The project “Cost-effective methods for tracking large scale vegetation physiology: Participatory phase and pilot experiments”, (Vihreävalo for short in Finnish, Greenlight in English) is a two year project (Oct 2016 – Sep 2018) funded by the Academy of Finland under the key Funding Call. The goal of the project is to to evaluate the feasibility and potential of SIF and PRI for tracking large scale vegetation physiology, and to identify novel and cost-effective methods to optimize vegetation management in real-case scenarios.

Society faces the combined challenge of a rising population increasingly concentrated in urban areas. Food production needs to increase sustainably in response to this challenge by the optimal use of water, fertilizers and pesticides. In addition urban forests are now valued as key ecosystem service providers, with potential to reduce city air pollution levels, temperatures and human stress levels. These developments require the adoption of modern vegetation management practices to rapidly and accurately monitor the dynamic health status of vegetation.

With the advent of unmanned aerial vehicles (UAVs) and hyperspectral imaging systems it is now possible to acquire optical information from vegetation anywhere and anytime. Optical indices such as greenness are being applied in the context of precision agriculture to estimate plant biomass or nitrogen content. However, variations in greenness (e.g. the yellowing of diseased leaves) reflect only the slow response of plants to stress making them less suitable for vegetation monitoring. Vegetation generates additional optical signals that cannot be seen with the naked eye and require the use of hyperspectral systems, e.g. the emission of solar-induced chlorophyll fluorescence (SIF) or the photochemical reflectance index (PRI). In contrast to greenness, SIF and PRI respond instantaneously to the plant’s physiological status making these indices particularly suitable for pre-visual stress detection and optimization of fertilizer, irrigation or pesticide application. Despite the intrinsic potential of SIF and PRI their use remains stuck at the basic research level or focused on scientific activities, largely due to the difficulties of interpreting the data.

In this project we will apply our models and  know-how to evaluate the feasibility and cost-effectiveness of SIF and the PRI for tracking large scale vegetation physiology in a number of real-case scenarios. Together with end-users and key stakeholders, we have identified real case-studies (both in city parks and farms) that will be used to conduct pilot activities during the project. Stakeholders and end-users from applied research institutes, industry, service and agricultural sectors, as well as city parks departments will be involved in the project.

Budget: c. 300 k€