Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data

Delenne, Carole; Durrieu, S.; Rabatel, Gilles; Deshayes, M.; Bailly, Jean-Stéphane; Lelong, Camille; Couteron, Pierre

doi:10.1080/01431160701311259

Cited by 37 publications

(26 citation statements)

References 12 publications

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“…The best imagery was acquired from Unmanned Aerial Vehicles (UAV) or UtraLight Aircrafts (ULA). Delenne et al [39] showed that simple local Fourier analysis and co-occurrence matrix-based indices, applied to very high spatial resolution images acquired with an ULA, detected vineyards and characterized intra-field cropping patterns (simple row vs. pergola), and rows and inter-row orientation and scale. Along the same lines, Amoruso et al [29] used a variogram-based texture algorithm to discriminate olive groves from forest and other vegetation land use classes.…”

Section: Tree Crop Planting Patternmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Section: Tree Crop Planting Patternmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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“…Hall et al, 2003;Johnson et al, 2003;Lamb et al, 2004;Zarco-Tejada et al, 2005;Rodríguez-Pérez et al, 2007;Hall and Wilson, 2013) and grape quality Martínez-Casasnovas et al, 2012;, or more sophisticated image processing aimed at either mapping terroir units (Pedroso et al, 2010;Vaudour et al, 2010;Urretavizcaya et al, 2013) or identifying vineyards (e.g. Wassenaar et al, 2002;Warner and Steinmaus, 2005;Rabatel et al, 2006Rabatel et al, , 2008Delenne et al, 2008Delenne et al, , 2010 ( Tables 1 and 2). Other remote-sensing studies have dealt with the incorporation of remote-sensing information into the prediction of soil properties and their monitoring in vineyards (Vaudour, 2008;Corbane et al, 2012;, with the assessment of soil erosion patterns Chevigny et al, 2014) or with the prediction of vineyard evapotranspiration (Galleguillos et al, 2011a, b;Bellvert et al, 2014).…”

Section: Remote Sensing Of Terroirmentioning

confidence: 99%

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

Vaudour

Costantini²,

Jones

et al. 2015

SOIL

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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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“…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. Moreover, a previous comparative study for vine plot detection [14] showed the superiority of a frequency analysis on a such textural approach (also based on a difference between cooccurrence features calculated along two perpendicular directions).…”

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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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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Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data

Cited by 37 publications

References 12 publications

Remote Sensing and Cropping Practices: A Review

Remote Sensing and Cropping Practices: A Review

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

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

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