{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T01:11:19Z","timestamp":1786669879035,"version":"build-2736575974"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,11]],"date-time":"2017-01-11T00:00:00Z","timestamp":1484092800000},"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>Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.<\/jats:p>","DOI":"10.3390\/rs9010059","type":"journal-article","created":{"date-parts":[[2017,1,11]],"date-time":"2017-01-11T10:36:49Z","timestamp":1484131009000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems"],"prefix":"10.3390","volume":"9","author":[{"given":"Craig","family":"Mahoney","sequence":"first","affiliation":[{"name":"Department of Geography, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Hopkinson","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9650-2184","authenticated-orcid":false,"given":"Natascha","family":"Kljun","sequence":"additional","affiliation":[{"name":"Department of Geography, Swansea University, Singleton Park, Swansea SA2 8PP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Van Gorsel","sequence":"additional","affiliation":[{"name":"CSIRO Oceans and Atmosphere, Wilf Crane Crescent, Yarralumla ACT 2600, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2008.09.012","article-title":"Testing LiDAR models of fractional cover across multiple forest ecozones","volume":"113","author":"Hopkinson","year":"2009","journal-title":"Remote Sens. 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