Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images

Zhang, Ning; Zhang, Xiaoli; Yang, Guijun; Zhu, Chenghao; Huo, Langning; Feng, Haikuan

doi:10.1016/j.rse.2018.08.024

Cited by 86 publications

(47 citation statements)

References 48 publications

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“…Results of recent studies [190,191] indicate that hyperspectral data both from aircraft and UAV platforms can help to identify infested trees whereas the UAV data provided more accurate results (81% classification accuracy) than the aircraft data (73%) due to the finer spatial resolution. In Zhang et al [192] the hyperspectral UAV based scanner was used to assess defoliation. The authors compared three waveband selection algorithms and reported 95.32% accuracy of detection of damaged individual trees utilizing the best of the algorithms mentioned.…”

Section: Assessing Forest Health and Physiology Statusmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.

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Section: Assessing Forest Health and Physiology Statusmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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“…However, although the deleterious effects of pest infestations are often linked to transmitted diseases, the insects themselves can cause considerable damage [158]. An effect that is common in forests and orchards is the defoliation of trees, a cue that has been explored in a few experiments [157,159]. Also, insects can disrupt physiological processes and photosynthesis, causing an impact on plant height, an effect that can be detected through photogrammetry [158,160].…”

Section: Othersmentioning

confidence: 99%

“…Such an angular movement alters the overlapping between images and deflect sensors from nadir, thus damaging the mosaicking process and introducing a variety of distortions. Correcting those distortions is not a trivial task, but it is unavoidable if they are very prominent or if the method to be applied relies heavily on the geometrical characterization of the image [19,55,59,64,67,81,107,114,127,142,153,159,167]. Another problem associated to high wind speeds is that canopies will move and change appearance, which may cause inconsistencies and may impede proper image alignment due to the impossibility of finding enough common key points between the photographs.…”

Section: Revisited Issuesmentioning

confidence: 99%

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Barbedo

2019

Drones

212

117

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Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.

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“…Furthermore, with the spectral disease indices (SDIs) developments, the proposed framework also can be used as the ROIs construction methods first, and then, the SDIs can be selected to determine the damage types or severity. Combined with the research the research presented by Zhang et al [40], the damaged tree crown extraction can be applied before extracting average reflectance of individual D.tabulaeformis damaged trees. This step can replace the manual drawing of tree crown ROIs, and realize the automatic assessment of defoliation.…”

Section: Further Applicationmentioning

confidence: 99%

Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images

2020

Self Cite

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Background Tree crown extraction is an important research topic in forest resource monitoring. In particular, it is a prerequisite for disease detection and mapping the degree of damage caused by forest pests. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is effective for surveying and monitoring forest health. This article proposes a spectral-spatial classification framework that uses UAV-based hyperspectral images and combines a support vector machine (SVM) with an edge-preserving filter (EPF) for completing classification more finely to automatically extract tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) in Jianping county of Liaoning province, China. Results Experiments were conducted using UAV-based hyperspectral images, and the accuracy of the results was assessed using the mean structure similarity index (MSSIM), the overall accuracy (OA), kappa coefficient, and classification accuracy of damaged Pinus tabulaeformis. Optimized results showed that the OA of the spectral-spatial classification method can reach 93.17%, and the extraction accuracy of damaged tree crowns is 7.50–9.74% higher than that achieved using the traditional SVM classifier. Conclusion This study is one of only a few in which a UAV-based hyperspectral image has been used to extract tree crowns damaged by D. tabulaeformis. Moreover, the proposed classification method can effectively extract damaged tree crowns; hence, it can serve as a reference for future studies on both forest health monitoring and larger-scale forest pest and disease assessment.

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Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images

Cited by 86 publications

References 48 publications

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images

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