Use Of Normalized Difference Vegetation Index Research Articles

The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics.

Evaluation of NDVI to assess avian abundance and richness along the upper San Pedro River

Remote-sensing models have become increasingly popular for identifying, characterizing, monitoring, and predicting avian habitat but have largely focused on single bird species. The Normalized Difference Vegetation Index (NDVI) has been shown to positively correlate with avian abundance and richness and has been successfully applied to southwestern riparian systems which are uniquely composed of narrow bands of vegetation in an otherwise dry landscape. Desert riparian ecosystems are important breeding and stopover sites for many bird species but have been degraded due to altered hydrology and land management practices. Here we investigated the use of NDVI, coupled with vegetation, to model the avian community structure along the San Pedro River, Arizona. We also investigated how vegetation and physical features measured locally compared to those data that can be gathered through remote-sensing. We found that NDVI has statistically significant relationships with both avian abundance and species richness, although is better applied at the individual species level. However, the amount of variation explained by even our best models was quite low, suggesting that NDVI habitat models may not presently be an accurate tool for extensive modeling of avian communities. We suggest additional studies in other watersheds to increase our understanding of these bird/NDVI relationships.

Scaling up: linking field data and remote sensing with a hierarchical model

Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we use a Hierarchical Bayesian model to assess the relationship between measurements collected on the ground at centimeter scales nested within 2 × 3 m quadrats, which are in turn nested within much larger (0.1–12 ha) plots. We compare these measurements with the Normalized Difference Vegetation Index (NDVI) derived from radiometrically and geometrically corrected 30-m resolution LANDSAT ETM+ data to assess the NDVI–Biomass relationship in the Cape Floristic Region of South Africa. Our novel modeling approach allows the data observed at submeter scales to be incorporated directly into the model and thus all the data (and variability) collected at finer scales are represented in the estimates of biomass at the LANDSAT scale. The model reveals that there is a strong correlation between NDVI and biomass, which supports the use of NDVI in spatiotemporal analysis of vegetation dynamics in Mediterranean shrubland ecosystems. The methods developed here can be easily generalized to other ecosystems and ecophysiological parameters.

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

The effects of phenology on indirect measures of aboveground biomass in annual grasses

Remote sensing is increasingly being used to quantify vegetation biomass across large areas, often with algorithms based on calibrated relationships between biomass and indices such as the normalized difference vegetation index (NDVI). To improve capacity to evaluate grassland dynamics over time, we examined the influence of phenological changes on NDVI–biomass relationships in annual grasses. Our findings support the use of NDVI throughout early growth and the beginning stages of canopy maturation, but suggest caution for later stages. In contrast, measurements of fractional photosynthetically active radiation (fAPAR) absorbed by the canopy and leaf area index (LAI) served as good season‐long surrogates for canopy biomass. Canopies reached maximum biomass approximately 40 days after maximum greenness, with biomass increasing by approximately 20% during senescence. For multi‐year studies of management impact (i) avoid using seasonal comparisons from dates much after the point of maximum greenness or (ii) consider non‐NDVI‐based approaches.

Effects of chlorophyll fluorescence on the estimation of microphytobenthos biomass using spectral reflectance indices

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675 nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluorescence emission peaks at 683 nm, counterbalancing the decrease in reflectance centered at 675 nm, thus causing the underestimation of NDVI. The interference of Chl fluorescence was found to be easily identified by a conspicuous double-peak feature in the 670–700 nm region of the second-derivative reflectance spectra. The fluorescence-induced NDVI underestimation was shown to be most pronounced for high surface biomass levels and low incident solar irradiance. Particular aspects of microphytobenthos biofilms, such as the increase in surface Chl fluorescence due the contribution of emission by subsurface layers, and vertical migratory responses by motile microalgae to changes in ambient light, further complicate the effects on biomass estimation using NDVI-like indices. By comparing NDVI with a fluorescence-independent biomass index for a wide range of natural light conditions, it was found that Chl fluorescence interference may cause the underestimation of microalgal biomass to reach over 25%, with errors above 10% being expected for more than half of the measuring occasions. These results indicate that the use of NDVI may compromise the correct assessment of important aspects of microphytobenthos ecology, such as the characterisation of migratory behaviour or the determination of biomass-specific productivity rates, and call for the use of alternative biomass indices, not based on the Chl a red absorption peak.

Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates

Summary In recent years, the Normalized Difference Vegetation Index (NDVI) has been used to assess the relationships between habitat quality and animal life‐history traits. Since numerous ecological studies now use NDVI rather than perform direct vegetation assessments, field validations are essential to provide confidence in the biological significance of NDVI estimates. While some studies have compared NDVI with plant biomass, very few examined the relationship between NDVI and changes in vegetation quality. Using data from two long‐term studies of alpine ungulates, we assessed the relationship between two NDVI indices and the date of peak in faecal crude protein (FCP), which represents temporal variability in the availability of high‐quality vegetation. We also evaluated if NDVI data could predict annual variation in the timing of spring green‐up. In both populations, integrated NDVI in June was negatively correlated with the date of the peak in FCP, indicating that high integrated NDVI values corresponded to early springs in alpine habitats. Maximum NDVI increase during spring green‐up was positively correlated with the timing of peak FCP, illustrating that rapid increases in NDVI represented delayed springs. Predicted values of date of peak FCP estimated each year from NDVI data satisfactorily fitted observed values, and prediction intervals included all observed values. These results suggest that NDVI can reliably predict variation over years in the timing of spring. Synthesis and applications. Our long‐term studies demonstrate that a multi‐year time series of Normalized Difference Vegetation Index (NDVI) can reliably measure yearly changes in the timing of the availability of high‐quality vegetation for temperate herbivores. This finding therefore supports the use of NDVI as a proxy for vegetation attributes in population ecology and wildlife management studies.

Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index?

AbstractAn early prediction of crop biomass at maturity and yield is important in different circumstances. The use of spectral reflectance indices, such as the Normalized Difference Vegetation Index (NDVI), has been proposed as a fast, nondestructive way of estimating crop growth capacity. In this study, we examined whether NDVI assessment relatively early in the crop cycle may be useful for predicting final biomass and yield in wheat. To that end, NDVI was measured and biomass quantified regularly from tillering to maturity for six different wheat genotypes grown under a contrasting range of N and water availabilities. In addition, final biomass and yield were measured at maturity. In line with expectations from the literature, we found that NDVI at milk‐grain stage was well correlated to final yield and biomass. However, it was also observed that NDVI at the onset of stem elongation was also reasonably correlated to both attributes. Because crop growth in wheat from the end of tillering to anthesis is related to the determination of grain number and yield, we propose the use of NDVI at the onset of stem elongation as a complementary criterion for establishing the required late crop management (N fertilisation, irrigation) practices.

The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie

Spectral vegetation indices (SVIs) calculated from remotely sensed data are routinely used to monitor spatial and temporal changes in vegetation biophysical characteristics. The most commonly used SVI, the Normalized Difference Vegetation Index (NDVI), has been criticized because of its sensitivity to atmospheric conditions and substrate reflectivity, as well as its insensitivity to increases in vegetation biomass past particular thresholds. Yet, the use of NDVI remains widespread and is attractive because of the ease with which it is calculated. This article examines the utility of NDVI for monitoring the biophysical characteristic of green vegetation fraction (GVF) in comparison to other SVIs suggested as improvements. Statistical relationships between spectral response, presented in the form of SVIs, and GVF of a native tallgrass prairie canopy are explored. Broadband spectra were gathered from close-range during the 1999 growing season at the Konza Prairie Biological Station (KPBS), located in the Flint Hills region of Kansas, USA. Through simple regression analyses, spectra were related to GVF estimates derived from digital color photographs. SVIs evaluated are the NDVI, the Soil Adjusted Vegetation Index (SAVI), and the square of scaled NDVI ( N* 2). Results show that NDVI and N* 2 were statistically related to GVF ( R 2 for NDVI=.77, N* 2=.78) throughout the growing season. The least-squares line defining the relationship between N* 2 and GVF approximated a 1:1 line. For June sample dates, all three SVIs were significant statistical predictors of GVF ( R 2 for NDVI=.89, N* 2=.91, SAVI=.89). Regression coefficients for late-season sample dates were weaker, yet still significant in statistical terms ( R 2 for NDVI=.70, N* 2=.70). While encouraging, these results suggest that further analyses are required to determine the usefulness of SVIs calculated from broadband devices for estimation of GVF when leaf litter dominates the scene.

Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains

Spectral mixture analysis (SMA) from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was used to understand regeneration patterns after fire in two semiarid shrub communities of the Santa Monica Mountains, California: northern mixed chaparral and coastal sage scrub. Two fires were analyzed: the Malibu Topanga fire (3 November 1993) and the Calabasas fire (21 October 1996). SMA was compared to the results of the Normalized Difference Vegetation Index (NDVI) to assess vegetation recovery. An unburned control plot (within the past 20 years), having similar environmental features, was used to generate two relative fire regeneration indices, Regeneration Index (RI) and Normalized Regeneration Index (NRI). Indices were calculated using the Green Vegetation (GV) endmember and the NDVI. These indices were determined to be largely independent of AVIRIS radiometric calibration uncertainty, minor errors in the atmospheric correction, topographic distortions, and differences in the phenological state of the vegetation because of interannual or seasonal differences. The temporal evolution of the two fires were combined to produce a longer observation period and used to fit a logarithmic regression model for each Mediterranean shrub community. The NRI developed from the GV endmember (NRI GV) produced the closest estimate for the time of recovery in both communities based on recovery times in the literature. The use of NDVI worked very well for recovery in the northern mixed chaparral, but was less successful in the coastal sage scrub, mainly because of extensive herbaceous cover during the first years of the regeneration process. Endmembers generated from hyperspectral images were more accurate because they are tuned to capture the greenness of the shrub type of vegetation. Use of matching plots having similar environmental features, but which were burned in different years were demonstrated to improve estimates of the recovery within each community.

Use of thermal and vegetation index data from earth observing satellites to evaluate the risk of schistosomiasis in Bahia, Brazil

A geographic information system (GIS) was constructed using maps of regional agroclimatic features, vegetation indices and earth surface temperature data from environmental satellites, together with Schistosoma mansoni prevalence records from 270 municipalities including snail host distributions in Bahia, Brazil to study the spatial and temporal dynamics of infection and to identify environmental factors that influence the distribution of schistosomiasis. In an initial analysis, population density and duration (months) of the annual dry period were shown to be important determinants of disease. In cooperation with the National Institute of Spatial Research in Brazil (INPE), day and night imagery data covering the state of Bahia were selected at approximately bimonthly intervals in 1994 (six day–night pairs) from the data archives of the advanced very high resolution radiometer (AVHRR) sensor of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite. A composite mosaic of these images was created to produce maps of: (1) average values between 0 and +1 of the normalized difference vegetation index (NDVI); and (2) average diurnal temperature differences (d T) on a scale of values between 0 and 15°C. For each municipality, NDVI and d T were calculated for a 3×3 pixel (9 km 2 area) grid and analyzed for relationships to prevalence of schistosomiasis. Results showed a statistically significant relationship of prevalence to d T ( ρ=−0.218) and NDVI ( ρ=0.384) at the 95% level of confidence by the Spearman rank correlation coefficient. Results support use of NDVI, d T, dry period climatic stress factors and human population density for development of a GIS environmental risk assessment model for schistosomiasis in Brazil.

Cotton yield estimation based on NOAA/AVHRR produced NDVI

The high temporal resolution of the NOAA/AVHRR satellite data can provide an efficient and consistent way for biomass and yield monitoring and assessment. The reflected radiation provides an indication of the type and density of canopy. A combination of spectral bands results in the vegetation indices, which are based on the spectral signatures of vegetation and account for the difference in response of the vegetation to the visible and near infrared channels. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI), which is the normalized difference of visible (red) and near infrared channels. The condition, distribution, structure and the development of the vegetation through the phenological stages can affect the relation between yield and NDVI. The primary objective of this study consists of examining the feasibility of applying a regression model for cotton yield monitoring by using the NDVI. NOAA/AVHRR images of six areas covered with cotton have been examined for a period of three years. The results show that the temporal variability of cotton yield from year to year can be monitored by the use of NDVI.

Land Surface Emissivity and Temperature Determination Using NOAA‐AVHRR Data

Studies on land surface processes using remote sensing data gains importance in the context of Geosphere Biosphere Programme. Present study addresses the applicability of split‐window method, in a tropical environment for mapping of surface temperature over heterogeneous surface from satellite data. The accuracy of the method is about +2.2°K, which is reasonable value taking into account the atmospheric attenuation in tropical environment. An attempt has been made to derive emissivity from normalized difference vegetation index (NDVI) by taking into account the fraction of vegetation cover of each pixel, which is determined by satellite data. The emissivity values estimated from satellite data found to be in reasonable agreement with an estimated error of less than 1%. The results of the study indicate the potential use of NDVI as a modulating parameter in the land surface temperature estimation from satellite data.

On the use of NDVI for estimating rainfall fields in the Kalahari of Botswana

This article presents the results of an experiment to estimate rainfall from the normalized difference vegetation index (NDVI). The study was carried out in the semi-arid region of south and southern Botswana, principally the Kalahari desert, where few rainfall-reporting stations exist. A statistical regression between NDVI and rainfall is developed for four areas with diverse soil conditions. These are used to estimate rainfall at an independent station or group of stations. The estimates reliably capture both the seasonal cycle and inter-annual variability. These regressions are used to produce a map of mean annual rainfall for the period 1982 to 1993, based on NDVI data, for the region 22 to 27°S and 20 to 26°E. The estimates are verified using all data available in the Meteorological Services' archive for these years.

Global analysis of the relationships among a vegetation index, precipitation and land surface temperature

Abstract Abstract. This study is designed to examine the spatial variability of the relationships among global NDVI (Normalized Difference Vegetation Index) data, remotely-sensed land surface temperature data, and gridded station precipitation data as well as to investigate the potential for the combined use of NDVI and temperature data for global bioclimate monitoring. The relationships among the three variables are examined using single and multiple temporal correlations and the analysis is augmented by the computation of the first annual harmonic of each parameter. In addition, the global variability of growing season liming is analysed using a proxy for the onset and conclusion of the growing season, based upon slopes of the NDVI time series. The NDVI data set as processed for this study has significant sources of systematic error, which include aerosol and cloud contamination, orbital drift, and instrument degradation. This analysis provides insight into the manner in which the relationships among NDVI, precipitation and remotely-sensed land surface temperature vary geographically, in spite of the data noise. Due to excessive systemic error, anomalies of this NDVI data set are not highly correlated with precipitation, or multiply correlated with temperature to precipitation. Greater immediate promise for interannual bioclimate monitoring is contained in the proxies presented here for the growing season onset and length.

Effects of management and topography on the radiometric response of a tallgrass prairie

Bidirectional reflectance measurements were obtained on grazed, burned ungrazed, and unburned ungrazed tallgrass prairie in eastern Kansas. Observations were also made on experimental plots on which vegetation height and biomass were manipulated by mowing. Foliage biomass and productivity (including off‐take estimates) were measured concurrently at all sites. While productivity of mowed or grazed sites was either equal to or greater than that on unmowed or ungrazed sites, individual or cumulative normalized difference vegetation index (NDVI) values tended to be positively correlated with biomass, not productivity. NDVI on grazed prairie was lower than on burned and unburned prairie during the first half of the growing season. After midseason, NDVI on grazed prairie was higher than on unburned prairie but no different than on burned prairie. Strong linear relationships among NDVI and canopy nitrogen and biomass existed early and late in the growing season but not at midseason. These relationships suggest that plant physiological processes associated with regrowth following defoliation are dominant influences on reflectance early in the season, whereas the accumulation of senescent material is the dominant process affecting reflectance in the latter half of the growing season. Canopy temperature was related to canopy nitrogen and biomass at midseason. The use of NDVI to estimate plant productivity and vegetation‐atmosphere exchange is complicated by changes in plant characteristics induced by grazing or mowing. Grazing tends to homogenize potential landscape‐induced differences in vegetation activity. Factors otherwise useful in estimating plant production, such as burning treatment and soil depth, are not strongly correlated with aboveground biomass or NDVI under grazing conditions. Results presented here suggest that concurrent use of thermal information provided by some satellite sensors may improve this relationship.

Tropical forest biomass and successional age class relationships to a vegetation index derived from landsat TM data

Forest stand structure and biomass data were collected using conventional forest inventory techniques in tropical, subtropical, and warm temperate forest biomes. The feasibility of detecting tropical forest successional age class and total biomass differences using Landsat-Thematic mapper (TM) data, was evaluated. The Normalized Difference Vegetation Index (NDVI) calculated from Landsat-TM data were not significantly correlated with forest regeneration age classes in the mountain terrain of the Luquillo Experimental Forest, Puerto Rico. The low sun angle and shadows cast on steep north and west facing slopes reduced spectral reflectance values recorded by TM at orbital altitude. The NDVI, calculated from low altitude aircraft scanner data, was significantly correlated with forest age classes. However, analysis of variance suggested that NDVI differences were not detectable for successional forests older than approximately 15–20 years. Also, biomass differences in young successional tropical forest were not detectable using the NDVI. The vegetation index does not appear to be a good predictor of stand structure variables (e.g., height, diameter of main stem) or total biomass in uneven age, mixed broadleaf forest. Good correlation between the vegetation index and low biomass in even age pine plantations were achieved for a warm temperate study site. The implications of the study for the use of NDVI for forest structure and biomass estimation are discussed.