Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 299-303, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W4-299-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
26 Aug 2015
LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS
I. Kalisperakis1, Ch. Stentoumis1, L. Grammatikopoulos2, and K. Karantzalos3 1up2metric, Athens, Greece
2Laboratory of Photogrammetry, Technological Educational Institute of Athens, Athens, Greece
3Remote Sensing Lab., National Technical University of Athens, Athens, Greece
Keywords: Precision agriculture, biomass, crop, narrow band indices Abstract. The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (i) hyperspectral data, (ii) 2D RGB orthophotomosaics and (iii) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (r2 > 73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.
Conference paper (PDF, 3850 KB)


Citation: Kalisperakis, I., Stentoumis, Ch., Grammatikopoulos, L., and Karantzalos, K.: LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 299-303, https://doi.org/10.5194/isprsarchives-XL-1-W4-299-2015, 2015.

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