drought. Studies on the impacts of severe droughts on forest ecosystems have suggested that evergreen forests have higher resistance than deciduous forests. However, whether the resistance and recovery rate of forest ecosystems vary under moderate droughts remained largely unknown. Here, we used the Standardized Precipitation Index to identify drought characteristics in China’s forest ecosystems from 2000 to 2018. We quantified the resistance and recovery rate to moderate droughts under different drought timings based on the Enhanced Vegetation Index. We then adopted random forest regression to evaluate the relative importance of climatic variables and species richness as drivers of resistance and recovery rate to moderate droughts. We found that China’s forest ecosystems mainly experienced moderate drought events, resulting in different resistance and recovery rate compared to severe droughts. Deciduous forests had high resistance (30.39 ± 21.32) and evergreen forests had high recovery rate (1.38 ± 1.10) due to their different response strategies under moderate droughts. The resistance and recovery rate of China’s forest ecosystems varied under different drought timings. The differences between deciduous and evergreen forests’ resistance (43.88 – 78.24 %) and recovery rate (-31.04 – −52.20 %) were remarkable in spring, autumn, and winter. In evergreen and deciduous forests, climatic variables were the main influencing factors of resistance and recovery rate to moderate droughts, similar to the leading role of climatic variables under severe droughts. Global radiation (41.06 %) and temperature (30.97 %) were the dominant factors contributing to resistance, whereas the recovery rate was primarily explained by global radiation (40.30 %) and precipitation (27.33 %). Thus, the high resistance in deciduous forests and the high recovery rate in evergreen forests under moderate droughts suggest that evergreen and deciduous forests can be mixed to improve the stability of forest ecosystems under climate change.

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 (R2) is found to be highest between rainfall and latent heat flux (R2 = 0.59) followed by rainfall and specific humidity (R2 = 0.35), and rainfall and sensible heat flux (R2 = 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. [ABSTRACT FROM AUTHOR]

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a−1, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province. [ABSTRACT FROM AUTHOR]

Migrating grazers and carnivores respond to seasonal changes in the environment and often match peaks in resource abundance. However, it is unclear whether and how frugivorous animals use phenological events to time migration, especially in the tropics.The straw‐coloured fruit bat Eidolon helvum, Africa's most gregarious fruit bat, forms large seasonal colonies throughout much of sub‐Saharan Africa. We hypothesized that aggregations of E. helvum match the timing of their migration with phenologies of plant growth or precipitation.Using monthly colony counts from across much of the species' range, we matched peak colony size to landscape phenologies and explored the variation among colonies matching the overall closest phenological event.Peak colony size was closest to the peak instantaneous rate of green‐up, and sites with closer temporal matching were associated with higher maximum greenness, short growing season and larger peak colony size. Eidolon helvum seem to time their migrations to move into highly seasonal landscapes to exploit short‐lived explosions of food and may benefit from collective sensing to time migrations.The link between rapid changes in colony size and phenological match may also imply potential collective sensing of the environment. Overall decreasing bat numbers along with various threats might cause this property of large colonies to be lost.Remote sensing data, although, indirectly linked to fruiting events, can potentially be used to globally describe and predict the migration of frugivorous species in a changing world. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives. [ABSTRACT FROM AUTHOR]

Extreme climate anomalies are expected to become more frequent under climate change, and rare extreme events, such as the 2021 western North American heat wave, provide an opportunity for comparative empirical analysis of ecosystem resilience. This study evaluates anomalies in a remotely sensed enhanced vegetation index (EVI) in the aftermath of the record-setting western North American heat wave in 2021, with temperatures approaching 50 °C in coastal and interior regions of the Pacific Northwest. The results show that the forest ecosystems most affected were not necessarily those that experienced the highest absolute temperature values. Instead, the greatest reductions in greenness were observed across northern coastal temperate rainforests. Most affected were the cooler, very wet, hyper-maritime ecosystems that are normally buffered from large temperature fluctuation by a strong oceanic influence. In contrast, moisture-limited forests of the interior plateau of British Columbia, where most of the all-time record temperatures occurred, generally showed normal or even increased productivity during and after the heat wave. A putative explanation for this heat resistance of interior forests was normal or above average precipitation leading up to the heat event, allowing for transpirational cooling. Nevertheless, the data suggest that the largest protected coastal temperate rainforest in the world, with 6.4 million hectares, is comparatively more vulnerable to extreme heat waves, which are expected to become more frequent under climate warming, than other ecosystems of the Pacific Northwest.

The seasonal dynamics of plant communities are important indicators for assessment of long‐term vegetation patterns and provide valuable information to predict ecosystem responses to climate change. However, increased frequency of extreme weather events can force ecosystems into unstable states, which leads to greater uncertainty in determining phenological metrics (e.g., growing season length). To better understand these uncertainties, we utilized 9 years of eddy covariance and remote sensing data to parameterize models of seasonal ecosystem respiration (Re) for two subtropical longleaf pine forests (mesic and xeric), with similar vegetation but different water holding capacity. We compared two commonly used algorithms to extract phenology metrics, the growth rate (GR) and third derivative (TD) methods, which are usually used without justification. We determined the impact of algorithm selection on estimating key biological dates related to plant community carbon dynamics (e.g., start, end, and length of physiologically active season, specifically Re), characterized the model's response to extreme weather events, and compared estimates to those derived via remotely sensed greenness from the enhanced vegetation index (EVI). We observed that periods of winter warming increased duration of physiological activity in terms of Re, and summer water limitation caused multi‐peaked, asymmetric behavior, creating significant uncertainties. We found that choice of phenology metric extraction algorithm significantly impacted biological event dates; the GR method estimated longer phenophases than the TD in both sites, as well as earlier starting and later ending dates for phenophases. Because the TD method was unable to give estimates during the buffer period of phenophase transition under certain weather conditions, the GR method may be more suitable for studies in subtropical forests. Dates derived from EVI greenness rarely matched those of plant community seasonal dynamics models, especially in spring and summer. The estimated length of Re from the model was significantly longer than that derived from EVI, indicating that the use of EVI could result in shorter growing season estimates and greater uncertainty. Our results provide direction for optimization of future approaches to extract phenological metrics and better scientific understanding of forest land surface phenology, as weather anomalies become more common with climate change. [ABSTRACT FROM AUTHOR]