Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests

Miraki, Mojdeh; Sohrabi, Hormoz

doi:10.1007/s10661-021-09695-7

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…A representation of the TLS and its measurements are shown in Figure 9. In forest health assessment, TLS systems are used to determine tree features and structure (Miraki and Sohrabi, 2021;Terryn et al, 2022;, and to estimate crown fuel and fuel hazard (Hillman et al, 2021).…”

Section: Terrestrial Laser Scanning Systemsmentioning

confidence: 99%

“…A similar study using RGB point clouds for calculating LAI was conducted by Lin L. et al (2021), and the models achieved a correlation of 0.92. Miraki and Sohrabi (2021) estimated LAI from RGB images and terrain model descriptors as input data, but the correlation was only 0.42, in the same study canopy height was also estimated, and using linear regression models the correlation achieved was 0.84. The study presented by (Qiao et al, 2022) also considered morphological features from the soil and the vegetation to improve the prediction of LAI, achieving correlation values of 0.93 but it depends on the growth stage of the vegetation.…”

Section: Linear Regressionmentioning

confidence: 99%

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Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

et al. 2023

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Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

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Section: Terrestrial Laser Scanning Systemsmentioning

confidence: 99%