Abstract
One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations.
Highlights
One of the main purposes of precision agriculture is the delineation of management zones (MZs) to support decisions according to the estimated variability to improve the resource use efficiency, productivity, quality, profitability and sustainability of agricultural production [1]
The lines passed by the Light detection and ranging (LiDAR) summary points; Buffer areas of 0.25 m radius were created around each line; A mask of the projected hedgerow canopy was created by means of supervised classification of the multispectral image; The mask of the projected canopy was vectorized; The previous buffers were clipped by the mask of the projected canopy; The clipped buffers were used as zones to summarize the vegetation indices; The areas of the clipped buffers were used to normalize the mean values of the indices in those areas
A variety of statistical methods were used to assess the relationship between vegetation indices and LiDAR-derived geometrical and structural orchard parameters: (a) an exploratory analysis of the variables by means of descriptive statistics; (b) a multivariate descriptive analysis to obtain the matrix of Pearson’s linear correlation coefficients; (c) a canonical correlation analysis to study the relationship between the two sets of variables (LiDAR-derived orchard parameters and vegetation indices); (d) a geostatistical interpolation to create the continuous spatial distribution of selected vegetation indices; (e) a cluster analysis of the continuous vegetation indices maps; and (f) an analysis of variance (ANOVA) to check if classes arising from the clustering of vegetation indices allowed for effective MZs in terms of differentiating the foliar canopy in almond trees
Summary
One of the main purposes of precision agriculture is the delineation of management zones (MZs) to support decisions according to the estimated variability to improve the resource use efficiency, productivity, quality, profitability and sustainability of agricultural production [1]. With the purpose of delineating MZs for pest management, MéndezVázquez et al [8] added an ecological layer to classic zoning approaches None of these approaches for MZ delineation rely on geometric and/or structural data of tree orchards, or are exclusively based on vegetation indices from remote sensing images. Other studies have considered proximal photogrammetry as an alternative to, or in combination with, terrestrial LiDAR to quantify the structural complexity of orchard trees at very high resolution [15,20]; the use of UAV imagery to assist in orchard management is increasing [21] In this respect, most applications have focused on the correlations between image-derived and field and biophysical parameters, or to map geometric properties using photogrammetry (structure-from-motion, SfM) [13]. Deal to in Fork its Farm to Fork in its Farm strategy [26].strategy [26]
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