Vegetation greenness modeling in response to climate change for Northeast Thailand

In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.

Combining gradual and abrupt analysis to detect variation of vegetation greenness on the loess areas of China

The annual peak growth and trend shift of vegetation are critical in characterizing the carbon sequestration capacity of ecosystems.As the well-known area with the fastest vegetation growth in the world,the Loess Plateau(LP)lands find an enhanced greening trend in the annual and growing-season.However,the spatiotemporal dynamics of vegetation peak growth and breakpoints characteristics on time series still needs to be explored.Here,we performed tendency analysis to characterize recent variations in annual peak vegetation growth through a satellite-derived vegetation index(NDVI_(max),Maximum Normalized Difference Vegetation Index)and then applied breakpoint analysis to capture abrupt points on the time series.The results demonstrated that the vegetation peak trend had been significantly increasing,with a growth rate at 0.68×10^(-2)·a^(-1) during 2001-2018,and most pixels(70.81%)have a positive linear greening trend over the entire LP.In addition,about 83%of the breakpoint type on the monthly NDVI time series is a monotonic increase at the pixel level,and most pixels(57%)have detected breakpoints after 2010.Our results also showed that the growth rate accelerates in the northwest and decelerates in the southeast after the breakpoint.This study indicates that combining abrupt analysis with gradual analysis can describe vegetation dynamics more effectively and comprehensively.The findings highlighted the importance of breakpoint analysis for monitor timing and shift using time series satellite data at a regional scale,which may help stakeholders to make reasonable and effective ecosystem management policies.

Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region

The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.

Complex causes and consequences of rangeland greening in South America -multiple interacting natural and anthropogenic drivers and simultaneous ecosystem degradation and recovery trends

Land-surface greening has been reported globally over the past decades.While often seen to represent ecosystem recovery,the impacts on biodiversity and society can also be negative.Greening has been widely reported from rangelands,where drivers and processes are complex due to its high environmental heterogeneity and societal dynamics.Here,we assess the complexity behind greening and assess its links to various drivers in an iconic,heterogeneous rangeland area,the IberáWetlands and surroundings,in Argentina.Time-series satellite imagery over the past 19 years showed overall net greening,but also substantial local browning both in protected and unprotected areas,linking to land use,temporal changes in surface water,fire,and weather.We found substantial woody expansion mainly in the unprotected land,with 37%contributed by tree plantations and the remaining 63%by spontaneous woody expansion,along with widespread transitions from terrestrial land to seasonal surface water.Fire occurrences tended to reduce greening with unprotected areas experiencing widespread and frequent fire.However,protected areas had more browning in unburnt areas than burned areas.Temporal variation in annual precipitation and temperature tended to nonlinearly influence fire occurrences with an interplay of human fire management,further shaping the vegetation greening,pointing to high complexity behind the observed rangeland greening involving interactions among local drivers.Our findings highlight that the observed overall greening is an outcome of multiple trends with clear negative impacts on biodiversity and the local livestock-oriented culture(notably expanding tree plantations)and spontaneous vegetation dynamics,partly involving spontaneous woody expansion.The latter has positive potential for biodiversity and ecosystem services in terms of woodland recovery,but can become negative in such a natural savanna region if expansions develop on a too broad scale,highlighting the importance of ensuring recovery of natural fire and herbivory regimes in protected areas along with sustainable rangeland management elsewhere.

Application of HPLC and ESI-MS techniques in the analysis of phenolic acids and flavonoids from green leafy vegetables (GLVs)

Diets containing high proportions of fruits and vegetables reduce the risk of onset of chronic diseases. The role of herbal medicines in improving human health is gaining popularity over the years, which also increases the need for safety and efficiency of these products. Green leafy vegetables （GLVs） are the richest source of phenolic compounds with excellent antioxidant properties. Increased consumption of diets containing phenolic compounds may give positive and better results to human health and significantly improves the immune system. Highly selective, susceptible and versatile analytical techniques are necessary for extraction, identifica- tion, and quantification of phenolic compounds from plant extracts, which helps to utilize their important biological properties. Recent advances in the pre-treatment procedures, separation techniques and spectro- metry methods are used for qualitative and quantitative analysis of phenolic compounds. The online coupling of liquid chromatography with mass spectrometry （LC-MS） has become a useful tool in the metabolic profiling of plant samples. In this review, the separation and identification of phenolic acids and flavonoids from GLVs by LC-MS have been discussed along with the general extraction procedures and other sources of mass spectrometer used. The review is devoted to the understanding of the structural configuration, nature and accumulation pattern of phenolic acids and flavonoids in plants and to highlighting the recent developments in the chemical investigation of these compounds by chromatographic and spectroscopic techniques. It concludes with the advantages of the combination of these two methods and prospects.

Spatial dynamics of aboveground carbon stock in urban green space:a case study of Xi'an,China

Greenhouse gas emission of carbon dioxide（CO2） is one of the major factors causing global climate change.Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO2.Quantifying the carbon stock,distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment.Remote sensing is a valuable and effective tool for monitoring and estimating aboveground carbon（AGC） stock in large areas.In the present study,different remotely-sensed vegetation indices（VIs） were used to develop a regression equation between VI and AGC stock of urban green space,and the best fit model was then used to estimate the AGC stock of urban green space within the beltways of Xi＇an city for the years 2004 and 2010.A map of changes in the spatial distribution patterns of AGC stock was plotted and the possible causes of these changes were analyzed.Results showed that Normalized Difference Vegetation Index（NDVI） correlated moderately well with AGC stock in urban green space.The Difference Vegetation Index（DVI）,Ratio Vegetation Index（RVI）,Soil Adjusted Vegetation Index（SAVI）,Modified Soil Adjusted Vegetation Index（MSAVI） and Renormalized Difference Vegetative Index（RDVI） were lower correlation coefficients than NDVI.The AGC stock in the urban green space of Xi＇an in 2004 and 2010 was 73,843 and 126,621 t,respectively,with an average annual growth of 8,796 t and an average annual growth rate of 11.9%.The carbon densities in 2004 and 2010 were 1.62 and 2.77 t/hm2,respectively.Precipitation was not an important factor to influence the changes of AGC stock in the urban green space of Xi＇an.Policy orientation,major ecological greening projects such as ＂transplanting big trees into the city＂ and the World Horticultural Exposition were found to have an important impact on changes in the spatiotemporal patterns of AGC stock.

Nutrient and Phytochemical Composition of Five Wild Green Leafy Vegetables Consumed in Erie-Biase Local Government Area of Cross River State

This work on nutrient and phytochemical composition of five wild green leafy vegetables consumed in Erei-Biase Local Government Area of Cross River State, Nigeria was aimed at identifying and determining the nutrient, and phytochemical compositions of Amaranthus viridis (Ikorodotaseach), Aeschylus glabra (Ididieriri), Alphanostylis lepthanta (Emornegbandip), Calcasia saxatilis, (Igbongonokpa) and Lonchocarpus sericeus (Ajuokoh). Analysis of moisture, protein, fiber, ash, fat and carbohydrate, micronutrients and phytochemicals was done using standard methods, while vitamins were done using AOAC, (1995). The data generated were subjected to T-test, standard deviation, standard error of the mean. The result of the proximate analysis showed that Amaranthus viridis had the highest protein value (9.73%) and the lowest carbohydrate value (4.60%) respectively. Aeschylea glabra recorded the highest fat value [9.73%]. The micro nutrient result revealed that copper (CU) and phosphorous (Ph) values were low and that the value for the other micro-nutrient differed significantly with the exception of calcium (Ca) values which did not differed significantly (P Amaranthus viridis had the highest value of Vitamin C. The phytochemicals and anti-nutrient contents of the vegetables were moderately high but not higher than the safe levels. Hence they are recommended for consumption.

Time-lagged and cumulative effects of drought and anthropogenic activities on China's vegetation greening from 1990 to 2018

Previous studies have confirmed the time-lagged and cumulative effects of drought and anthropogenic activities on vegetation growth,but these studies focus on the time-lagged effect of drought and are poorly known how vegetation productivity responds to anthropogenic activities.Here,based on the reconstructed Normalized Difference Vegetation Index,the Standardized Precipitation Evapotranspiration Index and land use degree comprehensive index,we diagnosed the spatiotemporal pattern of vegetation and drought,investigated time-lagged and cumulative effects of drought and anthropogenic activities over China through the month where the maximum correlation coefficient occurred.It revealed that the browning trend of 32.21%of vegetated lands was covered by overall greening,especially northwestern China.Drought intensified with a rate of 0.0014/year.in 66.41%and 54.57%of the vegetated lands had time-lagged and cumulative response to drought,with a shorter timescales of 1–4 months,indicating the higher sensitivity of vegetation growth to drought.There was a U-shaped relationship between moisture conditions and vegetation response time.49.9%of China’s vegetation showed time-lagged effects to anthropogenic activities,with a longer timescales of 6–10 years,demonstrating that anthropogenic activities triggered ecological changes but vegetation ecosystems cannot keep pace.The accumulated and time-lagged years declined with increased land use intensity.

Seasonal compensation implied no weakening of the land carbon sink in the Northern Hemisphere under the 2015/2016 El Niño

The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere(NH).However,we reported here a pervasive and continuous vegetation greening and no weakened land carbon sink in the maturation phase of the 2015/2016 El Niño event over the NH(mainly in the extra-tropics),based on multiple evidences from remote sensing observations,global ecosystem model simulations and atmospheric CO_(2)inversions.We discovered a significant compensation effect of the enhanced vegetation growth in spring on subsequent summer/autumn vegetation growth that sustained vegetation greening and led to a slight increase in the land carbon sink over the spring and summer of 2015(average increases of 23.34%and 0.63%in net ecosystem exchange from two independent datasets relative to a 5-years average before the El Niño event,respectively)and spring of 2016(6.82%),especially in the extra-tropics of the NH,where the water supply during the pre-growing-season(November of the previous year to March of the current year)had a positive anomaly.This seasonal compensation effect was much stronger than that in 1997 and 1998 and significantly alleviated the adverse impacts of the 2015/2016 El Niño event on vegetation growth during its maturation phase.The legacy effect of water supply during the pre-growing-season on subsequent vegetation growth lasted up to approximately six months.Our findings highlight the role of seasonal compensation effects on mediating the land carbon sink in response to episodic extreme El Niño events.

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction(GVF)can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction(NWP)models.However,the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well.In this study,a near real-time(NRT)GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model.The annual variability of the GVF over North China is examined in detail.Many differences between the two GVF datasets are found over dryland,grassland,and cropland/grassland mosaic areas.Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity.The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October.Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon.Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state,which in turn helps to improve short-range forecasts in arid and semiarid regions of North China.Moreover,our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

乡村人口迁出对生态脆弱地区植被覆被的影响——以内蒙古自治区为例（英文）

Different government departments and researchers have paid considerable atten- tion at various levels to improving the eco-environment in ecologically fragile areas. Over the past decade, large numbers of people have emigrated from rural areas as a result of the rapid urbanization in Chinese society. The question then remains： to what extent does this migra- tion affect the regional vegetation greenness in the areas that people have moved from？ Based on normalized difference vegetation index （NDVI） data with a resolution of 1 km, as well as meteorological data and socio-economic data from 2000 to 2010 in Inner Mongolia, the spatio-temporal variation of vegetation greenness in the study area was analyzed via trend analysis and significance test methods. The contributions of human activities and natural factors to the variation of vegetation conditions during this period were also quantita- tively tested and verified, using a multi-regression analysis method. We found that： （1）the vegetation greenness of the study area increased by 10.1% during 2000-2010. More than 28% of the vegetation greenness increased significantly, and only about 2% decreased evi- dently during the study period. （2） The area with significant degradation showed a banded distribution at the northern edge of the agro-pastoral ecotone in central Inner Mongolia. This indicates that the eco-environment is still fragile in this area, which should be paid close at- tention. The area where vegetation greenness significantly improved showed a concentrated distribution in the southeast and west of Inner Mongolia. （3） The effect of agricultural labor on vegetation greenness exceeded those due to natural factors （i.e. precipitation and tempera- ture）. The emigration of agricultural labor improved the regional vegetation greenness sig- nificantly.

Quantification of urban greenery using hemisphere-view panoramas with a green cover index

Urban greenery has positive impacts on the well-being of residents and provides vital ecosystem services.A quantitative evaluation of full-view green coverage at the human scale can guide green space planning and management.We developed a still camera to collect hemisphere-view panoramas(HVPs)to obtain in situ heterogeneous scenes and established a panoramic green cover index(PGCI)model to measure human-scale green coverage.A case study was conducted in Xicheng District,Beijing,to analyze the quantitative relationships of PGCI with the normalized difference vegetation index(NDVI)and land surface temperature(LST)in different land use scenarios.The results show that the HVP is a useful quantization tool:(1)the method adaptively distinguishes the green cover characteristics of the four functional areas,and the PGCI values are ranked as follows:recreational area(29.6)>residential area(19.0)>traffic area(15.9)>commercial area(12.5);(2)PGCI strongly explains NDVI and LST,and for each unit(1%)increase in PGCI,NDVI tends to increase by 0.007,and(3)LST tends to decrease by 0.21 degrees Celsius.This research provides government managers and urban planners with tools to evaluate green coverage in complex urban environments and assistance in optimizing human-scale greenery and microclimate.