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

AimTo evaluate the PocketLAI((R)) smart app for estimating leaf area index (LAI) in woody canopies. LocationNorthern Italy. MethodsPocketLAI - a smartphone application for LAI estimates based on gap fraction derived from the real-time processing of images acquired at 57 degrees 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. ResultsIn general, PocketLAI showed satisfactory performances in the case of broad-leaf plants (R-2=0.78, P<0.001) for all shrub and tree clusters. On the other hand, poor results were obtained for conifers (R-2=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 (R-2=0.95 for row-planted trees, R-2=0.87 for tall forest trees), although satisfying results were achieved also in the case of canopies made irregular and non-homogeneous by pruning (R-2=0.73 for small fruit trees). Concerning shrubs, the agreement between PocketLAI and hemispherical photography was higher for species with big leaves (R-2=0.72). ConclusionsThese 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.