Geology

Forest management

Mathematics

Biology

Boreal

Statistics

Mean squared error

Forest inventory

Geography

Ecology

Forestry

Environmental science

Agroforestry

Remote sensing

Taiga

Basal area

Remote Sensing of Environment

We compared the performance of Sentinel-2 and Landsat 8 data for forest variable prediction in the boreal forest of Southern Finland. We defined twelve modelling setups to train multivariable prediction models with either multilayer perceptron (MLP) or regression tree models with the brute force forward selection method. The reference data consisted of 739 circular field plots that had been collected by the Finnish Forest Centre concurrently with the Sentinel-2 and Landsat 8 acquisitions. The input data were divided into training, validation and test sets of equal sizes for 100 iterations in each modelling setup. The predicted forest variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We recorded the performance figures and the best predictive image bands for each modelling setup. The best average performance over the 100 modelling iterations was obtained using all Sentinel-2 bands. The plot-level relative root mean square errors (RMSE%) of the field observed mean were 38.4% for average stem diameter, 42.5% for stem basal area/ha, 30.4% for average tree height, and 59.3% for growing stock volume/ha with variables including all tree species. The corresponding best figures with all Landsat 8 bands were RMSE% = 44.6%, 50.2%, 36.6% and 72.2%, respectively. The Sentinel-2 outperformed Landsat 8 also when using near-equivalent image bands and Sentinel-2 data down-sampled to 30 m pixel resolution. The relative systematic error (bias%) did not show any significant differences between Sentinel-2 and Landsat 8 data: the average of the absolute value of bias% was 0.8% for Sentinel-2 and 1.2% for Landsat 8. The best predictive Sentinel-2 image band was the red-edge 1 (B05_RE1), when variable totals including all species were estimated. The short-wave infrared bands (B11_SWIR1 & B12_SWIR2) and the visible green band (B03_Green) were also among the best predictors. The median number of predictors in the best performing models was 4–6 for the Sentinel-2 and 4–5 for the Landsat 8 models, respectively. We conclude that Sentinel-2 Multispectral Instrument (MSI) data can be recommended as the principal Earth observation data source in forest resources assessment.

Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region