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.     

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.     

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.     

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).     

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.     

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.     

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.     

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.     

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.     

Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data

A vegetation index (VI) model for predicting evapotranspiration (ET) from data from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite and ground meteorological data was developed for riparian vegetation along the Middle Rio Grande River in New Mexico. Ground ET measurements obtained from eddy covariance towers at four riparian sites were correlated with MODIS VIs, MODIS land surface temperatures (LSTs), and ground micrometeorological data over four years. Sites included two saltcedar (Tamarix ramosissima) and two Rio Grande cottonwood (Populus deltoides ssp. Wislizennii) dominated stands. The Enhanced Vegetation Index (EVI) was more closely correlated (r=0.76) with ET than the Normalized Difference Vegetation Index (NDVI; r=0.68) for ET data combined over sites and species. Air temperature (T_a) measured over the canopy from towers was the meteorological variable that was most closely correlated with ET (r=0.82). MODIS LST data at 1- and 5-km resolutions were too coarse to accurately measure the radiant surface temperature within the narrow riparian corridor; hence, energy balance methods for estimating ET using MODIS LSTs were not successful. On the other hand, a multivariate regression equation for predicting ET from EVI and T_a had an r²=0.82 across sites, species, and years. The equation was similar to VI-ET models developed for crop species. The finding that ET predictions did not require species-specific equations is significant, inasmuch as these are mixed vegetation zones that cannot be easily mapped at the species level.     

NOAA-AVHRR data processing for the mapping of vegetation cover

The NOAA-AVHRR images have been widely used for global studies due to their low cost, suitable wavebands and high temporal resolution. Data from the AVHRR sensor (Bands 1 and 2) transformed to the Normalized Difference Vegetation Index (NDVI) are the most common product used in global land cover studies. The purpose of this Letter is to present the vegetation, soil, and shade fraction images derived from AVHRR, in addition to NDVI, to monitor land cover. Six AVHRR images from the period of 21 to 26 June 1993 were composed and used to obtain the above mentioned products over Sa o Paulo State, in the south-east of Brazil. Vegetation fraction component values were strongly correlated with NDVI values ( r 0.95; n 60). Also, the fraction image presented a good agreement with the available global vegetation map of Sao Paulo State derived from Landsat TM images.     

Estimating ground fractional vegetation cover using the double-exposure method

Fractional vegetation cover (FVC) is a key parameter in ecological models. It is important to determine the ground FVC quickly and accurately in studies of soil erosion, surface energy balance, and carbon cycling. As one of the FVC ground measurement methods, the photographic method is easy to operate with relatively high precision. However, its classification result showed poor accuracy when an image of a high-contrast scene contained a shadow region where a low signal-to-noise ratio (SNR) existed, because the single-exposure image in the photographic method did not contain sufficient surface information about both the illuminated and shadowed parts. This article presents application of a double-exposure photographic method to determine vegetation cover in the shadow region of an image. It consists of two measurements used in acquiring images (normal and over-exposure) and one image-processing part to handle the obtained images. Illuminated vegetation and soil, as well as the shadow region, was classified with the normally exposed image in the intensity, hue, and saturation (IHS) colour space, and the shadow region was further classified as shadowed vegetation and shadowed soil using the over-exposed image. The results indicate that the over-exposed image reduced the average bias of the FVC in the shadow region from 15.40% to ?4.14% and the root mean square error (RMSE) from 0.174 to 0.066. The RMSE of the entire scene was 0.055 in the over-exposed image and 0.092 in the single-exposed image. The double-exposure method also showed a better classification result than the high dynamic range method in deep shadow regions. This study shows that this method is capable of distinguishing vegetation and soil in the shadow region and thus it is an effective and accurate method for ground FVC measurement.     

Spectral characteristics of vegetation cover: factors of variability

Abstract

The dependence of spectral diffuse reflectance coefficients on the phenology of tree stands, observation and illumination conditions, ratio of scattered and total radiation was studied on a special testing ground by an automated system. The experimental dependences obtained for high density stands are satisfactorily described on the basis of the turbid layer theory (Ross-Nilson-Kuusk model)     

Diurnal patterns of bidirectional vegetation indices for wheat canopies

The perpendicular vegetation index (PVI) and normalized difference vegetation index (NDVI) were calculated from Mark II radiometer RED (0.63-0.69 μm) and NIR (0.76–0.90 μ) bidirectional radiance observations for wheat canopies. Measurements were taken over the plant development interval flag leaf expansion to watery ripeness of the kernels during which the leaf area index (LAI) decreased from 40 to 2-5. Spectral data were taken on four cloudless days five times (09.30, 11.00, 12.30, 14.00 and 15.30 hours (central standard time, C.S.T.) at five view zenith, Zv (0, 15, 30,45 and 60°) and eight view azimuth angles relative to the Sun, A_v (0, 45, 90, 135, 180, 225, 270 and 315°). The PVI was corrected to a common solar irradiance (PVIC) based on simultaneously observed insolation readings.

The PVIC at nadir view (?=0°) increased as (l/cosZ_s) increased on all the measurement days whereas the NDVI changed little as solar zenith angle (Z_s) changed. Thus, the PVIC responded to increasing path length through the canopy, or the number of leaves encountered, as solar zenith angle changed whereas the NDVI, which has saturated by the time an LAI of 2 was achieved, was nonresponsive.

Off-nadir PVIC ratioed to nadir PVIC increased as the view zenith and solar zenith angles increased (reciprocity in Sun and view angles), and as the view azimuth, A angle approached the Sun position (back scattering stronger that forwardscattering). In contrast, the DNVI was very stable for all view and solar angles consistent with saturation in its response. Even though the PVI is subject to bidirectional effects, it contains more useful information about wheat canopies at LAI > 2 than does the NDVI. The NDVI of the plant canopies changed rapidly at low vegetative cover but its bidirectional sensitivity at low LAI was not investigated.     

Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices

To predict the responses of the timing, duration, and density of photosynthetically active plant cover to a changing climate, it is necessary to first quantitatively describe the relationships between temporal and spatial patterns of vegetation cover and both spatial and inter-annual variation in temperature and precipitation. We examined these relationships at multiple scales in Taiwan using monthly maximum composite values of MODIS-NDVI and MODIS-EVI between 2000 and 2012. The two vegetation indices were highly correlated to each other on a monthly basis for non-forest land-cover types, but correlated poorly in forests, probably due to the saturation of NDVI. However, the two indices were equally sensitive in detecting the onset and offset of growing season for all vegetation types. We found that EVI was positively related to both precipitation and temperature on a monthly timescale, although the relationships were not significant at the annual timescale. The much greater variation in monthly than in annual precipitation and temperature probably explains this difference. At low elevations, precipitation had a positive effect and temperature had a negative effect on EVI; however, at high elevations, which are mostly forested, both were positively related to EVI (although precipitation effects were not significant). We interpret this as evidence of water limitation of photosynthetic cover in the warmer, low-elevation parts of the island, whereas in the higher-elevation areas precipitation was usually adequate to satisfy evapotranspirative demand. This study illustrates the importance of examining the effects of precipitation and temperature on plant growth at a range of spatial and temporal scales. Specifically, finer spatial and temporal scales of analysis may better reveal climatic controls over vegetation growth than broader scales of analysis.     

Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery

Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensing technology. However, combining ecological ground truth information and remote sensing datasets for mapping approaches is complicated by the complexity of ecological datasets. In this study, we present a new approach that uses high spatial resolution hyperspectral datasets to map vegetation units of a semiarid rangeland in Central Namibia. Field vegetation surveys provide the input to the workflow presented in this study. The collected data were classified by hierarchical cluster analysis into seven vegetation units that reflect different ecological states occurring in the study area. Spectral indices covering vegetation and soil characteristics were calculated from hyperspectral remote sensing imagery and used as environmental variables in a constrained ordination by applying redundancy analysis (RDA). The resulting statistical relationships between vegetation data and spectral indices were transferred into images of ordination axes, which were subsequently used in a supervised fuzzy c-means classification approach relying on a k-NN distance metric. Membership images for each vegetation unit as well as a confusion image of the classification result allowed a sound ecological interpretation of the resulting hard classification map. Classification results were validated with two independent reference datasets. For an internal and external validation dataset, overall accuracy reached 98% and 64% with kappa values of 0.98 and 0.53, respectively. Critical steps during the mapping workflow were highlighted and compared with similar mapping approaches.