Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island

<i>Acacia mearnsii</i>, an invasive alien species, is the main resource identified for the energy transition initiated on Reunion Island. It is, therefore, critical to identify the existent forest stands within the island’s large tropical forest area and to determine which of these stand...

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Main Authors: Hélène Bley Dalouman, François Broust, Annelise Tran
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Forests
Subjects:
Online Access:https://www.mdpi.com/1999-4907/14/10/2030
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author Hélène Bley Dalouman
François Broust
Annelise Tran
author_facet Hélène Bley Dalouman
François Broust
Annelise Tran
author_sort Hélène Bley Dalouman
collection DOAJ
description <i>Acacia mearnsii</i>, an invasive alien species, is the main resource identified for the energy transition initiated on Reunion Island. It is, therefore, critical to identify the existent forest stands within the island’s large tropical forest area and to determine which of these stands are accessible for logging operations. In this study, a very high-spatial-resolution image was used to map forest (<i>Acacia mearnsii</i>, <i>Acacia heterophylla</i>, <i>Cryptomeria japonica</i>) and non-forest land cover. To identify the accessible forest stands, we developed a geographic information system (GIS) processing chain, accounting for barriers, the distribution of slopes, and the location of forest roads. User-defined parameters, such as the slope threshold and maximal distance to forest roads, allow different scenarios to be explored. The classification yields an overall accuracy of 88% and a Kappa index of 0.86 and highlights the areas occupied by the different types of Acacia. By applying the GIS processing chain for a standard scenario, the results show that only 19% of the initial study area of 637 ha of <i>Acacia mearnsii</i> patches is suitable for exploitation. In conclusion, our results demonstrate how remote sensing (RS) and GIS can be combined to provide valuable tools for forest managers to design management plans for invasive alien forest species.
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spelling doaj.art-ca2d468b68af430cbb692362bd6813ea2023-11-19T16:32:43ZengMDPI AGForests1999-49072023-10-011410203010.3390/f14102030Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion IslandHélène Bley Dalouman0François Broust1Annelise Tran2UPR BioWooEB, French Agricultural Research Centre for International Development (CIRAD), F-97743 Saint-Denis, FranceUPR BioWooEB, French Agricultural Research Centre for International Development (CIRAD), F-97743 Saint-Denis, FranceUMR TETIS, French Agricultural Research Centre for International Development (CIRAD), F-34398 Montpellier, France<i>Acacia mearnsii</i>, an invasive alien species, is the main resource identified for the energy transition initiated on Reunion Island. It is, therefore, critical to identify the existent forest stands within the island’s large tropical forest area and to determine which of these stands are accessible for logging operations. In this study, a very high-spatial-resolution image was used to map forest (<i>Acacia mearnsii</i>, <i>Acacia heterophylla</i>, <i>Cryptomeria japonica</i>) and non-forest land cover. To identify the accessible forest stands, we developed a geographic information system (GIS) processing chain, accounting for barriers, the distribution of slopes, and the location of forest roads. User-defined parameters, such as the slope threshold and maximal distance to forest roads, allow different scenarios to be explored. The classification yields an overall accuracy of 88% and a Kappa index of 0.86 and highlights the areas occupied by the different types of Acacia. By applying the GIS processing chain for a standard scenario, the results show that only 19% of the initial study area of 637 ha of <i>Acacia mearnsii</i> patches is suitable for exploitation. In conclusion, our results demonstrate how remote sensing (RS) and GIS can be combined to provide valuable tools for forest managers to design management plans for invasive alien forest species.https://www.mdpi.com/1999-4907/14/10/2030<i>Acacia mearnsii</i>invasive alien speciesremote sensingobject-based classificationconfusion indexGIS
spellingShingle Hélène Bley Dalouman
François Broust
Annelise Tran
Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
Forests
<i>Acacia mearnsii</i>
invasive alien species
remote sensing
object-based classification
confusion index
GIS
title Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
title_full Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
title_fullStr Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
title_full_unstemmed Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
title_short Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of <i>Acacia mearnsii</i> on Reunion Island
title_sort combining remote sensing and a geographic information system to map and assess the accessibility of invasive alien species forest stands case of i acacia mearnsii i on reunion island
topic <i>Acacia mearnsii</i>
invasive alien species
remote sensing
object-based classification
confusion index
GIS
url https://www.mdpi.com/1999-4907/14/10/2030
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