| 1. |
- Persson, Henrik, et al.
(författare)
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Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 96
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Tidskriftsartikel (refereegranskat)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 x 2.5 km(2), 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 m(3)/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.
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| 2. |
- Persson, Henrik, et al.
(författare)
-
Combining TanDEM-X, Sentinel-2 and field data for prediction of species-wise stem volumes
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this study, stem volume measured by the Swedish National Forest Inventory were modelled using the k nearest neighbor (kNN) algorithm, with k=1, 3, or 5 neighbors. As independent variables, the combination of two satellite sensors were used: the active radar sensor TanDEM-X and the passive optical sensor Sentinel-2. The results indicate that stem volume per species can be predicted relatively accurately, mainly due to the inclusion of Sentinel-2 data, while the total stem volume is largely predicted well due to inclusion of the TanDEM-X phase height. The prediction of total stem volume was, however, not significantly improved with the additional spectral information from Sentinel-2 about the tree species. The kNN method is somewhat limited in the highest range of volumes, since no extrapolation is supported. Thus, it is important to have a reference dataset representing the entire range of the population for a successful application. The main advantage of combining the two data sources is the convenient procedure of obtaining both the tree species classification and volumes (divided per species) in a single method. It is concluded, that when sufficient reference data are available, the kNN approach with a combination of radar and optical data provides additional information about the stem volumes (in terms of tree species), but without improving the prediction of the total stem volume accuracy.
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| 3. |
- Wallerman, Jörgen, et al.
(författare)
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Nation-wide mapping of tree growth using repeated airborne laser scanning
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this study, mapping of tree growth was performed using data from the two nation-wide acquisitions of airborne laser scanning in Sweden. Following the successful first national acquisition performed in 2009 - 2015, a new, repeated, scanning is now launched and ongoing. The first scanning provided new, accurate (in accuracy as well as in spatial resolution) data about the forest and quickly found wide-spread use in the forest industry. It outperformed previous methods and provided a new standard of data capture for forest management planning. The addition of a second scanning provide information also about changes, where forest tree growth is of high interest in the industry. This study presents the first results from large-scale assessment of growth for basal area-weighted mean tree height (H) and mean stem volume (V), using the bi-temporal scannings and sample-plot data from the National Forest Inventory. Growth was most accurately assessed by the direct change metrics of the scannings, although the accuracies were moderate. The accuracy of forecasts, i.e. only utilizing the predicted forest state at the first scanning, were similar for H but inferior for V, though.
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| 4. |
- Wallerman, Jörgen, et al.
(författare)
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SLU Forest Map - Mapping Swedish Forests Since Year 2000
- 2021
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Konferensbidrag (refereegranskat)abstract
- SLU Forest Map are maps of the Swedish forest state, produced by the Swedish University of Agricultural Sciences (SLU) from satellite images using the Swedish National Forest Inventory sample plots as reference data. Until now, four maps have been produced, in raster format (12.5×12.5 m2 to 25×25 m2 cell sizes), with estimates of basal area-weighted mean tree height, basal area-weighted mean stem diameter, stand age, total as well as species-specific stem volume, for the years 2000, 2005, 2010, and 2015. These maps provide publicly available data, free of charge, supporting a wide range of applications; scientific research as well as operational uses in forest management planning, biodiversity assessment, and monitoring. This paper presents SLU Forest Map, the data and methods utilised in the production, and a new consistent evaluation of the estimation accuracy for each variable and mapped year.
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