Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

Optimization of leaf area index measurement method and correction of green plot ratio formula based on regional plant characteristics-a study in Chongqing, China

The quantification of green space green plot ratio (GPR) is mostly based on estimation formulas, and the leaf area index (LAI) estimation values in these estimation formulas have not been well verified by measured LAI values, resulting in errors and uncertainties in GPR quantification results. This study aims to address this gap by measuring the LAI of 113 regional plants in Chongqing, China, following a standardized measurement path for digital hemispherical photography (DHP). The results indicate that the optimal relative exposure value (REV) was - 1 under overcast conditions and - 2 under sunny and cloudy conditions. Among the threshold algorithms for hemispherical images, the Intermodes algorithm in ImageJ was the best. The LAI of regional plants is highest in summer, followed by spring and autumn, and lowest in winter. Tree height (h) and crown width (w) are key factors affecting LAI, but the LAI also varies with plant species. Overall, the LAI of evergreen trees is higher than that of deciduous trees. The LAI of evergreen trees and shrubs with a height shorter than 5 m is the largest, and that of deciduous trees and shrubs with a crown width larger than 8 m is the largest. The study further verified that the existing GPR estimation formula exhibited large errors in Chongqing, while there was a strong correlation (R2 = 0.973) between the GPR estimation value and the measured value. A conversion formula was developed to reduce estimation biases, and the corrected formula is capable of estimating GPR values more accurately when actual LAI measurements are insufficient. Overall, this study verifies the significance of measuring localized LAI values, promotes the understanding of LAI suitability for GPR calculations, and provides an empirical formula for GPR estimation in Chongqing, China.

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

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.