A: This was chosen together with our executive partners in Indonesia and Gabon as a workable scale. This means that it is not too large so that it cannot be easily covered in the field and not be too small so that there would be way too many predictions
A: This was chosen together with our executive partners in Indonesia and Gabon as a workable timeframe. Not too far in the future to be too vague to handle on, but not too short-term to not be able to make preparations for an intervention. We do produce preprocessed ground-truth data for one, three and twelve months as well for anyone who wants to build a model on those timeframes by using the open source path.
A: this has two reasons. The first is the coverage of the Early Warning System (the Integrated Alerts of Global Forest Watch) that we rely on to make our predictions. The second and more important reason is the huge biodiversity of the forests in this region and the urgency to preserve this area.
A: We only make predictions in areas that have forest according to the GLAD classification of 2000 with all tree cover loss removed between 2000 and 2020. This is because GLAD-L (a Landsat-based Early Warning System) also predicts in newly grown forest, which can also be plantations. We from Forest Foresight focus on primary tropical forest in our deforestation predictions and thus do not take all Integrated Alerts into account by using this mask.
A: We deem Global Forest Watch to be an independent and reliable source. Also, we work together with the Wageningen University in the Netherlands to further improve our predictions and they are the developers of the RADD alert system which is integrated in the Integrated Alerts.
A: We use all alerts, regardless of confidence level . If we would not include the low confidence alerts in our predictions/labeled (or ground-truth) data then this would be a loss because most of these alerts turn out to be actual deforestation. We also do not want to exclude them from our training data because then the system would be trying to predict these low confidence alerts as well without having the full knowledge available from the Early Warning System. We do include the confidence level as a feature for training but deem this to be most fair.
A: The definition of illegal is very hard to know without full local knowledge. Still then it can be vague and debatable, depending on the stakeholder. We find it important that whoever has the resources to prevent deforestation has all the information available and can then filter our predictions based on what they deem to find important.
A: Since our predictions have to cover everything and over a large area it is better to have wall-to-wall covering of our ground-truth dataset and for a longer period of time than to have high-quality sparse data for a short timeframe and a small area. The underlying datasets of the Integrated Alerts have all shown to be accurate, especially with regards to precision/user’s accuracy, for over 90%.
A: Given the work that goes into preparing a field intervention by our partners we have found that it is more important to be very sure about where deforestation will take place than to cover everything. Experience has taught us that most implementing partners do not have the resources to tackle the majority of the predicted forest loss
A: The two major reasons are that deforestation is a term well known to the general public. The second one is that it is really hard to prove with a Predictive Warning System based on an Early Warning System whether forest loss is permanent and whether the spatial scale means that it is complete or only part removal.
A: An Early Warning System is used as a general term in two instances: to alert before an event happens or to alert as soon as possible when an event has happened. Though the term Early Warning System is used way more often than our Predictive Warning System we have experienced that the term is mostly used to describe the latter (report as soon as possible after the fact). By changing the name of our System we want to differentiate ourselves from that.