The use of multispectral cameras deployed on unmanned aerial vehicles UAVs in land cover and vegetation mapping applications continues to improve and receive increasing recognition and adoption by resource management and forest survey practitioners. Comparisons of different camera data and platform performance characteristics are an important contribution in understanding the role and operational capability of this technology. In this article, object-based classification accuracies for different cover types and vegetation species of interest in central Ontario were examined using data from three UAV-based multispectral cameras. Five land-cover classes forest, shrub, herbaceous, bare soil, and built-up were determined to be up to 95% correct overall with calibrated multispectral Parrot Sequoia digital camera data compared to independent field observations. The levels of classification accuracy decreased approximately 10–15% when spectrally less capable consumer-grade RGB sensors were used. Multispectral Parrot Sequoia classification accuracy was approximately 89% when more detailed vegetation classes, including individual deciduous tree species, shrub communities and agricultural crops, were analysed. Additional work is suggested in the use of such UAV multispectral and point cloud data in ash tree discrimination to support emerald ash borer infestation detection and management, and in analysis of functional and structural vegetation characteristics e.g. leaf area index.

Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle

Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will hav...

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Mapping black ash dominated stands using geospatial and forest inventory data in northern Minnesota, USA

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted. Expected final online publication date for the Annual Review of Entomology, Volume 68 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

https://www.annualreviews.org/doi/pdf/10.1146/annurev-ento-120220-125410

Early Monitoring of Forest Wood-Boring Pests with Remote Sensing.

Since forest and fruit wood borer insects are very harmful, and the formed galleries are complex and not easy to observe, the 3D reconstruction and visual prediction simulation of their galleries are of great importance in agricultural and forestry research. A single image-based 3D reconstruction and visualization method is proposed. The method is divided into two steps: (1) photographing the complete insect galleries on different sample wood segments, correcting the images to obtain the complete insect tract outline, and then redefining the height of model expansion based on the distance from the outline to the midline of the outline via the sketch-based reconstruction method to reconstruct the 3D geometric model of insect tracts; (2) setting the influencing factors, such as forest and fruit wood borer pest species, host plants and insect population density, and simultaneously judging the newly added sample points and updating the original skeleton points according to the category of sample points and the comprehensive consideration of influencing factors, so as to obtain the changes of insect gallery structure under different conditions and achieve the predictive simulation of insect tract structure. We found that modeling 3D wood borer galleries by different pests on different host plants can be achieved. Compared to the hand drawing method, our method can obtain 3D models in a very short time, and the experimental models are all reconstructed within 1.5 s. The predicted variation in the range of insect tracts indicate that it was inversely proportional to the population density and positively proportional to the moth-eating ability of the pests, indicating that the method reflects the relationship between the range of insect tracts and the influencing factors. The proposed method provides a new approach to the study and control of wood borer galleries in the forest and fruit industry. In conclusion, we provide a method to reconstruct and predict the wood borer galleries in three dimensions.

https://www.mdpi.com/2073-4395/12/5/1087/pdf?version=1652758758

Three-Dimensional Reconstruction and Predictive Simulation Algorithm of Forest and Fruit Wood Borer Galleries Based on Two-Dimensional Images and Different Influencing Factors

The most visible symptoms of emerald ash borer (EAB) (Argilus planipennis Fairmaire) infestation do not usually appear until six years after the borer’s arrival, by which time the prognosis is so grim that many communities have resorted to either heavy chemical use that only slows mortality, or clear-cutting the entire ash tree population. We utilized leaf-level spectroscopy for early detection of invasive pest-related stress, focusing on EAB as it reaches the edges of the geographic range for green and white ash trees (Fraxinus pennsylvanica and Fraxinus americana) in North America. Over the course of two full growing seasons, we sampled trees in three study areas with EAB infestation in Johnson County, Kansas, and two sample groups without infestation in Riley County, Kansas. Our method utilizes field spectrometer readings for reflectance, along with lab spectrophotometry for estimation of leaf chlorophyll and carotenoid content at several points during the growing season. Results show significant differences between pigment ratios and hyperspectral indicators between infested and non-infested ash trees, although the extent of the separation varies across the foliar season. This work has the potential to make stress diagnosis more effective, thereby improving response, and decreasing both chemical application and plant loss.

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Leaf-Level Spectroscopy for Analysis of Invasive Pest Impact on Trees in a Stressed Environment: An Example Using Emerald Ash Borer (Agrilus planipennis Fairmaire) in Ash Trees (Fraxinus spp.), Kansas, USA

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

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B. Hall

Papers

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies