03675naa a2200265 a 450000100080000000500110000800800410001902400280006010000180008824501330010626000090023950010030024852017850125165300250303665300200306165300270308165300420310865300230315065300110317370000240318470000150320870000150322370000270323877301440326510615592020-12-10       2020    bl uuuu     u00u1 u    #d7 a10.3390/rs122339472DOI1 aHIRIGOYEN, A.  aStand characterization of eucalyptus spp. Plantations in uruguay using airborne lidar scanner technology.h[electronic resource]  c2020  aArticle history: Received: 16 October 2020 / Revised: 5 November 2020 / Accepted: 21 November 2020 / Published: 2 December 2020. Acknowledgments: The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful for the support of Roberto Scoz, Demian Gomez, Zenia Barrios and Gustavo Balmelli (INIA), Mariano Blanco, Santiago Heguaburu, Carola Odone and José Carlos de Mello (FOSA). R.M.N.-C. acknowledges the ISOPINE (UCO-1265298) and ESPECTRAMED (CGL2017-86161-R) projects for methodological support. We acknowledge the institutional support of the University of Cordoba-Campus de Excelencia CEIA3. We also thank the ERSAF group and, particularly. Cristina Acosta and Antonio Ariza. for their assistance during this research. We thank David Walker for his revisions of the different versions of this manuscript, and the anonymous referees for their comments and corrections.  aAbstract: Airborne lidar scanner (ALS) technology is used in a variety of applications, including forestry. ALS has enormous potential for the estimation of relevant biometric parameters in forest plantations. This study investigates the use of an object-oriented semi-automated segmentation algorithm for stands delineation, based on modeling ALS data, in plantations of Eucalyptus grandis and E. dunnii in Uruguay. The results show that non-parametric methods delivered more accurate and less biased results for total volume (TV) with R2 0.93, RMSE 20.04 m3 h ?1 for E. grandis and R2 0.93, RMSE 18.43 m3 h ?1 for E. dunnii; and above ground biomass (AGB) with R2 0.95, RMSE 70.2 kg h?1 for E. grandis and R2 0.96, RMSE: 71.2 Kg h?1 for E. dunnii. Parametric methods performed better for dominant height (Ho) with R2 0.98, RMSE 0.67 m and R2 : 0.96, RMSE: 0.8 m for E. grandis and E. dunnii, respectively. The most informative ALS metrics for the estimation of AGB and TV were metrics related to the elevation in parametric models (Elev.70 and Elev.75), while for the non-parametric models (k-NN) they were Elev.75 and canopy density. For Ho, the ALS metrics selected were also related to elevation both in the parametric (Elev.90 and Elev.99) and random forest models (Elev.max and Elev.75). The segmentation methodology proposed here matched closely the segments delineated by human operators, and provides a low-cost, cost-effective, easy to apply and update model aimed at generating AGB or TV maps for harvest tasks, based on rasters derived from ALS metrics. The present research shows the capacity of ALS metrics to improve extensive strategic inventories; validating and promoting the adoption of ALS technology for inventory forest stands of Eucalyptus spp. in Uruguay.  aABOVE GROUND BIOMASS  aDOMINANT HEIGHT  aINTENSIVE SILVICULTURE  aPARAMETRIC AND NON-PARAMETRIC METHODS  aSTAND SEGMENTATION  aVOLUME1 aVARO-MARTINEZ, M.A.1 aRACHID, C.1 aFRANCO, J.1 aNAVARRO-CERRILLO, R.M.  tRemote Sensing, 1 December 2020, Volume 12, Issue 23, Article number 3947, Pages 1-19. Open Access. Doi: https://doi.org/10.3390/rs12233947