Search

In this study we present a technique to discriminate between climate or human-induced dryland degradation, based on evaluations of AVHRR NDVI data and rainfall data. Since dryland areas typically have high inter-annual rainfall variations and rainfall has a dominant role in determining vegetation growth, minor biomass trends imposed by human influences are difficult to verify. By performing many linear regression calculations between different periods of accumulated precipitation and the annual NDVImax, we identify the rainfall period that is best related to the NDVImax and by this the proportion of biomass triggered by rainfall. Positive or negative deviations in biomass from this relationship, expressed in the residuals, are interpreted as human-induced. We discuss several approaches that use either a temporally fixed NDVI peaking time or an absolute one, a best mean rainfall period for the entire drylands or the best rainfall period for each individual pixel. Advantages and disadvantages of either approach or one of its combinations for discriminating between climate and human-induced degradation are discussed. Depending on the particular land-use either method has advantages. To locate areas with a high likelihood of human-induced degradation we therefore recommend combining results from each approach. [Copyright &y& Elsevier]

We use satellite data from different sensor systems to analyze and explain the causes, processes, and impacts of desertification in a Steppe grazing area in Syria, with the aim of supporting the formulation of a strategy for rehabilitating desertified areas. Through the mapping of parameters such as barley fields, eolian sand distribution, and drainage patterns from Thematic Mapper (TM) data, we identified barley cultivation as one major reason for increased sand erosion or its downhill deposition. With regard to the degradation of natural vegetation covers, we discriminate between climate-triggered and human-induced vegetation degradation by analyzing the natural response pattern of vegetation to rainfall. For the monitoring of vegetation covers, we used composited 10-day interval 8-km Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data from 1981 to 1996. A consistently changing response of vegetation to rainfall over this time period, expressed in the residuals of the NDVImax/Rainfall linear regression calculations, is interpreted as nonclimate or human driven, where correlations between residuals and the time of their occurrence produce correlation coefficients >|0.6|. Pixels showing a negative temporal trend in residuals coincide with areas that are most heavily used by humans. Heavily used areas were located through detecting nomadic campsites from Indian Remote Sensing Satellite (IRS)-1C data. By combining campsite distribution with census data, such as flock size, average annual offtake, and grazing habits, we assess grazing pressures and put them in relation to the natural resources. This information provided the basis for the definition of protected areas or rehabilitation plots, and for elaborating measures to support the Steppe dwellers. [Copyright &y& Elsevier]