TY - JOUR A1 - Böhnke, Denise A1 - Krehl, Alice A1 - Moermann, Kai A1 - Volk, Rebekka A1 - Lützkendorf, Thomas A1 - Naber, Elias A1 - Becker, Ronja A1 - Norra, Stefan T1 - Mapping urban green and its ecosystem services at microscale-a methodological approach for climate adaptation and biodiversity JF - Sustainability / Multidisciplinary Digital Publishing Institute (MDPI) N2 - The current awareness of the high importance of urban green leads to a stronger need for tools to comprehensively represent urban green and its benefits. A common scientific approach is the development of urban ecosystem services (UES) based on remote sensing methods at the city or district level. Urban planning, however, requires fine-grained data that match local management practices. Hence, this study linked local biotope and tree mapping methods to the concept of ecosystem services. The methodology was tested in an inner-city district in SW Germany, comparing publicly accessible areas and non-accessible courtyards. The results provide area-specific [m(2)] information on the green inventory at the microscale, whereas derived stock and UES indicators form the basis for comparative analyses regarding climate adaptation and biodiversity. In the case study, there are ten times more micro-scale green spaces in private courtyards than in the public space, as well as twice as many trees. The approach transfers a scientific concept into municipal planning practice, enables the quantitative assessment of urban green at the microscale and illustrates the importance for green stock data in private areas to enhance decision support in urban development. Different aspects concerning data collection and data availability are critically discussed. KW - climate adaptation KW - urban green KW - mapping KW - ecosystem service cascade KW - model KW - surface type-function-concept KW - planning indicators KW - city district KW - level KW - urban planning practice KW - climate change Y1 - 2022 U6 - https://doi.org/10.3390/su14159029 SN - 2071-1050 VL - 14 IS - 15 PB - MDPI CY - Basel ER - TY - JOUR A1 - Wilhelms, Andre A1 - Börsig, Nicolas A1 - Yang, Jingwei A1 - Holbach, Andreas A1 - Norra, Stefan T1 - Insights into phytoplankton dynamics and water quality monitoring with the BIOFISH at the Elbe River, Germany JF - Water N2 - Understanding the key factors influencing the water quality of large river systems forms an important basis for the assessment and protection of cross-regional ecosystems and the implementation of adapted water management concepts. However, identifying these factors requires in-depth comprehension of the unique environmental systems, which can only be achieved by detailed water quality monitoring. Within the scope of the joint science and sports event "Elbschwimmstaffel" (swimming relay on the river Elbe) in June/July 2017 organized by the German Ministry of Education and Research, water quality data were acquired along a 550 km long stretch of the Elbe River in Germany. During the survey, eight physiochemical water quality parameters were recorded in high spatial and temporal resolution with the BIOFISH multisensor system. Multivariate statistical methods were applied to identify and delineate processes influencing the water quality. The BIOFISH dataset revealed that phytoplankton activity has a major impact on the water quality of the Elbe River in the summer months. The results suggest that phytoplankton biomass constitutes a substantial proportion of the suspended particles and that photosynthetic activity of phytoplankton is closely related to significant temporal changes in pH and oxygen saturation. An evaluation of the BIOFISH data based on the combination of statistical analysis with weather and discharge data shows that the hydrological and meteorological history of the sampled water body was the main driver of phytoplankton dynamics. This study demonstrates the capacity of longitudinal river surveys with the BIOFISH or similar systems for water quality assessment, the identification of pollution sources and their utilization for online in situ monitoring of rivers. KW - water quality KW - phytoplankton KW - river dynamics KW - multisensor system KW - online KW - monitoring KW - high spatial resolution KW - multivariate statistics Y1 - 2022 U6 - https://doi.org/10.3390/w14132078 SN - 2073-4441 VL - 14 IS - 13 PB - MDPI CY - Basel ER -