APPLICATIONS OF HIGH RESOLUTION REMOTE SENSING IMAGERY TO PRECISION CROP AND VITICULTURE MANAGEMENT
G.I. Metternicht, K. Halloran, C. Baldacchino
Department of Spatial Sciences, Curtin
Precision crop management is a strategy that uses information technologies to integrate data from multiple resources (e.g. GPS, GIS, combine-mounted yield monitors, remote sensing) on decisions associated to crop production. Remotely sensed imagery provides many opportunities in the development of precision crop management techniques. High spatial resolution air- and satellite-borne imagery can aid in the development of information basis to rapidly map spatial variations in crop productivity, assisting managers to find the causes of such variability so that better management strategies can be implemented.
This research deals with two applications of high resolution multispectral remote sensing in precision crop management: crop yield prediction and precision viticulture.
The capability to predict crop yield before harvesting is an important factor, as it enables farm managers to change farming practices throughout the growing season in order to maximise profit and yield, while minimising costs. Remote yield prediction in this research was undertaken using imagery collected by an airborne system (DMSI) developed by SpecTerra Systems, a Perth-based company, able to provide four-bands multispectral imagery in the blue to NIR regions of the spectrum at a spatial resolution varying from 0.25 to 2 meters. Image transformations (e.g NDVIgreen, NDVI, Plant Pigment Index, Photosynthetic Vigour Index) and digital counts from raw bands were statistically regressed against yield data interpolated from yield points collected using a combine harvester and yield monitor, to assess the potential of developing algorithms for yield prediction of wheat, lupins, canola, and oats.
In regards to precision viticulture, the
main interest was on assessing the potential of DMSI imagery to accurately map
individual wine rows using advanced object-oriented classification
techniques. Grapevines, are typical of
row crops in that vigour is expressed not only as canopy density, but also in
canopy dimensions. Information on the spatial extent of the canopy enables
vineyard managers to determine where they should apply specific management
techniques that maximise final production.
In recent years yield maps produced by grape yield monitoring in
The use of an object oriented approach accounting for the targets’ structure, shape, size and spectral characteristics enabled accurate mapping of wine rows. Within eCognition, raw DMSI bands and spectral indices were segmented, and subsequently classified using a fuzzy classification technique.
The results of this research show a moderately weak relationship between the imagery and crop yield data. Correlations between vegetation indices, raw and data ad yield were below 0.4. Regressions were calculated using multiple regression techniques, incorporating imagery acquired early and late in the growing season. Only one regression equation was capable of describing nearly a quarter of the possible variation present. The paper discusses external influences not considered during the research that may have driven these low correlation results.
Good results were obtained in the mapping of wine rows using an fuzzy object oriented classification of DMSI imagery. An overall kappa index of 0.76 is reported, with a producer’s accuracy of 91% and user’s accuracy of 86% in the identification of wine rows.
The evidences gathered through this research suggest that high spatial resolution air- (e.g. DMSI) or space-borne multispectral imagery (e.g. Quickbird, Ikonos, OrbView-3) offer great potential to produce base information for precision viticulture management, though the use of such imagery for dry agricultural crop yield prediction offered unsatisfactory results.