Open AccessMulti-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations
Author(s) -
Gustavo Fluminense Carneiro,
Matheus Pinheiro Ferreira,
Carlos Frederico de Sá Volotão
Publication year - 2020
Publication title -
rbc. revista brasileira de cartografia/revista brasileira de cartografia
Language(s) - English
Resource type - Journals
eISSN - 1808-0936
pISSN - 0560-4613
DOI - 10.14393/rbcv72n1-50477
Subject(s) - random forest , remote sensing , eucalyptus , normalized difference vegetation index , shuttle radar topography mission , vegetation (pathology) , environmental science , geography , computer science , artificial intelligence , digital elevation model , leaf area index , agronomy , ecology , medicine , pathology , biology
It is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from multiple sources. We defined four vegetation classes: natural forest (NF), planted eucalyptus forest (PF), agriculture (A) and pasture (P), and sampled 410,251 pixels from 100 polygons of each class. Classification experiments were performed by using a random forest algorithm with images from Landsat-8, Sentinel-1, and SRTM. We considered four texture features (energy, contrast, correlation, and entropy) and NDVI. We used F1-score, overall accuracy and total disagreement metrics, to assess the classification performance, and Jeffries–Matusita (JM) distance to measure the spectral separability. Overall accuracy for Landsat-8 bands alone was 88.29%. A combination of Landsat-8 with Sentinel-1 bands resulted in a 3% overall accuracy increase and this band combination also improved the F1-score of NF, PF, P and A in 2.22%, 2.9%, 3.71%, and 8.01%, respectively. The total disagreement decreased from 11.71% to 8.71%. The increase in the statistical separability corroborates such improvement and is mainly observed between NF-PF (11.98%) and A-P (45.12%). We conclude that combining optical and radar remote sensing data increased the classification accuracy of natural and planted forests and may serve as a basis for large-scale semi-automatic mapping of forest resources.