TY - JOUR
T1 - Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region
AU - Astola, Heikki
AU - Häme, Tuomas
AU - Sirro, Laura
AU - Molinier, Matthieu
AU - Kilpi, Jorma
N1 - Funding Information:
We thank the anonymous reviewers for their helpful comments. This study was funded by the Ministry of Agriculture and Forestry of Finland (project OH300-S42100-03) and by VTT Technical Research Centre of Finland Ltd. The field reference data was provided by the Finnish Forest Centre.
Funding Information:
We thank the anonymous reviewers for their helpful comments. This study was funded by the Ministry of Agriculture and Forestry of Finland (project OH300-S42100-03 ) and by VTT Technical Research Centre of Finland Ltd . The field reference data was provided by the Finnish Forest Centre.
Publisher Copyright:
© 2019
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/3/15
Y1 - 2019/3/15
N2 - 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.
AB - 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.
KW - Boreal forest
KW - Forest variables
KW - Forestry
KW - Landsat 8
KW - Sentinel-2
UR - http://www.scopus.com/inward/record.url?scp=85060538152&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2019.01.019
DO - 10.1016/j.rse.2019.01.019
M3 - Article
AN - SCOPUS:85060538152
SN - 0034-4257
VL - 223
SP - 257
EP - 273
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -