HIGHLIGHTS:
The multispectral indexes successfully represented the spatial distribution of land use in a highly heterogeneous field.
Better results were obtained when anisotropy was considered.
Soil-Adjusted Vegetation Index represented better the spatial structure of the anisotropic field.
ABSTRACT
The study aimed to characterize the spatial structure of variability of biophysical indexes of vegetation through images obtained by Unmanned Aerial Vehicles under strong heterogeneity and anisotropy, using geostatistical procedures. Plots with different types and densities of culture were evaluated in a didactic vegetable garden. Five vegetation indexes obtained from aerial multispectral camera images were evaluated parallel with geostatistical analysis and anisotropy investigation for multiscale spatial modeling. For the studied domain, geometric anisotropy was identified for the biometric indexes. The spherical model presented a better fit when anisotropy was not considered, whereas the exponential model had the best performance in the anisotropic analysis. Contrasting targets were better identified in multispectral images and considering anisotropy. The Soil-Adjusted Vegetation Index is recommended for similar applications.
Key words:
UAV; spatial correlation; SAVI; precision agriculture
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T. Med - Mean temperature; T. Max - Maximum temperature; T. Min - Minimum temperature; RH. Med - Mean relative air humidity; SR - Solar radiation
X Coord and Y Coord - X and Y coordinates, respectively; SAVI - Soil Adjusted Vegetation Index; NDVI - Normalized Difference Vegetation Index; MGRVI - Modified Green Red Vegetation Index; NGRDI - Normalized Red Green Difference Index
SAVI - Soil Adjusted Vegetation Index; NDVI - Normalized Difference Vegetation Index; MGRVI - Modified Green Red Vegetation Index; NGRDI - Normalized Red Green Difference Index; C0 - Nugget effect; C0 + C - Sill; A - Range and model adjustment evaluated by the Sum of Squares of Error or Residuals (SSErr)
