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Investigating the long-term dynamics in the canopy proves to be difficult due to the short observational records of the normalized difference vegetation index (NDVI). To explore the linkage between tree growth, NDVI dynamics and largescale atmospheric circulation in the Greater Khingan Mountain, Northeast China, we established a chronology of Larix gmelinii tree ring width at three elevations (870-920 m, 1,100-1,150 m and 1,270-1,320 m) in the northern foothills of the mountain range. We then calculated the correlations between the tree ring chronologies and NDVI and climate factors, and reconstructed the NDVI time series from June to September 1759-2021 in the region based on the middle-elevation tree ring chronology. The results identify the positive effect of temperature (r = 0.56, p < 0.01) and the negative effect of precipitation (r = 0.44, p < 0.01) in the growing season as the main influencing factors of NDVI for the study period (1981-2019). The 11-year moving average of the reconstructed NDVI series reveals two periods of high canopy vigor (1898-1926 and 2009-2013) and three periods of low canopy vigor (1860-1962, 1882-1888 and 1968-1977) in the last 263 years. These periods correspond to drought events recorded in the historical literature. Wavelet analysis shows that the reconstructed sequences exhibited 11-13, 23-25, and 39-42 years period variations. Integrating this with spatial correlation analysis reveals that tree growth in the Mangui region was impacted by the combined effect of the North Pacific Decadal Oscillation and the North Atlantic Multidecadal Oscillation. The results of this paper provide a reference for the study of vegetation change patterns in the northern foothills of the Greater Khingan Mountains. [ABSTRACT FROM AUTHOR]

Over time, drought affects all regions of Morocco, especially in the arid climate region, which has negative consequences on agriculture, economic and environmental. The present study aims to describe the intensity of drought in Morocco and more specifically their impact on the distribution of vegetation. Spatial and temporal remote sensing data are used to monitor drought in the Doukkala region of Morocco, using a set of Landsat images, including Landsat 5 (ETM), and Landsat 7 (ETM+) captured during the period 1964-2014. This was determined based on remote sensing parameters: temperature condition index (TCI), vegetation condition index (VCI) and vegetation health index (VHI). The Normalized Difference Vegetation Index (NDVI) was determined for the years 1966, 1984, 1988, 2000 2006 and 2009, in order to identify the vegetation categories and quantify the vegetation density in the Doukkala region. The NDVI obtained was analyzed using the SPI (Normalized Precipitation Index) based on the rainfall data of the years 1966, 1984, 1988, 2000 2006 and 2009. The results obtained showed that the correlation between NDVI and SPI indicated negative values or less than 1. The calculation of VHI showed low values (VHI < 40%) in one part of the studied area that indicate severe to extreme drought conditions, while in the other part the VHI showed high values (VHI > 40%), which mainly reflect favorable conditions for crop development (no drought). The results of this study can be used for monitoring and evaluation of the drought for sustainable management of the area. [ABSTRACT FROM AUTHOR]

Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application. [ABSTRACT FROM AUTHOR]

The SCU Lightning Complex Fire started on 16 August 2020 and burned more than 395,000 acres of woodlands and grasslands in six California counties. Satellite images of pre-fire green vegetation biomass from both 2020 springtime (moist) and summertime (drier) periods, along with slope and aspect were used as predictors of burn severity patterns on the SCU Complex landscape using machine learning algorithms. The main finding from this analysis was that the overall burn severity patterns of the SCU Complex fires could be predicted from pre-fire vegetation biomass, slope, and aspect model input variables with high accuracies of between 50% and 80% using Random Forest machine learning techniques. The August and April biomass cover variables had the highest feature importance values. It can be concluded that the amount of dry biomass present at a given location was essential to predict how severely and completely the 2020 fires burned the vegetation cover and surface soils across this landscape. [ABSTRACT FROM AUTHOR]

Although accurate estimates of biomass loss during peat fires, and recovery over time, are critical in understanding net peat ecosystem carbon balance, empirical data to inform carbon models are scarce. During the 2019 dry season, fires burned through 133,631 ha of degraded peatlands of Central Kalimantan. This study reports carbon loss from surface fuels and the top peat layer of 18.5 Mg C ha-1 (3.5 from surface fuels and 15.0 from root/peat layer), releasing an average of 2.5 Gg (range 1.8-3.1 Gg) carbon in these fires. Peat surface change measurements over one month, as the fires continued to smolder, indicated that about 20 cm of the surface was lost to combustion of peat and fern rhizomes, roots and recently incorporated organic residues that we sampled as the top peat layer. Time series analysis of live green vegetation (NDVI trend), combined with field observations of vegetation recovery two years after the fires, indicated that vegetation recovery equivalent to fire-released carbon is likely to occur around 3 years after fires. [ABSTRACT FROM AUTHOR]

While air temperature has commonly been used to define the onset of the growing season (plant uptake of soil nutrients), there is evidence in the literature suggesting vegetation growth is sensitive to soil temperature. As soil temperature observations become increasingly available from monitoring networks, differences in the start of season (SOS) estimates based on both above and below‐ground temperatures should be explored. In this study, air, surface, and soil (at depths of 5, 10, and 20 cm) temperature from the U.S. Climate Reference Network were used to estimate SOS at 104 stations across the U.S.. Temperature thresholds of 0, 5, or 10°C were used to estimate the SOS as the earliest date of the year when temperatures remained above each threshold. SOS dates based on temperature were compared with MODIS‐satellite‐derived normalized difference vegetation index (NDVI). Results indicated that the day‐of‐year of SOS based on soil temperature occurred about two months earlier than SOS estimates from air and surface temperatures. Overall, 5 cm soil temperature SOS estimates using a 5°C threshold matched well with SOSNDVI; albeit, only slightly better than air temperature SOS estimates using the 0°C threshold. This was in part because air temperature conditions were more likely to dip back below a given threshold with the passage cold fronts than soil temperatures. This often resulted in later air temperature SOS estimates particularly in years with sub‐freezing late season cold fronts. This suggests soil temperature can improve SOS estimates for many locations across the U.S. Plain Language Summary: The start of the growing season is an important moment when vegetation cover begins to interact with and influence atmospheric conditions. Estimates on the timing of growing season start dates has traditionally been done using air temperature observations. However, recent studies have indicated that soil temperature conditions can also be an important indicator of plant growth and development. In this study, air, surface, and soil temperature data were used to estimate the start of the growing season. Overall, soil temperatures were found the earliest start dates followed by air (∼6 weeks later) and surface (8 weeks later) temperature estimates. Comparisons with satellite measures of vegetation development revealed that the earlier soil temperature growing season start dates were similar to satellite based estimates for more stations than air temperature start of season dates. However, these results varied regionally with air temperature start of season dates better aligned with satellite estimates in southern portions of the U.S. and soil temperature estimates having lower measures of error in the mid to northern regions of the U.S. These results suggest that soil temperature conditions can provide improved estimates of growing season start dates for many locations across the U.S. Key Points: Soil temperatures provided earlier estimates of growing season onset than air temperature5 cm soil temperature using a 5°C threshold agreed best with normalized difference vegetation index estimates of growing season onsetSoil temperatures can improve start of season estimates over air temperature [ABSTRACT FROM AUTHOR]

Vegetation growth plays a crucial role in the carbon cycle and climate change mitigation. However, the relative contribution of hydroclimatic variables (relative humidity, terrestrial water storage, day and night‐time land surface temperatures) on vegetation growth of agricultural and nonagricultural areas at the global scale remains unexplored. Using satellite‐based datasets, we examined the changes in Normalized Difference Vegetation Index (NDVI) and the four hydroclimatic variables during 2003–2014. Also, the relative contribution of the four hydroclimatic variables on vegetation growth in agricultural and nonagricultural areas was estimated. A significant (p‐value < 0.05) greening has occurred in the agricultural regions of India and Brazil during 2003–2014. Whereas in nonagriculture areas, a considerable greening occurred only in India and China during the 2003–2014 period. Among the four hydroclimatic variables, both day‐time and night‐time land surface temperature are the significant contributors of vegetation growth in the two‐thirds of the global landmass. Terrestrial water storage is a substantial contributor to the vegetation growth in the tropics and subtropics. Night‐time land surface temperature is strongly associated with the vegetation growth in the colder regions. The hydroclimatic variables do not explain the considerable amount of the total variance of vegetation growth over the agricultural areas in China, which is due to human agricultural management practices. Generally, the response of hydroclimate variables on vegetation growth in the agricultural and nonagricultural areas has significant implications in many areas, including food security, carbon sequestration, water resource management, and climate change. Key Points: A significant (p‐value < 0.05) greening has occurred in the agricultural ecosystems of India and Brazil during 2003–2014A significant greening occurred only in India and China in the nonagricultural ecosystems during 2003–2014Day and night‐time land surface temperature are the major contributors to vegetation growth over the two‐thirds of the globe [ABSTRACT FROM AUTHOR]

Based on the datasets of MODIS NDVI and SPOT VETGETATION NDVI from 1999 to 2018, this study has introduced the gravity centre model and time-lag analysis model to explore the spatial–temporal change patterns of the NDVI in China in different sub-regions and its response to climate factors. Results showed that: (1) In 1999–2018, there was an increasing trend in vegetation coverage in China on the whole, with a larger increase occurring in central south and northeast China. (2) The NDVIs of six land use types showed a similar change trend with temperature and precipitation with different time lags. (3) The maximum correlation coefficients between the NDVIs of different land types and climate factors appeared at Lag 0–1 (current month and previous one month) and Lag 0–2 (current month, previous one and two month), while the correlation coefficients were the lowest at Lag 3 (previous three month). (4) During the past twenty years, the gravity centres of the NDVI, temperature and precipitation moved northward as a whole with similar migration trajectories. These results could provide important decision supports for vegetation restorations and ecological protections in different sub-regions of China. [ABSTRACT FROM AUTHOR]

This study was carried out to seek the effect of soil salinity in different biophysical characteristics of vegetative covers and the effect of salinity on the behavior of red and near infrared wavelengths in the spectral reflectance curve to figure out the relationship between the density and type of vegetation in saline soils according to some vegetation indices, Salamiyat project was selected to conduct this study in. It's located within geographical coordinates (E44 296120 N 33 361311, N33 418853 E 44 153791) with 34 m high above sea level, in a total area about 14265 Acres. Samples were taken selectively from surface and sub surface soil from the studied area and according to the previously identified salinity units map that was update under this study. Two pedons were exposed in each salinity unit class and it has been morphologically described. Soil samples had been taken from each horizon, some chemical and physical properties were estimated and the spectral reflectance of each the surface layer (0-5 cm) and the natural plant of each site was measured. The study showed that there was a close correlation between the reflectance and type of common salts in soil, and it gave the highest reflectance for the soil of white salt crust while it gave a low reflectance for the dark soil surfaces. Location six (pedon 6) has given the highest reflectance value for soil in the red wavelength where it was 0.3504 and it also has given the highest reflectance value in the NIR wavelength at 0.4057 of the same pedon. The first location (pedon 1) showed less reflectance in the red wavelength curve where it was 0.0807, and it gave the least reflectance in the NIR wavelength where it was 0.1124 of the same pedon. The correlation graph of soil salinity and reflectance showed that reflectance is increasing up to some limits then starts to decrease with salts accumulation. Salinity effects the reflectance of plants through effecting its physiological characteristics particularly plant leaves therefor the reflectance decreases in the Red and NIR wavelengths in the plant that have no salts stress, water stress or disease stress. while there was a clear difference between the reflectance of the Red and NIR wavelength that can be used in distinguishing plant cover that suffers from salinity stress even if it was a perfectly growing in that soil. [ABSTRACT FROM AUTHOR]

Background: Different factors affect vegetation changes in all areas. Pointing out the climate factors is an important issue when considering vegetation. Methods: In Taif province, we studied the vegetation changes in Al-Hada and Al-Shafa areas in the period from 2000 to 2018. The correlation between vegetation and climate factors in the previously mentioned areas has been studied in spring. Results: Using NDVI images, we found that the highest coverage was in 2013 with 11.23% in Al-Hada area, while the lowest coverage was in 2018 with 3.48% in Al-Shafa area. The information about the climate factors was obtained from NOAH model data from NASA Global Land Data Assimilation System with spatial resolution (1.0° and 0.25°). We found that temperature and rain precipitation had the greatest impact. Conclusion: The results indicated that this method had the potential to be a useful guide to identify the impact of these two factors on the vegetation in this area. [ABSTRACT FROM AUTHOR]

Though the area under study has a paucity of major environmental resources, it is capable of building up it's own resource base. This can be achieved by developing the naturally thriving salt resistant plants. These in turn shower a positive impact on the economic and environmental resource base of the region and in turn aiming towards sustainable development. The Landsat TM/ETM+ data was used to monitor the spatio-temporal changes in vegetation condition and soil salinity. To cross check the vegetation cover information and soil salinity obtained through images, ground truth verification of certain sample locations through GPS device was done. Afterwards, vegetation cover map and soil salinity map of 1998 and 2018 were crossed to generate the map of change of vegetation cover and salinity cover for the respective dates and to find out the changing pattern of vegetation cover and soil salinity. Remote sensing based indices like NDVI and NDSI were employed to extract spatial information on vegetation condition and soil salinity of the study area respectively. The soil salinity in terms of NDSI increased in the study area. The Shannon entropy index was used to understand and quantify the salt effected growth and trend in the study area during the period 1998 to 2018 and to produce land use and cover map for the studied area through the use of the Geospatial techniques with Shannon's Entropy statistical method. For this purpose, three Landsat images were used for land use/land cover classification by using supervised maximum likelihood classification techniques to extract and assess the changes of salt effected area lands. It was concluded that the salinity of the soil has increased down the years. Entropy increased from 0.08 to in the first period to 0.42 in the second. Entropy value increased in the NE, NW, SE and SW zones showed a higher value. This was in bearing with the fact that their was an expansion in the saline land. [ABSTRACT FROM AUTHOR]

Productivity of northern latitude forests is an important driver of the terrestrial carbon cycle and is already responding to climate change. Studies of the satellite-derived Normalized Difference Vegetation Index (NDVI) for northern latitudes indicate recent changes in plant productivity. These detected greening and browning trends are often attributed to a lengthening of the growing season from warming temperatures. Yet, disturbance-recovery dynamics are strong drivers of productivity and can mask direct effects of climate change. Here, we analyze 1-km resolution NDVI data from 1989 to 2014 for the northern latitude forests of the Greater Yellowstone Ecosystem for changes in plant productivity to address the following questions: (1) To what degree has greening taken place in the GYE over the past three decades? and (2) What is the relative importance of disturbance and climate in explaining NDVI trends? We found that the spatial extents of statistically significant productivity trends were limited to local greening and browning areas. Disturbance history, predominately fire disturbance, was a major driver of these detected NDVI trends. After accounting for fire-, insect-, and human-caused disturbances, increasing productivity trends remained. Productivity of northern latitude forests is generally considered temperature-limited; yet, we found that precipitation was a key driver of greening in the GYE. [ABSTRACT FROM AUTHOR]

The prolonged 2012–2016 California drought has raised many issues including concerns over reduced vegetation health. Drought impacts are complicated by geographical differences in hydroclimatic variability due to a climatic dipole influenced by the Pacific. Analysis of MODIS‐derived Normalized Difference Vegetation Index and self‐calibrated Palmer Drought Severity Index from 2000 to 2018 reveals differences in drought and vegetation responses in Northern versus Southern California (NorCal vs SoCal, see definition in section). The greatest declines in Normalized Difference Vegetation Index were focused in the SoCal, while NorCal appears not severely affected thus far. It appears that both the strength of drought and the sensitivity of the vegetation to drought are larger in SoCal. The exacerbated aridity in SoCal is a trend extending throughout the past and present century. The spatial differences in hydroclimatology and vegetation responses are important considerations for statewide climate change adaptation—with SoCal potentially facing greater challenges. Plain Language Summary: In this study, we investigated California vegetation responses to the recent prolonged 2012–2016 drought, which was potentially the driest 4‐year span in the last 1,200 years. The overall vegetation of the state has been presumed to be severely affected. However, Normalized Difference Vegetation Index (NDVI) satellite data related to vegetation greenness show that the large‐scale drought impacts on observable vegetation health were principally focused in Southern California regions, where the vegetation communities experienced striking declines in greenness. By contrast, some areas of Northern California have demonstrated significant NDVI increase and this difference in NDVI trends is evident over the past 19 years. The distinct vegetation behavior in Northern and Southern California seems to be related to a climatic dipole in the western United States, which is influenced by the large‐scale ocean‐atmosphere interactions. Some climate models predict an extension of such a dipole with greater precipitation in Northern California and less in Southern California by the end of the 21st century. Increased water demand in the south could intensify statewide intersectoral and interregional water competition in the future. However, increasing drought damage to wildlands and their terrestrial ecosystems in the south will not be relieved by any type of traditional water reallocation and irrigation effort. Key Points: NDVI has decreased in Southern California but increased in the north and the higher elevations of the Sierra NevadaA hydroclimatic dipole contributes to the differing drought intensities and vegetation responses of Northern and Southern CaliforniaClimate change will likely intensify the north‐south hydroclimatic contrast in California [ABSTRACT FROM AUTHOR]

Extensive studies have focused on instantaneous and time‐lag impacts of climatic factors on vegetation growth; however, the chronical and accumulative indirect impacts of antecedent climatic factors carrying over for a period of time on vegetation growth, defined as cumulative effects, are less investigated. Here we aimed to disentangle the cumulative effects of climatic factors on vegetation growth by using vegetation indexes and accumulated meteorological data. First, we investigated the explanation and fit of climate changes on vegetation variations by applying stepwise multiple linear regression with Akaike information criterion. Then, we obtained the correlation coefficients and lagged time of climatic factors on vegetation growth whereby partial correlation and time‐lag effect analyses. Results showed that (i) consideration of cumulative climate effects increased the explanation and fit of climate changes on vegetation dynamics for more than 77% of vegetated surface with an average global explanation of 68.33%, which was approximately 3.35% higher than the scenario when only time‐lag effects were considered; (ii) big differences exhibited in the correlation coefficients and lagged times under the scenarios with cumulative climate effects considered or not; and (iii) positive accumulated temperature (accumulated solar radiation) effects with zero (three‐month) time lag dominates most mid‐high latitude ecosystems, and negative accumulated temperature effects with three‐month delay dominates the temperate arid and semiarid regions and tropical dry ecosystems. By comparison, accumulated precipitation had relatively complex cumulative effects on vegetation growth. We concluded that climatic factors had significant cumulative effects on vegetation growth; consideration of the cumulative effects helps us better understand the climate‐vegetation interactions. Key Points: The climatic factors had significant cumulative impacts on vegetation growth, which were varied by climatic factors and spacesAdditional consideration of the cumulative impacts increased the explanation and fit of climatic factors on plant growthThe correlation coefficients and lagged times of climatic factors on plant growth were very different if we included cumulative impacts [ABSTRACT FROM AUTHOR]

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. Widespread changes in satellite‐observed NDVI values have been recorded across the arctic and boreal region in recent decades. In an effort to characterize these changes, we measured the response of NDVI to changes in community composition and leaf area in two types of boreal peatland ecosystems. We found strong relationships between NDVI and the percent cover of different plant functional types, in particular of shrubs. Our results support recent studies that have shown strong correlations between species cover, productivity, and NDVI in far northern ecosystems. [ABSTRACT FROM AUTHOR]

Highlights • eMODIS NDVI data can be used to track vegetation degradation in mountain pastures. • Vegetation degradation was differentiated by high NDVI with small seasonal variations. • Prolonged high NDVI curves may be due to vegetation degradation, not climatic changes. • Conditional inference classification identified phenophases to discern degradation from remote sensing data. Abstract The detection and assessment of vegetation degradation of mountain pastures is of high interest given the increasing number of reports on degradation as well as the importance of pastures as a source of livelihood for many mountain communities globally. Due to its synoptic perspective, remote sensing is an attractive instrument to rapidly assess and diagnose degradation prevalence and status. While it is relatively straight-forward to assess degradation leading to a change in vegetation cover by remote sensing, it is more challenging to study changes in vegetation composition, i.e. vegetation degradation. Therefore, our aim was to investigate the use of the seasonal variation (phenophases) of zonal-specific pastures varying in vegetation degradation using the normalized difference vegetation index (NDVI). For ground-truthing, quantitative and qualitative relevé data were collected during field surveys 2008–2015 during the growing season, and have been used to validate the classification of vegetation degradation based on eMODIS NDVI composite data for selected normal and dry years. Using a 500 m buffer zone, vegetation features have been extracted and correlated with satellite reflectance spectra for classifying the remote sensing imagery. We found a significant difference in compositional structure among degradation classes for all ecozones. Since qualitative changes in vegetation cover were caused by a replacement of dominant grasses and forbs with unpalatable forbs or shrubs with different reflectance patterns, our results indicate that excluding the alpine zone, vegetation degraded pastures differed from non-degraded ones by higher NDVI values at each phenology phase, although this was not always significant. As vegetation degradation led to an earlier greenup and later senescence, i.e. an observed extension of the growing season in terms of NDVI data, shifts in species composition could also be driving effects that have so far mostly been interpreted as signs of climatic changes. The procedures of this study and the developed conditional inference classification trees enable cost-effective screenings of large areas to detect vegetation degraded pastures and to assess seasonal grazing impact from remote sensing based interpretation. We conclude that the procedures developed here using eMODIS NDVI composite datasets are well suited for vegetation degradation assessments. This can improve monitoring of vegetation degradation as well as land-use planning. [ABSTRACT FROM AUTHOR]

Aim: Radial growth and foliage dynamics of trees both play a significant role in the terrestrial carbon cycle. Yet, crucial knowledge gaps exist in how these two growth components are linked. Our goal is to help bridge these gaps by providing a Northern Hemispheric survey of the connections between, and drivers of, inter‐annual wood and canopy–landscape dynamics and phenology. Location: Northern (>30° N) forest ecosystems. Methods: We compared a multispecies network of ca. 700 annually resolved radial tree‐growth records with the global inventory modelling and mapping studies‐normalized difference vegetation index (GIMMS‐NDVI) estimates of foliage greenness between 1982 and 2012. Tree‐ring data were assimilated into the simple process‐based Vaganov–Shashkin Lite model to derive xylem phenology on a monthly basis and were contrasted against NDVI estimates of canopy phenology. We additionally determined the response of all these vegetation measures to temperature and precipitation. Results: We found broad‐scale agreement in the phenology and growing season climate response between radial tree growth and seasonally integrated canopy–landscape dynamics. On a monthly basis, however, a temporal asynchrony in the climate signals at mid‐ and high latitudes was observed, where the strongest climate response of the NDVI record occurred around leaf flush, whereas an early‐ to mid‐growing season signal dominated the tree‐ring growth. Main conclusions: Our comprehensive study helps to elucidate the unique contributions of foliar and radial growth to terrestrial carbon cycling and the time‐scales at which they operate. Although we observed that both measures have similar overall climate constraints, these two growth components are sensitive to distinct seasonal windows. Our study suggests that joint assessment of both leaf and stem growth is required to address productivity of forests and demonstrates that these seasonal sensitivities must be considered before combining and interpreting these two metrics. [ABSTRACT FROM AUTHOR]

We investigated drought activity and the relationship between drought and vegetation in Northwest China over the period 1982-2013 using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). The indexes were, respectively, calculated from ground-based meteorological data and from remotely sensed satellite data. The spatial and temporal distributions of drought (SPEI) and of vegetation cover (NDVI) were compared using annual trends, and the relationships between these trends were analyzed. The results are: (1) Overall, Northwest China had a drought trend during the study period, although some a few regions show a significant wetness trend; (2) the mean annual NDVI fluctuates, but overall shows an increasing trend, particularly in some mountainous areas that have at least adequate water and vegetation cover, while unused land becomes degraded; (3) most regions show a positive correlation between SPEI and NDVI, although the western parts of the Tarim basin, Qaidam basin, and some regions in the southeastern part of study area show a negative correlation; and (4) the various regions respond differently to global climate change, but in general regions with more vegetation cover show increased vegetation growth, while regions with less vegetation cover are becoming degraded and thus more vulnerable to the adverse effects of climate change. [ABSTRACT FROM AUTHOR]

The variation of land surface phenology responds to climate change and controls many feedbacks of vegetation to the climate system. In this study, spatial and temporal patterns of land surface phenology in Songnen Plain of China were investigated between 1983 and 2012 using the third generation NDVI (Normalized Difference Vegetation Index) dataset from GIMMS (Global Inventory Modelling and Mapping Studies). The contribution of climate factors on the start (SOS), end (EOS) and length of growing season (LOS) were identified by grid-based grey relational analysis. Our results indicate that average SOS and EOS dates in Songnen Plain showed clear spatial patterns. Delayed SOS accounted for 12.7% of the study area with a highly significant increased rate of 0.54 days per year (p<0.05). 12.2% of the total land presented prolonged EOS at highly significant rates of 0.38 days per year. Areas showing shortened and delayed LOS occupied 17.9% and 14.2%, respectively. Spatially, the most influential factor affecting SOS changes was sunshine duration, the relative humidity in winter and spring. EOS changes were mostly associated with spring sunshine hours, relative humidity and temperature in summer. Sunshine duration and relative humidity were the main influential factors in regulating the variations in LOS. The response of the land surface phenology to climate factors showed different spatial patterns in different seasons. The results may provide a basic reference for studying the ecosystem functioning in temperate semi-humid and semi-arid region. [ABSTRACT FROM AUTHOR]

In Nordic and Arctic regions, the rapidly warming climate sustains hydroclimatic and vegetation changes in the landscape. There is evidence for an increase in vegetation density in some regions, a trend that is expected as a response to increasing temperature and precipitation. If the hydroclimatic changes are linked to vegetation response, it could be viewed as a landscape‐scale nature‐based solution (NBS) that could moderate the runoff response, as denser vegetation should lead to increased evapotranspiration and lower runoff. In this paper, we investigate and compare hydroclimatic changes over a set of basins in the Nordic region and northwest America and compare with changes in vegetation density, analyzed using the normalized difference vegetation index (NDVI) for three time periods: 1973–1978, 1993–1998, and 2013–2016. Over the period of the 1970s to 1990s, the hydroclimate became warmer and wetter and vegetation density increased, but over a later period from the 1990s to 2010s, vegetation density decreased, despite a continuing warming and wetting of the climate. Although there was a tendency for runoff to decrease in basins where vegetation density increased, the relation between precipitation and runoff was much stronger. Overall, we found weak evidence for vegetation density changes, driven by hydroclimate, to act as NBS on the landscape scale over the studied regions. However, as hydroclimatic changes interact with vegetation changes and their ensuing hydrological responses in complex ways, more detailed investigations are needed to determine the potential NBS effect on the landscape scale across Nordic and Arctic regions. [ABSTRACT FROM AUTHOR]

Abstract: Arctic tundra vegetation has largely been “greening” in recent decades, resulting in major changes to terrestrial ecosystems, with implications for surface energy balance, permafrost, carbon and water cycling, herbivore populations, and human land use. While general greening trends have been well‐studied, more specific vegetation‐temperature dynamics are spatially and temporally heterogeneous and currently not well understood. This study uses Normalized Difference Vegetation Index (NDVI) and Summer Warmth Index (SWI) data to investigate patterns of arctic tundra vegetation and temperature dynamics over North American and Eurasian continents and by Arctic bioclimate subzones (essentially latitudinal‐based). Relative vegetation increases in northern subzones were muted compared to temperature increases, whereas relative vegetation increases in southern subzones were consistent with, or greater than, relative temperature changes. Detrended, interannual NDVI variances were greatest in middle and southern subzones, whereas interannual SWI variances were greatest in southern subzones. Annual SWI and NDVI relationships were strongest in midlatitude subzones. [ABSTRACT FROM AUTHOR]

Precipitation products with high spatial resolution are important for basin-scale hydrological and meteorological applications. Downscaling techniques commonly used with satellite-derived rainfall data build statistical regression relationships between the precipitation and land surface characteristics to obtain rainfall estimates with improved spatial resolution. However, these relationships tend to be extended mistakenly from the regional scale to the hill slope scale. This paper introduces a quadratic parabolic profile (QPP) model for downscaling precipitation. The proposed technique uses a quadratic parabolic equation to express the rule for changes of precipitation with elevation. It is assumed that precipitation is the primary factor restricting vegetation growth during the growing season. Therefore, an ordinary least square regression method is used to fit an “elevation–normalized difference vegetation index (NDVI)” function to determine the parameters of the QPP model. This method was implemented in the Three-River Headwaters Region (TRHR) during the growing seasons of 2009–2013 for both monthly and total precipitation. The results indicated that the precipitation estimates downscaled using the QPP method had higher accuracies than those of commonly used exponential regression, multiple linear regression, and geographically weighted regression models. The average root mean square errors (RMSEs) and mean absolute percent errors (MAPEs) of total precipitation during the growing season of the commonly used models were 17%–69% and 17%–92% higher, respectively, than those of the QPP model. Meanwhile, the precipitation downscaled using the QPP technique also had lower MAPEs and RMSEs than the PERSIANN-CCS, PERSIANN-CDR, GSMaP-RNL, and GSMaP-RNLG products. Downscaled precipitation estimates from the QPP model exhibited patterns with elevation that were more detailed and more reliable than from the commonly used downscaling methods and another four satellite products. In addition, the QPP model is insensitive to errors in the NDVI or elevation. These findings suggest the proposed approach could be implemented successfully to downscale both monthly and total precipitation of the Tropical Rainfall Measuring Mission (TRMM) 3B43 product throughout the growing season in the TRHR. [ABSTRACT FROM AUTHOR]

Abstract: The terrestrial forest ecosystems in the northern high latitude region have been experiencing significant warming rates over several decades. These forests are considered crucial to the climate system and global carbon cycle and are particularly vulnerable to climate change. To obtain an improved estimate of the response of vegetation activity, e.g., forest greenness and tree growth, to climate change, we investigated spatiotemporal variations in two independent data sets containing the dendroecological information for this region over the past 30 years. These indices are the normalized difference vegetation index (NDVI3g) and the tree‐ring width index (RWI), both of which showed significant spatial variability in past trends and responses to climate changes. These trends and responses to climate change differed significantly in the ecosystems of the circumarctic (latitude higher than 67°N) and the circumboreal forests (latitude higher and lower than 50°N and 67°N, respectively), but the way in which they differed was relatively similar in the NDVI3g and the RWI. In the circumarctic ecosystem, the climate variables of the current summer were the main climatic drivers for the positive response to the increase in temperatures showed by both the NDVI3g and the RWI indices. On the other hand, in the circumboreal forest ecosystem, the climate variables of the previous year (from summer to winter) were also important climatic drivers for both the NDVI3g and the RWI. Importantly, both indices showed that the temperatures in the previous year negatively affected the ecosystem. Although such negative responses to warming did not necessarily lead to a past negative linear trend in the NDVI3g and the RWI over the past 30 years, future climate warming could potentially cause severe reduction in forest greenness and tree growth in the circumboreal forest ecosystem. [ABSTRACT FROM AUTHOR]

This study examines the MODIS time series NDVI datasets to detect greenness regeneration over Western Rajasthan and the Gujarat region of India. Time series analysis was applied to 17-years (2000-2016). MODIS NDVI satellite data product. Rainfall data for the same period were also analyzed to understand its impact over vegetation. NDVI time series datasets of MODIS 16-day composite proved sufficient for deriving statistically significant trend values for identifying areas of change in vegetation cover. Areas showing positive changes in NDVI trend was clearly correlated with areas which were brought under irrigational network over these areas, indicating an increase in vegetation, due to availability of water supply. Trends in NDVI were also compared with the trends in rainfall over the selected locations from Gujarat and Western Rajasthan. NDVI was positively correlated with the rainfall in both the regions. The NDVI time series trend analysis successfully detected the changes in greenness regeneration areas in Gujarat and western Rajasthan. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the presence of vegetation patches, called holes remaining after forest fires. Holes are of interest to explore because their vegetation is affected by severe temperature stress nearby, although they can serve as an agent to regenerate a forest after the burn. Further, it is interesting to know why holes emerge at all, while little if anything is known about their structure and distribution in space. A statistical analysis of their presence and abundance and a spatial statistical analysis to analyze their positions was done within four forest fire footprints. Fractal dimension of the holes was compared to that of the forest fire footprint, whereas remote sensing imagery was used to identify the normalized difference vegetation index (NDVI) of the patches before and after the fire. Results showed that the fractal dimension of the holes is lower than that of the forest fire footprint, and that the NDVI is slowly recovering to the original NDVI. Differences with the NDVI of the surrounding areas remain large. We concluded that patches of vegetation after a forest fire are interesting to study, providing clues of why unburned patches occur despite the fire presence nearby, how they can be characterized spatially and how the vegetation composition responds to such nearby fire. The Recommendations for Resource Managers: Forest fires affect the forests, and have an effect on the population living within the forest and close to it. A forest fire commonly leaves behind a large number of unburnt vegetation patches. In this study we call them holes. These holes have been under severe heat and smoke pressure, but have similar tree species and forest structure as the original forest. They serve as the starting point to regenerate the forest. The primary implications for resource management are as follows: A better understanding of where they are, and how they are composed may help to understand the behavior of a fire. Their characterization may help to better understand the relation between vegetation as a fuel for forest fire. Their biodiversity will improve the fire spread modeling of burns that are carried out for management of a forest stand. [ABSTRACT FROM AUTHOR]

Changing climates have extended growing seasons and increased vegetation productivity in many northern ecosystems, but less is known about Southern Hemisphere counterparts. Among the more dramatic changes are reductions in winter snow cover in mountainous ecosystems in southeastern Australia; some forecasts predict almost complete absence of snow by 2050. We used the eddy covariance technique in a montane grassland (Nimmo) of the Snowy Mountains, which was a carbon sink of 26−185 g C m −2  yr −1 from 2007–2014 but was temperature- and moisture-limited. Higher soil temperatures increased net productivity at a rate of about 2.2 g C m −2  month −1 per 1 °C, when soil water content was not limiting. Carbon uptake of Australian mountain grasslands may therefore increase in a warming climate, provided growing season precipitation remains adequate. We used the MODIS normalized difference vegetation index (NDVI) to monitor phenology at Nimmo from 2001–2016. We also recorded daily estimates of greenness from digital imagery (i.e. PhenoCam) and found close correspondence between satellite and near-surface greenness indices in 2015, suggesting that remote sensing can reliably extend PhenoCam records. Mean growing season length (244 days) did not change over the study period and was two weeks longer than mountain grasslands in the Northern Hemisphere. The longer Australian growing season is not correlated with productivity and is likely attributable to the lower latitude of the Snowy Mountains (36°S) and mild Australian climate. Green-up of the grassland advanced by 16 days over the past 15 years, seemingly as a result of increasing August (late winter) temperature. Grass senescence (i.e. yellowing) was most closely related to vapor pressure deficit in March (early autumn). When high temperatures (≥14 °C) and low rainfall (≤50 mm) resulted in high March VPD (∼0.7 kPa), grass yellowed nearly 3 months early and the growing season was <200 days. These results suggest there is a tipping point beyond which high summer VPD prevents late-season recovery of Australian mountain grasslands. [ABSTRACT FROM AUTHOR]

Interactions between climate and ecosystem properties that control phenological responses to climate warming and drought are poorly understood. To determine contributions from these interactions, we used space-borne remotely sensed vegetation indices to monitor leaf development across climate gradients and ecoregions in the southeastern United States. We quantified how air temperature, drought severity, and canopy thermal stress contribute to changes in leaf flushing from mountainous to coastal plain regions by developing a hierarchical state-space Bayesian model. We synthesized daily field climate data with daily vegetation indices and canopy surface temperature during spring green-up season at 59 sites in the southeastern United States between 2001 and 2012. Our results demonstrated strong interaction effects between ecosystem properties and climate variables across ecoregions. We found spring green-up is faster in the mountains, while coastal forests express a larger sensitivity to inter-annual temperature anomalies. Despite our detection of a decreasing trend in sensitivity to warming with temperature in all regions, we identified an ecosystem interaction: Deciduous dominated forests are less sensitive to warming than are those with fewer deciduous trees, likely due to the continuous presence of leaves in evergreen species throughout the season. Mountainous forest green-up is more susceptible to intensifying drought and moisture deficit, while coastal areas are relatively resilient. We found that with increasing canopy thermal stress, defined as canopy-air temperature difference, leaf development slows following dry years, and accelerates following wet years. [ABSTRACT FROM AUTHOR]

Desertification of arid and semiarid land is a severe environmental problem. Better understanding of the desertification process, in the context of changing climate and growing human activities, is essential for policy-making on desertification control strategies and land-use management in these environmentally sensitive areas. In this study, we analyze vegetation change and dune activity over the years 1981-2013 in Mu Us, a semiarid dune field in north-central China, using remote sensing techniques and geomorphic analysis. During this period, we have observed an increasing trend in vegetation greenness, indicated by the Normalized Difference Vegetation Index, which is consistent with the decreasing trend of the dune mobility index. Changes in dune morphologies in the study area indicate that dunes are being stabilized under vegetation growth, and this morphological change could potentially be used to detect the long-term trend in desertification. Based on a detailed analysis of both climatic and socioeconomic data, we found that the decadal changes in wind strength have significant impact on the long-term vegetation rehabilitation in the study area and that the short-term variations in vegetation growth are associated with interannual fluctuations in precipitation. Policy-driven, large ecological restoration projects implemented in recent decades can exert a positive impact on vegetation restoration, especially under favourable climatic conditions. This study implies that such climatic conditions may offer a window of opportunity for land regeneration in semiarid northern China. [ABSTRACT FROM AUTHOR]

Global climate change has significantly affected vegetation variation in the third-polar region of the world - the Qinghai-Tibet Plateau. As one of the most important indicators of vegetation variation (growth, coverage and tempo-spatial change), the Normalized Difference Vegetation Index (NDVI) is widely employed to study the response of vegetation to climate change. However, a long-term series analysis cannot be achieved because a single data source is constrained by time sequence. Therefore, a new framework was presented in this paper to extend the product series of monthly NDVI, taking as an example the Yarlung Zangbo River Basin, one of the most important river basins in the Qinghai-Tibet Plateau. NDVI products were acquired from two public sources: Global Inventory Modeling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging spectroradiometer (MODIS). After having been extended using the new framework, the new time series of NDVI covers a 384 months period (1982-2013), 84 months longer than previous time series of NDVI product, greatly facilitating NDVI related scientific research. In the new framework, the Gauss Filtering Method was employed to filter out noise in the NDVI product. Next, the standard method was introduced to enhance the comparability of the two data sources, and a pixelbased regression method was used to construct NDVI-extending models with one pixel after another. The extended series of NDVI fit well with original AVHRR-NDVI. With the extended time-series, temporal trends and spatial heterogeneity of NDVI in the study area were studied. Principal influencing factors on NDVI were further determined. The monthly NDVI is highly correlated with air temperature and precipitation in terms of climatic change wherein the spatially averaged NDVI slightly increases in the summer and has increased in temperature and decreased in precipitation in the 32 years period. The spatial heterogeneity of NDVI is in accordance with the seasonal variation of the two climate-change factors. All of these findings can provide valuable scientific support for water-land resources exploration in the third-polar region of the world. [ABSTRACT FROM AUTHOR]

Effects of climate change on vegetation greenness have attracted considerable attention in the context of global change; however, the dependence of such climatic effects on elevation remains poorly understood. In this study, we examine the relationship between vegetation greenness change and climate change and, in particular, characterize how this relationship changes with elevation in the high mountains of southwest China by using the remotely sensed normalized difference vegetation index (NDVI), and observed temperature and precipitation data sets for the period of 1982–2013. The results show that vegetation exhibited a greening trend (slope: 0.0008 year−1, p < 0.01) under climate warming (slope: 0.04 °C year−1, p < 0.01) and drying (slope: −2.47 mm year−1, p > 0.05). The vegetation greening and climate warming trends were stronger in the higher elevation plateaus than in the lower elevation mountains. Statistical analysis showed that temperature was the main driving factor on vegetation greening, and the driving effect was elevation-dependent. A substantially more significant correlation between climate warming and vegetation greening was found in the higher elevation plateaus, which reveals a higher temperature sensitivity of these plateaus. In addition, a significant correlation between inter-annual standard deviations of NDVI and precipitation during 1982–2013 was tracked over the entire study area. [ABSTRACT FROM AUTHOR]

Vegetation activity plays a crucial role in the global carbon cycle and climate. Many studies have examined recent changes in vegetation growth and the associated local climatic drivers. They revealed a global greening trend during the recent decades. However, few studies have analyzed how remote oceanic conditions affect land vegetation growth through atmospheric teleconnection, and the causes of the recent greening needs further investigation. In this study, we investigate the spatio-temporal variations (including trends) of vegetation activity using satellite data of growing-season normalized difference vegetation index (NDVI gs ), and examine their relationship to local and remote climate oscillations and external anthropogenic forcing by statistical means. As expected, there is an increasing trend in global-mean NDVI gs from 1982–2013, with significant greening over Europe and many other land areas. NDVI gs is temperature-limited at northern high-latitudes, but water-limited in arid and semi-arid regions, and radiation-limited in the Amazon and eastern and southern Asia. Globally, El Niño-Southern Oscillation (ENSO) is the leading climatic driver of interannual variability of NDVI gs , especially over southern and eastern Africa, eastern Australia, northeastern Asia, and northern South America. Consistent with previous modeling studies, a regression-based attribution analysis suggests that historical anthropogenic forcing (mainly increases in greenhouse gases) explains about two thirds of the NDVI gs trend from 1982 to 2013, with the rest coming mainly from the Atlantic Multi-decadal Oscillation (AMO). Contributions to the recent NDVI gs trend from ENSO and Pacific decadal variability and Artctic Oscillation appear to be small. [ABSTRACT FROM AUTHOR]

Abstract: A series of Ecological Construction Programs have been initiated to protect the condition of grasslands in China during recent decades. However, grassland degradation is still severe, and conditions have not been restored as intended. This paper aims to empirically examine the effectiveness of these programs for protecting the grassland condition in the extensive pastoral areas of China. We focus on one major program that has been implemented widely on the grasslands, the Subsidy and Incentive System for Grassland Conservation (SISGC). The normalized difference vegetation index, measured with remote sensing technology, is used to quantify the grassland condition between 2001 and 2014. With data from 54 counties in the pastoral areas of Inner Mongolia, we estimate the impact of SISGC on the grassland condition. A fixed effects model is employed to control for livestock production, climate, time trends, and time‐invariant heterogeneity between counties. The model results provide quantitative evidence that the condition of the grasslands has improved significantly because of SISGC; but that the effectiveness of SISGC was offset to some extent by other socio‐economic and climate factors, such as increased producer prices and high temperature. This may explain why the actual grassland degradation has not been prevented as effectively as was expected. In addition, the impact of SISGC was stronger in counties with worse initial grassland condition. Furthermore, the effects of producer prices and climate changes were also more pronounced in these counties. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Vegetation gross primary productivity (GPP) is an important component in the global carbon cycle and its accurate estimation is essential in ecosystem monitoring and simulation. Previous studies show that ecosystem models usually overestimate GPP under drought and during spring, late fall and winter. In this study, these issues are addressed in the daily boreal ecosystem productivity simulator (BEPSd) by introducing a new water stress factor ( f w ) to replace the old one and a designed fraction in term of the normalised difference vegetation index (NDVI) ( f ndvi ) to indicate the effect of chlorophyll on photosynthesis. GPP simulations are conducted at 41 flux sites across Europe to test BEPSd with the new f w and f ndvi . The new f w captures drought conditions well and f ndvi expresses the chlorophyll constraint on photosynthesis. Although BEPSd with the old f w performs well for some plant function types (PFTs), it is unsatisfactory for others. BEPSd incorporating both the new f w and f ndvi gives better simulations than the old version, particularly for evergreen broadleaf forest, deciduous broadleaf forest and closed shrub with R (RMSE) value increasing (decreasing) from 0.69 (3.20 gCm −2 d −1 ) to 0.74 (1.65 gCm −2 d −1 ), 0.72 (4.01 gCm −2 d −1 ) to 0.82 (2.91 gCm −2 d −1 ), 0.54 (1.82 gCm −2 d −1 ) to 0.75 (1.59 gCm −2 d −1 ), respectively. Furthermore, the new f w effectively mitigates GPP overestimates under drought, and f ndvi counteracts GPP overestimates during spring, late fall and winter. Overall, the improved BEPSd shows a satisfactory performance at flux sites over Europe. [ABSTRACT FROM AUTHOR]

Importance of atmospheric aerosols through direct and indirect effects on hydrological cycle is highlighted through multiple studies. This study tries to find how much the aerosols can affect evapo-transpiration (ET), a key component of the hydrological cycle over high NDVI (normalized difference vegetation index)/dense canopy, over Dibrugarh, known for vast tea plantation. The radiative effects of aerosols are calculated using satellite (Terra-MODIS) and reanalysis data on daily and monthly scales. Aerosol optical depth (AOD) obtained from satellite and ground observations compares well. Aerosol radiative forcing (ARF), calculated using MERRA data sets of ‘clean-clear radiation’ and ‘clear-radiation’ at the surface, shows a lower forcing efficiency, 35 Wm −zs , that is about half of that of ground observations. As vegetation controls ET over high NDVI area to the maximum and that gets modified through ARF, a regression equation is fitted between ET, AOD and NDVI for this station as ET = 0.25 + (−84.27) × AOD + (131.51) × NDVI that explains 82% of ‘daily’ ET variation using easily available satellite data. ET is found to follow net radiation closely and the direct relation between soil moisture and ET is weak on daily scale over this station as it may be acting through NDVI. [ABSTRACT FROM AUTHOR]

Droughts have dramatic direct and indirect impacts on vegetation and terrestrial ecosystem stability, including decreases in growth and subsequent decreases in CO absorption. Although much research has been carried out on the response of vegetation to droughts, it remains unclear whether biomes are becoming more resistant or more vulnerable to drought. In this study, we used the Standardized Precipitation Evapotranspiration Index (SPEI, a multiscalar drought index) and the Normalized Difference Vegetation Index (NDVI, an indicator of vegetation growth) to detect the sensitivity of vegetation growth to droughts across 12-24 month timescales and to detect the change in this sensitivity over recent decades. We found that vegetation growth was most sensitive to 17-18 month droughts in water-limited regions, implying pronounce legacy effects from water conditions in previous years. In addition, we detected reduced coupling between drought and vegetation growth, probably caused by release moisture stress in water limited areas. Meanwhile, we observed a shortening of drought timescale to which vegetation most sensitively responded from an average of 18.1 to 17.2 months, suggesting the weakening of the drought legacy effect on vegetation growth. Results of this study contribute to the overall understanding of the resistance and resilience of ecosystems to drought conditions. [ABSTRACT FROM AUTHOR]

Although we often take the trees and plants around us for granted, almost every aspect of our lives depends upon them. They clothe us, feed us, provide us with oxygen, absorb carbon dioxide and give us building materials and medications. Vegetation, in Iasi County - the study area in this paper, is predominantly of forest-steppe. In this study is investigating correlation LST - sparse/dense vegetation extraction from NDVI, in three different moments of 2017. Landsat 8 images, which have undergone pre-processing, were used as based date. A lot of papers showed that the surface temperature of the work was confirmed from Landsat 5/7/8. Many researches indicated that between LST and NDVI is a linear relationship. It is noticeable that correlation coefficient has closer values to 1 for relationship LST - NDVI dense; R2 is about 0.9, while R2 is about 0.7 for relationship LST - NDVI sparse. In conclusion, this correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur. [ABSTRACT FROM AUTHOR]

Wetlands are the most important component of an ecosystem that provides ecological habitats and hence wetlands are supposed to be the indicators of healthy environment. It has been observed recently that the wetlands are affected by several anthropogenic impacts and climate change. Monitoring of the wetlands for their preservation has become mandatory. The present study uses MODIS satellite data of 2000 and 2014 for recording the temporal change in the status of the wetlands in Nagpur district, Maharashtra India. Least square regression correlation had been performed between wetlands area and corresponding (Normalized Difference Vegetation Index) NDVI of the selected wetlands. The study shows that the extent of the wetlands have reduced in the study area during 2000-2014. The Least square regression analysis result shows that vegetated areas within wetlands reduced during 2000 to 2014. This study highlights the present status of wetlands, which is helpful in future, conservation, assessment, and management of wetlands. The application of remote sensing and GIS techniques made analysis simpler and effective. [ABSTRACT FROM AUTHOR]

Two varieties of sweet corn were compared; Gold Rush and Chocolate in this study. Three fertilization regimes were used: 1. Organic (1200 kg/ha), 2. Organic (600 kg/ha) and half urea (86.5 kg/ha), and 3. Di amino phosphate (DAP) (50 kg/ha) and urea (173 kg/ha). Same irrigation amount was applied for all plots by using furrow irrigation method. NDVI was estimated by using RS technique to determine vegetation index. By using same technique canopy temperatures were estimated. Chocolate variety was highest in NDVI value (0.58) when Organic+Half Urea was applied. Plant height and leaf area were measured from each plot. Gold Rush variety produced the tallest plant with 119.4 cm at the Organic+Half Urea fertilizer application. The biggest leaf area (46.08 cm2) was measured from Gold Rush variety with DAP+Urea fertilizer application. Sugar content also determined by using Rea fracto meter. The highest sugar content was 14% for the Gold Rush variety by applying Organic+Half Urea and DAP+Urea. When all findings were evaluated, Organic+Half Urea fertilizer application was found to give better results. [ABSTRACT FROM AUTHOR]

Vegetation cover over Nigeria has been on the decrease recently, hence the need for adequate monitoring using geo-information technology. This study examined the spatio-temporal variation of vegetation cover over Nigeria for thirty years with a view to developing a strategy for enhancing environmental sustainability. In order to predict the spatial extent of vegetation cover in 2030, the study utilised satellite images from between 1981 and 2010 using the Normalised Difference Vegetation Index (NDVI) coupled with cellular automata and Markov chain techniques in ArcGIS 10.3. The results showed that dense vegetal areas decreased in area from 358,534.2 km2 in 1981 to 207,812 km2 in 2010, while non-vegetal areas increased from 312,640.8 km2 in 1981 to 474,436.4 km2 in 2010 with a predicted increase to 501,504.9 km2 by 2030, i.e. an increase of about 27,068.4 km2 between 2010 and 2030. The study concluded that geoinformation techniques are effective in monitoring long-term intra- and inter-annual variability of vegetation and also useful in developing sustainable strategies for combating ecological hazards. [ABSTRACT FROM AUTHOR]

Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOSC5 and SOSC6) and EOS (EOSC5 and EOSC6) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOSC5 and SOSC6 (EOSC5 and EOSC6) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOSC5 was more consistent than SOSC6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOSC5 (EOSC6) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied. [ABSTRACT FROM AUTHOR]

The recent deployment of ESA's Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015-November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded. [ABSTRACT FROM AUTHOR]

Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end-of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover. [ABSTRACT FROM AUTHOR]

ABSTRACT Station-based observed precipitation data are available in a limited way and only at low spatial resolution. High-resolution satellite rainfall products can help to monitor precipitation changes over large areas. The Tropical Rainfall Measuring Mission 3B43 ( TRMM 3B43) precipitation data with coarse spatial resolution and low data accuracy are capable of depicting the spatial variability of precipitation, but fail to estimate the accurate magnitude. These data available in Xinjiang, China, need to be evaluated, especially because Xinjiang has a complex terrain and application of such data becomes a challenging task. Based on the relation between precipitation and normalized difference vegetation index ( NDVI), a proxy for vegetation, a new statistical downscaling algorithm is proposed in this study. The calibration was based on geographical difference analysis ( GDA) and the monthly fractions derived from the un-calibrated TRMM data can be used to disaggregate high-resolution annual precipitation to high-resolution monthly precipitation. The accuracy of downscaled TRMM precipitation was evaluated based on station-based observed precipitation for a period of 1998-2010. Results indicated that: (1) optimal relations shown by R2 between TRMM precipitation and NDVI at different temporal and spatial scales were different: the largest R2 value was 0.69 for the period of 1998-2010 at a spatial resolution of 1.00° × 1.00°, was 0.70 for the dry year (2001 in this study) at a spatial resolution of 0.75° × 0.75°, and was 0.67 for the wet year (2010 in this study) at a spatial resolution of 1.25° × 1.25°; (2) the downscaled TRMM precipitation data obtained using NDVI described spatial patterns of precipitation reasonably well at a spatial resolution of 8 km × 8 km with more detailed information when compared to the raw TRMM precipitation at a spatial resolution of 0.25° × 0.25°; (3) the downscaled TRMM precipitation with GDA calibration, PDSGDA, can exclude the regions with irrigation- and/or ground water-induced vegetation coverage. Therefore, PDSGDA obtained in this study can be regarded as estimated alternative precipitation data across Xinjiang for management of water resources and agricultural irrigation activities. [ABSTRACT FROM AUTHOR]

Background: Baekdudaegan was designated in 2005 as a protected area to prevent destruction and conserve. However, there are many disturbed and destroyed areas. The total disturbed area amounts to 25.9 km² (0.94%), including 13.4 km² (0.49%) in the core area and 12.5 km² (0.45%) in the buffer area. This study aims to classify the vegetation types established in the disturbed areas and diagnose the current conditions for ecological restoration in the forest ecosystem. Methods: We surveyed the vegetation in the disturbed areas of Daegwallyeong and Chupungryeong and the surrounding natural areas. The survey conducted from July to September 2015 targeted a total of 54 quadrats by Braun-Blanquet method (Daegwallyeong, 22; Chupungryeong, 32). We also investigated the height and coverage of each layer. We classified the vegetation types based on the field data and analyzed the ratio of life form and the exotic plants, species richness, and vegetation index (Hcl). The Normalized Difference Vegetation Index (NDVI) was calculated from rapideye satellite imagery in 2014 and 2015. Results: Vegetation types were classified into 11 groups according to the criteria that included successional sere or plantation at first, followed by developmental stage and origins. As a result of the analysis of the survey data, species richness, vegetation index (Hcl), ratio of tree plants, and the NDVI tended to increase, while the ratio of the exotic plants tended to decrease with the time since disturbance. These indicators had the classified values according to the vegetation types with time since the disturbance. Conclusions: These indicators can be effectively used to diagnose the conditions of the present vegetation in the disturbed area of the Baekdudaegan area. In addition, the NDVI might be effective for the diagnosis of the disturbed status instead of the human efforts based on the higher spatial resolution of satellite imagery. Appropriate diagnosis of the disturbed forests in the Baekdudaegan area considering the established vegetation types is essential for the elaboration of restoration plans. In addition, restoration target and level should be different according to the disturbed status of restoration site. [ABSTRACT FROM AUTHOR]

Liu, W.; Feng, Q., and Zhang, F., 2017. Spatial-temporal variation characteristics of NPP in the Heihe River basin, Northwestern China, in a recent 10-year period calculated by CASA model. In: Zhi, Y. and Guido Aldana, P.A. (eds.), Sustainability of Water Resources and the Development of Coastal Environments: Select Proceedings from the 2016 International Conference on Water Resource and Environment (WRE2016). Journal of Coastal Research, Special Issue No. 80, pp. 36-47. Coconut Creek (Florida), ISSN 0749-0208. In this paper, the spatial-temporal variation characteristics of NPP and related factors in the Heihe River basin were analyzed using the meteorological data of the Heihe River basin, SPOT/NDVI data, and other modified mathematical statistical methods. The results show that during the 1999-2010 period the total NPP in the Heihe River basin exhibited a fluctuating increasing trend, reaching 0.3×1012 gC/a. The multiyear variation value of NPP was 0.19~601.09 gC/m2/a in the whole basin, 3.5 gC/m2/a in the upper reaches, 2.06 gC/m2/a in the middle reaches, and 0.31 g/m2/a in the lower reaches. The NPP of vegetation in the Heihe River basin during January and February tended to slightly decrease, and on average varied between −0.01 and −0.02 gC/m2/a, while in June, July, August, and September greatly increased. The NPP of 10 vegetation types exhibited an increasing trend in 2010 compared to 1999, on average increased by 21.02 gC/m2/a. Among various vegetation types the increase in the NPP value of swamp vegetation was the largest, reaching 46.24 gC/m2/a followed by planted vegetation, steppe types, and meadow types; there values were 39.22 gC/m2/a, 32.49 gC/m2/a, and 27.63 gC/m2/a, respectively. The accumulated NPP in grassland ecosystem in the Heihe River basin was the largest, which is of important significance to the eco-environment of the basin. As viewed for the whole Heihe River basin, the yearly mean cumulative value of NPP of vegetation was negatively related to annual mean temperature and annual total solar radiation but positively related to annual total precipitation, suggesting that water is a limiting factor to the NPP in the Heihe River basin. [ABSTRACT FROM AUTHOR]

Vegetation is an essential component of terrestrial ecosystems, and it plays an important role in regulating climate change, the carbon cycle, and energy exchange. And permafrost is extremely sensitive to climate change. In particular, aboveground vegetation on permafrost has great sensitivity to that change. The permafrost zone of northeastern China, within middle and high latitudes of the northern hemisphere, is the second-largest region of permafrost in China. It is at the southern edge of the Eurasian cryolithozone. This study analyzes growing-season spatiotemporal variation of the normalization difference vegetation index (NDVI) in this permafrost zone and the correlation between NDVI and climate variables during 1981-2014. Mean growing-season NDVI significantly increased by 0.0028 yr-1 over the entire permafrost zone. The spatial dynamics of vegetation cover in the zone had strong heterogeneity on the pixel scale. Pixels that showed increasing trends accounted for 80% of the permafrost area, and were mostly found in the permafrost zone with the exception of western steppe regions. Pixels that showed decreasing trends (approximately 20% of the permafrost area) were mainly in the cultivated and steppe portions of the study area. Our results indicated that temperature was the dominant influence on vegetation growth during the growing season in most permafrost zones. [ABSTRACT FROM AUTHOR]

This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April–October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China. [ABSTRACT FROM AUTHOR]

Based on Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI), we investigated vegetation response to different time-scales drought across different vegetation types and homogeneous clusters in China, by annual maximum Pearson correlation ( R max ) and the corresponding time-scales of drought. Results showed that: (1) 8 subregions with homogeneous climate-vegetation conditions were identified using Fuzzy C-Means algorithm; (2) SPEI and NDVI's annual R max were in significantly positive correlation in most regions of China, indicating that vegetation biomass were influenced mainly by the spatiotemporal characteristics of the water availability. The southeastern Yangtze River basin and the lower Pearl River basin are dominated by abundant precipitation, and vegetation is not sensitive to droughts in these regions. The northeastern Heilongjiang province, the Changbai Mountains and western Sichuan province are characterized by weak NDVI versus SPEI relations, indicating a relatively small effect of drought on vegetation; (3) The effects of annual average water balance, annual average annual precipitation, annual average effective accumulative temperature, and annual average daily sunshine hours on the NDVI versus SPEI correlation show that the annual average water balance is the key factor behind the change of vegetation vigor. It can therefore be concluded that the change of water availability is the key factor behind the change of vegetation activity and biomass. Regional precipitation or water balance was significantly related to the correlation between SPEI and NDVI. Vegetation in the regions with longer sunshine hours is more sensitive to droughts. In general, the sensitivity of grassland to droughts is the largest, followed by the sensitivity of shrubs and forests to droughts. [ABSTRACT FROM AUTHOR]