Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest
<p class="MDPI17abstract">The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
‘Marin Drăcea’ National Research-Development Institute in Forestry
2019-12-01
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| Series: | Annals of Forest Research |
| Subjects: | |
| Online Access: | https://www.afrjournal.org/index.php/afr/article/view/1267 |
| Summary: | <p class="MDPI17abstract">The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems.</p> |
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| ISSN: | 1844-8135 2065-2445 |