Integral Of Normalized Difference Vegetation Index Research Articles

Management practices should be strengthened in high potential vegetation productivity areas based on vegetation phenology assessment on the Qinghai-Tibet Plateau

Climate change and human activities have severely affected the structure and function of vegetation on the Qinghai-Tibet Plateau (QTP). Management practices have contributed to vegetation growth on the QTP, yet the response of vegetation productivity to management during the growing season remains unclear. Here, a fitted sixth-degree polynomial function was used to extract phenology-based vegetation productivity, which was quantified as the integrated normalized difference vegetation index (INDVI) over the length of the season during 2000–2019. Additionally, a new management practice intensity indicator was established to quantify the degree of ecological project and policy effectiveness on vegetation productivity. Three main results were obtained: (1) On a pixel-by-pixel basis, phenology-based vegetation productivity can avoid the impact of start-of-season (SOS) and end-of-season (EOS) variations. Vegetation productivity increased over 66% of the area of the QTP during 2000–2019, and the area with a significant increasing trend in vegetation productivity accounted for 9.15% of the QTP, including 5.65% in grassland and 0.23% in cultivated land. (2) Areas of increased vegetation productivity, grassland productivity, and cultivated land productivity affected by the positive impact from climate change accounted for 23.76%, 16.61%, and 0.31%, respectively, while they accounted for 41.29%, 28.10%, and 0.59% for human activities, respectively. (3) The regions of high-level management practice intensity accounted for 26.51% of the QTP but only 2.57% and 0.03% of grassland and cultivated land productivity, respectively. The regions of decreased grassland productivity still accounted for 21.85% of the QTP. Therefore, improving vegetation management practice intensity on grassland can increase regional vegetation productivity. These findings can provide effective guidance for regional vegetation management.

Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium

Crop-yield models based on vegetation indices such as the normalized difference vegetation index (NDVI) have been developed to monitor crop yield at higher spatial and temporal resolutions compared to agricultural statistical data. We evaluated the model performance of NDVI-based random forest models for sugar beet and potato farm yields in northern Belgium during 2016–2018. We also evaluated whether weather variables and root-zone soil water depletion during the growing season improved the model performance. The NDVI integral did not explain early and late potato yield variability and only partly explained sugar-beet yield variability. The NDVI series of early and late potato crops were not sensitive enough to yield affecting weather and soil water conditions. We found that water-saturated conditions early in the growing season and elevated temperatures late in the growing season explained a large part of the sugar-beet and late-potato yield variability. The NDVI integral in combination with monthly precipitation, maximum temperature, and root-zone soil water depletion during the growing season explained farm-scale sugar beet (R2 = 0.84, MSE = 48.8) and late potato (R2 = 0.56, MSE = 57.3) yield variability well from 2016 to 2018 in northern Belgium.

Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020

Vegetation dynamics play a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined the past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. In this study, we investigated the spatio-temporal variations in vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship with climate changes using correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning points from 2001 to 2020. As expected, there were greening trends in global karst SINDVI from 2001 to 2020, with significant increasing trends in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions. According to correlation analyses, SINDVI was water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude. Our results suggest that anthropogenic activities were mainly responsible for the increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) in China and Europe, and intensive farm in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed increasing trends in SINDVI. In general, climate factors were the dominant drivers for the variation in vegetation greenness in globally karst regions during research period.

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.

Temporal Changes in Coupled Vegetation Phenology and Productivity are Biome-Specific in the Northern Hemisphere

Global warming has greatly stimulated vegetation growth through both extending the growing season and promoting photosynthesis in the Northern Hemisphere (NH). Analyzing the combined dynamics of such trends can potentially improve our current understanding on changes in vegetation functioning and the complex relationship between anthropogenic and climatic drivers. This study aims to analyze the relationships (long-term trends and correlations) of length of vegetation growing season (LOS) and vegetation productivity assessed by the growing season NDVI integral (GSI) in the NH (&gt;30°N) to study any dependency of major biomes that are characterized by different imprint from anthropogenic influence. Spatial patterns of converging/diverging trends in LOS and GSI and temporal changes in the coupling between LOS and GSI are analyzed for major biomes at hemispheric and continental scales from the third generation Global Inventory Monitoring and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for a 32-year period (1982–2013). A quarter area of the NH is covered by converging trends (consistent significant trends in LOS and GSI), whereas diverging trends (opposing significant trends in LOS and GSI) cover about 6% of the region. Diverging trends are observed mainly in high latitudes and arid/semi-arid areas of non-forest biomes (shrublands, savannas, and grasslands), whereas forest biomes and croplands are primarily characterized by converging trends. The study shows spatially-distinct and biome-specific patterns between the continental land masses of Eurasia (EA) and North America (NA). Finally, areas of high positive correlation between LOS and GSI showed to increase during the period of analysis, with areas of significant positive trends in correlation being more widespread in NA as compared to EA. The temporal changes in the coupled vegetation phenology and productivity suggest complex relationships and interactions that are induced by both ongoing climate change and increasingly intensive human disturbances.

Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach

AbstractQuestionsCan herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to paddock scale?LocationNative plant communities and buffelgrass roller chopped pastures of the Arid Chaco, western Argentina (28–32° S, 64–67° W; area: 100 000 km2).MethodsWe decomposed normalized difference vegetation index (NDVI) data from MODIS (pixel size: 250 m × 250 m) into woody (W) and herbaceous (H) components. We calibrated the relationship between field estimates of herbaceous ANPP and the H component of NDVI using linear regression. The regression model fitted was applied to a 10‐yr MODIS database for four paddocks to estimate herbaceous ANPP. We analysed the relationship between herbaceous ANPP and watering point distance and growing season precipitation.ResultsThe annual integral of NDVI × proportion of the herbaceous component [H/(H + W)] explained 71% and 91% of herbaceous ANPP variation in native plant communities and buffelgrass roller chopped pastures, respectively. The regression model fitted, however, differed (P &lt; 0.05) between the two types of system. The NDVI annual integral explained a higher proportion of herbaceous ANPP variations than the NDVI annual peak or the growing season (December–April) integral. For native plant communities, herbaceous production increased significantly (P &lt; 0.05) with watering point distance, and marginally significantly (P &lt; 0.10) with growing season precipitation. For buffelgrass roller chopped pastures, the herbaceous production increased significantly (P &lt; 0.05) with growing season precipitation.ConclusionOur model was able to estimate herbaceous ANPP from the decomposition of an NDVI time series that included woody components. Thus, the model provides the basis for more accurate monitoring of spatial and temporal variability of herbaceous ANPP in areas where herbaceous and woody plant components co‐exist. Applying our models, we detected clear spatial and temporal patterns of herbaceous ANPP. The possibility of describing in a spatially explicit way the past 14 yrs of herbaceous ANPP allows designing livestock management strategies and devise alternatives to control degradation processes in the Arid Chaco.

Primary production estimation of Çankırı province’s rangelands using light use efficiency (LUE) model with satellite data and agrometshell module

In this study, monthly and annual gross primary production (GPP) of rangelands in Cankiri province for the period of 2000-2009 was calculated using light use efficiency (LUE) model with the inputs of satellite data and AgrometShell module. The average production of rangelands varied seasonally and annually (from 12630 to 37701 tons) and was approximately 17800 tons for the last ten years. The amount of rainfall and changing number of animal grazing in the region probably led to the variation. Model performance was tested with integrated normalized difference vegetation index (INDVI) approach which produced a moderate significant correlation (R2= 0.69, P 0.05 for 2008, r= 0.41, P>0.05 for 2009) due to some factors such as sampled plant type, scale differences between satellite data and ground sample size, and subjective sampling errors. This study indicates that LUE Model together with the inputs of AgrometShell module is suitable tool for estimation of rangeland primary production.

Tendencia del NDVI en el período 2000-2014 como indicador de la degradación de tierras en Argentina: ventajas y limitaciones

La degradación de tierras es uno de los mayores problemas ambientales de la Argentina. Para gestionar esta problemática se requieren sistemas de monitoreo que permitan detectar su tendencia y proporcionen alertas tempranas. Recientemente se han desarrollado metodologías de monitoreo basadas en índices de imágenes satelitales, siendo NDVI (Normalized Difference Vegetation Index) el más utilizado. Se analizó la tendencia del NDVI (del sensor MODIS), entre 2000-2014, como posible indicador de la degradación de tierras en la Argentina. Para cada año (del 01/07 al 30/06 del año siguiente) se calculó la integral anual del NDVI y su tendencia mediante regresión lineal simple para cada pixel. De acuerdo al signo y significancia estadística de la pendiente se cartografiaron áreas con tendencias negativas (TN; 37,9% del territorio nacional), positivas (TP; 5,0%) y sin tendencia (57,1%). Las TN se concentraron en Patagonia Norte (asociado a la deposición de cenizas volcánicas y a un ciclo seco) y en el Chaco Seco (asociado a desmontes para agricultura y a un ciclo seco). Las TP se concentraron en el noroeste del país (posiblemente asociado a un ciclo húmedo). Esta metodología, junto con indicadores de campo y datos climáticos, puede integrar un sistema para monitorear las tierras a escala nacional.

Relationship of cotton nitrogen and yield with Normalized Difference Vegetation Index and plant height

The strength of the associations of cotton (Gossypium hirsutum L.) yield and N nutrition with integrated Normalized Difference Vegetation Index (NDVI) and plant height measurements has been scarcely documented. The objective of this investigation was to compare the strength in terms of determination coefficient (R2) among the associations of cotton yield and leaf N concentration with integrated and respective NDVI and plant height measurements taken at key growth stages. A field experiment was carried out on no-till cotton at Jackson and Milan in Tennessee during 2008–2010. Six N treatments of 0, 45, 90, 135, 179, and 224 kg N ha−1 were implemented in a randomized complete block design with four replicates for all site years. Regressions of lint yield with NDVI × plant height and NDVI + plant height were sometimes stronger than those of lint yield with NDVI alone. Associations of leaf N concentration with NDVI × plant height and NDVI + plant height were similar to or variably stronger than those of leaf N with NDVI alone. Regressions of lint yield and leaf N with NDVI × plant height or NDVI + plant height were generally similar to those of lint yield and leaf N with plant height alone. Utilization of integrated NDVI and plant height measurements to predict cotton yield and/or assess N nutrition has variable advantages over the use of NDVI alone. Both integrated and respective NDVI and plant height measurements are more appropriate to be used to predict cotton yield than to assess N nutrition.

Rainfall, topography and primary production relationships in a semiarid ecosystem

AbstractA series of 15 Satellite Pour l'Observation de la Terra XS images, which were acquired during a single growing season, were used to study the spatial and temporal relationships between topography, rainfall and aboveground net primary production (ANPP) of annuals in a semiarid environment. A digital elevation model was used to locate five physiographic units: interfluve, shoulder, backslope, foot slope, and channel, along the slope catena. ANPP values were then surrogated by the normalized difference vegetation index (NDVI). Four phenological phases were interpreted from the seasonal NDVI profiles: germination, green‐up, drying, and senescence. A significant difference between NDVI values of all physiographic units was found, mainly during the green‐up phase. NDVI values at peak season were characterized as typical of semiarid ecosystems, except those of the footslope and the channel, which were found to have meaningfully high NDVI values, due to run‐off distribution and a thick soil profile. The seasonal NDVI integral was related positively and linearly to model‐derived water availability values for all physiographic units and the NDVI was correlated to multiple‐timescale rainfall data and the length of dry spells. The 1‐month rainfall data were found to have the highest correlation with the NDVI, indicating lag and cumulative effects of rainfall on production. This implies that the time required for the plants to use the water for production is around a month. It is concluded that studies of ecosystem functioning and capabilities in semiarid environments should consider not only mean annual rainfall amounts, but also the temporal rainfall distribution, mainly on a monthly scale, and the effect of physiographic units. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Drivers of megaherbivore demographic fluctuations: inference from elephants

AbstractEnvironmentally induced variation in survival and fecundity generates demographic fluctuations that affect population growth rate. However, a general pattern of the comparative influence of variation in fecundity and juvenile survival on elephant population dynamics has not been investigated at a broad scale. We evaluated the relative importance of conception, gestation, first year survival and subsequent survivorship for controlling demographic variation by exploring the relationship between past environmental conditions determined by integrated normalized difference vegetation index (INDVI) and the shape of age distributions at 17 sites across Africa. We showed that, generally, INDVI during gestation best explained anomalies in age structure. However, in areas with low mean annual rainfall, INDVI during the first year of life was critical. The results challenge Eberhardt's paradigm for population analysis that suggests that populations respond to limited resource availability through a sequential decrease in juvenile survival, reproductive rate and adult survival. Contrastingly, elephants appear to respond first through a reduction in reproductive rate. We conclude that this discrepancy is likely due to the evolutionary significance of extremely large body size – an adaptation that increases survival rate but decreases reproductive potential. Other megaherbivores may respond similarly to resource limitation due to similarities in population dynamics. Knowing how vital rates vary with changing environmental conditions will permit better forecasts of the trajectories of megaherbivore populations.

Forage Production of the Argentine Pampa Region Based on Land Use and Long-Term Normalized Difference Vegetation Index Data

Information about forage productivity and its interactions with cultural practices or climatic variation is necessary to plan livestock management and to increase production without damaging the environment. Remote sensing provides a valuable data source to achieve these goals. Here we characterize forage production over a large region (92 million hectares) by analyzing spatial, seasonal, and interannual variability with Normalized Difference Vegetation Index (NDVI) data. We identified 23 homogeneous zones that enclose multiple counties with similar characteristics of land use and productivity. A long-term series (1981–2000) of Advanced Very High Resolution Radiometer images were used to calculate monthly NDVI and the annual integral of NDVI (I-NDVI), which is an estimate of primary productivity, for each county. County agricultural land use data were used to resolve pure forage and crop NDVI patterns over time using a spectral unmixing model. The annual integral of NDVI was significantly associated with geographic longitude and average precipitation but not with latitude. Improved relationships between forage production and I-NDVI can be obtained by collecting more accurate forage estimates in the field and calculating radiation use efficiencies. Images of high temporal resolution allow the inference of seasonal changes, and images of high spatial resolution allow a more precise description of the forage resources.

EARLY ONSET OF VEGETATION GROWTH VS. RAPID GREEN-UP: IMPACTS ON JUVENILE MOUNTAIN UNGULATES

Seasonal patterns of climate and vegetation growth are expected to be altered by global warming. In alpine environments, the reproduction of birds and mammals is tightly linked to seasonality; therefore such alterations may have strong repercussions on recruitment. We used the normalized difference vegetation index (NDVI), a satellite-based measurement that correlates strongly with aboveground net primary productivity, to explore how annual variations in the timing of vegetation onset and in the rate of change in primary production during green-up affected juvenile growth and survival of bighorn sheep (Ovis canadensis), Alpine ibex (Capra ibex), and mountain goats (Oreamnos americanus) in four different populations in two continents. We indexed timing of onset of vegetation growth by the integrated NDVI (INDVI) in May. The rate of change in primary production during green-up (early May to early July) was estimated as (1) the maximal slope between any two successive bimonthly NDVI values during this period and (2) the slope in NDVI between early May and early July. The maximal slope in NDVI was negatively correlated with lamb growth and survival in both populations of bighorn sheep, growth of mountain goat kids, and survival of Alpine ibex kids, but not with survival of mountain goat kids. There was no effect of INDVI in May and of the slope in NDVI between early May and early July on juvenile growth and survival for any species. Although rapid changes in NDVI during the green-up period could translate into higher plant productivity, they may also lead to a shorter period of availability of high-quality forage over a large spatial scale, decreasing the opportunity for mountain ungulates to exploit high-quality forage. Our results suggest that attempts to forecast how warmer winters and springs will affect animal population dynamics and life histories in alpine environments should consider factors influencing the rate of changes in primary production during green-up and the timing of vegetation onset.

Spatial patterns of vegetation phenology metrics and related climatic controls of eight contrasting forest types in India – analysis from remote sensing datasets

Leaf phenology describes the seasonal cycle of leaf functioning and is essential for understanding the interactions between the biosphere, the climate and the atmosphere. In this study, we characterized the spatial patterns in phenological variations in eight contrasting forest types in an Indian region using coarse resolution NOAA AVHRR satellite data. The onset, offset and growing season length for different forest types has been estimated using normalized difference vegetation index (NDVI). Further, the relationship between NDVI and climatic parameters has been assessed to determine which climatic variable (temperature or precipitation) best explain variation in NDVI. In addition, we also assessed how quickly and over what time periods does NDVI respond to different precipitation events. Our results suggested strong spatial variability in NDVI metrics for different forest types. Among the eight forest types, tropical dry deciduous forests showed lowest values for summed NDVI (SNDVI), averaged NDVI (ANDVI) and integrated NDVI (I-NDVI), while the tropical wet evergreen forests of Arunachal Pradesh had highest values. Within the different evergreen forest types, SNDVI, ANDVI and INDVI were highest for tropical wet evergreen forests, followed by tropical evergreen forests, tropical semi-evergreen forests and were least for tropical dry evergreen forests. Differences in the amplitude of NDVI were quite distinct for evergreen forests compared to deciduous ones and mixed deciduous forests. Although, all the evergreen forests studied had a similar growing season length of 270 days, the onset and offset dates were quite different. Response of vegetative greenness to climatic variability appeared to vary with vegetation characteristics and forest types. Linear correlations between mean monthly NDVI and temperature were found to yield negative relationships in contrast to precipitation, which showed a significant positive response to vegetation greenness. The correlations improved much for different forest types when the log of cumulative rainfall was correlated against mean monthly NDVI. Of the eight forest types, the NDVI for six forest types was positively correlated with the logarithm of cumulative rainfall that was summed for 3–4 months. Overall, this study identifies precipitation as a major control for vegetation greenness in tropical forests, more so than temperature.

Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992–93, 1995) and SPOT Vegetation (1998–2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices.

Remote sensing of protected areas to derive baseline vegetation functioning characteristics

Abstract:Question: How can we derive baseline/reference situations to evaluate the impact of global change on terrestrial ecosystem functioning?Location: Main biomes (steppes to rain forests) of Argentina.Methods: We used AVHRR/NOAA satellite data to characterize vegetation functioning. We used the seasonal dynamics of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of the photosynthetic active radiation intercepted by vegetation (fPAR), and the surface temperature (Ts), for the period 1981–1993. We extracted the following indices: NDVI integral (NDVI‐I), NDVI relative range (Rrel), NDVI maximum value (Vmax), date of maximum NDVI (Dmax) and actual evapotranspiration.Results: f PAR varied from 2 to 80%, in relation to changes in net primary production (NPP) from 83 to 1700 g.m‐2.yr‐1. NDVI‐I, Vmax and fPAR had positive, curvilinear relationships to mean annual precipitation (MAP), NPP was linearly related to MAP. Tropical and subtropical biomes had a significantly lower seasonality (Rrel) than temperate ones. Dmax was not correlated with the defined environmental gradients. Evapotranspiration ranged from 100 to 1100 mm.yr‐1. Interannual variability of NDVI attributes varied across the temperature and precipitation gradients.Conclusions: Our results may be used to represent baseline conditions in evaluating the impact of land use changes across environmental gradients. The relationships between functional attributes and environmental variables provide a way to extrapolate ecological patterns from protected areas across modified habitats and to generate maps of ecosystem functioning.

Remote sensing of protected areas to derive baseline vegetation functioning characteristics

Question: How can we derive baseline/reference situations to evaluate the impact of global change on terrestrial ecosystem functioning? Location: Main biomes (steppes to rain forests) of Argentina. Methods: We used AVHRR/NOAA satellite data to characterize vegetation functioning. We used the seasonal dynamics of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of the photosynthetic active radiation intercepted by vegetation (fPAR), and the surface temperature (Ts), for the period 1981-1993. We extracted the following indices: NDVI integral (NDVI-I), NDVI relative range (R rel ), NDVI maximum value (V max ), date of maximum NDVI (D max ) and actual evapotranspiration. Results: fPAR varied from 2 to 80%, in relation to changes in net primary production (NPP) from 83 to 1700 g.m–2.yr–1. NDVI-I, Vmax and f PAR had positive, curvilinear relationships to mean annual precipitation (MAP), NPP was linearly related to MAP. Tropical and subtropical biomes had a significantly lower seasonality (Rrel) than temperate ones. Dmax was not correlated with the defined environmental gradients. Evapotranspiration ranged from 100 to 1100 mm.yr –1 . Interannual variability of NDVI attributes varied across the temperature and precipitation gradients. Conclusions: Our results may be used to represent baseline conditions in evaluating the impact of land use changes across environmental gradients. The relationships between functional attributes and environmental variables provide a way to extrapolate ecological patterns from protected areas across modified habitats and to generate maps of ecosystem functioning.

Using trend line and principal component analysis to study vegetation changes in Senegal 1986—1999 from AVHRR NDVI 8 km data

The objective of the study is to evaluate existing methods and techniques for analysing time series of remotely sensed vegetation index data. This was done within the framework of assessing long-term changes in the vegetation of Senegal. West Africa, using time series of NDVI (Normalized Difference Vegetation Index) data dating from 1986 to 1999. NDVI was derived from NOAA AVHRR 8 km data provided through the Pathfinder program. Two NDVI integrals were computed for each year, one covering the entire growing season to represent savannah biomass production, as well as an NDVI integral for the month of September, where NDVI has proven to be highly correlated with cereal crop yields. The existing literature identifies two appropriate methods. The temporal trend of annual values of integrals of NDVI was computed for each pixel. This yields a slope and an intercept value for each pixel, where the slopes indicated the long term trends in vegetative productivity. The slopes and the annual integrated NDVI values can be correlated to rainfall and biomass data. It appeared that the trends captured the general development in the vegetation in response to changing rainfall; however, it was equally apparent that variation in rainfall could not explain all the variation in integrated NDVI. Standard principal components (PC) were computed and analysed for the same study area with respect to rainfall, biomass and the original variation in i me grated NDVI. Changes in vegetation productivity were identified in the PCs; these interpretations are, however, not unambiguous. PCs tend to reflect specific events such as fires, drought periods and agricultural encroachment, rather than depicting the general development during the period. PC2 for the agricultural areas and PC3 and PC5 for the rangelands showed change patterns comparable to the respective slope images. Ii was concluded that the trend method was best suited for operational assessment of the state of the environment of Senegal. The approach is simple and reproducible, easy to interpret, and less sensitive to variations in time period and study area compared to the PC method.

PATTERNS AND CONTROLS OF PRIMARY PRODUCTION IN THE PATAGONIAN STEPPE: A REMOTE SENSING APPROACH*

We took advantage of regional gradients to study the spatial relationships between aboveground net primary production (ANPP) and climate in the Patagonian steppe of South America. We explored the same relationships through time, considering the natural variations of ANPP and climate for 11 yr. Based on NOAA/AVHRR satellite normalized difference vegetation index (NDVI) data, we evaluated the effects of climate on annual and seasonal ANPP across regional gradients of precipitation (100-500 mm/yr) and tem- perature (218-98C of annual mean). We studied ANPP climatic controls through time at four sites using NDVI and meteorological data. We used annual NDVI integral as a surrogate of annual ANPP. Annual NDVI integral increased linearly along regional gradients of precipitation, and its annual variability de- creased exponentially. Annual NDVI integral was, in most cases, unrelated to precipitation through time. We described the seasonality of ANPP using four variables derived from seasonal NDVI curves: the dates of growing season start and end, the date of maximum NDVI, and the length of the growing season. The growing season started later toward the cold extreme of the regional temperature gradients and, within a given site through time, during the coldest years. The dates of maximum NDVI and end of the growing season occurred later toward the humid or cold extremes of the regional gradients, whereas the length of the growing season was positively affected by precipitation and temperature along these gradients. These variables were not associated with climate through time. The response of the start of the growing season to temperature was greater in time, following the natural climatic fluctuations, than in space, accompanying regional temperature gradients. This difference probably resulted because the time required for shifts in community composition and plant adaptation is longer than one year. Climatic determinants of ANPP shifted from precipitation alone to precipitation plus temperature when the temporal scale of analysis changed from annual to seasonal. Our results indicate the feasibility of forecasting forage availability a few months prior to the beginning of the growing season, but not during the whole year. Longer term data sets and manipulative experiments are required to forecast annual ANPP and predict its response to climate change.

Land‐use impact on ecosystem functioning in eastern Colorado, USA

AbstractLand‐cover change associated with agriculture has had an enormous effect on the structure and functioning of temperate ecosystems. However, the empirical evidence for the impact of land use on ecosystem functioning at the regional scale is scarce. Most of our knowledge on land‐use impact has been derived from simulation studies or from small plot experiments. In this article we studied the effects of land use on (i) the seasonal dynamics and (ii) the interannual variability of the Normalized Difference Vegetation Index (NDVI), a variable linearly related to the fraction of the photosynthetically active radiation (PAR) intercepted by the canopy. We also analysed the relative importance of environmental factors and land use on the spatial patterns of NDVI. We compared three cultivated land‐cover types against native grasslands. The seasonal dynamics of NDVI was used as a descriptor of ecosystem functioning. In order to reduce the dimensionality of our data we analysed the annual integral (NDVI‐I), the date of maximum NDVI (DMAX) and the quarterly average NDVI. These attributes were studied for 7 years and for 346 sites distributed across eastern Colorado (USA). Land use did modify ecosystem functioning at the regional level in eastern Colorado. The seasonal dynamics of NDVI, a surrogate for the fraction of PAR intercepted by the canopy, were significantly altered by agricultural practices. Land use modified both the NDVI integral and the seasonal dynamics of this spectral index. Despite the variability within land‐cover categories, land use was the most important factor in explaining regional differences of the NDVI attributes analysed. Within the range of environmental conditions found in eastern Colorado, land use was more important than mean annual precipitation, mean annual temperature and soil texture in determining the seasonal dynamics of NDVI.