Spatial point pattern analysis of environmental effects on valley fever intensity in Phoenix, Arizona.

Impacts of climate change and human activities on vegetation NDVI in China’s Mu Us Sandy Land during 2000–2019

Changes in different land cover areas and NDVI values in northern latitudes from 1982 to 2015

Multiyear trends in Normalized Difference Vegetation Index (NDVI) have been used as metrics of high latitude ecosystem change based on the assumption that NDVI change is associated with ecological change, generally as changes in green vegetation amount (green leaf area index [LAI] or plant cover). Further, no change in NDVI is often interpreted as no change in these variables. Three canopy reflectance models including linear mixture model, the SAIL (Scattering from Arbitrarily Inclined Leaves) model, and the GeoSail model were used to simulate scenarios representing high latitude landscape NDVI responses to changes in LAI and plant cover. The simulations showed inconsistent NDVI responses. Clear increases in NDVI are generally associated with increases in LAI and plant cover. At higher values of LAI, the change in NDVI per unit change in LAI decreases, with very little change in spruce forest NDVI where crown cover is >50% and at the tundra–taiga ecotone with transitions from shrub tundra to spruce woodland. These lower responses may bias the interpretation of greening/browning trends in boreal forests. Variations in water or snow coverage were shown to produce outsized nonbiological NDVI responses. Inconsistencies in NDVI responses exemplify the need for care in the interpretation of NDVI change as a metric of high latitude ecosystem change, and that landscape characteristics in terms of the type of cover and its characteristics, such as the initial plant cover, must be taken into account in evaluating the significance of any observed NDVI trends.

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.

ABSTRACTUnderstanding the Tibetan Plateau’s role in environmental change has gained increasing scientific attention in light of warming and changes in land management. We examine changes in greenness over the Tibetan Plateau using the Normalized Difference Vegetation Index (NDVI) from the Global Inventory Monitoring and Modeling Study (GIMMS3g) to identify significant changes over the entire plateau, six ecoregions, and protected areas based on a multiyear time series of July imagery from 1982 to 2015. We also test whether there have been changes in human populations in protected areas. There has been relatively little change in mean NDVI over the Tibetan Plateau or ecoregions, however, there were significant changes at the pixel level. There are sixty-nine protected areas on the Tibetan Plateau; sixty-two protected areas had no significant change in mean NDVI and seven protected areas experienced a significant increase in NDVI. There has been an increase in population within protected areas from 2000 to 2015; however, mean populations significantly increased in two protected areas and significantly decreased in four protected areas. Results suggest a slow greening of the Tibetan Plateau, ecoregions, and protected areas, with a more rapid greening in northern Tibet at the pixel level. Most protected areas are experiencing minor changes in NDVI independent of human population.

Most nature and health research use the normalized difference vegetation index (NDVI) for measuring greenness exposure. However, little is known about what NDVI measures in terms of vegetation types (e.g., canopy, grass coverage) within certain analysis zones (e.g., 500 m buffer). Additionally, exploration is needed to understand how to interpret changes in average NDVI (e.g., per 0.1 increments) exposure in relation to changes in vegetation amount and types. In this study, we aimed to explore what vegetation types and amounts best explain the average NDVI and how changes in average NDVI values indicate changes in different vegetation coverages. We used spatial modeling to sample mean NDVI and percentages of vegetation for sample locations within the Greater Manchester case study area. We fitted linear, nonlinear, and mixed multivariate and univariate generalized additive models (GAMs) for multiple spatial scales to identify the relationships between NDVI and vegetation amount and types. Our results showed that the relationships between NDVI and individual vegetation types mostly follow nonlinear trends. We found that canopy and shrubs coverage exhibited a greater influence on mean NDVI exposure values than grass coverage at 300 and 500 m indicating that NDVI values are sensitive to certain types and amounts of vegetation within various buffer zones. We also identified increment in mean NDVI exposure values at lower, mid, and high ranges might be associated with varying changes in total greenspace percentage and individual vegetation types. For instance, at 300 m buffer, an increment of mean NDVI in the lower range (e.g., from 0.2 to 0.3) is associated with an about 17% increase in greenspace percentage. Overall, interpreting changes in NDVI values for urban greening interventions would require careful evaluation of the relative changes in types and quantities of vegetation for different buffer zones.

Multifaceted responses of vegetation to average and extreme climate change over global drylands

Vegetation coverage directly affects the quality of a regional ecological environment. Current analyzes of the factors driving vegetation coverage do not focus on human activities. Poyang Lake is an internationally important wetland. This study is based on 814 MODIS normalized difference vegetation index (NDVI) data from 2000 to 2017 in the Poyang Lake Basin. Natural effects (temperature, precipitation, terrain niche index) and human activity intensity are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: NDVI in the Poyang Lake Basin fluctuated and increased over the 18 years examined. Spatially, NDVI significantly decreased around the central city, and NDVI significantly increased on both sides of the river. NDVI changes are less affected by topographic factors and are more affected by climatic factors and human activities. Among the climatic factors, the correlation between NDVI and temperature was greater than that between NDVI and precipitation. Climatic factors had a greater impact on NDVI than human activities throughout the study area. However, in areas with significant NDVI changes, the correlation between climatic factors and NDVI was not obvious, and the correlation between human activities and NDVI was more obvious than in other areas. Therefore, from 2000 to 2017, the vegetation coverage of the Poyang Lake Basin generally showed an increasing trend; climatic factors had a greater impact on the overall vegetation coverage of the basin than human activities, but significant changes in vegetation coverage were caused by human activities.

Widespread changes in arctic and boreal Normalized Difference Vegetation Index (NDVI) values captured by satellite platforms indicate that northern ecosystems are experiencing rapid ecological change in response to climate warming. Increasing temperatures and altered hydrology are driving shifts in ecosystem biophysical properties that, observed by satellites, manifest as long-term changes in regional NDVI. In an effort to examine the underlying ecological drivers of these changes, we used field-scale remote sensing of NDVI to track peatland vegetation in experiments that manipulated hydrology, temperature, and carbon dioxide (CO2 ) levels. In addition to NDVI, we measured percent cover by species and leaf area index (LAI). We monitored two peatland types broadly representative of the boreal region. One site was a rich fen located near Fairbanks, Alaska, at the Alaska Peatland Experiment (APEX), and the second site was a nutrient-poor bog located in Northern Minnesota within the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found that NDVI decreased with long-term reductions in soil moisture at the APEX site, coincident with a decrease in photosynthetic leaf area and the relative abundance of sedges. We observed increasing NDVI with elevated temperature at the SPRUCE site, associated with an increase in the relative abundance of shrubs and a decrease in forb cover. Warming treatments at the SPRUCE site also led to increases in the LAI of the shrub layer. We found no strong effects of elevated CO2 on community composition. Our findings support recent studies suggesting that changes in NDVI observed from satellite platforms may be the result of changes in community composition and ecosystem structure in response to climate warming.

As important node cities in the Belt and Road region, Shenzhen and Bangkok are faced with similar environmental threats posed by the high‐speed social development process. Rapid urbanization leads to changes in vegetation growth and land cover types and then affects ecosystem services. In the current study, we used a time‐series normalized difference vegetation index dataset from 2000 to 2019 and two land cover type datasets from 2000 to 2018 to investigate and compare the spatiotemporal characteristics of the changes in vegetation and land cover types of the two cities. We found that the trend of vegetation change was mainly affected by the change in land cover types, while the interannual fluctuation of vegetation change was likely related to the extreme climate events caused by El Niño‐Southern Oscillation events. However, different urbanization strategies led to opposite vegetation change trends in Bangkok and Shenzhen after 2005. With urbanization, the vegetation coverage (Pv) of Shenzhen increased from 48% in 2000 to 62% in 2018. The total urban green spaces (except croplands) of Shenzhen have remained above 33% of the total area since 2006. However, the total urban green space in Bangkok accounted for only 8% of the total area in 2018, which was even lower than the area percentage of Shenzhen's forests in the same year. Rapid urbanization without adequate urban green spaces caused a decreasing trend of Pv in Bangkok. Green development under the Belt and Road Initiative requires serious considerations of environmental quality and urban livability during the rapid urbanization.

Gonsamo et al. (1) use 8-km satellite data from advanced very high-resolution imaging spectroradiometer (AVHRR) global inventory modeling and mapping studies (GIMMS) to demonstrate the role of climatic oscillations, specifically the East Atlantic-West Russia (EA-WR) pattern, on interannual dynamics of Amazon greenness. Hilker et al. (2) do not investigate EA-WR but focus on the El Nino southern oscillation (ENSO), a pattern that is well known to affect climate throughout South America and the Pacific region (3). Gonsamo et al. (1) do not challenge these results but claim that EA-WR, more than ENSO, may “explain the entire ensuing year Amazon vegetation greenness dynamics.” We are unable to judge this claim based on our findings (2), but argue that the authors do not present a convincing case. EA-WR is a teleconnection pattern whose anomalies result in above-average temperatures over eastern Asia and below-average temperatures over large portions of western Russia and northeastern Africa. A connection between North Atlantic sea surface temperature (SST) and the likelihood of an El Nino onset has been demonstrated (4). A direct approach to prove the superior explanatory power of EA-WR compared with ENSO would have been to use the same normalized difference vegetation index (NDVI) dataset shown in figure 1 of Hilker et al. (2) and demonstrate a better correlation between NDVI and EA-WR. Figure 1 A–D in Gonsamo et al. actually confirms a stronger connection between annual precipitation and ENSO than with annual precipitation and EA-WR (1). The lack of correlation in figure 1 E–G of Gonsamo et al. is not surprising given the high noise level in AVHRR GIMMS that largely prevents detection of trends over tropical vegetation (5). Proof of statistical significance of changes in GIMMS NDVI is missing. The spatial patterns in figure 1H seem unconnected to those in figure 1 C and D, which begs the question of what climate factor, if not precipitation, drives those changes in NDVI. The connections between EA-WR and ensuing year precipitation and EA-WR and ensuing year NDVI (figure 1 I and J) seem to contradict the findings in figure 1 D and H; at the very least, the distinction between those figures is not clear. The Pearson R values presented in figure 1 I and J are extremely low. Parts J and K in figure 1 are not comparable because figure 1K shows monthly mean values (2), whereas figure 1J shows interannual variation. The intent of the analysis shown in figure 1K was to demonstrate that Amazon forests initially respond positively to seasonal reductions in rainfall, whereas grasslands respond negatively. Why the authors included this figure in the presented context is unclear. On a side note, Gonsamo et al. wrongly claim that Hilker et al. (2) demonstrate that a lack of correlation between moderate resolution imaging spectroradiometer (MODIS) NDVI and ENSO can be attributed to normalizing MODIS reflectance to a common view and sensor geometry (1). Hilker et al. demonstrate that directionally normalized NDVI observations show seasonal variation, contrary to previous findings (6).

Due to the short-term observation record of the normalized difference vegetation index (NDVI), the research on long-term NDVI changes is scarce, which limits our understanding of the impacts of NDVI changes in the context of global warming. In this study, a regional tree-ring chronology was developed based on the tree-ring samples of Pinus tabuliformis in the middle Qinling Mountains. The results showed that tree-ring width of P. tabuliformis was significantly positively correlated with May-July NDVI (r=0.624, P<0.01, n=34). The Sig-Free tree-ring width chronology was used to reconstruct May-July NDVI during the period 1825-2018, which explained 38.9% of the total NDVI variance. Results of spatial analysis showed that the reconstructed series could better represent the NDVI changes in the study area. There were six high NDVI periods and five low NDVI periods in the past 194 years. The vegetation grew best in 2006-2018, indicating vegetation cove-rage in the middle of Qinling Mountains had been improved during the warming hiatus. Low NDVI periods in the reconstruction series were consistent with drought over much of study area. Results of wavelet analysis indicated the existence of 2-4 years and 12-16 years cycles in the reconstruction series. SEA analysis showed that the reconstruction series decreased significantly in the El Nino year, while increased significantly in the first to third years after the La Nina event. The growth of P. tabuliformis was predicted to increase slightly under the SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios.

The Qinling-Daba Mountains in central China (also known as the north–south transitional zone) comprise an ideal area to study land cover change, climate change, and human activities. The normalized difference vegetation index (NDVI) change and associated driving factors are highly sensitive to vegetation cover change. To discover the long-term vegetation trends in the transition zone and determine the driving factors of NDVI change in recent decades, this study analyzed the NDVI variation trend and its spatial variation with elevation, slope, and land-use type based on annual growing season NDVI data from 1990–2019 (Landsat 30 m; Google Earth Engine). The results show that NDVI values in the Qinling-Daba Mountains significantly increased and experienced a dynamic change process, involving an initial decrease and subsequent increase over this time period. The period of 2000–2005 showed a remarkable increasing stage of the NDVI in the transition zone. Such NDVI changes are sensitive to elevation and slope. For example, areas at elevations &lt; 1500 m or with slopes of 5°–25° exhibited a stronger rate of NDVI increase than in other places. The NDVI change was also found to be positively affected by human land use and climate warming, both of which had a stronger impact than precipitation. The area with rapid NDVI growth was also the region with the greatest impact of human cropland and host to the Grain-for-Green project. This demonstrates that human land use has had a positive impact on the NDVI change in recent decades, although urbanization had led to a decrease in the NDVI in surrounding areas. Land-use policies have contributed to the large increase in NDVI values, especially those for forest conservation and expansion programs such as the Grain-for-Green project.

In 1999, the Grain for Green Project was implemented by the Chinese government. Since then, the vegetation of Zuli River Basin, a semi-arid river basin of the Chinese Loess Plateau, has been greatly changed. Clearly understanding the impact of natural and artificial factors on vegetation change is important for policy making and ecosystem management. In this study, spatio-temporal variations in vegetation cover in Chinese Zuli River Basin during 1999–2016 were investigated using Landsat normalized difference vegetation index (NDVI) data. Analyses of several indicators, including changes in NDVI in different slopes and land use changes and the relationships between climatic factors and NDVI change, were presented to quantitatively evaluate the effects of agriculture, climate, and policy on NDVI change. The NDVI in the Zuli River Basin increased during the study period, and the main contributors to this change were forest in 1999–2011, cropland, abandoned farmland, and grassland in 2009–2016, and land with slopes ≤ 15°. Land with slope > 15°, where the “Project” was implemented, slightly contributed to the increase in regional NDVI. In 1999–2011, the project (−98.16%) combined with climate change (−68.18%) showed negative effects on the increase in NDVI in the Zuli River Basin, but agriculture (22.28%) played a positive role in increasing this index. In 2009–2016 and 1999–2016, the project (38.45% and 35.25%, respectively), the project combined with climate change (49.83% and 46.30%, respectively), agriculture (18.61% and 23.30%, respectively), promoted increases in NDVI in the basin.

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management.