Delineation of vine parcels by segmentation of high resolution remote sensed images

Costa, Jean-Pierre da; Michelet, Franck; Germain, Christian; Lavialle, Olivier; Grenier, Gilbert

doi:10.1007/s11119-007-9031-3

Cited by 52 publications

(30 citation statements)

References 10 publications

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“…a complete DOC region of about one hundred thousand hectares) are presently under progress, as well as a detailed comparison with other published methods (such as the one presented in [13]). …”

Section: B Characterization Resultsmentioning

confidence: 99%

“…These approaches aiming at vine plot detection only provide a vine/non-vine pixel classification without the determination of plot boundaries. In a recent study, Da Costa et al [13] applied a textural approach to meet this need. Even if the results obtained on several plots (less than 10) are good, it seems difficult to generalize this method as it is applied on a 0.15 cm resolution and needs the user to select a window inside the field he wants to process.…”

Section: Introductionmentioning

confidence: 99%

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An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

Delenne

Rabatel²,

Deshayes

2008

IEEE Geosci. Remote Sensing Lett.

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Abstract-The availability of an automatic tool for vine plot detection, delineation and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier Transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundaries determination and accurate estimation of inter-row width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard very high spatial resolution remotely sensed data. About 89% of vine plots are detected corresponding to more than 84% of vineyard area and 64% have correct boundaries. Compared to precise on-screen measurements, vine row orientation and inter-row width are estimated with an accuracy of respectively 1 o and 3.3 cm.

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Section: B Characterization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

Delenne

Rabatel²,

Deshayes

2008

IEEE Geosci. Remote Sensing Lett.

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“…However, much of the work in this area over the last decade is especially influenced by the advent of VHSR images, which have favoured innovative approaches for retrieving specific patterns of vineyard arrangements from helicopter colour images with ∼ 0.25 m resolution (Wassenaar et al, 2002), airborne multispectral images with ∼ 2 m resolution (Gong et al, 2003) or 0.5 m resolution (Rabatel et al, 2006), satellite panchromatic 1 m IKONOS images (Warner and Steinmaus, 2005), satellite panchromatic 0.6 m Quickbird images (Rabatel et al, 2006), and ultra-light motorized (ULM) colour 0.5 m images Delenne et al, 2010). Approaches for vineyard identification include grapevine field detection (Wassenaar et al, 2002;Rabatel et al, 2006), grapevine field delineation (Da Costa et al, 2007), grapevine row extraction (Hall et al, 2003;Delenne et al, 2010;Matthews and Jensen, 2013;Puletti et al, 2014) and the detection of missing plants (Chanussot et al, 2005;Delenne et al, 2010). These approaches have mostly used greylevel images (often the red band) and either relied on frequency analysis (Wassenaar et al, 2002;Rabatel et al, 2006;Delenne et al, 2010) or developed textural analysis, a branch of image processing focused on the spatial statistics of the grey levels of images, the variations of which are perceived as homogeneous areas by the human eye (Haralick et al, 1973).…”

Section: Identification And/or Characterization Of Vineyardsmentioning

confidence: 99%

An overview of the recent approaches to terroir functional modelling, footprinting and zoning

Vaudour

Costantini²,

Jones

et al. 2015

SOIL

102

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Abstract. Notions of terroir and their conceptualization through agro-environmental sciences have become popular in many parts of world. Originally developed for wine, terroir now encompasses many other crops including fruits, vegetables, cheese, olive oil, coffee, cacao and other crops, linking the uniqueness and quality of both beverages and foods to the environment where they are produced, giving the consumer a sense of place. Climate, geology, geomorphology and soil are the main environmental factors which make up the terroir effect on different scales. Often considered immutable culturally, the natural components of terroir are actually a set of processes, which together create a delicate equilibrium and regulation of its effect on products in both space and time. Due to both a greater need to better understand regional-to-site variations in crop production and the growth in spatial analytic technologies, the study of terroir has shifted from a largely descriptive regional science to a more applied, technical research field. Furthermore, the explosion of spatial data availability and sensing technologies has made the within-field scale of study more valuable to the individual grower. The result has been greater adoption of these technologies but also issues associated with both the spatial and temporal scales required for practical applications, as well as the relevant approaches for data synthesis. Moreover, as soil microbial communities are known to be of vital importance for terrestrial processes by driving the major soil geochemical cycles and supporting healthy plant growth, an intensive investigation of the microbial organization and their function is also required. Our objective is to present an overview of existing data and modelling approaches for terroir functional modelling, footprinting and zoning on local and regional scales. This review will focus on two main areas of recent terroir research: (1) using new tools to unravel the biogeochemical cycles of both macro-and micronutrients, the biological and chemical signatures of terroirs (i.e. the metagenomic approach and regional fingerprinting); (2) terroir zoning on different scales: mapping terroirs and using remoteand proxy-sensing technologies to monitor soil quality and manage the crop system for better food quality.

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“…Entity delineation: The goal is the delineation of selected spatial entities (e.g., buildings [32], vine parcels [33], crop fields [34]) with a focus on the precision of their boundaries.…”

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confidence: 99%

Good Practices for Object-Based Accuracy Assessment

Radoux

Bogaert

2017

Remote Sensing

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Thematic accuracy assessment of a map is a necessary condition for the comparison of research results and the appropriate use of geographic data analysis. Good practices of accuracy assessment already exist, but Geographic Object-Based Image Analysis (GEOBIA) is based on a partition of the spatial area of interest into polygons, which leads to specific issues. In this study, additional guidelines for the validation of object-based maps are provided. These guidelines include recommendations about sampling design, response design and analysis, as well as the evaluation of structural and positional quality. Different types of GEOBIA applications are considered with their specific issues. In particular, accuracy assessment could either focus on the count of spatial entities or on the area of the map that is correctly classified. Two practical examples are given at the end of the manuscript.

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Delineation of vine parcels by segmentation of high resolution remote sensed images

Cited by 52 publications

References 10 publications

An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

An Automatized Frequency Analysis for Vine Plot Detection and Delineation in Remote Sensing

An overview of the recent approaches to terroir functional modelling, footprinting and zoning

Good Practices for Object-Based Accuracy Assessment

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