Análisis de zonas de cultivo y cuerpos de agua mediante el cálculo de índices radiométricos con imágenes Sentinel-2

Los cultivos y cuerpos de agua son un tema de interés para los países. Tener información sobre las zonas de cultivo, fuentes de agua y su comportamiento en las distintas temporadas del año es de utilidad para la producción agrícola y para la toma de decisiones. Por otra parte, actualmente se han generado gran cantidad de datos satelitales de la Tierra y herramientas para el procesamiento de grandes volúmenes de imágenes satelitales que son fundamentales para el monitoreo forestal, análisis multitemporal de zonas de cultivo y cuerpos de agua, clasificación del uso del suelo, entre otros usos. Sentinel-2 es un programa de observación de la Tierra que consta de 13 bandas espectrales que proporcionan imágenes de alta resolución espacial y calidad radiométrica. En este artículo se presenta un análisis multitemporal basado en el Índice de Vegetación de Diferencia Normalizada y el Índice de Agua de Diferencia Normalizada, obtenidos con imágenes del satélite Sentinel-2, para la identificación de cambios que se presentan en una zona del sureste de México en el periodo 2018-2020. Los resultados obtenidos demuestran un bajo rango del índice radiométrico en las áreas de estudio durante el 2018. Asimismo, los mayores cambios durante la temporada de lluvia fueron registrados en 2018; esto evidencia la provocación en la disminución en la calidad de los cultivos y en el cuerpo de agua

CrawNet: Multimedia Crawler Resources for Both Surface and Hidden Web

The web is the most used information source in both academic, scientific and industry forums. Its explosive growth has generated billions of pages with information which may be categorized as surface web, composed of static pages that are indexed into a hidden web, accessible through search templates. This paper presents the development of a crawler that allows searching, queries, and analysis of information in the surface web and hidden in specific domains of the web

Absence of molecular evidence of Leptospira spp. in urine samples collected from rodents captured in Yucatán, México

Leptospira spp. is a spirochete bacteria, causal agent of leptospirosis, zoonotic disease endemic in México that represents a serious public health and veterinary problem. Rodents are recognised as the most important reservoirs of this bacteria, which is transmitted mainly through direct or indirect contact with the Leptospira spp. excreted in the urine of infected individuals. Theaim of this study was to evaluate the circulation of Leptospira spp. in urine samples of wild and synanthropic rodents from Yucatán, México. Eighty-four rodents were captured in the community of Cenotillo, Yucatán. Twenty-six urine samples were collected from the bladder and were used in the total DNA extraction. The identification of Leptospira spp. was intended through the polymerase chain reaction test in its endpoint variant. No evidence of Leptospira spp. was found in the urine samples. It is necessary to use other tissues for the identification of Leptospira spp., before concluding that the rodents used in the present study are not reservoirs of this bacteri

Molecular detection of pathogenic Leptospira in synanthropic and wild rodents captured in Yucatán, México

Introduction: Leptospirosis is a zoonotic disease caused by bacteria of the genus Leptospira, which is endemic in México and considered a public and veterinary health problem. Rodents are the most relevant reservoirs of Leptospira spp. because the bacteria establish and reproduce in its renal tissue and are excreted through the urine. Objective: To identify the presence of Leptospira spp. in renal tissue from rodents captured in Yucatán, México. Materials and methods: Synanthropic and wild rodents were captured in the rural municipality of Cenotillo, Yucatán, México. We collected one kidney from each rodent and extracted the total DNA. The identification of Leptospira spp. was done by detecting two fragments of the 16S rRNA gene using end-point polymerase chain reaction (PCR). We sequenced and analyzed positive products using alignment tools. Results: A total of 92 rodents belonging to seven different species were captured. The PCR yielded a global positivity of 5.4% (5/92). The alignment analysis of the sequenced products demonstrated a 100% of coverage and identity with Leptospira interrogans. This is the first molecular evidence of Leptospira spp. circulation in Heteromys gaumeri captured in Yucatán, México. Conclusion: Our results evidenced that rodents of Yucatán are reservoirs of Leptospira spp. and participate in the infection cycle of leptospirosis in the region

Sentinel-1 SAR Images and Deep Learning for Water Body Mapping

Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases

Análisis de zonas de cultivo y cuerpos de agua mediante el cálculo de índices radiométricos con imágenes Sentinel-2

Crops and bodies of water are a topic of interest to countries. Having information about growing areas, water sources and their behaviour in the different seasons of the year is useful for agricultural production and decision-making. On the other hand, a large amount of Earth satellite data and tools for the processing of large volumes of satellite images have now been generated that are critical for forest monitoring, multitemporal analysis of growing areas and bodies of water, classification of land use, among others. Sentinel-2 is an Earth observation program consisting of 13 spectral bands that provide high spatial resolution and radiometric quality images. This article presents a multitemporal analysis based on the Standardized Difference Vegetation Index and Standardized Difference Water Index obtained with images of the Sentinel-2 satellite, for the identification of changes that occur in an area of south-eastern Mexico in the period 2018-2020. The results obtained demonstrate a low range of radiometric indexes in the study areas during 2018. In addition, the biggest changes during the rainy season were recorded in 2018; this shows the provocation in the decrease in crop quality and in the body of water.Los cultivos y cuerpos de agua son un tema de interés para los países. Tener información sobre las zonas de cultivo, fuentes de agua y su comportamiento en las distintas temporadas del año es de utilidad para la producción agrícola y para la toma de decisiones. Por otra parte, actualmente se han generado gran cantidad de datos satelitales de la Tierra y herramientas para el procesamiento de grandes volúmenes de imágenes satelitales que son fundamentales para el monitoreo forestal, análisis multitemporal de zonas de cultivo y cuerpos de agua, clasificación del uso del suelo, entre otros usos. Sentinel-2 es un programa de observación de la Tierra que consta de 13 bandas espectrales que proporcionan imágenes de alta resolución espacial y calidad radiométrica. En este artículo se presenta un análisis multitemporal basado en el Índice de Vegetación de Diferencia Normalizada y el Índice de Agua de Diferencia Normalizada, obtenidos con imágenes del satélite Sentinel-2, para la identificación de cambios que se presentan en una zona del sureste de México en el periodo 2018-2020. Los resultados obtenidos demuestran un bajo rango del índice radiométrico en las áreas de estudio durante el 2018. Asimismo, los mayores cambios durante la temporada de lluvia fueron registrados en 2018; esto evidencia la provocación en la disminución en la calidad de los cultivos y en el cuerpo de agua

CrawNet: Multimedia Crawler Resources for Both Surface and Hidden Web

The web is the most used information source in both academic, scientific and industry forums. Its explosive growth has generated billions of pages with information which may be categorized as surface web, composed of static pages that are indexed into a hidden web, accessible through search templates. This paper presents the development of a crawler that allows searching, queries, and analysis of information in the surface web and hidden in specific domains of the web