Jie Pan , Xinquan Ye , Fan Shao , Gaosheng Liu , Jia Liu , Yunsheng Wang
{"title":"松树种类、感染反应和数据类型对利用近距离高光谱遥感技术检测木虱的影响","authors":"Jie Pan , Xinquan Ye , Fan Shao , Gaosheng Liu , Jia Liu , Yunsheng Wang","doi":"10.1016/j.rse.2024.114468","DOIUrl":null,"url":null,"abstract":"<div><div>The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can be detected during the pre-visual stage and, if so, how to achieve hyperspectral detection. This study aimed to investigate the impacts of pine species, infection responses, and data types on hyperspectral detection of PWD, particularly in the pre-visual stage. Artificial inoculation experiments were conducted across three locations with 76 sample trees of two pine species, and hyperspectral data were collected regularly using ground-based non-imaging and UAV imaging spectrometers Five infection responses were identified: keep healthy (KH), quick infection (QI), slow recovery (SR), quick recovery (QR), and slow infection (SI). Spectral analysis revealed dynamic changes in the indices RVI (680–550,750) and NDVI (560,680), corresponding well with the spectral characteristics of the five infection responses. The infected trees with QI response could be spectrally detected starting from day 14, with over 50 % accuracy. Importance analysis using RF identified RVI (554,677) and NDVI (531,570) as consistent in detecting pre-visual stages. In contrast, the six VIs determined by PCA-S (RARSb, RVI (900, 680), RVI (800, 680), RVI (760, 500), RVI (800, 635), and REP) exhibited high consistency and played a crucial role in identifying pre-visual stage infected trees. These VIs combined with specific color bands, enabled the creation of false-color images highlighting infected trees starting from day 14 post-inoculation. The study highlighted the importance of recognizing infection response patterns for accurate PWD detection, with only QI response trees showed a stable infection cycle, making day 14 post-infection a meaningful starting point for spectral detection. Additionally, imaging and non-imaging data types did not significantly affect the detection process, and the impact of spectral resolution variations between 1 nm and 3.5 nm was negligible. Further research is required to determine the threshold for larger differences of spectral resolution and to explore detection across various pine species and growth environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114468"},"PeriodicalIF":11.1000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impacts of pine species, infection response, and data type on the detection of Bursaphelenchus xylophilus using close-range hyperspectral remote sensing\",\"authors\":\"Jie Pan , Xinquan Ye , Fan Shao , Gaosheng Liu , Jia Liu , Yunsheng Wang\",\"doi\":\"10.1016/j.rse.2024.114468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can be detected during the pre-visual stage and, if so, how to achieve hyperspectral detection. This study aimed to investigate the impacts of pine species, infection responses, and data types on hyperspectral detection of PWD, particularly in the pre-visual stage. Artificial inoculation experiments were conducted across three locations with 76 sample trees of two pine species, and hyperspectral data were collected regularly using ground-based non-imaging and UAV imaging spectrometers Five infection responses were identified: keep healthy (KH), quick infection (QI), slow recovery (SR), quick recovery (QR), and slow infection (SI). Spectral analysis revealed dynamic changes in the indices RVI (680–550,750) and NDVI (560,680), corresponding well with the spectral characteristics of the five infection responses. The infected trees with QI response could be spectrally detected starting from day 14, with over 50 % accuracy. Importance analysis using RF identified RVI (554,677) and NDVI (531,570) as consistent in detecting pre-visual stages. In contrast, the six VIs determined by PCA-S (RARSb, RVI (900, 680), RVI (800, 680), RVI (760, 500), RVI (800, 635), and REP) exhibited high consistency and played a crucial role in identifying pre-visual stage infected trees. These VIs combined with specific color bands, enabled the creation of false-color images highlighting infected trees starting from day 14 post-inoculation. The study highlighted the importance of recognizing infection response patterns for accurate PWD detection, with only QI response trees showed a stable infection cycle, making day 14 post-infection a meaningful starting point for spectral detection. Additionally, imaging and non-imaging data types did not significantly affect the detection process, and the impact of spectral resolution variations between 1 nm and 3.5 nm was negligible. Further research is required to determine the threshold for larger differences of spectral resolution and to explore detection across various pine species and growth environments.</div></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":\"315 \",\"pages\":\"Article 114468\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425724004942\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724004942","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Impacts of pine species, infection response, and data type on the detection of Bursaphelenchus xylophilus using close-range hyperspectral remote sensing
The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can be detected during the pre-visual stage and, if so, how to achieve hyperspectral detection. This study aimed to investigate the impacts of pine species, infection responses, and data types on hyperspectral detection of PWD, particularly in the pre-visual stage. Artificial inoculation experiments were conducted across three locations with 76 sample trees of two pine species, and hyperspectral data were collected regularly using ground-based non-imaging and UAV imaging spectrometers Five infection responses were identified: keep healthy (KH), quick infection (QI), slow recovery (SR), quick recovery (QR), and slow infection (SI). Spectral analysis revealed dynamic changes in the indices RVI (680–550,750) and NDVI (560,680), corresponding well with the spectral characteristics of the five infection responses. The infected trees with QI response could be spectrally detected starting from day 14, with over 50 % accuracy. Importance analysis using RF identified RVI (554,677) and NDVI (531,570) as consistent in detecting pre-visual stages. In contrast, the six VIs determined by PCA-S (RARSb, RVI (900, 680), RVI (800, 680), RVI (760, 500), RVI (800, 635), and REP) exhibited high consistency and played a crucial role in identifying pre-visual stage infected trees. These VIs combined with specific color bands, enabled the creation of false-color images highlighting infected trees starting from day 14 post-inoculation. The study highlighted the importance of recognizing infection response patterns for accurate PWD detection, with only QI response trees showed a stable infection cycle, making day 14 post-infection a meaningful starting point for spectral detection. Additionally, imaging and non-imaging data types did not significantly affect the detection process, and the impact of spectral resolution variations between 1 nm and 3.5 nm was negligible. Further research is required to determine the threshold for larger differences of spectral resolution and to explore detection across various pine species and growth environments.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.