The Anthropocene is characterized by climate change, biodiversity loss, and depletion of ecosystem services. In Europe, traditional orchards (Streuobstwiesen) are an important agroecosystem that provide many ecosystem services (e.g. fruit production, pollination and carbon fixation). However, pressure on these systems is high due both land use intensification and abandonment, and monitoring is challenging as they are small landscape elements with scattered distribution (>20000 orchards in the Federal State of Hesse (21.115 km²)). The main objective is to develop a remote sensing based monitoring system to map orchard condition. So far, we have gathered 3D-laserscanning data for Hesse for 2010 and 2019, and in our first case studies, we mapped key indicators such as number of trees, height and crown size (proxies for age and carbon sequestration), and overgrowing trees (indicator of abandonment) using machine learning. Further, we have also mapped temporal changes using historical images for parts of Hesse, and we collected first drone data. Regarding impacts, using these results we convinced one important stakeholder, the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG), to fund a 3-year-research project, and we convinced landscape trusts as local stakeholders and multiplicators to join the project and share data and methodologies.

https://www.hlnug.de/themen/naturschutz/biodiversitaetsforschungsfonds/streuobstwiesen

• Federal State of Hesse, Germany

• Remote and rural communities
• Conservationists, Decision makers

We will use mostly free and open source software as well as public data in this project, and our workflows will be well documented e.g. through tutorials which we will also use in our regular teaching activites with local and international students/partners. Where Laserscanning data is available, our approach can be directly replicated. Of course, the availability of LiDAR data across countries is limited. However, such data can also be generated using low-cost drones which are widely available, and we will test the use of drones in our project. Another limitation could be computer power. This can be overcome through the use of free software such as R that can be run on servers.

The project supports sustainability in various ways. First, we use public data and free and open source software data, and we will produce tutorials. So the knowledge gained in this project can be used in future projects, too. Second, the findings of the project will be communicated and made available to stakeholders from local to Federal State level. We will also discuss our methodological approaches wirh these stakeholders. Third, we will provide baseline data for the status of orchards (e.g. number of trees and their height (as a proxy for age) for two time steps (2010 and 2019) using laserscanning data. So we can describe the past and current status to guide management. Using 3D data, we will also generate baseline information to estimate the carbon sequestration for the orchards, which is highly relevant ecosystem service of orchards in times of climate change. By analysing historical aerial imagery from the 1960s, we will identify trends of changes, and by using novel technologies such as drones and mobile lidar systems we will explore how a sustainable monitoring in the future could lool like. Last, but not least, we aim to promote this traditional land use systems as it provides several further ecosystem services related to culture and biodiversity.

In this project, we will use novel technologies such as 3D laserscanning, drones and machine learning using free software to gather information on the status of a traditional land use system (orchards), which potentially will play an important role to increase the resilience of our landscapes against future environmental challenges. Accordingly, agroforestry has just returned to the political agenda, i.e. in Germany. So, we primarily support the environmental sustainability aspect of the WISIS values. Firstly, we promote these values by sharing our knowledge with relevant stakeholders from site managers to Federal authority. Beside the science aspect, laserscanning and drone data are very strong means of communication. Especially in remote or rural areas, where orchards are abandoned, the use of novel technologies could emphasize the importance of these ecosystems, and could show that those regions are also very important for sustainable development and high-tech research. By extracting ecological knowledge from the digital geodata using machine learning, we also aim to promote good governance and democratic decision making by providing a tool that could partly objective the assessment of ecosystem degradation. Further, we will produce tutorials for our workflows for our teaching courses, which are also available together with the data to stakeholders. The main aim of these courses is to support the geospatial literacy of the students. Some courses are also attended by international students from development countries, so there is potential for technology and knowledge transfer. Finally, while women possibly are underrepresented in the remote sensing community, in our newest course, there is a positive bias towards female students, and we strongly support female tutors for the students as role models. Thus, this project promotes WSIS values at different levels by cooperating with relevant stakeholders, using novel technologies, respecting gender aspects, supporting capacity building and knowledge sharing.