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The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran / Hojatolah Ganjkhanlo in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1769 - 1782 Titre : The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran Type de document : Article/Communication Auteurs : Hojatolah Ganjkhanlo, Auteur ; Mehdi Vafakhah, Auteur ; Hossein Zeinivand, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1769 - 1782 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bassin hydrographique [Termes IGN] carte thématique [Termes IGN] classification hypercube [Termes IGN] eau de fonte [Termes IGN] échantillonnage de données [Termes IGN] épaisseur [Termes IGN] géostatistique [Termes IGN] Iran [Termes IGN] krigeage [Termes IGN] manteau neigeux [Termes IGN] neige [Termes IGN] précision de l'estimation [Termes IGN] zone semi-aride Résumé : (auteur) The aim of this study is to compare the effect of two sampling patterns: systematic sampling and Latin hypercube sampling (LHS), on estimation precision of snow water equivalent (SWE), and also comparing different geostatistics methods of kriging, cokriging and radial basin functions for mapping SWE. To achieve the study purpose, the semi-arid mountainous watershed of Sohrevard in Zanjan Province of Iran was selected. Snow depth in 150 points with systematic sampling and 150 points with LHS sampling and snow density in 18 points were randomly measured. In addition, SWE was calculated in the study area, and its map was derived based on both the sampling methods using geostatistical techniques. The results showed that the accuracy of the SWE map using LHS was higher than systematic sampling. According to the most statistical indicators, in both methods of sampling, accuracy of mapping using regular spline was better than other methods. Numéro de notice : A2020-725 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581267 Date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581267 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96328 [article]

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 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 IGN] biome [Termes IGN] cultures irriguées [Termes IGN] évapotranspiration [Termes IGN] gestion de l'eau [Termes IGN] Gossypium (genre) [Termes IGN] image thermique [Termes IGN] irrigation [Termes IGN] Leaf Area Index [Termes IGN] Mato Grosso 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]

Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1832 - 1849 Titre : Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal Type de document : Article/Communication Auteurs : Santa Pandit, Auteur ; Satoshi Tsuyuki, Auteur ; Timothy Dube, Auteur Année de publication : 2020 Article en page(s) : pp 1832 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multibande [Termes IGN] analyse texturale [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] forêt [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] Népal Résumé : (auteur) The potential of the improved resolution Sentinel-2 MSI data was explored through texture metrics, vegetation indices (VIs) and pooled dataset using the Random Forest (RF) machine learning algorithm to estimate Above-ground Biomass (AGB) in a sub-tropical forest of Nepal. Texture metrics were derived based on different working window sizes (3 × 3, 5 × 5, 7 × 7 and 9 × 9), and the results were compared with those obtained, using raw traditional bands (Analysis set 1: 2, 3, 4, 8, 11 and 12), raw traditional and red edge bands (Analysis set 2: Set 1 + Band 5, 6, 7 and 8A), and red edge bands (Analysis set 3) only. Comparatively, the use of pooled data (texture and VIs) yielded higher biomass estimates. The results from pooled data based on the 7 × 7 window size resulted in models with better model fitting parameters. For instance, pooled data produced an R2 = 0.99 and a RMSE = 4.51 t ha−1 (relRMSE = 2.82). Further, the RF model selected dissimilarity, variance and mean from Band 2 and SAVI (Soil adjusted vegetation index) as the most important AGB predictor variables. The results demonstrated that like the red-edge bands, traditional bands were equally important in AGB estimation. Numéro de notice : A2020-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588390 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1588390 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96334 [article]