{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:05:15Z","timestamp":1770746715636,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8\u201338.3%, 34.2\u201341.9% and 31.7\u201338.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.<\/jats:p>","DOI":"10.3390\/rs11141693","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T11:25:12Z","timestamp":1563362712000},"page":"1693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2757-0981","authenticated-orcid":false,"given":"Al\u00eds","family":"Novo-Fern\u00e1ndez","sequence":"first","affiliation":[{"name":"Department of Organisms and Systems Biology, University of Oviedo, 33071 Oviedo, Asturias, Spain"}]},{"given":"Marcos","family":"Barrio-Anta","sequence":"additional","affiliation":[{"name":"GIS-Forest Research Group, Department of Organisms and Systems Biology, University of Oviedo, Polytechnic School of Mieres, 33600 Mieres, Asturias, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9813-6798","authenticated-orcid":false,"given":"Carmen","family":"Recondo","sequence":"additional","affiliation":[{"name":"Remote Sensing Applications Research Group (RSApps), Area of Cartographic, Geodesic and Photogrammetric Engineering, Department of Mining Exploitation and Prospecting, University of Oviedo, Gonzalo Guti\u00e9rrez Quir\u00f3s s\/n, 33600 Mieres, Asturias, Spain"},{"name":"Institute of Natural Resources and Territorial Planning (INDUROT), University of Oviedo, Gonzalo Guti\u00e9rrez Quir\u00f3s s\/n, 33600 Mieres, Asturias, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3747-1642","authenticated-orcid":false,"given":"Asunci\u00f3n","family":"C\u00e1mara-Obreg\u00f3n","sequence":"additional","affiliation":[{"name":"GIS-Forest Research Group, Department of Organisms and Systems Biology, University of Oviedo, Polytechnic School of Mieres, 33600 Mieres, Asturias, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-5739","authenticated-orcid":false,"given":"Carlos A.","family":"L\u00f3pez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"GIS-Forest Research Group, Department of Organisms and Systems Biology, University of Oviedo, Polytechnic School of Mieres, 33600 Mieres, Asturias, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/forestry\/cpw041","article-title":"Methods for variable selection in Lidar-assisted forest inventories","volume":"90","author":"Moser","year":"2017","journal-title":"Forestry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. 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