Rahim Aguejdad

Relationships between soil seed bank, vegetation and soil fertility along an urbanisation gradient

ABSTRACT Questions: How does urbanisation influence soil mineral nitrogen stocks (nitrate and ammonium stocks) and what are the consequences of these modifications on the functional diversity of the herbaceous vegetation (vascular plants) and the seed bank? Location: Nine study sites were located on an urbanisation gradient in the city of Rennes, France. Methods: Three urbanisation levels were defined: urban areas (high grey/green ratio,), suburban (medium grey/green ratio) and periurban (low grey/green ratio). For each urbanisation level, nitrates and ammonium stocks were quantified; the herbaceous vegetation was surveyed as well as the soil seed bank (using the seedling emergence method). Results: Nitrate concentration increased with urbanisation (the nitrate level in urban plots was twice the concentration in periurban ones) whereas the ammonium level was higher in periurban areas than in urban areas. In urban plots, the vegetation and the seed bank were more nitrophilous, whereas the nitrogen requirement was lower for periurban species. The relationship between the seed bank and the above-ground vegetation was not significant. Conclusions: The higher nitrate concentration in the urban area appeared to be related to higher concentrations of atmospheric pollutants found in this area and lower ammonium levels may be related to the higher temperature in urban areas (leading to higher nitrification rates). The shift in the composition of the seed bank and vegetation appeared to be a consequence of higher nitrate stocks. The dissimilarity between the seed bank and vegetation may be caused by enhanced emergence of nitrophilous species in urban areas. Nomenclature: Flora Europaea (Tutin et al. 1964–1980).

Object-oriented Image Analysis for Mapping Urban Expansion in Western France

Monitoring urban expansion is critical to understanding the effects of urbanization on ecological processes. However, it presents important challenges due to the heterogeneity of the urban areas and the rapid changes in land cover and land use that occur over short time periods. Conventional surveying and mapping methods are not adequate to produce accurate and timely land-use and cover change information on urbanization. To remedy to these problems, object-oriented methods using remote sensing data with very high resolution have been recently developed. The main objective of this paper is to assess the ability of an object-oriented approach to classify urban areas and their spatial expansion with a moderate/high resolution satellite image time series. This paper focuses on the monitoring of urban expansion of a medium-size city (Rennes metropolitan area, North-western France, 384 000 inhabitants) during the 21 year period from 1984 until 2005 with a multiscale object-oriented method. The image object-oriented classification approach we used consists of a two step method : A segmentation technique has been performed prior to classification. Preliminary results show an important urban expansion (+ 62%) during the 21-year investigated period, with a large increase since the early nineties. The object-oriented method used in this research has proven to be reliable to discriminate pixels that are truly urban from pixels that have similar spectral properties, such as clear cuts or bare soils, from moderate/high resolution images, on the contrary of per-pixel classifications.

Spatial Validation of Land Use Change Models Using Multiple Assessment Techniques: A Case Study of Transition Potential Models

Using land use and cover change (LUCC) models for the urban growth planning, environmental assessment, and decision-making needs the establishment of an appropriate level of confidence in their performance. The objective of this research is to explore the importance of using multiple assessment techniques in order to fairly evaluate the performance of land use models. An application is conducted by using the Land Change Modeler for Ecological Sustainability (LCM) which is an empirical and transition potential model. LCM is applied to model the agricultural to developed areas transition in Rennes metropolitan area (France). The land demand is estimated using the Markov Chain model; whereas, the transition potential map is implemented using the Multi-Layer Perceptron Neural Network (MLP) method based on historical changes and driving variables. The model performance is assessed based on a variety of the most commonly used validation techniques. At the study area level, the correctness and disagreement analysis shows that LCM performs better at predicting the amount than the allocation of developed areas. Additionally, landscape metrics reveal that LCM tends to predict a fragmented urban form, which seems evident because of the large number of the individual urban patches. At the municipality level, the error budget analysis shows that the model performance, which varies highly between different subareas, needs to be improved. Moreover, the cross-tabulation between the transition potential map and both the observed and the predicted agricultural to developed areas transitions reveals that the order of the transition potential values does not perfectly fit the observed change; whereas, the predicted change is not solely limited to areas with high potential.