Several data collection activities related to forest cover, forest biomass and forest management have been coordinated by IIASA’s NoDES group in the past

Data on forests have been contributed by forestry experts from different regions around the world. The data are wide-ranging in nature, including in situ forest measurements and visual interpretation of very high-resolution imagery, including forest management. The primary motivation behind the data collection is for the calibration and validation of remote sensing products (i.e., forest, biomass and forest management maps), but we also envisage broader areas of application such as forest growth models, determination of climate change impacts, etc. Two key outputs are described in more detail below.

Global forest extent map

A number of global and regional maps of forest extent are available, but when they are compared spatially, there are large areas of disagreement between them. Moreover, in the past, there was no global forest map that was consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining the diverse set of data sources on forest extent into a single forest cover product, it was possible to produce a global forest map that is more accurate than the individual input layers, and it represents the first map that is consistent with FAO statistics.

We used the Geo-Wiki interface to visually interpret very high-resolution satellite and aerial imagery. The percentage forest cover was recorded at 21,975 locations globally (1 km pixels for the reference year 2000). The data collected were then used for calibration using a geographically weighted regression (GWR) model to integrate eight different forest products into global hybrid forest cover maps at a 1 km resolution for the reference year 2000. The input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where they were available, and the MODIS Vegetation Continuous Fields product. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input data sets. The data sets are available from Zenodo (Schepaschenko et al., 2015a) while details of the development of the data sets can be found in Schepaschenko et al. (2015b, 2019).

Data Set:

Schepaschenko D., See L., Lesiv M., McCallum I., Fritz S., et al. (2015a) Global hybrid forest mask for the year 2000 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5559652

Schepaschenko D., See L., Lesiv M., McCallum I., Fritz S., et al. (2015b) Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sensing of Environment, 162: 208-220. https://doi.org/10.1016/j.rse.2015.02.011

Schepaschenko D., See L., Lesiv M., Bastin J.-F., Mollicone D., Tsendbazar N.-E., Bastin L., McCallum I., et al. (2019). Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics 40 (4) 839-862. https://doi.org/10.1007/s10712-019-09533-z

The Forest Observation System (FOS)

The Forest Observation System (FOS) is an international cooperation to establish and maintain a global in situ forest biomass database (Schepaschenko et al. 2019b). Above ground biomass (AGB) and canopy height estimates, with their associated uncertainties, has been derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many remote sensing measurements. The FOS offers the potential to improve the accuracy of remotely sensed biomass products (Chave et al., 2019) while developing new synergies between the remote sensing and ground-based ecosystem research communities.

The live database, which is updated regularly, is available from the FOS portal. A snapshot of the database (Schepaschenko et al., 2019a) consists of 1645 sample plots from 19 countries ranging from latitudes of 36⁰S to 65⁰N and longitudes from 84⁰W to 149⁰E.

Data Set:

Schepaschenko D, Chave J, Phillips OL, Lewis SL, Davies SJ, Réjou-Méchain M, Sist P, Scipal K, et al. (2019a). A global reference dataset for remote sensing of forest biomass. The Forest Observation System approach. [Data Collection]. IIASA DARE https://doi.org/10.22022/ESM/03-2019.38

Schepaschenko D., Chave J., Phillips O.L., Lewis S.L., Davies S.J., Réjou-Méchain M., Sist P., Scipal K., et al. (2019b). The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Scientific Data 6 (1): 198. https://doi.org/10.1038/s41597-019-0196-1

Chave J., Davies S.J., Phillips O.L., Lewis S.L., Sist P., Schepaschenko D., et al. (2019) Ground Data are Essential for Biomass Remote Sensing Missions. Surveys in Geophysics 40: 863–880 https://doi.org/10.1007/s10712-019-09528-w

A map of forest management

Information on forest management at a global scale is needed for sustainable planning, restoration activities and implementing conservation measures. Although some data sets are available, a global map with multiple harmonized categories was lacking until recently. The global map of forest management produced at IIASA is the product of several crowdsourcing campaigns with Geo-Wiki, involving both forest experts and the Geo-Wiki network of volunteers. Using very high-resolution satellite imagery and information on time series, we collected reference data on forest management at 226,322 locations globally. Using these data, we developed a spatially explicit forest management map at a 100 m resolution for the year 2015 using machine learning. Details of this data set are available in Lesiv et al. (2022).

Data Set available from Zenodo.

Lesiv, M., Shchepashchenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F. , et al. (2022). Global forest management data for 2015 at a 100 m resolution. Scientific Data 9 (1) e199. 10.1038/s41597-022-01332-3.