Accurate estimates of canopy structure like canopy cover (CC), Leaf Area Index (LAI), crown volume (Vcr), as well as tree and stand structure like stem volume (V_st) and basal area (G), are considered essential measures to manage poplar plantations effectively as they are correlated with the growth rate and the detection of possible stress. This research exploits the possibility of developing a precision forestry application using an unmanned aerial vehicle (UAV), terrestrial digital camera and traditional field measurements to monitor poplar plantation variables. We set up the procedure using explanatory variables from the Grey Level Co-occurrence Matrix textural metrics (Entropy, Variance, Dissimilarity and Contrast) calculated based on UAV multispectral imagery. Our results show that the GCLM texture derived by multispectral ortomosaic provides adequate explanatory variables to predict poplar plantation characteristics related to plants’ canopy and stand structure. The evaluation of the models targeting the different poplar plantation variables (i.e. Vcr, G_ha, Vst_ha, CC and LAI) with the four GLCM explanatory variables (i.e. Entropy, Variance, Dissimilarity and Contrast) consistently higher or equal resulted to R2 ≥0.86.
Accurate estimates of canopy structure like canopy cover (CC), Leaf Area Index (LAI), crown volume (Vcr), as well as tree and stand structure like stem volume (V_st) and basal area (G), are considered essential measures to manage poplar plantations effectively as they are correlated with the growth rate and the detection of possible stress. This research exploits the possibility of developing a precision forestry application using an unmanned aerial vehicle (UAV), terrestrial digital camera and traditional field measurements to monitor poplar plantation variables. We set up the procedure using explanatory variables from the Grey Level Co-occurrence Matrix textural metrics (Entropy, Variance, Dissimilarity and Contrast) calculated based on UAV multispectral imagery. Our results show that the GCLM texture derived by multispectral ortomosaic provides adequate explanatory variables to predict poplar plantation characteristics related to plants’ canopy and stand structure. The evaluation of the models targeting the different poplar plantation variables (i.e. Vcr, G_ha, Vst_ha, CC and LAI) with the four GLCM explanatory variables (i.e. Entropy, Variance, Dissimilarity and Contrast) consistently higher or equal resulted to R2 ≥0.86. Read More
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