Normalized Difference Vegetation Index Variables Research Articles

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.

Assessing the effect of rotational grazing adoption in Iberian silvopastoral systems with Normalized Difference Vegetation Index time series

Adaptive Multi-Paddock (AMP) is a grazing system which combines intensive, rapid grazing livestock rotation with relatively short grazing periods and long recovery time after grazing. The study assesses, under Mediterranean silvopastoral systems, changes in pasture phenology and spatial variability after adopting the AMP under contrasting land cover (Wooded Grassland vs Grassland ) with a remote sensing approach based on the time-series analysis of Normalized Difference Vegetation Index (NDVI) from remote sensing through Landsat satellite. The study revealed an overall positive effect of rotational grazing on pasture phenology and NDVI spatial variability. The AMP adoption resulted in higher estimated values of NDVI at the beginning (under grassland land cover), the end, and the peak of the growing season, while no differences were observed in parameters estimating the length of the growing season. The spatial variability of NDVI was always lower under AMP than in continuously grazed areas, except in the early stages of the growing season under grassland land cover. The results suggested that in a relatively short period (4-5 years), the AMP grazing system can represent a strategy to improve forage availability and exploitation by grazing animals under low stocking rates in extensively managed Mediterranean silvopastoral systems.

Response Time of Vegetation to Drought in Weihe River Basin, China

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

Spatiotemporal trends of NDVI and its response to climate variability in the Abbay River Basin, Ethiopia

Woody vegetation plays a vital role in regulating the water budget and energy exchange in the Earth's system. This study aimed at analyzing the spatiotemporal variability of Normalized Difference Vegetation Index (NDVI) and its response to Potential Evapotranspiration (PET), rainfall (RF), soil moisture (SM), and temperature (TEM) in the study area. The trends, correlations, and relationships between NDVI and climate variables were executed using Mann-Kendall monotonic trend (MKMT), partial correlation coefficients (PCC), and multiple linear regression (MLR) methods, respectively. Over the last 26 years, the interannual NDVI increased by 0.0065 yr−1 (R2 = 0.159, p = 0.157). The spatiotemporal MKMT and Theil-Sen slope analysis showed that interannual NDVI increased significantly in 78% of the basin's total area. Of the 78% of the basin, 31%, and 47%, of the total area showed extremely significant increasing (Zmk = 4.706, p ≤ 0.01), and significant increasing trends (Zmk = 2.378, p ≤ 0.05) respectively. The interannual variation of NDVI was well explained (R2 = 0.88, Adjusted R2 = 0.84) by the climate variables in the eastern, southeastern, and central sub-basins where agriculture, grass, sparse vegetation and barelands are the predominant land use land cover (LULC) classes. The main climatic factors that control vegetation growth and greenness during the rainy season were found to be PET, SM, and RF with 0.91, 0.99, and 0.86 PCC with NDVI respectively. The current study broadens the scientific community's understanding of the relationship between climate variables and vegetation growth in highland ecosystems. Understanding the seasonal and long-term relationship between climate and NDVI contributes to the scientific knowledge of highland ecosystems, which are extremely vulnerable to climate change.

Traits of dominant plant species drive normalized difference vegetation index in grasslands globally

AbstractAimTheoretical, experimental and observational studies have shown that biodiversity–ecosystem functioning (BEF) relationships are influenced by functional community structure through two mutually non‐exclusive mechanisms: (1) the dominance effect (which relates to the traits of the dominant species); and (2) the niche partitioning effect [which relates to functional diversity (FD)]. Although both mechanisms have been studied in plant communities and experiments at small spatial extents, it remains unclear whether evidence from small‐extent case studies translates into a generalizable macroecological pattern. Here, we evaluate dominance and niche partitioning effects simultaneously in grassland systems world‐wide.LocationTwo thousand nine hundred and forty‐one grassland plots globally.Time period2000–2014.Major taxa studiedVascular plants.MethodsWe obtained plot‐based data on functional community structure from the global vegetation plot database “sPlot”, which combines species composition with plant trait data from the “TRY” database. We used data on the community‐weighted mean (CWM) and FD for 18 ecologically relevant plant traits. As an indicator of primary productivity, we extracted the satellite‐derived normalized difference vegetation index (NDVI) from MODIS. Using generalized additive models and deviation partitioning, we estimated the contributions of trait CWM and FD to the variation in annual maximum NDVI, while controlling for climatic variables and spatial structure.ResultsGrassland communities dominated by relatively tall species with acquisitive traits had higher NDVI values, suggesting the prevalence of dominance effects for BEF relationships. We found no support for niche partitioning for the functional traits analysed, because NDVI remained unaffected by FD. Most of the predictive power of traits was shared by climatic predictors and spatial coordinates. This highlights the importance of community assembly processes for BEF relationships in natural communities.Main conclusionsOur analysis provides empirical evidence that plant functional community structure and global patterns in primary productivity are linked through the resource economics and size traits of the dominant species. This is an important test of the hypotheses underlying BEF relationships at the global scale.

Drought Vulnerability Curves Based on Remote Sensing and Historical Disaster Dataset

As drought vulnerability assessment is fundamental to risk management, it is urgent to develop scientific and reasonable assessment models to determine such vulnerability. A vulnerability curve is the key to risk assessment of various disasters, connecting analysis of hazard and risk. To date, the research on vulnerability curves of earthquakes, floods and typhoons is relatively mature. However, there are few studies on the drought vulnerability curve, and its application value needs to be further confirmed and popularized. In this study, on the basis of collecting historical disaster data from 52 drought events in China from 2009 to 2013, three drought remote sensing indexes were selected as disaster-causing factors; the affected population was selected to reflect the overall disaster situation, and five typical regional drought vulnerability curves were constructed. The results showed that (1) in general, according to the statistics of probability distribution, most of the normalized difference vegetation index (NDVI) and the temperature vegetation drought index (TVDI) variance ratios were concentrated between 0 and ~0.15, and most of the enhanced vegetation index (EVI) variance ratios were concentrated between 0.15 and ~0.6. From a regional perspective, the NDVI and EVI variance ratio values of the northwest inland perennial arid area (NW), the southwest mountainous area with successive years of drought (SW), and the Hunan Hubei Jiangxi area with sudden change from drought to waterlogging (HJ) regions were close and significantly higher than the TVDI variance ratio values. (2) Most of the losses (drought at-risk populations, DRP) were concentrated in 0~0.3, with a cumulative proportion of about 90.19%. At the significance level, DRP obeys the Weibull distribution through hypothesis testing, and the parameters are optimal. (3) The drought vulnerability curve conformed to the distribution rule of the logistic curve, and the line shape was the growth of the loss rate from 0 to 1. It was found that the arid and ecologically fragile area in the farming pastoral ecotone (AP) region was always a high-risk area with high vulnerability, which should be the focus of drought risk prevention and reduction. The study reduces the difficulty of developing the vulnerability curve, indicating that the method can be widely used to other regions in the future. Furthermore, the research results are of great significance to the accurate drought risk early warning or whether to implement the national drought disaster emergency rescue response.

Does weather drive habitat use and movement of a nonmigratory bird?

AbstractClimate change is predicted to increase the intensity and frequency of weather extremes (e.g., extreme heat and drought), which will likely affect wildlife species in significant ways. Maintaining landscape heterogeneity has been suggested as a potential conservation strategy to buffer animals from weather extremes. Because animal movement influences survival and population connectivity, understanding animal space use and movement in response to shifts in weather is useful for the conservation of wildlife. Nonmigratory species are predicted to be more negatively affected by climate change because they have a limited ability to disperse to find resources. We studied the Northern Bobwhite (Colinus virginianus; hereafter, bobwhite) to understand how weather alters habitat use and movement of a nonmigratory animal across a landscape. We collected telemetry locations on bobwhite across western Oklahoma during 2019–2020 and paired these data with meteorological and vegetation data. We analyzed the data at two temporal scales—hourly locations and 12‐h paths. At the hourly scale, we analyzed tree cover use, shrub cover use, and normalized difference vegetation index (NDVI) use using generalized or linear‐mixed models. At the 12‐h scale, we calculated three different movement metrics: cumulative distance, net displacement, sinuosity and analyzed each movement metric separately using generalized linear‐mixed models. We found that bobwhite used denser tree cover and higher NDVI values as air temperature increased in conjunction with solar radiation. Bobwhite used denser shrub cover as air temperature increased and average wind speed was high as well as when air temperature and average wind speed were low. The interaction between variability in vegetation and climate influenced bobwhite movement. Bobwhite in areas with higher NDVI variance moved farther when mean air temperatures were >0°C than bobwhite in areas with low NDVI variance. When mean air temperatures were ≤0°C, NDVI variance had little effect on the cumulative distance moved of bobwhite. Our findings show that some animals use different vegetation types during different weather conditions and that variability in vegetation on the landscape and weather together alters animal movement. Managing for landscape heterogeneity is a solution that may buffer animals from increased climate variability in the future.

Remote Sensing Image-Based Analysis of the Urban Heat Island Effect in Relation to the Normalized Difference Vegetation Index (NDVI): A Case Study of Patna Municipal Corporation

Abstract: The consequences of population growth and urbanization on the urban environment, climate, and water supply have caused a wide range of issues. As a result of the rapid urbanization process, vegetation covers are being converted by impervious and dry concrete covers. This has led to the growth of "heat islands," where urban areas experience warmer temperatures than the areas surrounding them. Urban Heat Island (UHI) is a human-caused environmental phenomenon that has a wide range of impacts on city dwellers, including changing the vegetation cover and its usage, which alters the thermal energy flow and raises surface and air temperatures. Such heat islands have far-reaching impacts for cities, the most significant of which is an increase in the expense of maintaining a safe living and working environment. The major objective of this study is to examine multitemporal Land Surface Temperature (LST)/Urban Heat Island (UHI) and Normalized Difference Vegetation Index (NDVI) in the Patna Municipal Corporation (PMC) from 1990 to 2022. The Landsat satellite data sets for the years 1990, 2001, 2011, and 2022 have used to investigate the impact of UHI/LST in relation to NDVI in the study area. The most significant change has been observed in vegetation cover (NDVI), which has declined compared to other types of land use land cover. According to the study, built-up area and barren land have high temperatures, whereas vegetated covers and water bodies have lower temperatures. The LST of some portion of study area is high due to the high population density and high percentage of built-up and concrete cover. The LST over the study area has risen on average by 6.88 °C between 1990 and 2022. The regression line provided a conclusive answer, demonstrating a strong negative relationship between the normalized difference vegetation index (NDVI) and UHI of PMC. According to the findings of this study, a major transition has occurred in PMC in terms of a decline in NDVI due to a rapid growth in urban expansion, and other infrastructure projects. To study urban climate and interactions between people and the environment, land surface temperature (LST) variations within cities are of utmost importance in respect to ascertain the LST/Urban Heat Island (UHI) effect and NDVI variations in PMC. While developing urbanization, the environmental impact must be taken into account.

Wetland vegetation changes in response to climate change and human activities on the Tibetan Plateau during 2000–2015

The Tibetan Plateau (TP), known as the third pole, is the highest plateau in the world. It has numerous wetlands, which are important ecological security barriers and plays an important role in mitigating global climate change. This paper employed breaks for additive seasonal and trend (BFAST) algorithm for the mutation detection of the monthly normalized difference vegetation index (NDVI) in wetlands. In addition, correlation analysis and residual analysis were used to study the response of climate change and human activities to NDVI of alpine wetland vegetation in the TP during 2000–2015. The results indicate that the NDVI showed a weak upward trend of 0.009/10a (P < 0.05) with the climate presenting a trend of dry heat development. The NDVI of the growing season was greatly affected by temperature factors with the highest correlation coefficient of 0.686 (P < 0.01). The temperature in the month before and solar radiation in the 3 months before also presented a time lag effect on NDVI, and their correlation coefficients were 0.574 (P < 0.05) and 0.636 (P < 0.05), respectively. Additionally, human activities may have a positive impact on the wetland after 2008. This study explored, for the first time, the NDVI variations of the dynamic wetland and their correlations with temperature, precipitation and solar radiation.

Spatiotemporal dynamics of vegetation in response to climate variability in the Borana rangelands of southern Ethiopia

In this paper, we analyze the spatiotemporal dynamics of vegetation and its association with climate variables in the Borana rangelands of southern Ethiopia. Moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, namely, MODIS/TERRA, MOD09Q1 16-day L3 Global 250 m Version 6 from National Aeronautics and Space Administration (NASA) United States Geological Survey (USGS), and climate data from 2000 to 2018 obtained from the National Meteorological Agency (NMA) of Ethiopia are used to determine the vegetation dynamics both at annual and seasonal timescales. The results indicate that the NDVI varies between .31 and .41, and the study area is dominated by sparse vegetation, especially shrubs and grasslands. The Borana rangelands were previously dominated by open grasslands, perennial herbaceous and woody vegetations, and some encroaching woody vegetations. The NDVI revealed poorer vegetation growth than the vegetation condition index (VCI) between 2000 and 2018, although variations were observed spatiotemporally. Rainfall was more highly variable both at annual and seasonal scales than the temperature during the studied years. The climate variables significantly determined the condition of vegetation, particularly during the growing seasons compared to other periods. A significant strong positive correlation was observed between the NDVI, VCI, and climate variables during the main (Belg) and short (Meher) rainy seasons in the Borana rangelands. The results of this study are beneficial for the development of proper intervention policies and provide information for sustainable rangeland management.

Analysis of factors influencing spatiotemporal differentiation of the NDVI in the upper and middle reaches of the Yellow River from 2000 to 2020

Surface vegetation represents a link between the atmosphere, water, and human society. The quality of the ecological environment in the upper and middle reaches of the Yellow River (UMRYR) has a direct impact on the downstream basin. However, only few studies have investigated vegetation changes in the UMRYR. Therefore, we used the coefficient of variation and linear regression analyses to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI). Further, we used the geographical detector model (GDM) to determine the spatial heterogeneity of the NDVI and its driving factors and then investigated the factors driving the spatial distribution of the NDVI in different climatic zones and vegetation types. The results showed that the NDVI in the UMRYR was high during the study period. The NDVI was distributed in a spatially heterogeneous manner, and it decreased from the southeast to the northwest. We observed severe degradation in the southeast, mild degradation in the northwest and the Yellow River source region, and substantial vegetation recovery in the central basin. Precipitation and vegetation type drove the spatial distribution of the NDVI. Natural factors had higher influence than that of anthropogenic factors, but the interactions between the natural and anthropogenic factors exhibited non-linear and bivariate enhancements. Inter-annual variations in precipitation were the main natural factor influencing inter-annual NDVI variations, while precipitation and anthropogenic ecological restoration projects jointly drove NDVI changes in the UMRYR. This study provides a better understanding of the current status of the NDVI and mechanisms driving vegetation restoration in the UMRYR.

Spatiotemporal correlation and multivariate analysis between vegetation health, terrestrial water storage and precipitation

Vegetation health is an essential indicator in the global hydrologic cycle as it is interrelated with the hydrological components. In tropical areas where vegetation dominates, analysing their correlation at a regional scale helps forecast the hydrologic cycle and understand vegetation’s response to climate change. However, the interactions between vegetation, terrestrial water storage and climate factors such as precipitation remain poorly understood in this region. Therefore, using Landsat and Gravity Recovery and Climate Experiment (GRACE) remote sensing and observed precipitation data, this study analysed the spatiotemporal correlation of Normalized Difference Vegetation Index (NDVI), Terrestrial Water Storage Anomaly (TWSA) and precipitation for the whole Peninsular Malaysia. The correlation coefficient (R) was used to assess the temporal variability of NDVI with TWSA and precipitation separately. Furthermore, a Geographically Weighted Regression (GWR) model was constructed to evaluate the spatial non-stationarity and heterogeneity relationships between the multi variables. The findings revealed complex interactions between the variables, where the strength of the correlations varied depending on the localised region and study period. The results suggest that downscaled GRACE-derived TWSA data would be helpful for detailed vegetation modelling and water resources management.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Processes and mechanisms of vegetation ecosystem responding to climate and ecological restoration in China.

Vegetation is an essential component of the earth's surface system and its dynamics is a clear indicator of global climate change. However, the vegetation trends of most studies were based on time-unvarying methods, cannot accurately detect the long-term nonlinear characteristics of vegetation changes. Here, the ensemble empirical mode decomposition and the Breaks for Additive Seasonal and Trend algorithm were applied to reconstruct the the normalized difference vegetation index (NDVI) data and diagnose spatiotemporal evolution and abrupt changes of long-term vegetation trends in China during 1982-2018. Residual analysis was used to separate the influence of climate and human activities on NDVI variations, and the effect of specific human drivers on vegetation growth was obtained. The results suggest that based on the time-varying analysis, high vegetation browning was masked by overall vegetation greening. Vegetation growth in China experienced an abrupt change in the 1990s and 2000s, accounting for 50% and 33.6% of the whole China respectively. Of the area before the breakpoint, 45.4% showed a trend of vegetation decrease, which was concentrated mainly in east China, while 43% of the area after the breakpoint also showed vegetation degradation, mainly in northwest China. Climate was an important driving force for vegetation change in China. It played a positive role in south China, but had a negative effect in northwest China. The impact of human activities on vegetation growthchanged from an initial negative influence to a positive one. In terms of human activities, an inverted-U-shaped relation was detected between CO2 emissions and vegetation growth; that is, the fertilization effect of CO2 had a certain threshold. Once that threshold was exceeded, it would hinder vegetation growth. Population density had a slight constraint on vegetation growth, and the implementation of ecological restoration projects (e.g., the Grain for Green Program) can promote vegetation growth to a certain extent.

Environmental correlates of body size influence range size and extinction risk: A global study in rodents

AbstractAimSpecies‐level traits, such as body and range sizes, are important correlates of extinction risk. However, both are often related and are driven by environmental factors. Here, we elucidated links between environmental factors, body size, range size and susceptibility to extinction, across the whole order of rodents.LocationGlobal.Time periodCurrent.Major taxa studiedRodents (order Rodentia).MethodsWe compiled an unprecedentedly large database of rodent morphology, phylogeny, range size, conservation status, global climate and normalized difference vegetation index (NDVI), comprising >86% of all described species. Using phylogenetic regressions, we initially explored the environmental factors driving body size. Next, we modelled the relationship between body size and range size. From this relationship, we computed and mapped (at the assemblage level) an index of relative range size, corresponding to the deviation from the expected range size of each species, given its body size. Finally, we tested whether relative range was correlated with the risk of extinction of the species derived from an assessment by the International Union for Conservation of Nature.ResultsWe found that, contrary to the expectations of Bergmann's rule, the body size of rodents was mostly influenced by variation in NDVI (rather than latitude/temperature). Body size, in turn, imposed a constraint on species range size, as evidenced by a triangular relationship that was segmented at the lower bound. The relative species range size derived from this relationship highlighted four geographical regions where rodents with small relative range were concentrated globally. We demonstrated that lower relative range size was associated with increased risk of extinction.Main conclusionsSpecies that, given their body size, are distributed across ranges that are smaller than expected have elevated extinction risk. Therefore, investigating the relationships between environmental drivers, body size and range size might help to detect species that could become threatened in the near future.

Suitability Analysis for Yam Production in Nigeria using Satellite and Observation Data

Identification of suitable areas for yam production is critical for ensuring yield in yam production in Nigeria. The study is aimed at determining suitable lands for yam production in Nigeria. Climate, soil, and environmental parameters that have a high contribution to yam production were used in developing a yam production suitability map using the Analytical Hierarchy Process (AHP). The AHP was used in deriving weights through a pairwise comparison technique. According to the findings, highly suitable (HS), suitable (S), marginally suitable (MS), and not suitable (NS) regions accounted for 11.79, 82.68, 4.05, and 1.47% of the study area, respectively. The Normalized Difference Vegetation Index (NDVI), a measure of vegetation vigor, was higher in HS, followed by S regions, and then MS regions. Similarly, climate variables in HS regions were more favorable for plant growth, followed by S regions and MS regions. The correlation between precipitation and temperature is high and significant only in the HS class, despite the fact that NDVI and climate variables are significantly connected in all the suitability classes. The output map, thus determined, provides information on highly suitable, suitable or marginally suitable lands that are of practical importance to agriculturists.

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

Average climatic events describe the occurrence of weather or climate at an average value, whereas extreme events are defined as events that exceed the upper or lower threshold value of statistical or observational average climatic events. This study investigated the impacts of both average climate change (ACC) (i.e., average precipitation, temperature, and potential evapotranspiration [PET]) and extreme climate change (ECC) (i.e., five precipitation and five temperature extremes) on dryland vegetation based on the Normalized Difference Vegetation Index (NDVI). The spatial divergences of ACC and ECC in affecting changes in NDVI over drylands were determined using the geographical detector model. In this study, the growth of vegetation in 40.29 % of global drylands was driven by average precipitation and this dominant effect also occurred in all the plant species, particularly shrubs. However, the sensitivity of grassland to average precipitation exceeded that of most of the woody vegetation. The average temperature and PET controlled 28.64 % and 31.07 % of the changes in NDVI, respectively. Precipitation extremes (except for consecutive dry days and consecutive wet days) and warm temperature extremes (WTE) had positive influences on dryland vegetation, and the effect of WTE on NDVI exceeded that of the remaining temperature extremes. Temperature extremes exerted more significant effects than precipitation extremes for changes in the grassland NDVI. In contrast, the variations in shrub NDVI were primarily dominated by precipitation extremes. We also found that the impacts of parts of average and extreme climatic factors on vegetation had changed over time. Furthermore, temperature extremes had far exceeded the average temperature in affecting vegetation growth at the spatial scale, and this action gradually intensified from 1982 to 2015. The influences of all precipitation extremes were weaker than those of the average precipitation. Those can offer scientific references for ecosystem protection in drylands.

Inter-annual variations of vegetation dynamics to climate change in Ordos, Inner Mongolia, China

To reveal the characteristics of climate change and the controlling factors for vegetation dynamics in the Ordos, Inner Mongolia, China, 34 years (1982-2015) of regional climate variables and vegetation dynamics were investigated. The results show that: Annual mean air temperature (TMP) significantly increased with a linear slope of 0.473°C/10yr. Annual precipitation (PRE) had a non-significant positive trend nearly 5 times lower than the trend of potential evapotranspiration (PET). The average Normalized Difference Vegetation Index (NDVI) computed for the region was found to show a significant positive trend (6.131×10-4/yr). However, all climate variables displayed non-significant correlations with NDVI at annual scale. The reduction of desert and the increase of grassland over the past decades were accountable for the increased NDVI. Principal components analysis revealed that the regional climate change can be characterized as changes in temperature, humidity and the availability of radiant energy. Based on principal components regression coefficients, NDVI was mostly sensitive to humidity component, followed by growing season warmth (WMI). Spatially, 93.1% of the pixels displayed positive trend and 61.8% of the pixels displayed significant change over the past decades. Both principal regression analysis and partial correlation analysis revealed that NDVI in eastern part of Ordos was sensitive to TMP, whereas, NDVI in southern and western areas of Ordos displayed the high sensitivity to combined effects of PRE and cloud coverage (CLD). Partial correlation analyses also revealed that TMX was a surrogate for aridity, TMN was a representative of humidity, and temperature variations below the threshold of 5°C (CDI) were less important than WMI. We conclude that regional climate change can be characterized by warming and increased aridity. The significant positive trend of regional NDVI and the non-significant correlations between NDVI and climate variables at annual scale suggests the hidden role of the human activities.