Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging
文献类型：期刊论文
| 作者 | Wang, Jingxu1; Lin, Qinan2; Meng, Shengwang3; Huang, Huaguo4; Liu, Yangyang5; Vanderwel, Mark |
| 刊名 | FORESTS
 |
| 出版日期 | 2024 |
| 卷号 | 15期号:1页码:16 |
| 关键词 | pine shoot beetle shoot damage ratio canopy temperature thermal infrared imagery LiDAR |
| DOI | 10.3390/f15010112 |
| 通讯作者 | Lin, Qinan(qinan_lin@zafu.edu.cn) ; Meng, Shengwang(mengsw@igsnrr.ac.cn) |
| 英文摘要 | The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused. |
| WOS关键词 | MOUNTAIN PINE-BEETLE ; SPECIES CLASSIFICATION ; IPS-TYPOGRAPHUS ; LIDAR ; OUTBREAK ; SPRUCE ; ATTACK ; DAMAGE ; DEFOLIATION |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001149208100001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202454]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lin, Qinan; Meng, Shengwang |
| 作者单位 | 1.Henan Acad Sci, Inst Geog, Key Lab Remote Sensing & Geog Informat Syst Henan, Zhengzhou 450052, Peoples R China 2.Zhejiang Agr & Forestry Univ, State Key Lab Subtrop Silviculture, Hangzhou 311300, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Qianyanzhou Ecol Res Stn, Beijing 100101, Peoples R China 4.Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China 5.China Siwei Surveying & Mapping Technol Co Ltd, Beijing 100086, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jingxu,Lin, Qinan,Meng, Shengwang,et al. Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging[J]. FORESTS,2024,15(1):16. |
| APA | Wang, Jingxu,Lin, Qinan,Meng, Shengwang,Huang, Huaguo,Liu, Yangyang,&Vanderwel, Mark.(2024).Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging.FORESTS,15(1),16. |
| MLA | Wang, Jingxu,et al."Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging".FORESTS 15.1(2024):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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