<p>Green spaces, from small-scale structures such as green roofs and individual trees in cities to large grasslands and forests, fulfill climate-relevant, ecological and social functions. The protection and monitoring of these spaces as well as dissemination and awareness raising in the field of nature conservation is of &#160;socio-politically relevant concern. The project SEMONA RELOADED (funded by the Austrian Research Promotion Agency, FFG) aims to identify these functions through inventories and change detection. The classification and monitoring of areas with biodiversity worthy of protection (e.g. Natura 2000), as well as green infrastructure in settlement areas (e.g. green space monitoring of the City of Vienna - GRM) are obligatory within the framework of nature conservation laws and are also required within the framework of national and international reporting obligations. Currently, such studies are often based on expert-based mapping in the field (biotope types) and/or indices derived from individual remote sensing data.<br />The motivation for SEMONA RELOADED is to support this labor-intensive process by linking regionally available very high spatial resolution remote sensing data such as airborne laser scanning (ALS) and aerial photography (AP) with high temporal resolution sentinel data (S1, S2). In addition to assisting with the initial identification and classification of green space, including remote sensing data in the workflow should enable constant monitoring of the areas. This builds on successful results from the feasibility study completed in 2021 (SeMoNa22).&#160;<br />The processing of test areas in Vienna has shown that the combination of S1 and S2 as well as high-resolution AP and ALS data has high potential for the differentiation of biotope types and green infrastructure in urban areas. By training classification algorithms using combined features, different biotope types could be successfully identified in test areas. In the inner-city area, green roofs could be successfully identified as a sub-area of green infrastructure monitoring better than with previously applied methods.<br />In the presented follow-up study, the research area is enlarged to a regional scale including the protected areas of Nationalpark Donau-Auen, the Vienna Woods Biosphere Reserve and the Natura 2000 area Wachau, the City of Krems as well as the whole area or the City of Vienna. In addition, different Stakeholders (provincial administration, national park and biosphere park administration, federal forestry office) are included in the research process to ensure the applicability of the developed methods for the applied use in mapping and monitoring.&#160;<br />In the presented poster, the relevant outcomes of the previous feasibility study will be presented and an overview of the planned research activities of the current SEMONA RELOADED project will be given.&#160;</p>
Abstract. Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union’s Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1 m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3 × 1.3 km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.
<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna&#8217;s city area were covered, however only 2&#160;% of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (&#8594; time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (&#8594; multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto &#8594; various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>
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