An automatic Sentinel-2 forest types classification over the Roncal Valley, Navarre: Spain

A. Fernandez-Carrillo; D. de la Fuente; F. W. Rivas-Gonzalez; A. Franco-Nieto

doi:10.1117/12.2533059

3 October 2019 An automatic Sentinel-2 forest types classification over the Roncal Valley, Navarre: Spain

A. Fernandez-Carrillo, D. de la Fuente, F. W. Rivas-Gonzalez, A. Franco-Nieto

Proceedings Volume 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X; 111561N (2019) https://doi.org/10.1117/12.2533059
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Forests cover 36.5% of Spanish land. Natural and man-induced disturbances are causing important changes in species distribution. As Spanish National Forest Inventory is updated every 10 years, a more recurrent periodic data source providing information on species distribution is needed in order to predict changes in forest area and composition. Remote Sensing meets this demand, as it provides periodic and spatially continuous data on forest status. In this context, MySustainableForest (MSF) H2020 project aims at providing remote sensing-based geo-information services through a web service platform. One of MSF products is a classification of main forest types, whose preliminary development was tested over a 950 km2 area located in Northern Spain. A Random Forest model was trained with data delineated with the help of local forest data. The output was validated using stratified k-fold cross-validation. Validation metrics were computed from the confusion matrix for each class separately and for the total set of classes. Although overall metrics were high (OA = 95%; DC = 85.1%), they varied significantly for different classes (e.g., Fagus sylvatica was classified with higher accuracy than Pinus nigra, which was mainly confused with other Pinus species), showing that species with higher seasonal variations were easier to identify. Random Forest feature importance ranking showed that bands in the near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths were essential to discriminate forest species, since they explained more than 40% of the variations alone and 82% in combination with Red wavelength.

Citation Download Citation

A. Fernandez-Carrillo, D. de la Fuente, F. W. Rivas-Gonzalez, and A. Franco-Nieto "An automatic Sentinel-2 forest types classification over the Roncal Valley, Navarre: Spain", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111561N (3 October 2019); https://doi.org/10.1117/12.2533059

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KEYWORDS

Data modeling

Remote sensing

Climate change

Clouds

Forestry

Gases

Image classification

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