Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated.

There is no doubt that respirometry is a useful measurement principle in the field of wastewater treatment. Although a large variety of methods and case studies have been published, respirometry has become neither a standard tool for control nor for assessment and optimisation of treatment plants. The drawback of the conventional method for determining nitrification kinetics is the long experimental time. This disadvantage can be avoided by “turning over” the experiment. Starting with low ammonia concentrations the steep slope of the Monod curve is measured first. The low concentration branch of the Monod curve is also the part where e.g. inhibition can be detected. Therefore the proposed procedure allows us to speed up the measurement of nitrification kinetics and to measure nitrification inhibition on-line.

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 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.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). 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.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. 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.