Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley, France

Ducati, Jorge Ricardo; Sarate, Rafael Eberhardt; Fachel, Jandyra Maria Guimarães

doi:10.20870/oeno-one.2014.48.3.1574

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…However, the very limited dataset used in that study (two fields) considerably limits its scope. Ducati et al [18] assessed the discrimination level between 46 conventional vineyards and 12 organic ones in the Loire Valley in France with Terra-ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite images. Three statistical linear discriminant analyses using the two visible spectral bands, the seven near-infrared and short-wave infrared spectral bands or all nine spectral bands resulted in 69%, 91%, and 91% classification accuracy, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize

Denis

Desclée²,

Migdall³

et al. 2020

Remote Sensing

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The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process.

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Section: Introductionmentioning

confidence: 99%

Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize

Denis

Desclée²,

Migdall³

et al. 2020

Remote Sensing

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“…They make use of a large range of sensors providing various spectral capabilities. They include multispectral data from Landsat [176] and ASTER [38,177,180], as well as hyperspectral data acquired from onboard satellite Hyperion [181,182], and airborne CASI (Compact Airborne Spectrographic Imager) sensor [183,184]. These studies that use different sensors are difficult to compare because of different experimental conditions (number of varieties, size of the area, etc.…”

Section: Crop Varietiesmentioning

confidence: 99%

“…Spectral properties are important in the visible wavelengths because the color of the foliage can be cultivar-dependent, but reflectance in the near infrared is the most often cited wavelength for variety discrimination, due to the different structures of the plants. In this context, the images acquired during the vegetative season are preferred when the soil is well-covered by the canopy [180]. To increase the chance of successful spectral discrimination of varieties, the sources of canopy reflectance variation must be eliminated as much as possible.…”

Section: Crop Varietiesmentioning

confidence: 99%

“…However, sources of spectral variation due to factors other than crop variety can persist, such as the soil, terrain slope, and row orientation that play relevant roles in the reflectance responses [180], particularly through the fraction of shadowed/sunlit soil for perennial crops. However, these external factors can also be the ultimate differentiation factors for variety discrimination, as with vineyards where Chardonnay is often grown on south and south-west facing parcels, and Pinot noir tends to occupy the higher parts of the hilly landscape [180]. We believe that the next significant methodological advance in the discrimination of crop varieties will come with the availability of new hyperspectral sensors, but all the issues associated with this application remain challenging.…”

Section: Crop Varietiesmentioning

confidence: 99%

“…This can be achieved by classifying the pixels inside a crop type mask in a given area [177] (Figure 5), by classifying only vegetation objects even with a small size of foliage [187], or by selecting fields with homogeneous cropping practices such as the crop age [176]. However, sources of spectral variation due to factors other than crop variety can persist, such as the soil, terrain slope, and row orientation that play relevant roles in the reflectance responses [180], particularly through the fraction of shadowed/sunlit soil for perennial crops. However, these external factors can also be the ultimate differentiation factors for variety discrimination, as with vineyards where Chardonnay is often grown on south and south-west facing parcels, and Pinot noir tends to occupy the higher parts of the hilly landscape [180].…”

Section: Crop Varietiesmentioning

confidence: 99%

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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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