scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

scholarly journals Classifying vineyards from satellite images: a case study on Burgundy’s Côte d’Or

OENO One ◽  
2014 ◽  
Vol 48 (4) ◽  
pp. 247 ◽  
Author(s):  
Jorge R. Ducati ◽  
Magno G. Bombassaro ◽  
Jandyra M. G. Fachel
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Vegetation Index ◽  
Pinot Noir ◽  
Grape Variety ◽  
Spectral Bands ◽  
Remote Sensing Techniques ◽  
Grape Varieties

<p style="text-align: justify;"><strong>Aim</strong>: To use Remote Sensing imagery and techniques to differentiate categories of Burgundian vineyards.</p><p style="text-align: justify;"><strong>Methods and results</strong>: A sample of 201 vine plots or “climats” from the Côte d’Or region in Burgundy was selected, consisting of three vineyard categories (28 Grand Cru, 74 Premier Cru, and 99 Communale) and two grape varieties (Pinot Noir and Chardonnay). A mask formed by the polygons of these vine plots was made and projected on four satellite images acquired by the ASTER sensor, covering the Côte d’Or region in years 2002, 2003 (winter image), 2004 and 2006. Mean reflectances were extracted from pixels within each polygon for each of the nine spectral bands (visible and infrared) covered by ASTER. The database had a total of 797 reflectance spectra assembled over the four images. Statistical discriminant analysis of percentage classification accuracy was made separately for Côte de Nuits and Côte de Beaune, and for each year. Results showed that for individual years and Côtes, classification accuracy for vineyard category was as high as 73.7% (Beaune 2002) and as low as 66.7% (Beaune 2003). There were no significant differences in accuracy between spring, summer and winter images. Classification accuracy for grape variety in Côte de Beaune over the four study years was between 73.5% for Pinot Noir climats in 2004 and 91.9% for Chardonnay climats in 2006, including the winter image. Concerning the vegetation index NDVI, there were no significant differences between vineyard categories.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Satellite data is shown to be functional to reveal vineyard quality. Spectral differences between categories of Burgundian vineyards are at least partially due to terroir characteristics, which are transmitted to vine and vine canopy.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: This work indicates that Remote Sensing techniques can be used as an auxiliary tool for the monitoring of vineyard quality in established viticultural regions and for the study of quality potential in new regions.</p>

scholarly journals The Analysis of Geospatial Information for Validating Some Numbers of Islands in Indonesia

2017 ◽  
Vol 49 (2) ◽  
pp. 204 ◽  
Author(s):  
Sukendra - Martha
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Geospatial Data ◽  
The Other ◽  
Remote Sensing Images ◽  
Geospatial Information ◽  
Internal Affair ◽  
High Resolution Satellite Images ◽  
Data Source

This article discusses a comparison of various numbers of islands in Indonesia; and it addresses a valid method of accounting or enumerating numbers of islands in Indonesia. Methodology used is an analysis to compare the different number of islands from various sources.  First, some numbers of  Indonesian islands were derived from: (i) Centre for Survey and Mapping- Indonesian Arm Forces (Pussurta ABRI) recorded as 17,508 islands; (ii) Agency for Geospatial Information (BIG) previously known as National Coordinating Agency for Surveys and Mapping (Bakosurtanal) as national mapping authority reported with 17,506 islands (after loosing islands of  Sipadan and Ligitan); (iii) Ministry of Internal Affair published 17,504 islands. Many parties have referred the number of 17,504 islands even though it has not yet been supported by back-up documents; (iv) Hidrographic Office of Indonesian Navy has released with numbers of 17,499; (v) Other sources indicated different numbers of islands, and indeed will imply to people confusion. In the other hand, the number of 13,466 named islands has a strong document (Gazetteer). Second, enumerating the total number of islands in Indonesia can be proposed by three ways: (i) island census through toponimic survey, (ii) using map, and (iii) applying remote sensing images. Third, the procedures of searching valid result in number of islands is by remote sensing approach - high resolution satellite images. The result of this work implies the needs of one geospatial data source (including total numbers of islands) in the form of ‘One Map Policy’ that will impact in the improvement of  Indonesian geographic data administration. 

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

scholarly journals Mapping of Polar Areas Based on High-Resolution Satellite Images: The Example of the Henryk Arctowski Polish Antarctic Station

2017 ◽  
Vol 104 (1) ◽  
pp. 65-78
Author(s):  
Zdzisław Kurczyński ◽  
Sebastian Różycki ◽  
Paweł Bylina
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Mean Squared Error ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Polar Regions ◽  
Technical Requirements ◽  
Digital Elevation ◽  
High Resolution Satellite Images

Abstract To produce orthophotomaps or digital elevation models, the most commonly used method is photogrammetric measurement. However, the use of aerial images is not easy in polar regions for logistical reasons. In these areas, remote sensing data acquired from satellite systems is much more useful. This paper presents the basic technical requirements of different products which can be obtain (in particular orthoimages and digital elevation model (DEM)) using Very-High-Resolution Satellite (VHRS) images. The study area was situated in the vicinity of the Henryk Arctowski Polish Antarctic Station on the Western Shore of Admiralty Bay, King George Island, Western Antarctic. Image processing was applied on two triplets of images acquired by the Pléiades 1A and 1B in March 2013. The results of the generation of orthoimages from the Pléiades systems without control points showed that the proposed method can achieve Root Mean Squared Error (RMSE) of 3-9 m. The presented Pléiades images are useful for thematic remote sensing analysis and processing of measurements. Using satellite images to produce remote sensing products for polar regions is highly beneficial and reliable and compares well with more expensive airborne photographs or field surveys.

scholarly journals TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 97-104
Author(s):  
R. G. C. J. Kapilaratne ◽  
S. Kaneta
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Global Climate ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Flood Events ◽  
Large Area ◽  
High Resolution Satellite Images ◽  
Catastrophic Impact

Abstract. Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5 m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/km2 with a competitive accuracy and minimal system requirements than ResNet101.

scholarly journals Photo Interpretation and GIS as a support tool for Archaeology

2017 ◽  
Vol 3 (1) ◽  
pp. 116
Author(s):  
Ariele Câmara ◽  
Teresa Batista
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Aerial Photographs ◽  
Photo Interpretation ◽  
Support Tool ◽  
Integration Techniques ◽  
Remote Sensing Techniques ◽  
New Buildings

This work presents photo interpretation integration techniques of high resolution aerial photographs and satellite images. Through the use of this methodology, it is possible to identify Dolmens located in the Center Alentejo - Portugal, and recover archaeological information. From the observation of dolmens it was perceived the shape of these objects visualised in vertical images. The use of Remote Sensing techniques in conjunction with ArcGIS allowed to confirm and to know the interpretation keys of these monuments. This feature keys allow to identify and recognise sites already identified as well as new buildings.  

Ecologico-economic Substantiation of Environmental Remediation of Rock Disposal Dump at the Coal-mining Regions of Siberia and the Far East

2021 ◽  
Vol 25 (11) ◽  
pp. 48-53
Author(s):  
I.V. Zenkov ◽  
Hung Trinh Le ◽  
V.N. Vokin ◽  
E.V. Kiryushina ◽  
T.A. Veretenova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Remediation ◽  
Satellite Images ◽  
Far East ◽  
Remote Sensing Data ◽  
The Far East ◽  
Coal Deposits ◽  
High Resolution Satellite Images ◽  
Mining Regions

Based on the remote sensing data, the aggregated information has been provided on rock disposal dumps of the surface and abandoned coal deposits in the mining regions of Siberia and the Far East. High-resolution satellite images have helped to square the acreage of slope grade horizontal alignments of rock disposal dumps, as well as the yielding capacity of plant ecosystems on the dump slopes. The rock dumps architecture has been proposed to ensure the ecological balance generation at the appropriate pace. The economic indicators of the remedial ecology package work at the rock dumps have been provided.

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