Next Article in Journal
Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information
Previous Article in Journal
Quantification of Polychlorinated Biphenyl (PCB) Concentration in San Francisco Bay Using Satellite Imagery
Previous Article in Special Issue
Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2018, 10(7), 1111; https://doi.org/10.3390/rs10071111

Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

Department of Geoinformatics, Cartography and Remote Sensing, University of Warsaw (UW), ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 27 June 2018 / Accepted: 10 July 2018 / Published: 12 July 2018
View Full-Text   |   Download PDF [5400 KB, uploaded 12 July 2018]   |  

Abstract

Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest. View Full-Text
Keywords: classification; hyperspectral data; protected areas; neural networks classification; hyperspectral data; protected areas; neural networks
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Raczko, E.; Zagajewski, B. Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images. Remote Sens. 2018, 10, 1111.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top