Abstract
In this study, data from the satellite sensors TanDEM-X and Sentinel-2 were combined with national field inventory data to predict forest above-ground biomass (AGB) and stem volume (VOL) over a large area in Sweden. The data sources were evaluated both separately and in combination. The study area covers approximately 20,000,000 ha and corresponds to about 70% of the Swedish forest area. The study area was divided into tiles of 2.5 × 2.5 km2, which were processed sequentially. The field plots were inventoried on 7 m and 10 m circular plots by the Swedish National Forest Inventory, and plot AGB and VOL at the year of the satellite data were estimated based on a 10-year period of field data. The AGB and VOL were modelled using the k nearest neighbor (kNN) algorithm, with k = 5 neighbors. The combined use of two data sources with different scene extents enabled the generation of seamless AGB and VOL maps. Moreover, the kNN algorithm provided the VOL divided per tree species, which was used for classification of the dominant tree species at stand-level. The overall accuracy for the dominant tree species classification was 77%. The predicted AGB and VOL rasters were evaluated using 549 field inventoried forest stands distributed over Sweden. The RMSE for the predictions based on both data sources were 31.4 t/ha (29.1%) for AGB, and 59.0 m3/ha (30.2%) for VOL. By estimating and removing the variance due to sampling (the stand values were estimated from sample plots), the RMSE was improved to 18.0 t/ha (16.6%). The evaluated approach of using kNN was suitable for estimating forest variables from a combination of different satellite sensors, provided sufficient field reference data are available. The TanDEM-X data were most important for the AGB and VOL predictions, while Sentinel-2 data were essential to map the tree species.
Highlights
Remote sensing (RS) has proved invaluable for many sectors that rely on forest, since it enables predictions of variables with complete coverage in terms of raster maps
In Sweden, wall-to-wall raster maps with predictions of common forest variables (e.g., above-ground biomass (AGB), stem volume (VOL), tree height, and basal area) have been produced for the entire country using laser scanning data and field data from the Swedish National Forest Inventory (NFI) (Nilsson et al, 2017)
This study demonstrated the use of the k nearest neighbor (kNN) method with k = 5 neighbors, applied to data from TanDEM-X, Sentinel-2, and their com bination, together with field reference data, to predict above-ground biomass and stem volume of a large area in Sweden
Summary
Remote sensing (RS) has proved invaluable for many sectors that rely on forest, since it enables predictions of variables with complete coverage in terms of raster maps. In Sweden, wall-to-wall raster maps with predictions of common forest variables (e.g., above-ground biomass (AGB), stem volume (VOL), tree height, and basal area) have been produced for the entire country using laser scanning data and field data from the Swedish National Forest Inventory (NFI) (Nilsson et al, 2017). In the past, such maps were produced on a five years cycle (starting in 2000), based on Landsat or SPOT imagery. For efficient co-use of resources, this study is based on the frameworks developed within the two projects
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