{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:35Z","timestamp":1787029535802,"version":"build-2736575974"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,2,15]],"date-time":"2017-02-15T00:00:00Z","timestamp":1487116800000},"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 spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics\/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773\u20130.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1\u201321 pls\/m2) or the height threshold (0\u20132 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches.<\/jats:p>","DOI":"10.3390\/rs9020156","type":"journal-article","created":{"date-parts":[[2017,2,15]],"date-time":"2017-02-15T10:09:07Z","timestamp":1487153347000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection"],"prefix":"10.3390","volume":"9","author":[{"given":"Jonathan","family":"Dash","sequence":"first","affiliation":[{"name":"Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Pearse","sequence":"additional","affiliation":[{"name":"Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Watt","sequence":"additional","affiliation":[{"name":"Scion, P.O. Box 29237, Fendalton, Christchurch 8041, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Paul","sequence":"additional","affiliation":[{"name":"Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1111\/j.1366-9516.2004.00096.x","article-title":"Conifers as invasive aliens: A global survey and predictive framework","volume":"10","author":"Richardson","year":"2004","journal-title":"Divers. Distrib."},{"key":"ref_2","unstructured":"Richardson, D.M. (1998). 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