Universität Bonn

Center for Remote Sensing of Land Surfaces (ZFL)

03. September 2024

WetlandHealth4UNgoals WetlandHealth4UNgoals

Quantitative optical remote sensing methods for monitoring vegetation stress indicators for assessing ecosystem services and wetland health with regard to global sustainability goals

 

Lake Baringo.jpeg
Lake Baringo.jpeg © Tatenda Chiuya
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WetlandHealth4UNgoals

The aim of WetlandHealth4UNgoals is to develop targeted EO-based information products on vegetation vitality, the condition of wetlands and the associated provision of ecosystem services using the examples of endorheic wetlands in the Rift Valley of Kenya. The project aims to support a sustainable wetland management, as well as to contribute to the fight against global challenges (climate change, disasters, biodiversity loss and land degradation) through implementing an improved monitoring and reporting service.

The project is a collaborative effort between ZFL and UNU-EHS (UNU-EHS | United Nations University) and focuses on several core SDG areas that are closely interrelated. One particular research aspect is on generating insights into wetland health that support loss and damage monitoring in the context of UNFCCC and the Sendai Framework, as well as national SDG reporting.

An important and innovative aspect of the project is vegetation characterization using hyperspectral EnMAP data, radiative transfer models and deep learning to develop highly accurate vegetation stress data products for wetlands. These data products will be transferred for use  of Copernicus Seninel-2 satellite imagery. It is to be expected that a significant gain in scientific knowledge can be achieved, which will also be reflected in scientific publications and UN policy reports.

The ZFL will develop plant traits such as leaf area index or crown chlorophyll content, drought stress or available water content as a remotely sensed and validated data product. During the accompanying field work, a spectral database with the physical input parameters is created in order to create a named training data set for to establish machine learning.

The data access of the data products will be implemented as a web service on a cloud platform. This will be designed so that its services can be used for SDG or ecosystem service reporting.

 

Principal Investigator:

Dr. Michael Schmidt, PD

Scientific Coordinator

Center for remote sensing of land surfaces (ZFL)

Genscher Allee 3

53113 Bonn

+49 (0)228 73-2092

Web: https://www.zfl.uni-bonn.de/

Dr. Yvonne Walz,

Head of Environmental Vulnerability & Ecosystem Services Division

United Nation University - Institute for Environment and Human Security (UNU-EHS), Bonn

Platz der Vereinten Nationen 1
53113 Bonn

Tel.: +49 (0)228 815 0202
e-mail: info@ehs.unu.edu

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