journal article
Download Only Collection
Home
GOWARD, S. N.; DYE, D. G.; TURNER, S.; YANG, J.
doi: 10.1080/01431169308904453pmid: N/A
Abstract For the last 10 years the U.S. National Oceanic and Atmospheric Administration has produced an experimental Global Vegetation Index (GVI) data set for terrestrial vegetation research. These data, sampled from advanced very high resolution radiometer (AVHRR) observations, have served as a primary stimulus for global-scale vegetation research but have, so far, not been adequately evaluated. This study reviews the GVI production procedures and compares the resultant observations with a more comprehensive compilation of the AVHRR data being produced at the NASA Goddard Space Flight Center. There are many aspects of the GVI production procedures which could be improved to achieve the desired objectives. In particular, the mapping and sampling procedures employed provide measurements which only approximate the observed GAC measurements. The GVI NDVI record varies more than ±NDVI units (∼ 7 per cent of signal) from the GAC record and, in general, seriously underestimates the GAC NDVI measurements. The NDVI portion of the GVI record is compromised through use of digital numbers rather than calibrated reflectance. NDVI measurements from the calibrated channels of the GVI data set produces values that compare favourably with the GAC measurements, but with considerable residual variance. Calculation of a 3 by 3 pixel average of the GVI NDVI measurements reduces residual variance between the data sets to ±0.018 NDVI units (∼3 per cent of signal). Decay of sensor calibration and orbital overpass time, experienced by all the AVHRR sensors, as well as differences in these parameters between the sensors are not addressed but the results suggest the importance of accounting for these factors. These results indicate that GVI data sets, following adequate reprocessing, provide reasonable estimates of major regional contrasts in vegetation activity but should not be employed to evaluate local or minor trends.
DUNCAN, J.; STOW, D.; FRANKLIN, J.; HOPE, A.
doi: 10.1080/01431169308904454pmid: N/A
Abstract We assessed the statistical relations between spectral vegetation indices (SVIs) derived from SPOT multi-spectral data and semi-arid shrub cover at the Jornada LTER site in New Mexico. Despite a limited range of shrub cover in the sample the analyses resulted in r2 values as high as 0.77. Greenness SVIs (e.g., Simple Ratio, NDVI, SAVI, PVI and an orthogonal Greenness index) were shown to be more sensitive to shrub type and phenology than brightness SVIs (e.g., green, red and near-infrared reflectances and a Brightness index). The results varied substantially with small-scale changes in plot size (60 m by 60 m to 100 m by 100 m) as a consequence of landscape heterogeneity. The results also indicated the potential for the spectral differentiation of shrub types, and shrubs from grass, using multi-temporal, multi-spectral analysis.
FERENCZ, Cs.; TARCSAI, Gy.; LICHTENBERGER, J.
doi: 10.1080/01431169308904455pmid: N/A
Abstract A new correction method for atmospheric effects in Landsat-MSS and NOAA AVHRR data is presented which uses only the remotely-sensed multispectral data. The method is based on a new quasi-single-variable radiative transfer model, and as a first step we assumed that the surface is covered by vegetation. For Landsat-MSS data the method was developed for the tasseled cap indices using known empirical relationships among them. For NOAA AVHRR data ‘ cap-like’ indices and the average reflectance of the average canopy in the visible band known from Landsat-MSS data were used. The method was used in yield forecasting project in north-eastern part of Hungary and provided a significant enhancement in the quality of remotely sensed data.
GALLEGO, F. J.; DELINCE, J.; RUEDA, C.
doi: 10.1080/01431169308904456pmid: N/A
Abstract Inventories of the MARS Project of the IRSA that is briefly described. Some aspects concerning stability are discussed. Regression results seem to be more reliable if training pixels are chosen at random in a random subset of segments (elements of the area sampling frame). Some risk of bias is observed if segments with training pixels are included to compute the regression parameters.
ZHUANG, H. C.; SHAPIRO, M.; BAGLEY, C. F.
doi: 10.1080/01431169308904457pmid: N/A
Abstract In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI is where X i is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R 2 reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.
MASELLI, F.; CONESE, C.; PETKOV, L.; GILABERT, M. A.
doi: 10.1080/01431169308904458pmid: N/A
Abstract Several investigations have shown that NOAA NDVI data accumulated during a rainy season can be related to total rainfall or final primary productivity in the Sahel. However, serious problems can arise when looking for quantitative relations to monitor and forecast crop yield from NDVI values. Geographical variability can affect such relations, while the use of data taken from a whole season is impractical for forecasting. The present paper proposes a complete methodology of NDVI data processing which only utilizes NOAA AVHRR scenes from the first part of successive rainy seasons. A series of basic corrections are first applied to the original data to obtain reliable NDVI maximum value composites at the middle of the rainy seasons considered. Next, the variability in land resources is accounted for by means of a standardization process which normalizes the mean NDVI levels of some areas on the relevant multi-temporal averages and standard deviations. In this way, good estimates of the actual condition of vegetation can be obtained in relation to the local seasonal trend The methodology was applied to the Sahelian sub-departments of Niger with data from four years (1986–1989). The most interesting result achieved concerns the estimation of final grain (millet and sorghum) yield for the sub-departments by the end of July with a mean error of about 0·08 T ha −1. This timely evaluation could be of great utility in the context of an efficient drought early warning system.
doi: 10.1080/01431169308904459pmid: N/A
Abstract The effects of the drought of 1988 in Kansas, U.S.A., have been evaluated by calculating the difference in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index values recorded in 1988 from values recorded in 1987, a year of normal precipitation. Landscape parameters including land cover, soil permeability, available water capacity, soil texture, depth to water table, and slope were examined to determine if certain landscape elements imperilled an area to potential drought damage. Land cover, slope and depth to water table were found to have an effect on an area's drought susceptibility.
Showing 1 to 9 of 9 Articles