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ABSTRACT 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. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes. [Copyright &y& Elsevier]

Abstract: The projected influence of climate change on the timing and volume of phytomass production is expected to affect a number of ecosystem services. In order to develop coherent and locally effective adaptation and mitigation strategies, spatially explicit information on the observed changes is needed. Long-term variations of the vegetative growing season in different environmental zones of Europe for 1982–2006 have been derived by analysing time series of GIMMS NDVI data. The associations of phenologically homogenous spatial clusters to time series of temperature and precipitation data were evaluated. North-east Europe showed a trend to an earlier and longer growing season, particularly in the northern Baltic areas. Despite the earlier greening up large areas of Europe exhibited rather stable season length indicating the shift of the entire growing season to an earlier period. The northern Mediterranean displayed a growing season shift towards later dates while some agglomerations of earlier and shorter growing season were also seen. The correlation of phenological time series with climate data shows a cause-and-effect relationship over the semi natural areas consistent with results in literature. Managed ecosystems however appear to have heterogeneous change pattern with less or no correlation to climatic trends. Over these areas climatic trends seemed to overlap in a complex manner with more pronounced effects of local biophysical conditions and/or land management practices. Our results underline the importance of satellite derived phenological observations to explain local nonconformities to climatic trends for climate change impact assessment. [Copyright &y& Elsevier]

The analysis of landscape pattern through remote sensing data is relatively widespread in landscape ecology and landscape planning. However, the lack of comparability of results between different image‐processing methods and across spatial resolutions limits the potential usefulness of landscape pattern indices. In this study, 96 sampling plots in Switzerland were investigated covering land‐use intensities ranging from old‐growth forest to intensive agricultural landscapes. The sampling plots were captured using fused Landsat ETM–IRS, Quickbird and aerial photograph data. In order to quantify landscape patterns, seven patch indices (derived by object‐oriented classification) and six grey‐value indices were extracted from the sampling plots. Principal component analysis was applied to the datasets, with the amount of variance in the first four axes compared among the sampling plots. Biplots of indices and sampling plots derived from all datasets were investigated with respect to land‐use intensity patterns. PCA results indicated that increasing spatial resolution corresponded to a slight increase in explained variance. Moreover, image grey‐values explained more variance between the sampling plots than segmented patch indices. Furthermore, biplots of grey‐value indices were capable of grouping sampling plots according to the land‐use intensity gradient, while segmented patch indices failed to adequately represent these. [ABSTRACT FROM AUTHOR]