Season Normalized Difference Vegetation Index Research Articles

Operational Google-Earth-Engine Workflow to Monitor Irrigated Areas in a Semi-Arid Climate

Abstract. Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

Temporal variation characteristics in the association between climate and vegetation in Northwest China

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations.

Quantitatively analyzing the driving factors of vegetation change in China: Climate change and human activities

Understanding the impact of climate change and human activities on vegetation dynamics is crucial for ecosystem management. Employing the Residual Trend method and integrating Normalized Difference Vegetation Index (NDVI) data with land use/cover, this study assesses the impacts of climate change and human activities on vegetation dynamics across China from 2000 to 2018. The findings indicate a consistent upward trend of China's Growing Season NDVI (GSN), averaging a change rate of 0.0032/yr. Human activities are the primary drivers of this change, contributing 82.47% to GSN in China, while climate change accounts for 17.53%. The effect of human activities on vegetation dynamics showed considerable variation across different river basins, with the Huaihe River Basin experiencing the highest human impact (93.53%) and the Continental Basin the lowest (76.27%). Conversely, the Continental Basin experienced the greatest impact from climate change (23.73%), compared to the minimal influence in the Huaihe River Basin (6.47%). The study results offer contribution rates for each type of changed and unchanged land use, with persistent forestland, persistent grassland, persistent cropland, and grassland to forest conversion contributing 28.65%, 22.09%, 13.76%, and 4.61%, respectively. Persistent forestland emerges as the most efficacious land use type for facilitating vegetation restoration. Within the persistent forestlands of the Yangtze, Pearl, and Southeast River Basins, human activities accounted for 26.99%, 42.18%, and 43.50% of the vegetation alterations, respectively. These findings provide a scientific basis for formulating effective ecosystem management and protection strategies.

Temporal and Spatial Influences on Fawn Summer Survival in Pronghorn Populations: Management Implications from Noninvasive Monitoring.

Monitoring vital rates allows managers to estimate trends in growth rates of ungulate populations. However, connecting the influence of nutrition on ungulate demography is challenging. Noninvasive sampling offers a low-cost, low-effort alternative for measuring nutritional indices, allowing for an increased understanding of the mechanistic relationships between environmental factors, nutrition, and specific population vital rates. We examined the temporal influence of intrinsic and extrinsic factors on pronghorn (Antilocapra americana) fawn recruitment. We collected fresh fecal samples from adult female pronghorn in five subpopulations spanning three sampling periods associated with critical maternal life-history stages (late gestation, early lactation, breeding season) for 2 years to investigate both intra- and interannual influences. Intrinsic factors were fecal glucocorticoid metabolites (FGMs), nutritional indices (fecal nitrogen (FN) and 2,6-diaminopimelic acid (DAPA)), and dietary composition (protein intake of forbs, graminoids, legumes, other, shrubs), while the extrinsic factor was vegetative greenness (normalized difference vegetation index (NDVI)). We found variations in DAPA, protein intake of forbs, variation in forb protein intake, and protein intake of legumes during late gestation positively influenced fawn recruitment. Fecal nitrogen during early lactation showed the strongest positive influence on the recruitment of any measured parameter. Finally, breeding season NDVI and the variation in DAPA values positively influenced the subsequent year's fawn recruitment. Our longitudinal study enabled us to investigate which parameter was most important to specific periods of fawn development and recruitment. We combined the results across five subpopulations, but interpretation and subsequent management decisions should be made at the subpopulation level such that pronghorn subpopulations with low recruitment can be positively influenced by increasing nitrogen on the landscape available to adult females during the early lactation period. As the use of noninvasive monitoring methods continues to expand, we believe our methodologies and results can be broadly applied to other ungulate monitoring programs.

Study loss of vegetative cover and increased land surface temperature through remote sensing strategies under the inter-annual climate variability in Jinhua-Quzhou basin, China.

The Jinhua-Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982-2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST).Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies.The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua-Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua-Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua-Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

In this work, we propose that small wood core samples (≤10 cm length), taken from 0.3 m above the soil, represent an accurate means of correlating forest growth with remote sensing data. Short-term correlations between the Normalized Difference Vegetation Index (NDVI) and tree-ring components of the last three to four decades were tested, using 210 Pinus hartwegii wood cores collected at two locations, Tláloc (TLA) and Jocotitlán (JOCO) in central Mexico. The NDVI time series were generated with the Google Earth Engine (GEE) using Landsat 8 images. Also, seasonal trends in NDVI (e.g., spring, summer, autumn, winter) were analyzed through longitudinal analysis. The results showed more statistically significant dendrochronological indices in TLA than in JOCO, but both locations consistently showed an NDVI decrease in 2018 and 2020, indicating a reduction in vegetation vigor. At the two locations, the minimum and maximum NDVI occurred in April and October, respectively. Seasonal NDVI changes for spring were mainly seen at TLA with a decreasing trend, which may be related to a less defined dry season. The significant correlations (p < 0.05) between tree-ring components and the NDVI occurred in the dry season, indicating that the productivity of a given year is defined by the tree vigor shown in April and May, in the case of TLA, and between January and March, for JOCO. Although the NDVI values of JOCO were higher than those of TLA, tree growth, expressed by tree-ring indices, was lower. Our proposed field method to correlate tree-ring information and the NDVI is reliable and can be used in other coniferous forests.

Contribution of Climatic Factors and Human Activities to Vegetation Changes in Arid Grassland

Clarifying the changing trend in vegetation and its affecting variables is extremely valuable for natural resource management. Vegetation changes in the Yinshanbeilu grassland region, which is situated in the centre of Inner Mongolia in northern China and is part of the arid steppe region, are extremely sensitive to climatic factors. In this study, we investigated the changes in vegetation in the Yinshanbeilu grassland zone from the year 2000 to 2020 using the Normalized Difference Vegetation Index (NDVI) data. The contribution of climatic conditions and human activities to the annual and growing season vegetation changes was quantified. The findings revealed that vegetation cover in the Yinshanbeilu grassland zone increased at a rate of 0.00267/a between 2000 and 2020. Throughout the year and during the growing season, precipitation had a greater influence on the growth of vegetation than other climatological factors. In most places, there was a significant positive correlation between the NDVI and precipitation, which negatively correlated with other climatic factors. The average rates at which precipitation, temperature, cumulative sunshine hours, and potential evapotranspiration contributed to changes in NDVI were 0.00173/a, −0.00027/a, 0.00006/a, and 0.00074/a, respectively, for the entire year, and 0.00180/a, −0.00001/a, 0.00021/a, and 0.00059/a for the growing season. The impact of climate change on vegetation activities was more pronounced, accounting for 84.76% of annual NDVI change and 97.36% of growing season NDVI change. Humans contributed 15.24% of total annual NDVI change and 2.64% of growing season NDVI change. This research’s findings serve as scientific support for preserving the environment in the Yinshanbeilu grassland region, as well as an essential reference for government decision making.

Exploring NDVI change patterns across the Tibetan Plateau at the hillslope scale using geomorphons

ABSTRACTWithin the Tibetan Plateau (TP), grassland degradation is of great concern, and there is a need to better understand the spatial variability and landscape patterns in grassland degradation across the TP. This study explores potential patterns in grassland degradation, estimated using Landsat-derived normalized difference vegetation index (NDVI) growing season change products generated by a prior study. Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM)-derived geomorphons, topographic slope and the topographic position index (TPI) were used to explore grassland degradation and assess whether changes in growing season NDVI are associated with specific grasslands or landforms. An increase in NDVI within the TP was observed especially during the 1990 to 2018 and 2000 to 2018 time periods. Higher growing season median NDVI change values were found for the Southeast Tibet Shrublands and Meadows (SETSM) and TP Alpine Shrublands and Meadows (TPASM) grasslands in comparison to the other grassland types analysed, suggesting a more pronounced greening trend in the eastern portion of the plateau in comparison to the western portion. Small differences in NDVI change were observed for different geomorphon-based landforms, while topographic slope and TPI showed only modest correlations with NDVI change for all time periods and grassland types. More localized patterns of grassland degradation were masked by the widespread greening trends. Further analysis of only pixels that experienced a decreasing NDVI magnitude of less than or equal to −0.1 also generally suggested more change in the SETSM and TPASM. While this study generally supports the use of geomorphons as an analysis and aggregating unit for studying change patterns at the hillslope scale, more research is required to fully grasp the dynamics of landscape change at the hillslope scale in the TP.

Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

ABSTRACT Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.

Analysis of Growing Season Normalized Difference Vegetation Index Variation and Its Influencing Factors on the Mongolian Plateau Based on Google Earth Engine.

Frequent dust storms on the Mongolian Plateau have adversely affected the ecological environmental quality of East Asia. Studying the dynamic changes in vegetation coverage is one of the important means of evaluating ecological environmental quality in the region. In this study, we used Landsat remote sensing images from 2000 to 2019 on the Mongolian Plateau to extract yearly Normalized Difference Vegetation Index (NDVI) data during the growing season. We used partial correlation analysis and the Hurst index to analyze the spatiotemporal characteristics of the NDVI before and after the establishment of nature reserves and their influencing factors on the GEE cloud platform. The results showed that (1) the proportion of the region with an upwards trend of NDVI increased from 52.21% during 2000-2009 to 67.93% during 2010-2019, indicating a clear improvement in vegetation due to increased precipitation; (2) the increase in precipitation and positive human activities drove the increase in the NDVI in the study region from 2000 to 2019; and (3) the overall trend of the NDVI in the future is expected to be stable with a slight decrease, and restoration potential is greater for water bodies and grasslands. Therefore, it is imperative to strengthen positive human activities to safeguard vegetation. These findings furnish scientific evidence for environmental management and the development of ecological engineering initiatives on the Mongolian Plateau.

Greening and browning of urban lawns in Geneva (Switzerland) as influenced by soil properties

Urban green spaces with healthy vegetation play a key role in improving the quality of life in cities. However, urban soils, the basis of the urban greenery, are under strong anthropogenic influence and can considerably differ from natural soils. In this study, we observed short-term greening and browning of lawns during one vegetation period in urban parks of Geneva (Switzerland). We related the temporal trajectory of seasonal Normalized Difference Vegetation Index (NDVI) (8 days median) as a proxy of vegetation condition at different test sites to the physical soil properties (soil depth, coarse material, bulk density, conditioned air and water content) and Soil Organic Carbon (SOC). Strong drops of NDVI during dry periods in summer were related to shallow soil depths (<40 cm) and a high amount of coarse material (>10%) as well as lower SOC. Bulk density of the fine earth and the soil structure quality (expressed by air and water content of soil cores conditioned at a soil water potential of −100 hPa) had a significant influence on grass growth in spring but not in summer. Dense soils with conditioned air content closer to the trigger value of degraded soil structure resulted in lower NDVI values in spring. Our approach of using Earth Observation (EO) data for observing short-term greening and browning patterns, in this case the rise and decline of NDVI values, revealed that the role of the soil properties changed with the season. This approach may contribute to digital soil mapping and the assessment of soil ecosystem services in urban contexts. Urban planners are advised to save natural soils from over-building and keep them for green spaces. If soil has to be restored to create new green spaces, it should be deep and should not contain much coarse material, even for grassy vegetation.

Impacts of Climate Change and Human Activities on NDVI Change in Eastern Coastal Areas of China

Based on the datasets of normalized difference vegetation index (NDVI), temperature, precipitation, and solar radiation and the methods of trend, partial correlation, and residual analyses, this study explored the spatiotemporal variation in NDVI and its response to climate change from 1982 to 2019 in eastern coastal areas of China. Then, the effects of climate change and non-climatic factors (e.g., human activities) on NDVI trends were analyzed. The results showed that:① the NDVI trend varied greatly in different regions, stages, and seasons. On average, the growing season NDVI increased faster during 1982-2000 (stage I) than that during 2001-2019 (stage Ⅱ) in the study area. Moreover, NDVI in spring showed a more rapid increase than that in other seasons in both stages. ② For a given stage, the relationships between NDVI and each climatic factor varied in different seasons. For a given season, the major climatic factors associated with NDVI change were different between the two stages. The relationships between NDVI and each climatic factor showed great spatial differences in the study period. In general, the increase in growing season NDVI in the study area from 1982 to 2019 was closely related to the rapid warming. The increase in precipitation and solar radiation in stage Ⅱ also played a positive role. ③ In the past 38 years, climate change played a greater role in the change in growing season NDVI than non-climatic factors, including human activities. Whereas non-climatic factors dominated the increase in growing season NDVI during stage I, climate change played a major role during stage Ⅱ. We suggest that more attention should be paid to the impacts of various factors on vegetation cover variation during different periods to promote the understanding of terrestrial ecosystem changes.

Seasonal Spatiotemporal Changes in the NDVI and Its Driving Forces in Wuliangsu Lake Basin, Northern China from 1990 to 2020

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development.

Long-term evidence of differential resistance and resilience of grassland ecosystems to extreme climate events

Grassland ecosystems are affected by the increasing frequency and intensity of extreme climate events (e.g., droughts). Understanding how grassland ecosystems maintain their functioning, resistance, and resilience under climatic perturbations is a topic of current concern. Resistance is the capacity of an ecosystem to withstand change against extreme climate, while resilience is the ability of an ecosystem to return to its original state after a perturbation. Using the growing season Normalized Difference Vegetation Index (NDVIgs, an index of vegetation growth) and the Standardized Precipitation Evapotranspiration Index (a drought index), we evaluated the response, resistance, and resilience of vegetation to climatic conditions for alpine grassland, grass-dominated steppe, hay meadow, arid steppe, and semi-arid steppe in northern China for the period 1982-2012. The results show that NDVIgs varied significantly across these grasslands, with the highest (lowest) NDVIgs values in alpine grassland (semi-arid steppe). We found increasing trends of greenness in alpine grassland, grass-dominated steppe, and hay meadow, while there were no detectable changes of NDVIgs in arid and semi-arid steppes. NDVIgs decreased with increasing dryness from extreme wet to extreme dry. Alpine and steppe grasslands exhibited higher resistance to and lower resilience after extreme wet, while lower resistance to and higher resilience after extreme dry conditions. No significant differences in resistance and resilience of hay meadow under climatic conditions suggest the stability of this grassland under climatic perturbations. This study concludes that highly resistant grasslands under conditions of water surplus are low resilient, but low resistant ecosystems under conditions of water shortage are highly resilient.

The impact of residential greenness on psychological distress among Hurricane Katrina survivors.

Residential greenness may support mental health among disaster-affected populations; however, changes in residential greenness may disrupt survivors' sense of place. We obtained one pre- and three post-disaster psychological distress scores (Kessler [K]-6) from a cohort (n = 229) of low-income mothers who survived Hurricane Katrina in New Orleans, Louisiana, USA. Greenness was assessed using average growing season Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in the 300 m around participants' homes at each time point. We used multivariable logistic regressions to evaluate two hypotheses: 1) that cross-sectional greenness (above vs. below median) was associated with reduced psychological distress (K6≥5); and 2) that changes in residential greenness were associated with adverse mental health. When using EVI, we found that a change in level of greenness (i.e., from high to low [high-low], or from low to high [low-high] greenness, comparing pre- and post-Katrina neighborhoods) was associated with increased odds of distress at the first post-storm survey, compared to moving between or staying within low greenness neighborhoods (low-high odds ratio [OR] = 3.48; 95% confidence interval [CI] = 1.40, 8.62 and high-low OR = 2.60; 95% CI: 1.05, 6.42). Results for NDVI were not statistically significant. More research is needed to characterize how residential greenness may impact the health of disaster survivors, and how these associations may change over time.

Enhanced Dependence of China's Vegetation Activity on Soil Moisture Under Drier Climate Conditions

AbstractVegetation growth is known to be largely dependent on the availability of soil moisture (SM), yet the changes in response to climate change are still not clear. Here, we use the long‐term satellite‐observed normalized difference vegetation index (NDVI) and reanalysis SM to examine changes in the dependence of vegetation on SM across the vegetated lands of China during 1982–2015. The results show that the strength of the relationship between the interannual variability of the growing season NDVI and SM (partial correlation coefficient RSM‐NDVI) significantly increased in over 45% land fractions from 1982 to 2015. The strongest increasing trend occurred in the originally SM‐dependent areas, indicating an enhancing regime. The increase in RSM‐NDVI coincided with a decrease in precipitation and SM, highlighting the key role of drying in driving the increase in RSM‐NDVI. We also demonstrate that North China is a hotspot region which is more vulnerable to soil dryness. Our findings would be valuable for vegetation planning and management in China under future drying and intensified drought conditions.

Associations between Residential Greenspace and Fecundability in a North American Preconception Cohort Study.

Residential green space can have positive physiological effects on human health through various mechanisms, including reducing stress and/or depression or facilitating physical activity. Although green space has been consistently associated with improved birth outcomes in several studies, there has been limited study of its effect on other reproductive outcomes, including fertility. We examined associations between residential green space and fecundability, the per-cycle probability of conception. We analyzed data from 8,563 female participants enrolled between 2013 and 2019 in Pregnancy Study Online (PRESTO), a prospective preconception cohort study of North American couples attempting conception. Participants completed a baseline questionnaire on sociodemographic, behavioral, and reproductive factors, and bimonthly follow-up questionnaires for up to 12 months to ascertain pregnancies. Using geocoded addresses, we calculated residential green space exposure using the Normalized Difference Vegetation Index (NDVI) within 50-, 100-, 250-, and buffers across multiple temporal scales: annual maximum, seasonal maximum, and seasonal mean. We used proportional probabilities regression models to estimate fecundability ratios (FRs), adjusting for sociodemographic, behavioral, and neighborhood characteristics. We also evaluated the extent to which associations were mediated by reductions in perceived stress or depressive symptoms and increased physical activity. When comparing the highest () with the lowest () NDVI exposures within , we observed positive associations in the annual maximum NDVI [FR: 1.33; 95% confidence interval (CI): 1.06, 1.67] and seasonal maximum NDVI (FR: 1.19; 95% CI: 1.00, 1.41) models, but little association in the seasonal mean NDVI models (FR: 0.98; 95% CI: 0.73, 1.30). Restricted cubic splines showed evidence of nonlinearity in this association. Results were similar across buffer distances. Perceived stress, depressive symptoms, and physical activity explained of mediation across all NDVI metrics. In this cohort, greater residential green space was associated with a modest increase in fecundability. https://doi.org/10.1289/EHP10648.

Assessing vegetation restoration prospects under different environmental elements in cold and arid mountainous region of China

Assessing vegetation restoration prospects (VRPs) in degraded terrestrial ecosystems is of great importance for planning ecological restoration projects. However, few existing studies have systematically analyzed the relationship between vegetation trend persistence and VRPs and their drivers, especially in cold and arid mountainous areas. The typical cold and arid mountainous region-the Qilian Mountains was taken as the study area, and the trends and long-term memory of the growing season normalized difference vegetation index (GSNDVI) from 2000 to 2020 were explored. We constructed a VRPs assessment framework and identified the main environmental elements influencing vegetation restoration. The results showed that from 2000 to 2020, the vegetation improvement area of the Qilian Mountains was much larger than the degraded area, especially in the northwestern area, and the vegetation improvement was significant. About 31% of the study area showed an anti-persistent positive development trend, which requires continued attention and increased recovery efforts in the future. The terrain niche index (TNI), drought index and soil types were the environmental elements that had a strong influence on the VRPs. Regions with low TNI (< 0.6), high drought index (> 5), desert areas and organic soil environments were more likely to be restored with lower uncertainty. This study can provide a new framework for assessing VRPs in the Qilian Mountains and is expected to provide scientific guidance for ecosystem restoration planning in other regions worldwide, supporting the achievement of sustainable development goals.