Urban vegetation extraction from VHR (tri-)stereo imagery : a comparative study in two central European cities

The present study proposes a workflow to extract vegetation height for urban areas from Pléiades stereo and tri-stereo satellite imagery. The workflow was applied on a stereo image pair for Szeged, Hungary and on tri-stereo imagery for Salzburg, Austria. Digital surface models (DSMs) of the study areas were computed using the semi-global matching algorithm. Normalised digital surface models (nDSMs) were then generated. Objects of vegetation and non-vegetation were delineated based on the spectral information of the multispectral images by applying multi-resolution segmentation and support vector machine classifier. Mean object height values were then computed from the overlaid pixels of the nDSMs and assigned to the objects. Finally, the delineated vegetation was classified into six vegetation height classes based on their assigned height values by using hierarchical classification. The vegetation discrimination resulted in very high accuracy, while the vegetation height extraction was moderately accurate. The results of the vegetation height extraction provided a vertical stratification of the vegetation in the two study areas which is readily applicable for decision support purposes. The elaborated workflow will contribute to a green monitoring and valuation strategy and provide input data for an urban green accessibility study.DK W 1237N23(VLID)251709

Degradation of haloaromatic compounds

An ever increasing number of halogenated organic compounds has been produced by industry in the last few decades. These compounds are employed as biocides, for synthetic polymers, as solvents, and as synthetic intermediates. Production figures are often incomplete, and total production has frequently to be extrapolated from estimates for individual countries. Compounds of this type as a rule are highly persistent against biodegradation and belong, as "recalcitrant" chemicals, to the class of so-called xenobiotics. This term is used to characterise chemical substances which have no or limited structural analogy to natural compounds for which degradation pathways have evolved over billions of years. Xenobiotics frequently have some common features. e.g. high octanol/water partitioning coefficients and low water solubility which makes for a high accumulation ratio in the biosphere (bioaccumulation potential). Recalcitrant compounds therefore are found accumulated in mammals, especially in fat tissue, animal milk supplies and also in human milk. Highly sophisticated analytical techniques have been developed for the detection of organochlorines at the trace and ultratrace level

Comparison of SVM and boosted regression trees for the delineation of lacustrine sediments using multispectral ASTER data and topographic indices in the lake Manyara Basin

The lower member of the so called Manyara Beds is a distinct lacustrine sedimentary layer which indicates, with an elevation of more than 140 m above today's lake level, a high stand of the paleolake Manyara in the Monduli District in northern Tanzania. The Manyara Beds are rich in Pleistocene vertebrate fossils. In this study we focus on the delineation of this specific stratigraphic layer in order to yield new insights into paleontological settings, landscape evolution and to plan paleontological fieldwork. We compare the performance of a support vector classifier with a linear as well as a Gaussian kernel, with boosted regressiontree approaches to identify the lithostratigraphic layers of the Manyara Beds. For the identification of the lacustrine sediments, multispectral informationof ASTER satellite imagery and topographic indices derived from a digital elevation model were utilized as input feature sets. Acceptable classification accuracies were obtained with all methods. Thus, the Manyara Beds can be delineated and new sites with paleolake sediments were detected. The highest overall accuracy with 92% was provided by the support vector machine approach with a linear kernel for a binary classification problem. For a multi-class classification problem with three target classes the support vector classifier achieved 80% accuracy with a linear, as well as a Gaussian kernel. © 2015 E. Schweizerbart'sche Verlagsbuchhandlung, Stuttgart, Germany

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy)

This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north-eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence-only-based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of failure mechanisms for flow or avalanche type propagation. The set of predictors used in this experiment comprised primary and secondary topographic attributes, derived by processing a high resolution digital elevation model, CORINE land cover data and a set of vegetation and mineral indices obtained by processing multispectral ASTER images. All the selected data sources are dated before the disaster. A spatially random partition technique was adopted for validation, generating 50 replicates for each of the two considered movement typologies in order to assess accuracy, precision and reliability of the models. The debris slide and debris flow susceptibility models produced high performances with the first type being the best fit. The evaluation of the probability estimates around the mean value for each mapped pixel shows an inverted relation, with the most robust models corresponding to the debris flows. With respect to the role of each predictor within the modelling phase, debris flows appeared to be primarily controlled by topographic attributes whilst the debris slides were better explained by remotely sensed derived indices, particularly by the occurrence of previous wildfires across the slope. The overall excellent performances of the two models suggest promising perspectives for the application of presence-only methods and remote sensing derived predictors. Copyright © 2016 John Wiley & Sons, Ltd

Assessment of flash floods in a small Mediterranean catchment using terrain analysis and remotely sensed data: A case study in the Torrente Teiro, Liguria, Italy

Mediterranean catchments are particularly sensitive to temperature oscillations, rainfall intensity and human activities. Especially intensive precipitation events, changing land-use and thin soil layer trigger surface runoff generation and hence, soil erosion, sediment transport, flooding and related damages. In this study, we propose a methodology using remotely sensed data, terrain analysis and stochastic modeling to characterize the soil hydrological and physical components of the Teiro catchment. Particularly, we focus on the triggering land-cover and soil information that can be derived with multispectral remote sensing techniques. To study the hydrological dynamics of the Teiro catchment we applied the Soil Conservation Service Curve Number method implemented in a GIS system for different precipitation events related to various return periods. The input data was calculated based on multispectral indices describing the heterogeneity of soils and vegetation. The discharges obtained show reasonable values that have been validated with mapped flooded areas of the 4th October 2011 flood event. This event corresponds roughly to a 10 years return period. However, it is striking that a 50 years return period event was calculated to yield the double amount of discharge and thus, implies a major hazard for the local populatio

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy)

This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north-eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence-only-based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of failure mechanisms for flow or avalanche type propagation. The set of predictors used in this experiment comprised primary and secondary topographic attributes, derived by processing a high resolution digital elevation model, CORINE land cover data and a set of vegetation and mineral indices obtained by processing multispectral ASTER images. All the selected data sources are dated before the disaster. A spatially random partition technique was adopted for validation, generating 50 replicates for each of the two considered movement typologies in order to assess accuracy, precision and reliability of the models. The debris slide and debris flow susceptibility models produced high performances with the first type being the best fit. The evaluation of the probability estimates around the mean value for each mapped pixel shows an inverted relation, with the most robust models corresponding to the debris flows. With respect to the role of each predictor within the modelling phase, debris flows appeared to be primarily controlled by topographic attributes whilst the debris slides were better explained by remotely sensed derived indices, particularly by the occurrence of previous wildfires across the slope. The overall excellent performances of the two models suggest promising perspectives for the application of presence-only methods and remote sensing derived predictors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd