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We implemented a time\u2010series monitoring framework in a township in Zhejiang Province, China, acquiring seven sequences of unmanned aerial vehicle (UAV) orthomosaics between 2022 and 2024. An enhanced YOLOX\u2010based change detection model was developed and trained on 300\u2009000 samples. This model exploits phenological variations to automatically identify PWD\u2010discolored pines, effectively filtering confounding objects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The model demonstrated robust performance, achieving an Average Precision (AP) of 0.89, with Precision and Recall exceeding 85%. Analysis revealed a consistent westward expansion and a progressive increase in disease hotspots. Crucially, winter surveys detected substantial delayed\u2010symptom pines missed in autumn, roughly equivalent to the autumn baseline. Consequently, the annual cumulative mortality caused by PWD was nearly double (2\u00d7) the autumn count. Over 90% of trees newly identified in autumn were located within 300\u2009m of infections detected the previous spring, indicating strong spatial clustering. Furthermore, 80% of infected trees occurred at elevations &lt;\u2009400\u2009m and slopes &lt;\u200929\u00b0, aligning with prevailing easterly winds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>This research establishes a validated framework bridging remote sensing and on\u2010the\u2010ground sanitation. By quantifying the symptom lag effect (which doubles mortality estimates relative to traditional autumn surveys) and elucidating environmentally driven spread mechanisms, we provide a scientific basis for correcting census biases and optimizing resource allocation for precise PWD management. \u00a9 2026 Society of Chemical Industry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ps.70873","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:25:21Z","timestamp":1778214321000},"page":"8225-8240","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unraveling spatiotemporal dynamics of pine wilt disease via time\u2010series\n                    <scp>UAV<\/scp>\n                    monitoring and deep 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"You","sequence":"additional","affiliation":[{"name":"Jiuling (Shanghai) Intelligent Technology Co., Ltd  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guomo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Environment and Resources, College of Carbon Neutrality Zhejiang A&amp;F University  Hangzhou Zhejiang China"},{"name":"Zhejiang Key Laboratory of Carbon Sequestration and Emission Reduction in Agriculture and Forestry Zhejiang A&amp;F University  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1163\/187529272X00296"},{"key":"e_1_2_9_3_1","first-page":"949","article-title":"Aphelenchoides xylophilus n. sp., a nematode associated with blue\u2010stain and other fungi in 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