Prioritization of Near-Real-Time Forest Alerts to suit User-Specific Needs

Organised by Laboratory of Geo-information Science and Remote Sensing

Thu 28 April 2022 09:00 to 09:30

Venue Gaia, building number 101
Room 1

By Louisa Heilinger

Monitoring forest disturbances and deforestation all around the globe has become an important tool to combat biodiversity loss, ecosystem degradation and to reduce greenhouse gas emissions. To identify when and where forest disturbance is happening as soon as possible is essential to stopping it. In recent year, several systems evolved that monitor forest disturbance in near-real-time (NRT). Among them is the RADD system, which provides forest alerts every 6 to 12 consecutive days and can accurately map disturbance events. However, for such a system to be effective, the alerts must be prioritized to enable the users of the data to act upon them. As different users of the data have different needs towards prioritization, the research identified the user needs towards forest alerts. Moreover, this study identified how forest alerts can be prioritized to suit different user needs and how this prioritization can be developed and applied to suit the identified needs of a specific user group (park rangers) in the context of the Congo. The methods used in the research included literature review, analysis of interviews with key experts and geospatial analysis in Google Earth Engine (EE). The research identified a wide range of users of the forest alerts, including users in the field, decision makers, private sector users, users that inform the public and technical users. The main use of the alerts includes investigating illegal activities, managing protected areas, asserting land rights and community forest monitoring, monitoring, and enforcing conservation agreements, monitoring deforestation and concessions and raising public awareness. The user needs of park rangers and private sector timber companies were identified, focusing on spatial and technical needs. Existing prioritization techniques were explored, and it has been determined which existing prioritization method suits the identified user needs the most. Based on this analysis, it was outlined which components a prioritization technique should entail and how the existing prioritization method must be adapted to suit the identified user needs. The identified components were implemented in GEE. This resulted in the development of a prioritization method of forest alerts that suits user specific needs. The method is flexible and can be adapted to different user needs by incorporating additional spatial data layers.

The developed system is a work in progress, and although the development of the prioritization technique constitutes an important achievement, the possibility of further developing tools is vital. The developed prioritization is just the first step in an effort to create an automated method for forest alert prioritization that suits user needs. Therefore, additional research should be conducted on several components that make forest alerts more effective.

Keywords: NRT; forest alerts; forest monitoring; RADD; alert prioritization; user-specific prioritization