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  • 1
    Language: English
    In: Plant and Soil, 2013, Vol.370(1), pp.497-509
    Description: Aims: We analysed current carbon (C) stocks in fine root and aboveground biomass of riparian forests and influential environmental parameters on either side of a dike in the Donau-Auen National Park, Austria. Methods: On both sides of the dike, carbon (C) stock of fine roots (CFR) under four dominant tree species and of aboveground biomass (CAB) were assessed by topsoil cores (0-30 cm) and angle count sampling method respectively (n=48). C stocks were modeled, performing boosted regression trees (BRT). Results: Overall CFR was 2.8 t ha super(-1), with significantly higher C stocks in diked (DRF) compared to flooded riparian forests (FRF). In contrast to CFR, mean CAB was 123 t ha super(-1) and lower in DRF compared to FRF. However, dike construction was consistently ruled out as a predictor variable in BRT. CFR was influenced by the distance to the Danube River and the dominant tree species. CAB was mainly influenced by the magnitude of fluctuations in the groundwater table and the distances to the river and the low groundwater table. Conclusions: Despite pronounced differences in FRF and DRF, we conclude that there is only weak support that dikes directly influence C allocation in floodplain forests within the time scale considered (110 years).
    Keywords: Aboveground biomass ; Belowground biomass ; Carbon distribution ; Carbon sequestration ; Dike ; Ecosystem services ; Floodplain forest
    ISSN: 0032-079X
    E-ISSN: 1573-5036
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  • 2
    Language: English
    In: The Science of the Total Environment, July 1, 2015, Vol.520, p.49(10)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.scitotenv.2015.03.030 Byline: Albert H. Baur, Steffen Lauf, Michael Forster, Birgit Kleinschmit Abstract: Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available. Article History: Received 21 November 2014; Revised 26 January 2015; Accepted 6 March 2015 Article Note: (miscellaneous) Editor: P. Kassomenos
    Keywords: Emissions (Pollution) – Analysis ; Emissions (Pollution) – Models ; Cities and Towns – Analysis ; Cities and Towns – Models ; Pollution Control – Analysis ; Pollution Control – Models ; Global Temperature Changes – Analysis ; Global Temperature Changes – Models ; Greenhouse Gases – Analysis ; Greenhouse Gases – Models ; Air Pollution – Analysis ; Air Pollution – Models
    ISSN: 0048-9697
    Source: Cengage Learning, Inc.
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  • 3
    Language: English
    In: Science of the Total Environment, 01 July 2015, Vol.520, pp.49-58
    Description: Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available.
    Keywords: Urban Greenhouse Gas Emissions ; Sustainable Urban Development ; Household Size ; Landscape Metrics ; Multiple Regression Model ; Climate Change Mitigation ; Environmental Sciences ; Biology ; Public Health
    ISSN: 0048-9697
    E-ISSN: 1879-1026
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  • 4
    Language: English
    In: Science of the Total Environment, 01 November 2016, Vol.569-570, pp.527-539
    Description: The climate change and the proceeding urbanization create future health challenges. Consequently, more people around the globe will be impaired by extreme weather events, such as heat waves. This study investigates the causes for the emergence of surface urban heat islands and its change during heat waves in 70 European cities. A newly created climate class indicator, a set of meaningful landscape metrics, and two population-related parameters were applied to describe the Surface Urban Heat Island Magnitude (SUHIM) – the mean temperature increase within the urban heat island compared to its surrounding, as well as the Heat Magnitude (HM) – the extra heat load added to the average summer SUHIM during heat waves. We evaluated the relevance of varying urban parameters within linear models. The exemplary European-wide heat wave in July 2006 was chosen and compared to the average summer conditions using MODIS land surface temperature with an improved spatial resolution of 250 m. The results revealed that the initial size of the urban heat island had significant influence on SUHIM. For the explanation of HM the size of the heat island, the regional climate and the share of central urban green spaces showed to be critical. Interestingly, cities of cooler climates and cities with higher shares of urban green spaces were more affected by additional heat during heat waves. Accordingly, cooler northern European cities seem to be more vulnerable to heat waves, whereas southern European cities appear to be better adapted. Within the ascertained population and climate clusters more detailed explanations were found. Our findings improve the understanding of the urban heat island effect across European cities and its behavior under heat waves. Also, they provide some indications for urban planners on case-specific adaptation strategies to adverse urban heat caused by heat waves.
    Keywords: Surface Urban Heat Island Magnitude ; Modis ; Landscape Metrics ; Heat Waves ; Urban Green ; Climate Class Indicator ; Environmental Sciences ; Biology ; Public Health
    ISSN: 0048-9697
    E-ISSN: 1879-1026
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  • 5
    Language: English
    In: Marine Pollution Bulletin, 15 September 2016, Vol.110(1), pp.250-260
    Description: The European Water Framework Directive requires a good ecological potential for heavily modified water bodies. This standard has not been reached for most large estuaries by 2015. Management plans for estuaries fall short in linking implementations between restoration measures and underlying spatial analyses. The distribution of emergent macrophytes – as an indicator of habitat quality – is here used to assess the ecological potential. Emergent macrophytes are capable of settling on gentle tidal flats where hydrodynamic stress is comparatively low. Analyzing their habitats based on spatial data, we set up species distribution models with ‘elevation relative to mean high water’, ‘mean bank slope’, and ‘length of bottom friction’ from shallow water up to the vegetation belt as key predictors representing hydrodynamic stress. Effects of restoration scenarios on habitats were assessed applying these models. Our findings endorse species distribution models as crucial spatial planning tools for implementing restoration measures in modified estuaries.
    Keywords: European Water Framework Directive ; Northern Sea Estuaries ; Phragmites Australis ; Scirpus Maritimus ; Scirpus Tabernaemontani ; Restoration Management ; Environmental Sciences ; Oceanography
    ISSN: 0025-326X
    E-ISSN: 1879-3363
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  • 6
    Language: English
    In: IEEE Transactions on Geoscience and Remote Sensing, June 2014, Vol.52(6), pp.3453-3463
    Description: Ancillary geodata can supply information to enhance classification accuracy for a variety of remote-sensing applications. To understand the integration of different data into a knowledge-based multisource classification process, this paper evaluates the significance of geodata for the classification accuracy of a very high spatial resolution satellite image for the identification of forest types in Germany. The approach utilizes a fuzzy-logic classifier for the integration of a knowledge base, which combines spectral information with ancillary data layers. The results of the classification were used to test a method for evaluating the influence of the integration of single geodata, the effects on different classes, and the impacts of the applied rules. A microarray significance analysis (MSA) was used to evaluate the significance of the classification results, whereas an ISODATA clustering was utilized for visualizing. A sequence of 50 accuracy assessments of classifications with possible combinations of geodata and rules for the identified classes was derived. The resulting microarray of accuracy percentages of single classes and the overall classification was used for further investigation. The MSA supplies the measure of significance, called relative difference d(i). The MSA identified 11 classifications of positive significance (d(i) greater than 1.44) and three classifications of negative significance (d(i) lower than -2.87). In particular, classifications that contain all rules were rated as positive significant.
    Keywords: Accuracy ; Knowledge Based Systems ; Remote Sensing ; Soil ; Vegetation Mapping ; Training ; Image Segmentation ; Additional Information ; Fuzzy Logic ; Geodata ; Image Classification ; Microarray Significance Analysis (Msa) ; Object-Based Image Analysis ; Quickbird ; Engineering ; Physics
    ISSN: 0196-2892
    E-ISSN: 1558-0644
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  • 7
    Language: English
    In: Computers and Geosciences, 2015, Vol.84, p.86(10)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cageo.2015.08.006 Byline: Simon Nieland, Niklas Moran, Birgit Kleinschmit, Michael Forster Abstract: Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology. The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study. Author Affiliation: Geoinformation in Environmental Planning Lab, Technische Universitat Berlin, Stra[sz]e des 17. Juni 145, 10623 Berlin, Germany Article History: Received 23 February 2015; Revised 14 June 2015; Accepted 24 August 2015
    Keywords: Remote Sensing ; Biodiversity
    ISSN: 0098-3004
    Source: Cengage Learning, Inc.
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  • 8
    Language: English
    In: Remote Sensing of Environment, Feb 5, 2014, Vol.141, p.52(12)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.rse.2013.10.018 Byline: Adina Tillack, Anne Clasen, Birgit Kleinschmit, Michael Forster Abstract: The leaf area index (LAI), as a key indicator of physical and biological processes related to vegetation dynamics, is valuable in monitoring the biomass of forests. Based on the phenological development of trees, the LAI shows high seasonal variability. This study estimated the LAI through field measurements and satellite-derived spectral vegetation indices (SVIs) in two alluvial forest sites at species level (black alder). The primary objective of this study was the validation of seasonal relationships between field-measured LAI, using a LI-COR 2200 plant canopy analyzer (PCA), and four red edge and non-red edge satellite-derived spectral vegetation indices (SVIs) of 10 high spatial resolution RapidEye images: the normalized difference vegetation index (NDVI), the red edge NDVI (NDVI-RE), the modified red edge simple ratio (mSR-RE), and the curvature. The indices were compared using 4 phenological phases (leaf flushing until crown closure, leaf growth under crown closure, decreasing leaf chlorophyll content, and leaf senescence) over the entire vegetation period in 2011 using regression analyses, t-test and root mean square error (RMSE). The results suggest that the LAI-SVI relationships varied seasonally. Strong to weak linear relationships were obtained during different periods. For each phase, a different SVI fitted best: NDVI-RE during leaf flushing until crown closure (R.sup.2 =0.62, RMSE=0.47), mSR-RE during leaf growth under crown closure (R.sup.2 =0.422, RMSE=0.71), NDVI-RE during decreasing leaf chlorophyll content (R.sup.2 =0.182, RMSE=0.58), and NDVI during leaf senescence (R.sup.2 =0.829, RMSE=0.53). Thus, implementing the red edge channel improved the LAI-SVI relationships, particularly during periods with few variations in the LAI. An analysis of the entire vegetation period revealed that NDVI had the best regression (R.sup.2 =0.942, RMSE=0.507) because it was the most stable index due to moderate LAI values (average max. LAI=4.63). The satellite-based vegetation indices used in this study provided reliable estimates and described the temporal changes and spatial variability in the LAI well. It can be concluded that a LAI-SVI relation cannot be established by a single linear regression throughout a year. Hence, a multi-temporal approach is recommended when monitoring alluvial forest dynamics. Future research on estimating the LAI based on satellite imagery should include the phenological phases into the calculation. Article History: Received 21 November 2012; Revised 15 October 2013; Accepted 17 October 2013
    Keywords: Remote Sensing -- Analysis ; Vegetation Dynamics -- Analysis ; Chlorophyll -- Analysis
    ISSN: 0034-4257
    Source: Cengage Learning, Inc.
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  • 9
    Language: English
    In: Journal of Urban Planning and Development, March, 2014, Vol.140(1), p.4013003(12)
    Description: As climate change mitigation becomes pervasive on all spatial scales, mitigation options related to urban spatial planning and behavioral change become increasingly important. Because transport energy consumption seems to scale inversely with population density, increased attention focuses on the role of urban form. This study specifically analyzes the importance of population density for the reduction of urban greenhouse gas emissions in Europe. For this, drivers of both carbon dioxide (C[O.sub.2]) emissions from transport (for 134 cities) and total urban greenhouse gas emissions (C[O.sub.2]eq emissions) of 62 cities across Europe are investigated. Results indicate that population density is not, per se, a strong determinant of greenhouse gas emissions in European cities. Crucially, the spatial scale of the analysis matters and national influences modulate C[O.sub.2]eq emissions in the analyzed urban areas. Results show that greenhouse gas emissions of European urbanites increase significantly with decreasing household sizes and increasing personal wealth. Although the results are bound by data quality, it is assumed that the relative similarity of European cities is also leading to a lesser degree of importance of population density with respect to climate change mitigation. The results further encourage more thorough analyses of the role of household size and personal wealth for effective mitigation of climate change, additional spatially explicit econometric studies, and detailed, city-specific causal models of urban areas. DOI: 10.1061/(ASCE)UP.1943-5444.0000165. Author keywords: European cities; Climate change mitigation; Urban greenhouse gas emissions; Population density; Urban design.
    Keywords: Climate Change -- Environmental Aspects ; Climate Change -- Control ; Population Density -- Research ; Environmental Impact Analysis -- Methods ; Sociological Research
    ISSN: 0733-9488
    Source: Cengage Learning, Inc.
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  • 10
    Language: English
    In: International Journal of Applied Earth Observations and Geoinformation, Sept, 2014, Vol.31, p.110(12)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jag.2014.03.004 Byline: Philipp Gartner, Michael Forster, Alishir Kurban, Birgit Kleinschmit Abstract: acents We detect changes of Populus euphratica tree crowns by means of two very high spatial resolution imageries. acents We examine changes at the tree crown level by means of OBIA approach. acents We evaluate accuracies for tree crown diameter measures with reference data of visual tree delineation and field-based tree crown measurements. Author Affiliation: (a) Geoinformation in Environmental Planning Lab, Technische Universitat Berlin, Berlin 10623, Germany (b) Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830046, China Article History: Received 2 October 2013; Accepted 10 March 2014
    Keywords: Remote Sensing
    ISSN: 0303-2434
    Source: Cengage Learning, Inc.
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