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Byline: Molly E. Brown (1), Jorge E. Pinzon (2), Stephen D. Prince (3) Abstract: Systematic evaluation of food security throughout the Sahel has been attempted for nearly two decades. Food security analyses have used both food prices to determine the ability of the population to access food, and satellite-derived vegetation indices that measure vegetation production to establish how much food is available each year. The relationship between these two food security indicators is explored here using correspondence analysis and through the use of Markov chain models. Two sources of quantitative data were used: 8 km normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) carried on the NOAA series of satellites, and monthly millet prices from 445 markets in Mali, Niger and Burkina Faso. The results show that the growing season vegetation production is related to the price of millet at the annual and the seasonal time scales. If the growing season was characterized by erratic, sparse rainfall, it resulted in higher prices, and well-distributed, abundant rainfall resulted in lower prices. The correspondence between vegetation production and millet prices is used to produce maps of millet prices for West Africa. Author Affiliation: (1) Department of Geography, University of Maryland, NASA Goddard Space Flight Center, Code 923, Greenbelt, MD, 20771, USA (2) Science Systems and Applications Inc., NASA Goddard Space Flight Center, Code 923, Greenbelt, MD, USA (3) Department of Geography, University of Maryland, College Park, MD, USA Article History: Registration Date: 20/02/2006 Received Date: 25/05/2004 Accepted Date: 24/06/2005 Online Date: 01/09/2006

Systematic evaluation of food security throughout the Sahel has been attempted for nearly two decades. Food security analyses have used both food prices to determine the ability of the population to access food, and satellite-derived vegetation indices that measure vegetation production to establish how much food is available each year. The relationship between these two food security indicators is explored here using correspondence analysis and through the use of Markov chain models. Two sources of quantitative data were used: 8 km normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) carried on the NOAA series of satellites, and monthly millet prices from 445 markets in Mali, Niger and Burkina Faso. The results show that the growing season vegetation production is related to the price of millet at the annual and the seasonal time scales. If the growing season was characterized by erratic, sparse rainfall, it resulted in higher prices, and well-distributed, abundant rainfall resulted in lower prices. The correspondence between vegetation production and millet prices is used to produce maps of millet prices for West Africa.

This paper evaluates the consistency of the Normalized Difference Vegetation Index (NDVI) records derived from Advanced Very High Resolution Radiometer (AVHRR), SPOT-Vegetation, SeaWiFS, Moderate Resolution Imaging Spectroradiometer, and Landsat ETM+. We used independently derived NDVI from atmospherically corrected ETM+ data at 13 Earth Observation System Land Validation core sites, eight locations of drought, and globally aggregated one-degree data from the four coarse resolution sensors to assess the NDVI records agreement. The objectives of this paper are to: 1) compare the absolute and relative differences of the vegetation signal across these sensors from a user perspective, and, to a lesser degree, 2) evaluate the possibility of merging the AVHRR historical data record with that of the more modern sensors in order to provide historical perspective on current vegetation activities. The statistical and correlation analyses demonstrate that due to the similarity in their overall variance, it is not necessary to choose between the longer time series of AVHRR and the higher quality of the more modern sensors. The long-term AVHRR-NDVI record provides a critical historical perspective on vegetation activities necessary for global change research and, thus, should be the basis of an intercalibrated, sensor-independent NDVI data record. This paper suggests that continuity is achievable given the similarity between these datasets. Index Terms--Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT, vegetation.

Independent component analysis (ICA) provides a powerful new method to spatially and temporally deconvolve image sequences into components that capture variability arising from independent physical sources. To do this, ICA uses information contained in higher order cross-moments of multivariate data. We use remotely sensed time series of the normalized difference vegetation index to illustrate the utility of this technique. Index Terms--Decorrelation, image sequence analysis, independent component analysis (ICA), neural network applications, normalized difference vegetation index (NDVI), remote sensing.

Smoothness membership in Besov spaces [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](I) is used to compare the spatial coherence of satellite images. Smoothness is given by a complexity index computed as the rate of decay of the approximation error [Epsilon](M) when the image is approximated by its M-largest quantized wavelet coefficient. The technique was applied to a set of nine normalized difference vegetation index (NDVI) time series data as a quantitative quality measure of spatial coherence. The NDVI data set comprises different compositing and atmospheric correction techniques. The estimates of the complexity index give a quantitative measure of the performance of these techniques that agrees well with visual evaluation and with the physics of the image collection process. We demonstrate the maximum value NDVI composites with Rayleigh, ozone, and water vapor correction consistently provide the highest spatial coherence among the compositing and atmospheric correction techniques evaluated. We also show the complexity index is regionally dependent and is higher in dry periods than in wet periods where residual cloud interference is more likely to appear. Index Terms--Remote sensing, smoothness of image, wavelets.

A hierarchical procedure was developed for quantitative estimation of foliar chemistry from remote reflectance spectra. We based our analysis on a new methodology called Hierarchical Foreground and Background Analysis (HFBA) that derives sequentially a series of weighting vectors which simultaneously extract important discriminant features, in this case, leaf anatomy and chemical concentration at different levels of detection from the spectral information. In this study, we focused on the application of detecting carbon, cellulose, nitrogen concentrations, and water content. The goal of the derived vectors is twofold: 1) create a robust detection and classification system of constituent materials and 2) create a good information packing system that minimizes extraneous undesired interference, like noise, in the analysis. In our study, two data sets were examined: a fresh leaf (FL) data set, LOPEx [from the Joint Research Center (JRC), Ispra, Italy], and a dry leaf data set, Blackhawk Island (BH), WI [University of New Hampshire (UNH), Durham]. We tested the robustness of the derived vectors with four other data sets: fresh leaf data from Jasper Ridge Biological Preserve (JRBP) (chemistry from UNH, spectra from University of California, Davis), Santa Monica Mountains (SMM), CA, [from University of California, Davis (UCDavis)], and dry leaf data from two ACCP sites (UNH): Howland (HO), ME, and Harvard Forest (HF), MA. The results support the robustness of the HFBA system and demonstrate an advantage in classification accuracy (first level) as well as in predicting the biochemical composition (subsequent levels) over classical forms of analysis that ignore effects of the nonlinear variation that contribute to reflectance at different (subpixel and spectral) scales. HFBA primarily deals with the spectral scaling issue. Index Terms - Leaf biochemistry spectral characteristics, spectral feature extraction, vegetation mapping.