CHARACTERISATION OF PRODUCTIVITY LIMITATION OF SALT-AFFECTED LANDS IN DIFFERENT CLIMATIC REGIONS OF EUROPE USING REMOTE SENSING DERIVED PRODUCTIVITY INDICATORS

Soil salinity is a global issue and one of the major causes of land degradation. The large scale monitoring of salt-affected areas is therefore very important to shed light on necessary rehabilitation measures and to avoid further land degradation. We address the productivity limitation of salt-affected soils across the European continent by the usage of soil maps and high temporal resolution time series of satellite images derived from the SPOT vegetation sensor. Using the yearly dynamism of the vegetation signal derived from the Normalised Difference Vegetation Index, we decomposed the spectral curve into its base fraction and seasonal dynamism fractions next to an index approximating gross primary productivity. We observe gross primary productivity, base fraction and seasonal dynamism productivity differences of saline, sodic and not salt-affected soils under croplands and grasslands in four major climatic zones of the European continent. Analysis of variance models and post hoc tests of mean productivity values indicate significant productivity differences between the observed salt-affected and salt free areas, between management levels of soils as well as between the saline and sodic character of the land. The analysis gives insight into the limiting effect of climate in relation to the productivity of salt-affected soils. The proposed indicators are applicable on the global level, are objective and readily repeatable with yearly updates, thus, might contribute to the global operational monitoring and assessment of degraded lands

Prediction of biodiversity - regression of lichen species richness on remote sensing data

The objective of the present study was to develop a model to predict lichen species richness for six test sites in the Swiss Pre-Alps following a gradient of land use intensity combining airborne remote sensing data and regression models. This study ties in with the European Union Project „BioAssess”, which aimed at quantifying patterns in biodiversity and developing „Biodiversity Assessment Tools” that can be used to rapidly assess biodiversity. For this study, lichen surveys were performed on a circular area of 1 ha in 96 sampling plots in the six test sites. Lichen relevés were made on three different substrates: trees, rocks and soil. In the first step, ecologically meaningful variables derived from airborne remote sensing data were calculated using two levels of detail. 1'st level variables were processed using both spatial and spectral information of the CIR orthoimages. 2'nd level variables - based on 1'st level variables - were implemented using additional lichen expert knowledge. In the second step, all variables were calculated for each sampling plot and correlated with the different lichen relevés. Multiple linear regression models were built, containing all extracted variables, and a stepwise variable selection was applied to optimize the final models. The predictive power of the models (correlation between predicted and measured diversity) in a reference data set can be regarded as good. The obtained R ranging from 0.48 for lichens on soil to 0.79 for lichens on trees can be regarded as satisfactory to good, respectively. The accuracy of models could be further improved by adapting the model and by using additional calibration data and sampling plots. Species richness for each pixel within the six test sites was then calculated. This ecological modeling approach also reveals two main restrictions: 1) this method only indicates the potential presence or absence of species, and 2) the models may only be useful for calculating species richness in neighboring regions with similar landscape structures