Estimating leaf area index in tree species using the PocketLAI smart app
2015
Orlando, Francesca | Movedi, Ermes | Paleari, Livia | Gilardelli, Carlo | Foi, Marco | Dell'Oro, Michele | Confalonieri, Roberto | Hermy, Martin
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.
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