{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T21:07:06Z","timestamp":1771016826519,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JURO KAWACHI DONATION FUND","award":["16H04946"],"award-info":[{"award-number":["16H04946"]}]},{"name":"JURO KAWACHI DONATION FUND","award":["D2203615-202006551-J011"],"award-info":[{"award-number":["D2203615-202006551-J011"]}]},{"DOI":"10.13039\/501100004532","name":"Japan International Cooperation Agency (JICA)","doi-asserted-by":"publisher","award":["16H04946"],"award-info":[{"award-number":["16H04946"]}],"id":[{"id":"10.13039\/501100004532","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004532","name":"Japan International Cooperation Agency (JICA)","doi-asserted-by":"publisher","award":["D2203615-202006551-J011"],"award-info":[{"award-number":["D2203615-202006551-J011"]}],"id":[{"id":"10.13039\/501100004532","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer\u2013broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.<\/jats:p>","DOI":"10.3390\/rs16101783","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T09:10:22Z","timestamp":1715937022000},"page":"1783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving the Estimation of Structural Parameters of a Mixed Conifer\u2013Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7604-788X","authenticated-orcid":false,"given":"Jeyavanan","family":"Karthigesu","sequence":"first","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"},{"name":"Department of Agronomy, Faculty of Agriculture, University of Jaffna, Jaffna 40000, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9227-4177","authenticated-orcid":false,"given":"Toshiaki","family":"Owari","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hokkaido Forest, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Furano 079-1563, Japan"}]},{"given":"Satoshi","family":"Tsuyuki","sequence":"additional","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"given":"Takuya","family":"Hiroshima","sequence":"additional","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"ref_1","unstructured":"Seidler, R., and Bawa, K.S. 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