Tree Species Classification in Boreal Forests With Hyperspectral Data

By Michele Dalponte, Hans Ole Ørka, Terje Gobakken, Damiano Gianelle, Erik Næsset. IEEE Transactions on Geoscience and Remote Sensing, 2013

340 citations recorded by the source corpus.

Tree species mapping in forest areas is an important topic in forest inventory. In recent years, several studies have been carried out using different types of hyperspectral sensors under various forest conditions. The aim of this work was to evaluate the potential of two high spectral and spatial resolution hyperspectral sensors (HySpex-VNIR 1600 and HySpex-SWIR 320i), operating at different wavelengths, for tree species classification of boreal forests. To address this objective, many experiments were carried out, taking into consideration: 1) three classifiers (support vector machines (SVM), random forest (RF), and Gaussian maximum likelihood); 2) two spatial resolutions (1.5 m and 0.4 m pixel sizes); 3) two subsets of spectral bands (all and a selection); and 4) two spatial levels (pixel and tree levels). The study area is characterized by the presence of four classes 1) Norway spruce, 2) Scots pine, together with 3) scattered Birch and 4) other broadleaves. Our results showed that: 1) the HySpex VNIR 1600 sensor is effective in boreal tree species classification with kappa accuracies over 0.8 (with Pine and Spruce reaching producer's accuracies higher than 95%); 2) the role of the HySpex-SWIR 320i is limited, and its bands alone are able to properly separate only Pine and Spruce species; 3) the spatial resolution has a strong effect on the classification accuracy (an overall decrease of more than 20% between 0.4 m and 1.5 m spatial resolution); and 4) there is no significant difference between SVM or RF classifiers.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6d468a2da00db67904fd63a58e3bf6d3a1dfeb07

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Citations in the corpus

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, methodology): “SVM was widely used for tree species classification purposes (Dalponte, Orka, Gobakken, Gianelle, & Naesset, 2013; Ghiyamat, Shafri, Amouzad, Shariff, & Mansor, 2013; Peerbhay et al., 2013), but there are only a limited number of studies discussing the use of ANN in tree species classification…”
Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “For instance, in a recent study, Dalponte et al. (2013) obtained up to 96% producer accuracies when mapping conifers in Norwegian boreal forests.”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, result): “Many studies have compared RF and SVM accuracies showing comparable performances between both classifiers using different remote sensing data ( Waske & van der Linden, 2008; Duro et al., 2012; Hasan et al., 2012; Dalponte et al., 2013; Löw et al., 2013; Ghosh et al., 2014; Meyer et al., 2016).”