MODIS Normalized Difference Vegetation Index Research Articles

Higher susceptibility of beech to drought in comparison to oak

The expected increase in drought severity and frequency as a result of anthropogenic climate change leads to concerns about the ability of native tree species to cope with these changes. To determine the susceptibility of Fagus sylvatica (European beech) and Quercus robur (pedunculate oak) – the two dominant deciduous tree species in Central Europe – to drought, we quantified the climate sensitivity and drought-response of radial growth for both species using an array of dendroecological methods. Tree-ring data were collected from a site east of Coburg, Bavaria which had shown pronounced stress-symptoms (early leaf coloration) during the record drought of 2018. Climate-growth relationships were used to establish the sensitivity of radial growth to multiple climatic variables. The impact of specific drought events on tree growth was quantified using tolerance indices. In addition, we employed a Principal Component Gradient Analysis (PCGA) and remote sensing data (MODIS Normalized Difference Vegetation Index (NDVI)) to delineate the species specific drought responses. Using these methods we were able to show a clear difference in drought susceptibility between beech and oak. Beech displayed a higher sensitivity to temperature and the standardized precipitation evapotranspiration index (SPEI) and showed lower resistance and resilience to drought events than oak. In particular, beech was unable to fully recover from the 2003 drought, after which it expressed a stark growth decline, i.e. drought legacies, which was not observed for oak. The PCGA revealed a clear differentiation in the grouping of drought responses between beech and oak, supporting the findings of the climate-growth analysis and the tolerance indices. Correlations of NDVI and ring-width indices (RWI) indicated that under normal climatic conditions NDVI variability is linked to the start of the growing season. This is in contrast to drought years, such as 2003, where summer NDVI mirrored the drought response of beech and oak. These results reveal beech to have both a higher sensitivity to summer temperature and SPEI and a higher susceptibility to drought events. Although, in the past high plasticity and adaptability to drought have been attributed to both beech and oak, our study assigns beech a higher risk than oak to suffer from anticipated increases in drought frequency and intensity as a consequence of climate change.

Trends and land surface phenological responses to climate variability in the Argentina Pampas

Understanding the interaction between land surface and atmosphere processes is fundamental for predicting the effects of future climate change on ecosystem functioning and carbon dynamics. The objectives of this work were to analyze the trends in land surface phenology (LSP) metrics from remote sensing data, and to reveal their relationship with precipitation and ENSO phenomenon in the Argentina Pampas. Using a time series of MODIS Normalized Difference Vegetation Index (NDVI) data from 2000 to 2014, the start of the growing season (SOS), the annual integral of NDVI (i-NDVI, linear estimator of annual productivity), the timing of the annual maximum NDVI (t-MAX) and the annual relative range of NDVI (RREL, estimator of seasonality) were obtained for the Argentina Pampas. Then, spatial and temporal relationships with the Multivariate ENSO Index (MEI) and precipitation were analyzed. Results showed a negative trend in annual productivity over a 53.6% of the study area associated to natural and semi-natural grassland under cattle grazing, whereas a 40.3% of Argentina Pampas showed a significant positive trend in seasonality of carbon gains. The study also reveals that climate variability has a significant impact on land surface phenology in Argentina Pampas, although the impact is heterogeneous. SOS and t-MAX showed a significant negative correlation with the precipitation indicating an earlier occurrence. 23.6% and 28.4% of the study area showed a positive correlation of the annual productivity with MEI and precipitation, respectively, associated to rangelands (in the first case) and to both rangeland and croplands, in the second case. Climate variability did not explain the seasonal variability of phenology. The relationships found between LSP metrics and climate variability could be important for implementation of strategies for natural resource management.

Responses of NDVI to climate factors in Inner Mongolia using geographically weighted regression

Using the MODIS normalized difference vegetation index (NDVI) datasets along with the climate data (precipitation and temperature), geographically weighted regression (GWR) was constructed to explore the spatial nonstationarity and heterogeneity relationships between NDVI and climate factors in Inner Mongolia, China. Our work compared the results of GWR model accuracy with ordinary least squares (OLS) model. The results indicated that GWR method yielded better goodness of fit and higher model accuracy than OLS. Moreover, the GWR model could deeply reveal the complex relationship between NDVI and climate factors. At the same time, the research results could also provide scientific basis for vegetation modeling in Inner Mongolia and similar areas.

Availability analysis of the Chen NDVI model in MOD13 Q1 validation

The MODIS normalized difference vegetation index (NDVI) product plays an important role in the eco-environmental monitoring of natural disasters. However, its validation has been a long standing and important scientific problem. The paper proposed a method to integrate accurate classification information for medium-high spatial resolution remote sensing images to improve the traditional Chen NDVI scale conversion model and perform MOD13 Q1 validation. The authors had verified the method in the research area of Xiamen, Fujian Province, China, and the experimental results proved its effectiveness. This paper focuses on the availability research of the model in different experimental areas. Taking Fuzhou City of Jiangxi Province, China, as the study area, the MOD13 Q1 validation experiment was implemented. The conclusions are obtained from the experimental results: the Chen NDVI scale transformation model is not robust, and in some experimental areas there is significant transformation error when the conversion factor is too large (such as eightfold from 30 m OLI NDVI to 240 m up-scaled NDVI). In these bad cases, other more robust scale transformation models should be elected for the validation of the low-resolution land surface parameter images.

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.

Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park

The Sanjiangyuan National Park is the first Chinese national park system, and the ecological environment is inherently fragile and sensitive. Therefore, for environmental protection, it is imperative to understand the spatiotemporal variation characteristics of the ecological quality of vegetation and its climate influence factors. We used the MODIS normalized difference vegetation index (NDVI) dataset, meteorological dataset, and Carnegie–Ames–Stanford Approach (CASA) model to investigate the spatiotemporal patterns and change trends of the NDVI and the net primary productivity (NPP) of the vegetation in the Sanjiangyuan National Park from 2000 to 2016. A linear regression model was used to explore the influence of the ecological quality of vegetation and climatic factors. The results showed that (1) the NDVI and NPP were high in the southeast area and low in the northwest area. The Yangtze River headwater region had the lowest NDVI (0–0.3) and NPP (0–100 gC/m2). The Lancang River had the highest NDVI (0.4–0.8) and NPP (100–250 gC/m2). (2) From 2000–2016, approximately 23.46% of the area showed a significant positive trend of the NDVI that was mainly distributed in the prairie areas in the midlands and the north of the Yangtze River headwater region, and was scattered in the midlands and the north of Yellow River headwater region. Furthermore, 24.32% of the NPP was determined to have increased significantly, which was mainly distributed in the midlands and the north of the Yangtze River headwater region, as well as the midlands and the east of the Yellow River headwater region. (3) The vegetation growth in the Sanjiangyuan National Park was regulated by both water and heat conditions. The NDVI was significantly affected by precipitation during the growing season and by the annual precipitation. In addition, the NPP was significantly affected by temperature during the growing season and by the annual average temperature of the study area.

Usage ofMODIS NDVIto evaluate the effect of soil and water conservation measures on vegetation in Burkina Faso

AbstractThe dramatic droughts in West Africa from the 1960s to the 1990s compelled Burkina Faso to implement soil and water conservation (SWC) measures in the late 1970s. The purpose was to combat land degradation (e.g., desertification) in areas that experienced a decrease in vegetation productivity. In this study, normalized difference vegetation index (NDVI) trends from 2002 to 2016 were analyzed using the Mann‐Kendall test to explore the spatio‐temporal variations of vegetation at SWC and non‐SWC sites in selected regions of Burkina Faso. On average, NDVI increased by 3.6% annually from 2002 to 2016. Approximately 81% of the SWC sites experienced increasing (greening) trends, whereas 78% of the non‐SWC sites showed negative (browning) trends. Typically, SWC measures in Burkina Faso, such aszai(traditional planting pits use to foster the retention of soil moisture and increase soil nutrients) and half‐moons, are considered to have significant impacts on vegetation changes as visible from the NDVI. The results demonstrate that the implementation of SWC measures distinctly influences the vegetation growth in Burkina Faso. Thus, these findings could support regional and local informed decision‐making in the context of sustainable land use planning and evaluation of ecosystem services.

Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia, China

Grassland fires results in carbon emissions, which directly affects the carbon cycle of ecosystems and the carbon balance. The grassland area of Inner Mongolia accounts for 22% of the total grassland area in China, and many fires occur in the area every year. However, there are few models for estimation of carbon emissions from grassland fires. Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon. In this study, the regression equations for aboveground biomass (AGB) of grassland in growing season and MODIS NDVI (Normalized Difference Vegetation Index) were established through field experiments, then AGB during Nov.–Apr. were retrieved based on that in Oct. and decline rate, finally surface fuel load was obtained for whole year. Based on controlled combustion experiments of different grassland types in Inner Mongolia, the carbon emission rate of grassland fires for each grassland type were determined, then carbon emission was estimated using proposed method and carbon emission rate. Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978 × 1012 kg. The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2, with the annual average area of 311.69 km2. The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia. The total carbon emissions from grassland fires amounted to 2.24 × 107 kg with an annual average of 1.32 × 106 for the study area. The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner. The spatial characteristics of carbon emission depend on the location of grassland fire, mainly in the northeast of Inner Mongolia include Hulunbuir City, Hinggan League, Xilin Gol League and Ulanqab City. The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions. This study provides a reference for estimating carbon emissions from steppe fires. The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.

Linkage between residential green spaces and allergic rhinitis among Asian children (case study: Taiwan)

BackgroundThe prevalence of allergic rhinitis (AR) continues to increase, and the greatest increases were observed mostly in non-Western regions, particularly Asian and Pacific regions; however, only a few studies have been conducted in Asia. ObjectivesUsing Taiwan as an example of a typical Asian country, this study aimed to evaluate the association between green areas and the frequency of clinical visits for AR among children in Asia. MethodsWe used the Longitudinal Health Insurance Database to identify subjects diagnosed with AR at four years old in 2003, who were then followed up with until 2011. Greenness exposure was assessed using the MODIS Normalized Difference Vegetation Index (NDVI). The Generalized Additive Mixed Model (GAMM) was applied to examine the association between green areas and the rate of AR after controlling for confounders. ResultsThe major results indicate a positive correlation, as green areas significantly increased the risk of AR by up to 8.4%. Further, sensitivity test analyses confirmed the robustness of the model estimates, even after adjusting for confounders. Stratified analysis results also display areas with high humidity and low temperatures increasing the risk of AR by up to 14% and 27%, respectively. In contrast, areas with low humidity and high temperatures decreased the risk of AR by up to 4% and 22%, respectively. Moreover, the positive relationship between greenness and AR among children was consistent across different sub-populations. ConclusionsGreenness exposure contributes to an increased risk of allergic rhinitis (AR). This finding could serve as a reference for designing green landscapes, especially in residential areas.

Variation in the vegetation fade stage and its relationships with climate and vegetation productivity in Inner Mongolia, China

Based on the MODIS NDVI data from 2001 to 2018, we extracted the fade stage of vegetation in Inner Mongolia and the characteristics of its temporal and spatial changes using the Logistic curve maximum curvature method of the cumulative normalized difference vegetation index (NDVI). We analyzed the responses of fade stage to climatic factors and NDVI at the ecological zone level. The results showed that the fade stage in Inner Mongolia occurred between the day of year (DOY) 260 and 280 during the study period. It was DOY 270-280 in the forest ecological zone, and was delayed from the south to north. The fade stage in the grassland ecological zone was the earliest and ranged between DOY 257 and 273, and was delayed from the northeast to southwest. The fade stage in the desert ecological zone ranged between DOY 270 and 283, which was delayed from the northeast to southwest. From 2001 to 2018, the fade stage in three ecological zones showed an insignificant delaying trend. Spatially, vegetation productivity decreased from the northeast to southwest, with a larger area of an increasing temporal trend than that of a decreasing trend. The fade stage in the entire Inner Mongolia and three ecological zones was greatly affected by precipitation during the two or three months before the fade stage and was positively correlated with the pre-season average temperature, maximum temperature, and minimum temperature, respectively. The increases/decreases in vegetation productivity in August and September delayed/advanced the fade stage in entire Inner Mongolia and three ecological zones. In contrast, the increases/decreases in vegetation productivity in June and July advanced/delayed the fade stage in the grassland and desert ecological zone.

Elevation and Climate Effects on Vegetation Greenness in an Arid Mountain-Basin System of Central Asia

Mountain-basin systems (MBS) in Central Asia are unique and complex ecosystems, wherein their elevation gradients lead to high spatial heterogeneity in vegetation and its response to climate change. Exploring elevation-dependent vegetation greenness variation and the effects of climate factors on vegetation has important theoretical and practical significance for regulating the ecological processes of this system. Based on the MODIS NDVI (remotely sensed normalized difference vegetation index), and observed precipitation and temperature data sets, we analyzed vegetation greenness and climate patterns and dynamics with respect to elevation (300–3600 m) in a typical MBS, in Altay Prefecture, China, during 2000–2017. Results showed that vegetation exhibited a greening (NDVI) trend for the whole region, as well as the mountain, oasis and desert zones, but only the desert zone reached significant level. Vegetation in all elevation bins showed greening, with significant trends at 400–700 m and 2600–3500 m. In summer, lower elevation bins (below 1500 m) had a nonsignificant wetting and warming trend and higher elevation bins had a nonsignificant drying and warming trend. Temperature trend increased with increasing elevation, indicating that warming was stronger at higher elevations. In addition, precipitation had a significantly positive coefficient and temperature a nonsignificant coefficient with NDVI at both regional scale and subregional scale. Our analysis suggests that the regional average could mask or obscure the relationship between climate and vegetation at elevational scale. Vegetation greenness had a positive response to precipitation change in all elevation bins, and had a negative response to temperature change at lower elevations (below 2600 m), and a positive response to temperature change at higher elevations. We observed that vegetation greenness was more sensitive to precipitation than to temperature at lower elevations (below 2700 m), and was more sensitive to temperature at higher elevations.

Impacts of Land Use Change on NDVI in Shaanxi Province of China

Land use change is one of the main impacts of human activities and has a profound impact on vegetation change. Based on the MODIS normalized difference vegetation index (NDVI) from 2000 to 2018 and land use data in 2000, 2005, 2010, and 2015, the land use and its impacts on NDVI change in the Shaanxi province of China were analyzed. This study showed that the land use structure in Shaanxi province is stable, with forest land, grassland and farmland as the main components. The main feature of land use change from 2000 to 2015 was the large area of farmland converted to construction land, mainly distributed in the Guanzhong Plain in the central Shaanxi province, especially in the vicinity of the capital city of Xi’an. The impact of land use changes on NDVI showed a significant decreasing trend in the area converted to construction land during 2000-2018, while the NDVI for areas converted to forest, farmland and grassland all showed a significant increasing trend. The growth rate of NDVI in the area converted to farmland was higher than in the areas converted to forest land and grassland, the reason for which needs further study.

Evaluation of Terra/Aqua MODIS and Sentinel-2 MSI NDVI data for predicting actual evapotranspiration in Mediterranean regions

ABSTRACT Conventional meteorological data and remotely sensed Normalized Difference Vegetation Index (NDVI) images can be proficiently combined to predict actual evapotranspiration (ETA) on different spatial and temporal scales. Up to now, however, the operational application of this approach in heterogeneous Mediterranean regions has found difficulty due to the insufficient spatial resolution of satellite sensors having high acquisition frequency (i.e. 250 m of Terra/Aqua Moderate Resolution Imaging Spectroradiometer, MODIS). The current study investigates the advantages brought for this objective by the recent availability of NDVI data taken from the Sentinel-2 MultiSpectral Instrument (MSI), which has a spatial resolution of 10 m. The investigation has been performed in two Mediterranean areas characterized by different spatio-temporal variability of vegetation cover. The first is a mountain coniferous forest, where such variability is low, while the second is a relatively small (around 10 ha) irrigated tomato field surrounded by other annual crops showing diversified growing cycles. An ETA estimation method based on NDVI data is applied in the two study areas and its performances are evaluated against ground references obtained through the elaboration of site measurements (i.e. meteorological, soil water content, and crop coefficient observations). The advantage of using MSI over MODIS NDVI images is marginal in the first case study, while is evident in the second. More particularly, such advantage is outstanding when the remote sensing method is applied in an operational mode, i.e. without using the information on the water supplied to the tomato crop by irrigation. This confirms that the utility of higher spatial resolution data is dependent not only on the fragmentation of the observed landscapes but also on the synchronicity of major vegetation growing cycles, which is influenced by both environmental and human-induced factors.

Dynamics of Vegetation Change and Its Relationship with Nature and Human Activities — A Case Study of Poyang Lake Basin, China

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

Spatial and Temporal Characteristics and Driving Forces of Vegetation Changes in the Huaihe River Basin from 2003 to 2018

In this study, MODIS normalized difference vegetation index (NDVI), TRMM3B43 precipitation, and MOD11A2 land-surface temperature (LST) data were used as data sources in an analysis of temporal and spatial characteristics of vegetation changes and ecological environmental quality in the Huaihe River basin, China, from 2003 to 2018. The Mann–Kendall (MK) non-parametric test and the Theil–Sen slope test were combined for this analysis; then, when combined with the results of the MK mutation test and two introduced indexes, the kurtosis coefficient (KU) and skewness (SK) and correlations between NDVI, precipitation (TRMM), and land-surface temperature (LST) in different time scales were revealed. The results illustrate that the mean NDVI in the Huaihe River basin was 0.54. The annual NDVImax curve fluctuations for different land cover types were almost the same. The main reasons for the decrease in or disappearance of vegetation cover in the Huaihe River basin were the expansion of towns and impact of human activities. Furthermore, vegetation cover around water areas was obviously degraded and wetland protections need to be strengthened urgently. On the same time scale, change trends of NDVI, TRMM, and LST after abrupt changes became consistent within a short time period. Vegetation growth was favored when the KU and SK of TRMM had a close to normal distribution within one year. Monthly TRMM and LST can better reflect NDVI fluctuations compared with seasonal and annual scales. When the precipitation (TRMM) is less than 767 mm, the average annual NDVI of different land cover types is not ideal. Compared with other land cover types, dry land has stronger adaptability to changes in the LST when the LST is between 19 and 22.6 °C. These trends can serve as scientific reference for protecting and managing the ecological environment in the Huaihe River basin.

Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China.

Net primary productivity (NPP) of terrestrial ecosystems is an important metric of ecosystem functioning; however, the understanding of response mechanism of NPP to influencing factors and driving mechanisms are still limited. In this study, taking Anhui province as an example, spatio-temporal changes of NPP and its response to influencing factors were investigated for evaluating the effects of climate change and land use and land cover change (LUCC) on regional NPP. The Carnegie-Ames-Stanford Approach (CASA) model was employed for NPP simulation by using the MODIS normalized difference vegetation index (NDVI) data and meteorological data over 2001-2016. Combined domestic LUCC, the spatiotemporal distribution pattern and dynamic change characteristics of NPP under a long time series and its response to climate factors and human activities were analyzed in the Anhui province. The results indicated that from 2001 to 2016, total NPP had a fluctuated and decreased trend with the variation range between 30.52 and 38.07TgC in Anhui province. The multi-year average of total NPP was about 34.62TgC. The highest value was in 2008 and the lowest value was in 2011. Among them, amount of forestland NPP was the most. The spatial distribution of NPP shows that the high value area was mainly distributed in southern Anhui mountain areas and western Anhui Dabie mountain areas; the lower value was distributed in the middle in the study area. The area of which the NPP showed a slight decrease and essentially unchanged accounted for 59.35% and 31.82%, respectively. In general, the correlation between vegetation NPP and temperature was greater than that between precipitation. The vegetations NPP of eight land use types were all positively correlated with temperature. However, the other seven types of land use were negatively correlated with precipitation except cultivated land. In the past 16years, the decrease of cultivated land areas and the increase of urban and construction land areas contributed a lot to the decrease of vegetation NPP in Anhui province. The NPP changes of different land use types were closely related to climatic factors, land cover area, and vegetation types.

Spatial heterogeneity of climate variation and vegetation response for Arctic and high-elevation regions from 2001–2018

In recent decades, an amplification of warming in Arctic and high-elevation regions has been widely observed, along with a general enhancement of vegetation growth. However, driven by variability in controlling factors and complex mechanisms, climate and vegetation changes can be highly heterogeneous in space and time. In this study, an analysis is performed separating Arctic and Tibetan Plateau (TP) vegetated areas into various units according to a map of terrestrial ecoregions. The most recent variations of heat, moisture, and vegetation growth (MODIS Normalized Difference Vegetation Index) are evaluated over 2001–2018. Relationships among the climate and vegetation variables are assessed. Six distinct change patterns are identified: (1) synchronized increase of day and night temperature and precipitation during April to October coinciding with strong vegetation greening, (2) profound warming with no change in precipitation and vegetation, (3) an increase of summer temperature and vegetation with a negative latitudinal gradient, (4) greening under increased precipitation without warming, (5) browning not likely being driven by climate, (6) warming only during the nighttime and moderately enhanced vegetation growth. It is demonstrated that vegetation growth in the Arctic and TP is largely controlled by nighttime temperature and precipitation, as opposed to daytime temperature. The exception is the Canadian Arctic, where greening is directly related to summer daytime warming, and a contrasting relationship is observed on the TP. The underlying causes of these patterns are discussed, relating them to multiple mechanisms reported in the literature. These findings may help to further understand the changing Arctic and high-elevation climate and its effects on vegetation growth.

Integrating Standard Precipitation Index and Normalised Difference Vegetation Index for near-real-time drought monitoring in Eswatini.

Eswatini, as the rest of southern Africa, is being frequented by drought over the last decade, and modelling experts are predicting that drought years will become more and severe. The expected increase in extreme climatic events makes the use of drought indices essential for drought monitoring and early warning. To enable Eswatini to better prepare, analyse and respond to drought, this study analysed the use of Normalised Difference Vegetation Index (NDVI) and Standard Precipitation Index (SPI) for near-real-time drought monitoring through the development of a model for drought severity. Meteorological stations across all agro-ecological zones with data for the period 1986–2017 were selected for analysis. The SPI computation was achieved through DrinC software. Primary NDVI data sources were CHIRPS gridded rainfall dataset and the MODIS NDVI CMG data. Results of the 3-month SPI indicated that moderate droughts were experienced in 1990/1991, 2005/2006, 2011/2012, 2012/2013 and 2015/2016. The Highveld and Middleveld had the lowest drought occurrence percentage of 3.3%, whereas the likelihood of having a moderate, severe and extreme drought was higher in the Lowveld. The study determined a positive correlation between the SPI and the NDVI at 3-month time scale, and a value of Y (drought severity) greater than 0.54 indicated a significant dry spell and could be used as a drought trigger threshold for early warning. The combined use of NDVI and SPI was deemed capable of providing a near-real-time indicator for drought conditions allowing planners to provide timely information for drought preparedness, mitigation and response planning, thereby helping to lower the eventual drought relief costs, protect food security and reduce the humanitarian impact on the population.

Application of MODIS NDVI for Monitoring Kenyan Rangelands Through a Web Based Decision Support Tool

The Kenyan rangelands contribute significantly to the country’s GDP through livestock production and tourism. With dependence on rain-fed pastures, climate variability coupled with human induced factors such as overgrazing have adversely affected the rangeland ecosystems. And while indigenous communities and conservation experts already use their knowledge of the landscape to make decisions, this information is usually localized. Earth observation imagery provides a bigger picture that can complement indigenous knowledge and improve decision making. This research leverages on data from the MODIS receiver located at the Regional Centre for Mapping of Resources for Development(RCMRD) to develop the indices for the web-based Rangelands Decision Support Tool(RDST) . The tool automates data processing and provides an easy to use interface for accessing indices for rangeland monitoring. MODIS Normalized Difference Vegetation Index (NDVI), anomalies and deviation indices are provided on the tool at decadal, monthly and seasonal time steps .Users begin their assessments by selecting their monitoring units and an NDVI index that responds to their specific questions. These questions respond to assessing current conditions, monitoring trends and changes in vegetation, and evaluating proxies for drought conditions . The information can then be overlaid with other ancillary data sets (roads, water sources, invasive species, protected areas, conflict areas), for context. At the click of a button, the information can be downloaded as a map for further analysis or application in sub regional decision making. Information generated by the tool are being used decision making tool by rangeland managers and county officers. Specifically, to inform adjustments to existing grazing plans, managing movement of livestock from designated grazing areas in wet and dry season, monitoring the success of rehabilitation efforts and resilience of the rangeland ecosystems, monitoring drought, managing scarce water resources and monitoring the spread of invasive species. Successful implementation and application for decision making has relied heavily on local indigenous knowledge and capacity building on use of the earth observation indices. The SERVIR project service planning engagement approach was used in engagements with stakeholders. This improved their participation in co-development of the tool and indices; and in adoption of the tools for decision making.

Interannual variability of seasonal rainfall in Cordoba, Argentina, evaluated from ENSO and ENSO Modoki signals and verified with MODIS NDVI data

El Nino–Southern Oscillation (ENSO) is an atmospheric and oceanic signal closely linked with the short-term (interannual) climate variability and, as part of the global atmospheric circulation, influences for several months the weather in different regions of the world. As a proxy of ENSO, the Southern Oscillation Index (SOI) has shown a moderate ability to predict rainfall behavior on the territory of Cordoba province, Argentina. Several studies last years have postulated for the existence of the El Nino Modoki, “a similar but different” type of El Nino, with an increasing interest in determining the characteristics of its evolution and the impact of associated teleconnections over the weather around the world. Moreover, spectral indicators such as the normalized difference vegetation index (NDVI) have also been used to monitor the ENSO cycle. The influence in a predictive way of both ENSO and ENSO Modoki over seasonal rainfall indicators: amount (PP), frequency (DPP) and maximum daily precipitation (PPmax), was analyzed during the period from 1960 to 2017 in Cordoba Province, Argentina. Complementarily, the ENSO influence on vegetation behavior was also assessed during the rainfall season by using NDVI data from MODIS-TERRA for the period 2000–2017 to verify the predictive potential of the climate signal. Before the beginning of the rainy season, the August and September SOI average (SOIas) shows negative linear relationships with the rainfall attributes in almost all the territory of Cordoba that, although significant in statistical terms, are relatively weak. El Nino Modoki Index (EMI) used to represent the ENSO Modoki did not improve predictions on the rainfall behavior in Cordoba compared to SOI. Although the ENSO (SOIas) explains a little portion of interannual variability on subsequent seasonal rainfall, just between 14 and 22% for the entire region, when ENSO phases’ fluctuations upon NDVI data were analyzed, a very similar scheme was established. It is important to realize these responses show a strong local dependence associated with the environment resources and agricultural scheme own of each place. These results validate the use of the ENSO as a warning signal to anticipate the productive risk of the region, particularly when the Neutral years are discarded.