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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.