Relationships between vegetation indices,fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation

This study explores the use of the relationship between the normalized difference vegetation index (NDVI) and the shortwave infrared ratio (SWIR32) vegetation indices (VI) to retrieve fractional cover over the structurally complex natural vegetation of the Cerrado of Brazil using a time series of imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Data from the EO-1 Hyperion sensor with 30 m pixel resolution is used to sample geographic and seasonal variation in NDVI, SWIR32, and the hyperspectral cellulose absorption index (CAI), and to derive end-member values for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS) from a suite of protected and/or natural vegetation sites across the Cerrado. The end-members derived from relatively pure 30 m pixels are then applied to a 500 m pixel resolution MODIS time series using linear spectral unmixing to retrieve PV, NPV, and BS fractional cover (F_PV, F_NPV, and F_BS). The two-way interaction response of MODIS-equivalent NDVI and SWIR32 was examined for regions of interest (ROI) collected within protected areas and nearby converted lands. The MODIS NDVI, SWIR32 and retrieved F_PV, F_NPV, and F_BS are then compared to detailed cover and structural composition data from field sites, and the influence of the structural and compositional variation on the VIs and cover fractions is explored. The hyperion ROI analysis indicated that the two-way NDVI–SWIR32 response behaved as an effective surrogate for the two-way NDVI–CAI response for the campo limpo/grazed pasture to cerrado sensu stricto woody gradient. The SWIR32 sensitivity to the NPV and BS variation increased as the dry season progressed, but Cerrado savannah exhibited limited dynamic range in the NDVI–CAI and NDVI–SWIR32 two-way responses compared to the entire landscape, which also comprises fallow croplands and forests. Validation analysis of MODIS retrievals with Quickbird-2 images produced an RMSE value of 0.13 for F_PV. However, the RMSE values of 0.16 and 0.18 for F_BS and F_NPV, respectively, were large relative to the seasonal and inter-annual variation. Analysis of site composition and structural data in relation to the MODIS-derived NDVI, SWIR32 and F_PV, F_NPV, and F_BS, indicated that the VI signal and derived cover fractions were influenced by a complex mix of structure and cover but included a strong year-to-year seasonal effect. Therefore, although the MODIS NDVI–SWIR32 response could be used to retrieve cover fractions across all Cerrado land covers including bare cropland, pastures and forests, sensitivity may be limited within the natural Cerrado due to sub-pixel heterogeneity and limited BS and NPV sensitivity.     

Potential vegetation indices for determining global forest cover

The potential of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data acquired in middle (MIR) and thermal infrared (TIR) wavelengths for deriving global forest cover information was examined. As an exploratory step, these wavelengths were related to percentage cover of temperate coniferous forests in the Cascade Range of Oregon, U.S.A. These wavelengths individually were not strongly related to percentage forest cover. However, their inclusion within vegetation indices served to strengthen relations between remotely-sensed data and forest cover. One such index was the complex division index (C3/(C1*C2*C4*C5)), which was also seen to separate among the four forest successional stages present at this site. These findings have implications for the use of remotely-sensed data acquired in MIR and TIR wavelengths for deriving global forest cover information.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape

This paper addresses a few issues that are fundamental for the understanding of vegetation-topography relations: scale dependency, seasonal variability, and importance of observing individual properties. Particularly, it uses two statistical tools - Pearson's r and Moran's I - to define relationships of several topographic attributes with the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and their seasonal changes (from May to July and then September) in the Mediterranean-type landscape of the Santa Monica Mountains, California. The analyses are conducted at both the original data resolution and 20 aggregated resolutions, covering a total range of 30 m to 1500 m, so that topography-vegetation relationships can be compared at different scales. Large sample sizes have supported the significance of the following main findings for this landscape. First, elevation, slope, and southness are the most relevant primary topographic attributes among the tested attributes and their importance changes across seasons. Second, NDVI, NDII, and their seasonal variations have notably different relationships (including no relationship) with topography. Third, the observed topography-vegetation correlations (r) tend to change - typically increase - with the coarsening of spatial scale. Lastly, the spatial autocorrelation of vegetation variables and topographic attributes are often comparable, and the comparability is more apparent when topography-vegetation correlations are stronger. In all, the topography-NDVI/NDII relations defined in this paper may improve the understanding of the multi-scale and property-specific role that mountain topography plays in the formation and seasonal change of vegetation patterns.     

Dynamics of the relationship between NDVI and SWIR32 vegetation indices in southern Africa: implications for retrieval of fractional cover from MODIS data

Fractional cover of photosynthetic vegetation (F_PV), non-photosynthetic vegetation (F_NPV), and bare soil (F_BS) has been retrieved for Australian tropical savannah based on linear unmixing of the two-dimensional response envelope of the normalized difference vegetation index (NDVI) and short wave infrared ratio (SWIR)32 vegetation indices (VI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The approach assumes that cover fractions are made up of a simple mixture of green leaves, senescent leaves, and bare soil. In this study, we examine retrieval of fractional cover using this approach for a study area in southern Africa with a more complex vegetation structure. Region-specific end-members were defined using Hyperion images from different locations and times of the season. These end-members were applied to a 10-year time series of MODIS-derived NDVI and SWIR32 (from 2002 to 2011) to unmix F_PV, F_NPV, and F_BS. Results of validation with classified high-resolution imagery indicated major bias in estimation of F_NPV and F_BS, with regression coefficients for predicted versus observed data substantially less than 1.0 and relatively large intercept values. Examination with Hyperion images of the inverse relationship between the MODIS-equivalent SWIR32 index and the Hyperion-derived cellulose absorption index (CAI) to which it nominally approximates revealed: (1) non-compliant positive regression coefficients for certain vegetation types; and (2) shifts in slope and intercept of compliant regression curves related to day of year and geographical location. The results suggest that the NDVI–SWIR32 response cannot be used to approximate the NDVI–CAI response in complex savannah systems like southern Africa that cannot be described as simple mixtures of green leaves, dry herbaceous material high in cellulose, and bare soil. Methods that use a complete set of multispectral channels at higher spatial resolution may be needed for accurate retrieval of fractional cover in Africa.     

Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation

Thirty‐five stands of mature, closed canopy black spruce (Picea mariana), white spruce (Picea glauca) and balsam fir (Abies balsamea) in Prince Albert National Park, Saskatchewan, were assessed for cumulative defoliation caused by eastern spruce budworm (Choristoneura fumiferana). Multitemporal Landsat 5 TM images (15 June 1992 and 18 July 2004) and a single‐date SPOT 4 HRVIR (high resolution visible and infrared) image (19 August 2004) were obtained over these stands. Correlation analysis suggested that the strength of the relationship between the defoliation and various vegetation indices was generally moderate. The SPOT HRVIR indices were more highly correlated to cumulative defoliation than the Landsat indices, and the multitemporal Landsat TM index outperformed the single‐date Landsat TM index. These results may help in the design of defoliation assessment procedures that integrate satellite remotely‐sensed data and aerial sketch mapping techniques.     

Estimating percent impervious cover from Landsat-based land cover with a simple and transferable regression model

Percent impervious cover (PIC) is a widely used metric in ecological and hydrological analyses because it is highly correlated with pollutant and storm water run-off. The moderate-resolution satellite data (e.g. Landsat), that are typically used to calculate PIC, tend to overestimate PIC for all but very rural and very urban landscapes. Existing models for calibrating PIC estimates (e.g. ISAT, ETIS) are limited in that they are applicable only for specific land cover datasets and may also require population data; furthermore, these models have not been tested for performance outside of the geographic locations in which they were developed. The goal of this study was to explore simple but widely applicable regression models as tools for calibrating PIC estimates based on moderate resolution satellite data. The regression models used impervious land cover, from Landsat-based datasets, as the sole predictor of actual PIC. PIC was calculated for analysis units, ranging in size from 2.25 ha to ≥100 ha, for locations in Connecticut, Massachusetts, and Ohio in the United States. Regression models were fit for each size class of analysis unit at each study location; generalized versions of the models were created by fitting a regression to all size classes of analysis units at a given study location. Calibrated PIC estimates had root mean square error (RMSE) values that ranged from 1.5–10.7%; these values were considerably better than RMSE values for uncalibrated PIC estimates which ranged from 10.1–23.3%. For both calibrated and uncalibrated PIC, the accuracy of the estimates improved with the increasing size of the analysis units. Model regression coefficients were similar regardless of the analysis unit size, geographic location, or land cover dataset; model performance declined only slightly when applied outside the area in which it was developed. The simple regression models developed in this study had similar performance to previous calibration models (i.e. ISAT, ETIS) but are easier to apply and more widely applicable.     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.     

Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: a comparative study using a close-range sensing approach

Understanding the relationships between root zone soil moisture and vegetation spectral signals will enhance our ability to manage water resources and monitor drought-related stress in vegetation. In this article, the relationships between vegetation indices (VIs) and in situ soil moisture under maize and soybean canopies were analysed using close-range reflectance data acquired at a rainfed cropland site in the US Corn Belt. Because of the deep rooting depths of maize plants, maize-based VIs exhibited significant correlations with soil moisture at a depth of 100 cm (P < 0.01) and kept soil moisture memory for a long period of time (45 days). Among the VIs applied to maize, the chrolophyll red-edge index (CI_red-edge) correlated best with the concurrent soil moisture at 100 cm depth (P < 0.01) for up to 20 day lag periods. The same index showed a significant correlation with soil moisture at a 50 cm depth for lag periods from 10 (P < 0.05) to 60 days (P < 0.01). VIs applied to soybean resulted in statistically significant correlations with soil moisture at the shallower 10 and 25 cm depths, and the correlation coefficients declined with increasing depths. As opposed to maize, soybean held a shorter soil moisture memory as the correlations for all VIs versus soil moisture at 10 cm depth were strongest for the 5 day lag period. Wide dynamic range VI and normalized difference VI performed better in characterizing soil moisture at the 10 and 25 cm depths under soybean canopies when compared with enhanced VI and CI_red-edge.     

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.     

A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

Abstract

The imaging frequency and synoptic coverage of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) make possible for the first time a phenological approach to vegetation cover classification in which classes are defined in terms of the timing, the duration and the intensity of photosynthetic activity. This approach, which exploits the strong, approximately linear relationship between the amount of solar irradiance absorbed by plant pigments and shortwave vegetation indices calculated from red and near-infrared reflectances, involves a supervised binary decision tree classification of phytophenological variables derived from multidate normalized difference vegetation index (NDVI) imagery. A global phytophenological classification derived from NOAA global vegetation index imagery is presented and discussed. Although interpretation of the various classes is limited considerably by the quality of global vegetation index imagery, the data show clearly the marked temporal asymmetry of terrestrial photosynthetic activity.     

Characterizing vegetation indices derived from active and passive sensors

New ‘active’ sensors containing their own light source may provide consistent measures of plant and soil characteristics under varying illumination without calibration to reflectance. In 2006, an active sensor (Crop Circle) and various passive sensors were compared in a wheat (Triticum aestivum L., c.v. Chara) experiment in Horsham, VIC, Australia. The normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were calculated from plot data with a range of canopy cover, leaf area and biomass. The active sensor NDVI and SAVI data were slightly less effective than corresponding passive sensor data at estimating green cover (r ²?=?0.80?0.90 vs. ～0.95). Passive sensor measurements showed strong non-linearity for estimating dry biomass and green leaf area index (GLAI), whereas SAVI calculated from the active sensor was linear (r ²?=?0.86 and 0.90). Scaling effects were not apparent when point, transect and plot areas were compared at the given level of spatial variation. Sensor height above the target confounded SAVI data probably due to differential irradiance from the light sources and the unbalanced effect of the ‘L’ factor within the algorithm. The active sensor was insensitive to light conditions (r ²?=?0.99 for cloudy vs. clear skies) and had no requirement for optical calibration.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

Close-range remote sensing of aquatic macrophyte vegetation cover

This study used ground-based hyperspectral radiometry to examine variations in visible and near-infrared spectral reflectance of spatterdock (Nuphar polysepalum Engelm.) as a function of vegetation cover. Sites were sampled in Swan Lake in Grand Teton National Park, Wyoming, using a 512-band spectroradiometer to measure reflectance over the range 326.5-1055.3nm (visible-nearinfrared) and simultaneous estimates of spatterdock cover. Linear correlations between spatterdock cover and spectral reflectance were statistically significant at the 0.05 significance level in two specific ranges of the spectrum: 518-607 nm; and 697-900nm. Predictability of spatterdock cover using spectral variables was best using an NDVI transformation of the data in a non-linear equation (r ² = 0.95).     

Specularly modified vegetation indices to estimate photosynthetic activity

The hypothesis tested was that some part of the ecosystem-dependent variability of vegetation indices was attributable to the effects of light specularly reflected by leaves. ‘Minus specular’ indices were defined excluding effects of specular light which contains no cellular pigment information

Results, both empirical and theoretical, show that the ‘minus specular’ indices, when compared to the traditional vegetation indices, potentially provide better estimates of the photosynthetic activity within a canopy—and therefore canopy primary production—specifically as a function of Sun and view angles.     

The derivation of vegetation indices from AVHRR data

Abstract

The reliability of a 1-weck composited normalized difference vegetation index has been evaluated by using a cloud-screening algorithm applied to the visible and near-infrared data from the Advanced Very High Resolution Radiometer on NOAA-9. It is found that in some areas ofthe U.S. Great Plains this satellite sensor product may not be reliable due to the high frequency of cloud occurrence. Using the example of day-to-day variation in (he observed clear-sky radiances for one target, the vegetation index is shown to have maxima at high off-nadir and low solar zenith angles: this behaviour has been examined in detail. Some recommendations to improve the compositing technique are given.     

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989-2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI-SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.     

Intercalibration of vegetation indices from different sensor systems

Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR-2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 1-2%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change.     

Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model (ω–τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.     

Climatic and demographic determinants of vegetation cover in northern Cameroon

The objective of the study was to evaluate the spatio-temporal impacts of seasonal rainfall and urban population growth on the variations in normalized difference vegetation index (NDVI) in north Cameroon, which includes climates from south to north, the Sudanese and Sahelian climates. To this end, 48 points of measured rainfall were interpolated based on the kriging method at a spatial resolution of 8 km in accordance with the NOAA-AVHRR NDVI data set. Relationships between rainfall and NDVI, on the one hand, and urban population growth and NDVI, on the other, were analysed considering the 79 administrative units (AUs) in Cameroon. Seasonal (rainy season) variations of the vegetation cover were studied for the period 1987–2002 using the NDVI product at 8 km (NOAA-AVHRR) and 1 km (SPOT-VEGETATION) of spatial resolution. This article emphasizes the importance of the urban signal for the NDVI studies at finer scales, specifically in tropical areas.