Estimating leaf area index in tree species using the PocketLAI smart app

Aim: To evaluate the PocketLAI® smart app for estimating leaf area index in woody canopies.
Location: Northern Italy.
Methods: PocketLAI – a smartphone application for leaf area index estimates based on gap fraction derived from the real-time processing of images acquired at 57° below the canopy – was tested on continuous forest stands, plantations, spotted shrub-lands and spotted tree-lands. Leaf area index data from hemispherical photography (images post-processed with Can-eye software) were taken as reference values. Plants were clustered on the basis of leaf type and canopy structure.
Results: In general, PocketLAI showed satisfactory performances in case of broadleaf plants (R2 = 0.78, p<0.001) for all shrub and tree clusters. On the other hand, poor results were obtained for conifers (R2 = 0.16), likely because of the unfavorable leaf area to perimeter ratio. Best performances were observed for dense broadleaf canopies characterized by a regular arrangement of the crowns (R2 = 0.95 for row-planted trees, R2 = 0.87 for tall forest trees), although satisfying results were achieved also in case of canopies made irregular and non-homogeneous by pruning (R2 = 0.73 for small fruit trees). Concerning shrubs, the agreement between PocketLAI and hemispherical photography was higher for species with big leaves (R2 = 0.72).
Conclusions: These results suggest that PocketLAI can be an alternative to other methods in case of broadleaf woody species, especially in contexts where resources and portability are key issues, whereas further improvements are required for conifers.