{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:09:58Z","timestamp":1773385798663,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["CRDPJ 462973 \u2013 14"],"award-info":[{"award-number":["CRDPJ 462973 \u2013 14"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatial models that provide estimates of wood quality enable value chain optimization approaches that consider the market potential of trees prior to harvest. Ecological land classification units (e.g., ecosite) and structural metrics derived from Airborne Laser Scanning (ALS) data have been shown to be useful predictors of wood quality attributes in black spruce stands of the boreal forest of Ontario, Canada. However, age drives much of the variation in wood quality among trees, and has not been included as a predictor in previous models because it is poorly represented in inventory systems. The objectives of this study were (i) to develop a predictive model of mean stem age of black spruce-dominated stands, and (ii) refine models of black spruce wood density by including age as a predictor variable. A non-parametric model of stand age that used a k nearest neighbor (kNN) classification based on a random forests (rf) distance metric performed well, producing a root mean square difference (RMSD) of 15 years and explaining 62% of the variance. The subsequent random forests model of black spruce wood density generated from age and ecosite predictors was useful, with a root mean square error (RMSE) of 59.1 kg\u00b7m\u22123. These models bring large-scale wood quality prediction closer to becoming operational by including age and site effects that can be derived from inventory data.<\/jats:p>","DOI":"10.3390\/rs11172022","type":"journal-article","created":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T11:23:18Z","timestamp":1566991398000},"page":"2022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario"],"prefix":"10.3390","volume":"11","author":[{"given":"Rebecca R.M.","family":"Wylie","sequence":"first","affiliation":[{"name":"Department of Biology and Chemistry, Nipissing University, North Bay, ON P1B 8L7, Canada"}]},{"given":"Murray E","family":"Woods","sequence":"additional","affiliation":[{"name":"Ontario Ministry of Natural Resources and Forestry, Forest Resource Inventory Unit, North Bay, ON P1A 4L7, Canada"}]},{"given":"Jeffery P.","family":"Dech","sequence":"additional","affiliation":[{"name":"Department of Biology and Chemistry, Nipissing University, North Bay, ON P1B 8L7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/rs4040830","article-title":"LiDAR sampling density for forest resource inventories in Ontario, Canada","volume":"4","author":"Treitz","year":"2012","journal-title":"Remote Sens."},{"key":"ref_2","first-page":"128","article-title":"Forest stand age from LiDAR\u2014Derived predictors and nearest neighbor imputation","volume":"60","author":"Racine","year":"2014","journal-title":"For. 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