Collecting training data to map forest management at global scale

Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.

In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.

In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.