Measurement of the chi_{c2} Polarization in psi(2S) to gamma chi_{c2}

The polarization of the chi_{c2} produced in psi(2S) decays into gamma chi_{c2} is measured using a sample of 14*10^6 psi(2S) events collected by BESII at the BEPC. A fit to the chi_{c2} production and decay angular distributions in psi(2S) to gamma chi_{c2}, chi_{c2} to pi pi and KK yields values x=A_1/A_0=2.08+/-0.44 and y=A_2/A_0=3.03 +/-0.66, with a correlation rho=0.92 between them, where A_{0,1,2} are the chi_{c2} helicity amplitudes. The measurement agrees with a pure E1 transition, and M2 and E3 contributions do not differ significantly from zero.Comment: 6 pages, 4 figures, 1 tabl

Aplicación del enfoque multi-índice con imágenes Sentinel-2 para obtener áreas urbanas en la estación seca (Zonas semiáridas en el noreste de Argelia)

[EN] The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.[ES] La cartografía de las zonas urbanas presenta una gran dificultad, especialmente en los entornos áridos y semiáridos. Por esa razón, en esta investigación esperamos aumentar la precisión de la cartografía de la ciudad de Bordj Bou Arreridj en las regiones semiáridas (noreste de Argelia) centrándose en la identificación de la combinación adecuada de los índices espectrales obtenidos por teledetección. El estudio aplica el clasificador ‘k-means’. A este respecto, se seleccionaron cuatro índices espectrales, a saber, el índice de labranza de diferencia normalizada (NDTI) para el área construida, el índice de suelo desnudo (BSI) y el índice de suelo desnudo seco (DBSI), que están relacionados con el suelo desnudo, así como el índice de vegetación de diferencia normalizada (NDVI). Todos los índices espectrales anteriores mencionados se derivaron de datos Sentinel-2 adquiridos durante la estación seca (agosto). Se generaron dos combinaciones de ellas utilizando el proceso de superposición de capas, manteniendo constante tanto el índice NDTI como el índice NDVI en ambas combinaciones, de modo que el multi-índice NDTI/BSI/NDVI fue la primera combinación de conjuntos de datos, y el multi-índice NDTI/DBSI/NDVI fue el segundo componente. Los resultados muestran que el índice BSI funciona mejor con NDTI en comparación con el uso de DBSI. Por lo tanto, BSI proporciona mejoras: las clases de suelo desnudo y la de construcciones fueron mejor discriminadas, aumentando la precisión global en un 5,67%, y el coeficiente kappa un 12,05%. El uso de k-means como clasificador no supervisado proporciona una detección del área urbana automática y rápida. Por lo tanto, el conjunto de datos de varios índices NDTI/ BSI/ NDVI fue adecuado para cartografiar las ciudades en clima seco, y podría proporcionar una mejor gestión urbana y futuras aplicaciones de teledetección en zonas semiáridas en particular.Rouibah, K.; Belabbas, M. (2020). Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria). Revista de Teledetección. 0(56):89-101. https://doi.org/10.4995/raet.2020.13787OJS89101056Al-Quraishi, A. ( 2011). Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region. 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Aplicação de índices espectrais na avaliação do aporte de sedimento aos reservatórios das Usinas Hidrelétricas Itumbiara e Batalha (Brasil)

In this work, two spectral indexes were implemented using Landsat images, the Normalized Difference Water Index (NDWI) for the delimitation of the water mirror and the Normalized Difference Turbidity Index (NDTI) to evaluate the sediment contribution to the reservoirs of the hydroelectric power plants Itumbiara and Batalha, located on the borders of the states of Goiás and Minas Gerais. The acquisition and processing were carried out in the Google Earth Engine platform and the post-processing in the QGIS software. The NDTI was applied to the reservoirs considering the variation in water level between the dry and rainy seasons in 2020. The highest concentration of turbidity in the Itumbiara reservoir occurred in September, and the lowest in June. August was the month with the highest turbidity concentration and July the lowest in the Batalha reservoir.En este trabajo se implementaron dos índices espectrales utilizando imágenes Landsat, el Índice de Diferencia de Agua Normalizada (NDWI) para la delimitación del espejo de agua y el Índice de Turbidez de Diferencia Normalizada (NDTI) para evaluar el aporte de sedimentos a los embalses de las centrales hidroeléctricas Itumbiara y Batalha, ubicadas en los límites de los estados de Goiás y Minas Gerais. El NDTI se aplicó en los embalses considerando la variación del nivel del agua entre las épocas seca y lluviosa de 2020. La mayor concentración de turbiedad en el embalse de Itumbiara se presentó en septiembre y la menor en junio, siendo agosto el mes con mayor concentración de turbidez y julio con la menor en el embalse de Batalha.Dans ce travail, deux indices spectraux ont été implémentés à l'aide d'images Landsat, le Normalized Difference Water Index (NDWI) pour la délimitation du miroir d'eau et le Normalized Difference Turbidity Index (NDTI) pour évaluer la contribution des sédiments aux réservoirs des centrales hydroélectriques. Itumbiara et Batalha, situées aux confins des états de Goiás et Minas Gerais. Le NDTI a été appliqué dans les réservoirs en tenant compte de la variation du niveau d'eau entre les saisons sèche et pluvieuse de 2020. La plus forte concentration de turbidité dans le réservoir d'Itumbiara s'est produite en septembre et la plus faible en juin. Dans le réservoir de Batalha, août étant le mois avec la plus forte concentration de turbidité et la plus faible en juillet.In questo lavoro sono stati implementati due indici spettrali utilizzando immagini Landsat, il Normalized Difference Water Index (NDWI) per la delimitazione dello specchio d'acqua e il Normalized Difference Turbidity Index (NDTI) per valutare il contributo dei sedimenti ai serbatoi delle Centrali Idroelettriche Itumbiara e Batalha, situate ai confini degli stati di Goiás e Minas Gerais. L'acquisizione e l'elaborazione sono state effettuate nella piattaforma Google Earth Engine e la post-elaborazione nel QGIS. L'NDTI è stato applicato nei bacini considerando la variazione del livello dell'acqua tra la stagione secca e quella piovosa del 2020. La più alta concentrazione di torbidità nel bacino di Itumbiara si è verificata a settembre, e la più bassa a giugno. Nel bacino di Batalha, agosto è il mese con la più alta concentrazione di torbidità e il più basso a luglio.Neste trabalho foram implementados dois índices espectrais utilizando imagens Landsat, o Índice da Diferença Normalizada de Água (NDWI) para delimitação do espelho d’água e o Índice de Turbidez por Diferença Normalizada (NDTI) para avaliação do aporte de sedimentos aos reservatórios das Usinas Hidrelétricas Itumbiara e Batalha, localizadas nas divisas dos estados de Goiás e Minas Gerais. A aquisição e o processamento foram realizados na plataforma Google Earth Engine e o pós-processamento no software QGIS. O NDTI foi aplicado nos reservatórios considerando a variação do nível de água entre as estações seca e chuvosa de 2020. A maior concentração de turbidez no reservatório de Itumbiara se deu em setembro, e a menor em junho. Já no reservatório de Batalha, sendo agosto o mês com maior concentração de turbidez, e a menor em julho

An analysis of flooding coverage using remote sensing within the context of risk assessment

Results of research of the identification of flooding as a result of groundwater table fluctuations on the example of the valley of the River Vistula, with the use of multi-spectral Sentinel-2 images from the years 2017–2018 are presented. An analysis of indexes of water use, calculated on the basis of green, red and shortwave infrared (SWIR) bands, for extraction of water objects and flooded areas was carried out. Based on the analyses conducted, a mapping method was developed, using three water indexes (MNDWI Modified Normalised Difference Water Index, NDTI Normalised Difference Index and NDPI Normalised Difference Pond Index). Results show that the 10 metre false colour composite RNDTIGNDPIBMNDWI obtained significantly improved submerged extractions more than did individual water indexes. Moreover, the 10-m-images of MNDWI and NDPI, obtained by the sharpening High Pass Filter (HPF), may represent more detailed spatial information on floods than the 20-m-MNDWI and NDPI, obtained from original images