National mapping and estimation of forest area by dominant tree species using Sentinel-2 data

Abstract

Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine- and deciduous-dominated forest in Norway at a 16m×16m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42,000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by approximately 10%. The produced map was subsequently used to generate model-assisted (MA) and post stratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.

The Norwegian Forest Resource Map SR16: Mapping Forest Attributes Using Heterogeneous Sets of Airborne Laser Scanning and National Forest Inventory Data

“…The availability of remotely sensed data -such as from airborne laser scanning (ALS) or satellite imagery -opens up new possibilities for NFIs (McRoberts and Tomppo, 2007). By combining the field registrations in the NFI with remotely sensed data, estimates can be improved (McRoberts et al, 2002) which, for example, enables estimates for smaller regions or even local scale (Breidenbach et al, 2021;Breidenbach and Astrup, 2012). The principle is to model the relationship between field measured forest attributes and metrics derived from the remotely sensed data.…”

“…SR16 is planned to be updated annually by utilizing the newest NFI data and canopy cover loss maps (Hansen et al, 2013). The latter also opens up for new map layers such as annual volume and biomass loss (Breidenbach et al, 2021;NIBIO, 2020) which in Norway is largely driven by harvests.…”

“…do also occur, typically towards the mountains. More information on the spatial distribution of the main tree species in Norway can be found in Breidenbach et al (2021).…”

“…For the predictions we used a map of the main species in order to decide which strata a prediction unit (pixel) belonged to. The species map is based on Sentinel-2 and other auxiliary data, and is further described in Breidenbach et al (2021). As an alternative to using the field observed species, we also tried the predicted tree species for stratifying the model dataset.…”

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

“…Tree species map The tree species layer (Breidenbach et al 2020b) of the Norwegian Forest Resource Map SR16 (Astrup et al 2019) with a 16 m × 16 m pixel size was used to apply our age models for spruce, pine, and birch pixels. The tree species map was based on multitemporal S2 data and the random forests classifier using NFI plots as a reference.…”