D3. Earth Observation (EO) for ecosystem services monitoring

Edyta Woźniak (Space Research Centre of the Polish Academy of Sciences), ewozniak@cbk.waw.pl

Mariasilvia Giamberini (National Research Council of Italy)

Ioannis Manakos (Centre for Research and Technology Hellas)

Lluís Pesquer Mayos (Centre de Recerca Ecològica i Aplicacions Forestals)

Summary

The symposium will be focused on the use of remote sensing for ecosystem services (ES) monitoring. The access to new kind of satellite data and on-line services, as well as, the development of advanced image-processing technologies have opened novel opportunities to track changes in ecosystems and landscapes and their impact on the provided services. During the symposium, new EO methods and applications will be presented.

Description

The ecosystem service concept implies a direct link between biodiversity and human well-being. Research addressing Ecosystem Services (ES) has been rapidly increasing since 2005. However, it is still challenging to quantify and value ecosystem services. Assessing ES, like provisioning, regulation and maintenance or cultural ones, require for quantitative, spatially continuous, and timely information. The lack of such data is very often the main constrain in ES studies. As a consequence, ES are addressed using land-cover based concepts. Earth Observation (EO) can play an important role in filling in the gap. EO techniques can supply, alternatively to currently available point data, continuous surface data in a cost-effective way; allowing for studying cross-scale interactions and for the upscaling from point measurements and individual ecosystems to whole landscapes.

Currently, we are living a Remote Sensing Revolution. For the first time, there is regular access to many types of data, which vary in spatial, spectral and temporal resolutions. On the one hand, there is the Copernicus program with the fleet of Sentinel satellites. On the other hand, many constellations of CubeSats are launched (e.g. Planet, ICEYE, etc.). Also airborne and drone technologies have become more accessible. Moreover, advances in data processing possibilities occur such as cloud computing, big data solutions, artificial intelligence development, etc. All these progresses bring novel opportunities for the monitoring of habitats, landscapes, ecosystems and their services.

Despite these advances and high potentials of EO in ES assessment, its assimilation in everyday business is still quite scarce. Currently, the main applications of EO for ES monitoring are focused on CO2 sequestration evaluation, biodiversity studies and yield estimation. Their limited use is due to a number of reasons – often non-technical. These can be related to training and access to information, or training resources, or policies, as well as with the accounting methodologies of ES and their perception by users. This symposium will bring together experts from fields of remote sensing, life sciences, social sciences and economy to share experiences and new ideas for the use of modern EO technologies in landscape ecology, ecosystem services and their changes’ assessment, including the interactions between geodiversity and biodiversity across a range of scales. The latest applications of EO in ES evaluation will be presented. We will try to determine the possibility of filling gaps of information with the use of EO and their assimilation into novel modelling frameworks able to bridge the scale gap between climate projections, landscape dynamics and local ecosystem changes. Thanks to this symposium we will strengthen the collaboration among researches from different fields (landscape ecology, remote sensing, geoscience, economy, etc.), practitioners and decision makers for the better ES assessment using EO.

Impact

This symposium should deliver intangible outcomes, including enhanced knowledge, improvement of community perception of the capacity of EO for ecosystems services monitoring, increased motivation, exchange of experience, strengthening of links between different communities, networks.