{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:44:11Z","timestamp":1771332251526,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"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":["CRDPJ462973-14"],"award-info":[{"award-number":["CRDPJ462973-14"]}],"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>Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey\u2019s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.<\/jats:p>","DOI":"10.3390\/rs12132109","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T02:44:25Z","timestamp":1593657865000},"page":"2109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6894-7925","authenticated-orcid":false,"given":"Tristan R.H.","family":"Goodbody","sequence":"first","affiliation":[{"name":"Faculty of Forestry, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5841-6530","authenticated-orcid":false,"given":"Piotr","family":"Tompalski","sequence":"additional","affiliation":[{"name":"Faculty of Forestry, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0151-9037","authenticated-orcid":false,"given":"Nicholas C.","family":"Coops","sequence":"additional","affiliation":[{"name":"Faculty of Forestry, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Hopkinson","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2109-9671","authenticated-orcid":false,"given":"Paul","family":"Treitz","sequence":"additional","affiliation":[{"name":"Department of Geography and Planning, Mackintosh-Corry Hall, Room E208, Queens University, Kingston, ON, K7L 3N6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karin","family":"van Ewijk","sequence":"additional","affiliation":[{"name":"Department of Geography and Planning, Mackintosh-Corry Hall, Room E208, Queens University, Kingston, ON, K7L 3N6, Canada"},{"name":"Lim Geomatics Inc., 2685 Queensview Drive, Suite 102, Ottawa, ON K2B 8K2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"722","DOI":"10.5558\/tfc2013-132","article-title":"A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach","volume":"89","author":"White","year":"2013","journal-title":"For. 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