Publication
Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data
| dc.contributor.author | Guerra-Hernández, Juan | |
| dc.contributor.author | Cozensa, Diogo N. | |
| dc.contributor.author | Cardil, Adrian | |
| dc.contributor.author | Silva, Carlos Alberto | |
| dc.contributor.author | Botequim, Brigite | |
| dc.contributor.author | Soares, Paula | |
| dc.contributor.author | Silva, Margarida | |
| dc.contributor.author | González-Ferreiro, Eduardo | |
| dc.contributor.author | Diaz-Varela, Ramón | |
| dc.date.accessioned | 2019-11-04T11:26:12Z | |
| dc.date.available | 2019-11-04T11:26:12Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Forests 2019, 10, 905 | pt_PT |
| dc.identifier.doi | 10.3390/f10100905 | |
| dc.identifier.uri | http://hdl.handle.net/10400.5/18568 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation.publisherversion | www.mdpi.com/journal/forests | pt_PT |
| dc.subject | unmanned aerial vehicles (UAV) | pt_PT |
| dc.subject | forest inventory | pt_PT |
| dc.subject | volume | pt_PT |
| dc.subject | canopy height model (CHM) | pt_PT |
| dc.subject | object based image analysis (OBIA) | pt_PT |
| dc.subject | structure from motion (SfM) | pt_PT |
| dc.title | Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Forests | pt_PT |
| person.familyName | Botequim | |
| person.familyName | Soares | |
| person.givenName | Brigite | |
| person.givenName | Paula | |
| person.identifier | 943186 | |
| person.identifier | 639834 | |
| person.identifier.ciencia-id | 891C-A412-594F | |
| person.identifier.ciencia-id | 0219-879B-E8AA | |
| person.identifier.orcid | 0000-0002-6661-190X | |
| person.identifier.orcid | 0000-0002-7603-5467 | |
| person.identifier.rid | F-8251-2010 | |
| person.identifier.scopus-author-id | 36766730000 | |
| person.identifier.scopus-author-id | 55702522029 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | cde6b896-3b50-492e-972a-538aef76137f | |
| relation.isAuthorOfPublication | 9b96eb5a-3ccf-4974-b302-cdda02af8f02 | |
| relation.isAuthorOfPublication.latestForDiscovery | 9b96eb5a-3ccf-4974-b302-cdda02af8f02 |