Abstract
ABSTRACTKnowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor. The aim of this paper is to evaluate the three nonparametric classification algorithms (SVM, RF and ANN) in an attempt to classify the five most common tree species of the Szklarska Poręba area: spruce (Picea alba L. Karst), larch (Larix decidua Mill.), alder (Alnus Mill), beech (Fagus sylvatica L.) and birch (Betula pendula Roth). To avoid human introduced biases a 0.632 bootstrap procedure was used during evaluation of each compared classifier. Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%. Analysis of the stability of results concluded that RF and SVM had the lowest variance of overall accuracy and kappa coefficient (12 percentage points) while ANN had 15 percentage points variance in results.
Highlights
Knowledge about the vegetation condition of a forested area is important both for monitoring of protected areas (Nagendra et al, 2013) and estimating the potential economic value of forests (Ashutosh, 2012)
The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor
Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%
Summary
Knowledge about the vegetation condition of a forested area is important both for monitoring of protected areas (Nagendra et al, 2013) and estimating the potential economic value of forests (Ashutosh, 2012). A section of the Karkonosze National Park forms part of the valuable Karkonosze Mountains ecosystem. More than 30 years ago, a rapid expansion of industry in the surrounding areas combined with the particular landscape of Karkonosze to cause acid rains that exposed the fragile mountain ecosystem to insect infestation (Jadczyk, 2009). The synergic effect of acid rains, air pollution, drought and insect outbreak which all happened at that time contributed to the severity of the damage done to the ecosystem. Detailed information about vegetation recovery is crucial for the national park administration and foresters alike. The large amount of information contained in hyperspectral data allows for much more accurate and detailed classifications of tree species and vegetation (Masaitis & Mozgeris, 2013; Thenkabail, Lyon, & Huete, 2012). More traditional ways of classifying tree species demand a lot of manpower and money (Peerbhay et al, 2013)
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