Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification

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http://hdl.handle.net/10138/246858

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Piiroinen , R , Fassnacht , F E , Heiskanen , J , Maeda , E , Mack , B & Pellikka , P 2018 , ' Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification ' , Remote Sensing of Environment , vol. 218 , pp. 119-131 . https://doi.org/10.1016/j.rse.2018.09.018

Title: Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification
Author: Piiroinen, Rami; Fassnacht, Fabian Ewald; Heiskanen, Janne; Maeda, Eduardo; Mack, Benjamin; Pellikka, Petri
Contributor: University of Helsinki, Department of Geosciences and Geography
University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
University of Helsinki, TreeD lab - Terrestrial Ecosystem Dynamics
University of Helsinki, Department of Geosciences and Geography
Date: 2018-12-01
Language: eng
Number of pages: 13
Belongs to series: Remote Sensing of Environment
ISSN: 0034-4257
URI: http://hdl.handle.net/10138/246858
Abstract: Eucalyptus spp. and Acacia mearnsii are common exotic tree species in eastern Africa that have shown (strong) invasive behavior in some regions. Acacia mearnsii is considered a highly invasive species that is replacing native species and Eucalyptus spp. are known to consume high amounts of groundwater with suspected effects on native flora. Mapping the occurrence of these species in the Taita Hills, Kenya (part of the Eastern Arc Mountains Biodiversity Hotspot) is important as there is lack of knowledge on their occurrence and ecological impact in the area. Mapping methods that require a lot of fieldwork are impractical in areas like the Taita Hills, where the terrain is rugged and the infrastructure is poor. Our aim was hence to map the occurrence of these tree species in a 100 km(2) area using airborne imaging spectroscopy and laser scanning. We used a one class biased support vector machine (BSVM) classifier as it needs labeled training data only for the positive classes (A. mearnsii and Eucalyptus spp.), which potentially reduces the amount of required fieldwork. We also introduce a new approach for parameterizing and setting the threshold level simultaneously for the BSVM classifier. The second aim was to link the occurrence of these species to selected environmental variables. The results showed that the BSVM classifier is suitable for mapping Acacia mearnsii and Eucalyptus spp., holding the potential to improve the efficiency of field data collection. The introduced parametrization/threshold selection method performed better than other commonly used approaches. The crown level Fl-score was 0.76 for Eucalyptus spp. and 0.78 for A. mearnsii. We show that Eucalyptus spp. and A. mearnsii trees cover 0.8% and 1.6% of the study area, respectively. Both species are particularly located on steeper slopes and higher altitudes. Both species have significant occurrences in areas close to the biggest remaining native forest patch (Ngangao) in the study area. Nonetheless, follow-up studies are needed to evaluate their impact on the native flora and fauna, as well as their impact on the water resources. The maps created in this study in combination with such follow-up studies could serve as base data to generate guidelines that authorities can use to take action in handling the problems these species are causing.
Subject: Biased support vector machine
Invasive tree species
Imaging spectroscopy
Africa
One class classification
AIRBORNE IMAGING SPECTROSCOPY
ECOSYSTEM SERVICES
SOUTH-AFRICA
ESTABLISHMENT
DISTRIBUTIONS
PERFORMANCE
GRASSLAND
LANDSCAPE
TANZANIA
MAXENT
1172 Environmental sciences
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