{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T02:06:28Z","timestamp":1770775588854,"version":"3.50.0"},"reference-count":81,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese National Natural Science Foundation","award":["31870642"],"award-info":[{"award-number":["31870642"]}]},{"name":"Beijing\u2019s Science and Technology Planning Project","award":["Z201100008020001"],"award-info":[{"award-number":["Z201100008020001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The red turpentine beetle (Dendroctonus valens LeConte) has caused severe ecological and economic losses since its invasion into China. It gradually spreads northeast, resulting in many Chinese pine (Pinus tabuliformis Carr.) deaths. Early detection of D. valens infestation (i.e., at the green attack stage) is the basis of control measures to prevent its outbreak and spread. This study examined the changes in spectral reflectance after initial attacking of D. valens. We also explored the possibility of detecting early D. valens infestation based on spectral vegetation indices and machine learning algorithms. The spectral reflectance of infested trees was significantly different from healthy trees (p &lt; 0.05), and there was an obvious decrease in the near-infrared region (760\u20131386 nm; p &lt; 0.01). Spectral vegetation indices were input into three machine learning classifiers; the classification accuracy was 72.5\u201380%, while the sensitivity was 65\u201385%. Several spectral vegetation indices (DID, CUR, TBSI, DDn2, D735, SR1, NSMI, RNIR\u2022CRI550 and RVSI) were sensitive indicators for the early detection of D. valens damage. Our results demonstrate that remote sensing technology could be successfully applied to early detect D. valens infestation and clarify the sensitive spectral regions and vegetation indices, which has important implications for early detection based on unmanned airborne vehicle and satellite data.<\/jats:p>","DOI":"10.3390\/rs14061373","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"1373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2171-231X","authenticated-orcid":false,"given":"Bingtao","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Linfeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China"},{"name":"School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0333-0681","authenticated-orcid":false,"given":"Lili","family":"Ren","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China"},{"name":"Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University\u2014French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing 100083, China"}]},{"given":"Zhongyi","family":"Zhan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Youqing","family":"Luo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China"},{"name":"Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University\u2014French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s10531-004-0697-9","article-title":"The red turpentine beetle, Dendroctonus valens LeConte (Scolytidae): An exotic invasive pest of pine in China","volume":"14","author":"Yan","year":"2005","journal-title":"Biodivers. 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