{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:26:33Z","timestamp":1773714393154,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012448","name":"Soil and Water Conservation Bureau","doi-asserted-by":"publisher","award":["SWCB-109-024"],"award-info":[{"award-number":["SWCB-109-024"]}],"id":[{"id":"10.13039\/501100012448","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2313-B-005-019-MY3"],"award-info":[{"award-number":["MOST 108-2313-B-005-019-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R2 = 0.72) can also obtain LWD volume to save time and workforce.<\/jats:p>","DOI":"10.3390\/rs14040998","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:23:29Z","timestamp":1645431809000},"page":"998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-0179","authenticated-orcid":false,"given":"Min-Chih","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Soil and Water Conservation, National Chung Hsing University, 145 Xingda Road, Taichung 40227, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0662-4044","authenticated-orcid":false,"given":"Samkele S.","family":"Tfwala","sequence":"additional","affiliation":[{"name":"Department of Geography, Environmental Science and Planning, University of Eswatini, Kwaluseni M201, Eswatini"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4796-5435","authenticated-orcid":false,"given":"Su-Chin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Soil and Water Conservation, National Chung Hsing University, 145 Xingda Road, Taichung 40227, Taiwan"},{"name":"Innovation and Development Centre of Sustainable Agriculture (IDCSA), National Chung Hsing University, 145 Xingda Road, Taichung 40227, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.foreco.2011.04.026","article-title":"Tree type and forest management effects on the structure of stream wood following wildfires","volume":"262","author":"Vaz","year":"2011","journal-title":"For. 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