Defining Grapevine and Vineyard Characteristics from High Spatial Resolution Remotely Sensed Optical Imagery

    Research output: ThesisDoctoral Thesis

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    This thesis presents a methodology for processing high spatial resolution remotely sensed imagery of vineyards to acquire spatially referenced values that describe the foliar canopies of individual vines over entire vineyard blocks. A method of processing vineyard image data to extract spatially referenced quantitative information about attributes of individual vine canopies was developed. Quantitative descriptors of vine canopies were related to fruit yield and composition, and the utility of different remotely sensed canopy descriptors to infer fruit quality and yield were compared. Image derived canopy descriptors were compared to fruit composition, yield and vine performance over two consecutive growing seasons for 58 vines in a block of Cabernet Sauvignon. The results indicated that the methodology of producing quantitative descriptors of canopies was accurate in terms of locating individual vines and that, at this scale, correlations (p < 0.01) were found to exist between image derived descriptors of vine canopy size and density and fruit composition, particularly anthocyanin and phenolic concentration and berry size. Yield was correlated (p < 0.01) with canopy descriptors acquired from imagery taken in the preceding season. The algorithm developed to quantify the vine canopy biomass was able to separate canopy size from canopy foliage density, enabling a calculation of quantitative descriptors for each characteristic. Vine canopy size rather than apparent measures of foliage density had stronger and more consistent relationships with many of the proximate measures of fruit composition and vine canopy characteristics including leaf area index. Modelling of the radiation regime within vine canopies revealed that foliage density had little impact on the characteristics of the reflected radiation from the vine canopy. Canopy size was found to be the main factor contributing to the ability of the remote sensing instrument to discriminate between relative levels of biomass. On the basis of the image processing protocols established in this study, a methodology for partitioning vines into sets according to fruit composition was defined. An experiment was conducted where fruit was harvested into lots from vines with similar foliage characteristics as defined by the remotely sensed canopy descriptors. Each fruit lot was assessed for its composition. Results of the analysis confirmed that fruit composition was related to canopy density descriptors derived solely from remotely sensed imagery.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • Charles Sturt University
    • Lamb, David, Principal Supervisor, External person
    Award date01 Sep 2003
    Publication statusPublished - Sep 2003


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