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

Aim: To evaluate the PocketLAI® smart app for estimating leaf area index (LAI) in woody canopies. Location: Northern Italy. Methods: PocketLAI - a smartphone application for LAI 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. LAI 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 the case of broad-leaf 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 unfavourable leaf area to perimeter ratio. Best performances were observed for dense broad-leaf canopies characterized by a regular arrangement of crowns (R2  = 0.95 for row-planted trees, R2 = 0.87 for tall forest trees), although satisfying results were achieved also in the 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 broad-leaf woody species, especially in contexts where resources and portability are key issues, whereas further improvements are required for conifers.