Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
Source abstract
Abstract
Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Forest Insect Ecology and Management · Remote Sensing in Agriculture · Plant responses to elevated CO2
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450000, China
Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Subtropical Silviculture, Zhejiang Agriculture and Forest University, Hangzhou 311300, China
PIESAT International Information Technology Limited, Beijing 100195, China
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
Funding
National Natural Science Foundation of China · Science and Technology Project of Henan Province · Special Project for Team Building of Henan Academy of Sciences · Talents Training Program of Henan Academy of Sciences
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