Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR
| dc.contributor.author | Saad, Felipe | en |
| dc.contributor.author | Biswas, Sumalika | en |
| dc.contributor.author | Huang, Qiongyu | en |
| dc.contributor.author | Corte, Ana Paula Dalla | en |
| dc.contributor.author | Coraiola, Márcio | en |
| dc.contributor.author | Macey, Sarah | en |
| dc.contributor.author | Carlucci, Marcos Bergmann | en |
| dc.contributor.author | Leimgruber, Peter | en |
| dc.coverage.country | Brazil | en |
| dc.date.accessioned | 2021-12-09T19:58:32Z | en |
| dc.date.available | 2021-12-09T19:58:32Z | en |
| dc.date.issued | 2021-11-30 | en |
| dc.date.updated | 2021-12-09T14:32:15Z | en |
| dc.description.abstract | The Brazilian Atlantic Forest is a global biodiversity hotspot and has been extensively mapped using satellite remote sensing. However, past mapping focused on overall forest cover without consideration of keystone plant resources such as <i>Araucaria angustifolia.</i> <i>A. angustifolia</i> is a critically endangered coniferous tree that is essential for supporting overall biodiversity in the Atlantic Forest. <i>A. angustifolia’s</i> distribution has declined dramatically because of overexploitation and land-use changes. Accurate detection and rapid assessments of the distribution and abundance of this species are urgently needed. We compared two approaches for mapping <i>Araucaria angustifolia</i> across two scales (stand vs. individual tree) at three study sites in Brazil. The first approach used Worldview-2 images and Random Forest in Google Earth Engine to detect <i>A. angustifolia</i> at the stand level, with an accuracy of >90% across all three study sites. The second approach relied on object identification using UAV-LiDAR and successfully mapped individual trees (producer’s/user’s accuracy = 94%/64%) at one study site. Both approaches can be employed in tandem to map remaining stands and to determine the exact location of <i>A. angustifolia</i> trees. Each approach has its own strengths and weaknesses, and we discuss their adoptability by managers to inform conservation of <i>A. angustifolia</i>. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Saad, F.; Biswas, S.; Huang, Q.; Corte, A.P.D.; Coraiola, M.; Macey, S.; Carlucci, M.B.; Leimgruber, P. Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR. Land 2021, 10, 1316. | en |
| dc.identifier.doi | https://doi.org/10.3390/land10121316 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/106905 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Atlantic Forest | en |
| dc.subject | Araucaria angustifolia | en |
| dc.subject | Parana pine | en |
| dc.subject | Google Earth Engine | en |
| dc.subject | UAV-LiDAR | en |
| dc.subject | Worldview-2 | en |
| dc.subject | conservation | en |
| dc.subject | Brazil | en |
| dc.subject | multi-scale assessment | en |
| dc.title | Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR | en |
| dc.title.serial | Land | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |