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Article

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

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Department of Geoinformatics Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, Poland
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Geodetski Zavod Celje d.o.o., 3000 Celje, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editors: David Sheeren, Jean-Baptiste Féret, Laurence Hubert-Moy and Sophie Fabre
Remote Sens. 2021, 13(13), 2581; https://doi.org/10.3390/rs13132581
Received: 11 May 2021 / Revised: 23 June 2021 / Accepted: 28 June 2021 / Published: 1 July 2021
(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)
Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species. View Full-Text
Keywords: ecological disaster; conservation; biodiversity; forest mapping; species diversity; Sentinel-2; Landsat; SVM; Random Forest; machine learning ecological disaster; conservation; biodiversity; forest mapping; species diversity; Sentinel-2; Landsat; SVM; Random Forest; machine learning
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MDPI and ACS Style

Zagajewski, B.; Kluczek, M.; Raczko, E.; Njegovec, A.; Dabija, A.; Kycko, M. Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sens. 2021, 13, 2581. https://doi.org/10.3390/rs13132581

AMA Style

Zagajewski B, Kluczek M, Raczko E, Njegovec A, Dabija A, Kycko M. Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing. 2021; 13(13):2581. https://doi.org/10.3390/rs13132581

Chicago/Turabian Style

Zagajewski, Bogdan, Marcin Kluczek, Edwin Raczko, Ajda Njegovec, Anca Dabija, and Marlena Kycko. 2021. "Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve" Remote Sensing 13, no. 13: 2581. https://doi.org/10.3390/rs13132581

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