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Quantification of cotton water consumption by remote sensing / Jefferson Vieira José in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1800 - 1813 Titre : Quantification of cotton water consumption by remote sensing Type de document : Article/Communication Auteurs : Jefferson Vieira José, Auteur ; Niclene Ponce Rodrigues de Oliveira, Auteur ; Tonny José de Araújo da Silva, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1800 - 1813 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] biome [Termes descripteurs IGN] cultures irriguées [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] gestion de l'eau [Termes descripteurs IGN] Gossypium (genre) [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] irrigation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Mato Grosso [Termes descripteurs IGN] SEBAL (algorithme) Résumé : (auteur) Quantifying crop water consumption is essential for water resource management. The objective was to estimate the current evapotranspiration (ETa) of the cotton crop (Gossypium hirsutum L.) in the rainfed system, as well as the components of the radiation and energy balance in the Cerrado biome conditions using orbital images and the SEBAL algorithm and validate the estimates of evapotranspiration (ET) using FAO guidelines for crop coefficient (K c) of the cotton crop. Research was carried out in the State of Mato Grosso, Brazil, in areas with three cotton cultivars. Images of the Operational Land Imager and Thermal Infrared Sensor sensors were used and ET estimation was made based on the SEBAL algorithm. Mean ETa in the cotton cycle was 3.5 mm dia−1 and the K c values ranged from 0.22 and 1.12, on average, in the smaller and larger leaf area, respectively. Numéro de notice : A2020-726 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583777 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583777 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96329 [article]

An integration of bioclimatic, soil, and topographic indicators for viticulture suitability using multi-criteria evaluation: a case study in the Western slopes of Jabal Al Arab—Syria / Karam Alsafadi in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 13 [01/10/2020] . - pp 1466 - 1488 Titre : An integration of bioclimatic, soil, and topographic indicators for viticulture suitability using multi-criteria evaluation: a case study in the Western slopes of Jabal Al Arab—Syria Type de document : Article/Communication Auteurs : Karam Alsafadi, Auteur ; S. Mohammed, Auteur ; H. Habib, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1466 - 1488 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] bioclimatologie [Termes descripteurs IGN] climat [Termes descripteurs IGN] fertilité [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] qualité du sol [Termes descripteurs IGN] Syrie [Termes descripteurs IGN] topographie locale [Termes descripteurs IGN] viticulture Résumé : (auteur) In the 21st century, geographic information systems (GIS) have become one of the leading technologies in different sectors for development and planning, particularly in modern agricultural management. Moreover, recent advances in GIS tools and methods have helped decision-makers as well as farmers to find optimal sites for production of different crops. The cultivation of vineyards and grapes is one of the most important agricultural activities in the Al-Sweidaa governorate—Syria, which has been suffering from a decrease in annual productivity in conjunction with an increase in the annual demand for grapes and wine products, particularly in recent decades. Therefore, the aim of this research was to establish a new method for analyzing the optimum regions for economic viticulture production in the Western Slopes of Jabal Al Arab in the Al-Sweidaa governorate by using multi-criteria evaluation (MCE). To this end, a field survey was conducted and a soil sample was collected for physical and chemically analysis, and a 1984–2014 MRm.30-meter resolution dataset of climatic variables for the Al-Sweidaa governorate was set up as well. The results show that suitable areas are concentrated in the higher part of the study area (the eastern part) where climate and soil are favourable, and did not show any relevant limitations. Conversely, the lower part of the study area (the western) has unfavourable climate and soil chemical and physical fertility; therefore grape production is only possible if irrigation is applied and the fertility properties of the soil are improved, particularly the percentage of organic matter and the soil texture. Numéro de notice : A2020-609 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583291 date de publication en ligne : 14/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583291 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95970 [article]

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Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432 Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] régression des moindres carrés partiels [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] spectroradiomètre Résumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967 [article]

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Analysis of chlorophyll concentration in potato crop by coupling continuous wavelet transform and spectral variable optimization / Ning Liu in Remote sensing, vol 12 n° 17 (September 2020)  

Public [article]  inRemote sensing > vol 12 n° 17 (September 2020) . - 22 p. Titre : Analysis of chlorophyll concentration in potato crop by coupling continuous wavelet transform and spectral variable optimization Type de document : Article/Communication Auteurs : Ning Liu, Auteur ; Zizheng Xing, Auteur ; Ruomei Zhao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] azote [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] étalonnage de modèle [Termes descripteurs IGN] pomme de terre [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] régression des moindres carrés partiels [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (R2c) of 0.93 and the smallest R2c−R2cv value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (R2v) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops. Numéro de notice : A2020-600 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12172826 date de publication en ligne : 31/08/2020 En ligne : https://doi.org/10.3390/rs12172826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95950 [article]

Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 73 - 83 Titre : Counting of grapevine berries in images via semantic segmentation using convolutional neural networks Type de document : Article/Communication Auteurs : Laura Zabawa, Auteur ; Anna Kicherer, Auteur ; Lasse Klingbeil, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] comptage [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] extraction semi-automatique [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] viticulture Résumé : (auteur) The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH. Numéro de notice : A2020-252 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.002 date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.002 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94996 [article]

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Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data / Dipanwita Haldar in Geocarto international, vol 35 n° 4 ([15/03/2020])  

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Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Optimising drone flight planning for measuring horticultural tree crop structure / Yu-Hsuan Tu in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Feasibility study of vegetation indices derived from Sentinel-2 and PlanetScope satellite images for validating the LAI biophysical parameter to monitoring development stages of winter wheat / Radoslaw Gurdak in Geoinformation issues, Vol 10 n°1 (2018)  

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Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

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