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Desertification is a challenge in north-eastern Brazil (NEB) that needs to be understood to develop sustainable land-use strategies. This study analyses regional vegetation dynamics in NEB and the compatibility of two NDVI data sets to support future desertification assessment studies in the semi-arid Caatinga biome. Vegetation variability and trends in NEB are analysed for 1982-2006, based on monthly AVHRR (GIMMS) NDVI data. The GIMMS data are compared with MODIS NDVI for the overlapping period [2001][2002][2003][2004][2005][2006]. Existing statistical methods are applied and existing NDVI analyses in NEB expanded in respect to vegetation trend analysis and data set comparison.

Abstract:In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESSRemote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

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HighlightsA rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed.The intervention impact is evaluated by a before–after control-impact sampling design.The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change.The method is applicable to NDVI and other remote sensing-derived variables.