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French to English: Sample - earth observation and vegetation monitoring General field: Science Detailed field: Environment & Ecology
Source text - French Note conceptuelle du service
Le NDVI est de loin, l’indice de végétation le plus utilisé dans le suivi de la dynamique saisonnière de la végétation (Junich Imanishi et al, 2007). Les données spectrales provenant des capteurs de plusieurs satellites comme : Advanced Very High Resolution Radiometer de la National Oceanic and Atmospheric Administration (NOAA/AVHRR), VEGETATION du satellite pour l’observation de la Terre (SPOT VGT), Moderate Resolution Spectroradiometer (MODIS TERRA), METEOSAT Second Generation (MSG) sont utilisées pour le produire. Les acquisitions de plusieurs jours sont combinées à travers des méthodes diverses et variées pour obtenir des images plus ou moins propres, en réduisant les contaminations des nuages, les effets de réflectance directionnelle et ceux de vue Off Nadir et minimisant les effets de somme angulaires, des aérosols et de la vapeur d’eau (Brent N. HOLBEN, 1986).
Plusieurs auteurs, au nombre desquels, Justice et al (1984), Hiernaux et al (1986), Huete et al (2002), David et al, (2004) ont montré les performances, mais aussi les limites de cet indice dans le suivi et la caractérisation de la végétation à l’échelle globale. Au vu des limites que présente le NDVI, l’indice de végétation amélioré (EVI) a été proposé (Liu et Huete, 1995). L’étude comparée des performances des indices, NDVI et EVI, montre que le premier sature vite dans les zones de végétations denses tandis que le second maintient une bonne sensibilité sur les caractéristiques du couvert végétal (Huete et al, 2002 ; Brun, 2003).
Par ailleurs, plusieurs autres indices utilisant, la différence ou le ratio entre les bandes rouges et proche infrarouge comme : le Ratio vegetation index (RVI), le difference vegetation index (DVI), le Atmospherically resistant vegetation index (ARVI), le perpendiculaire vegetation index (PVI), le soil-adjusted vegetation index (SAVI) ont été développés pour mieux détecter la densité du couvert végétal, en vue de pallier aux limites du NDVI (Junishi et al, 2007). La comparaison des séries d’images d’indice de végétation (IV) d’une culture de maïs (NDVI, EVI, LAI et faPAR) provenant de MODIS TERRA et SPOT VEGETATION faite au Mexique montre certes les limites des images d’IV en matière de suivi de la dynamique de la végétation mais révèle que les indices de végétation issus de SPOT VEGETAION sont plus recommandables. Les images MODIS à 250 m de résolution n’apportent pas d’informations plus précises sur la dynamique de la végétation que celles de 500 et 1000 m (PEI-Yu Chen et al, 2006).
Les séries temporelles d’images NDVI sont utilisées pour l’étude de la dynamique de la végétation notamment dans le cadre du suivi et l’évaluation qualitatifs et quantitatifs des productions agricoles et pastorales ; c’est-à-dire pour estimer les rendements agricoles et les productions de biomasse des parcours. Ainsi, les séries temporelles d’images NDVI et de pluies estimées sont analysées dans un contexte de variabilité des pluies suivant différentes zones agroécologiques.
Translation - English Service concept note
NDVI is by far the most widely used vegetation index in monitoring the seasonal dynamics of vegetation (Junich, Imanishi et al, 2007). Spectral data from the sensors of several satellites such as: the Advanced Very High Resolution Radiometer of the National Oceanic and Atmospheric Administration (NOAA/AVHRR), the VEGETATION instrument of the SPOT satellite (SPOT VGT), the Moderate Resolution Imaging Spectroradiometer (MODIS TERRA), and METEOSAT Second Generation (MSG) are used to produce it. Multi-day acquisitions are combined through various methods to obtain more or less clean images, by reducing cloud contamination, the effects of directional reflectance and off-nadir view, and by minimising the effects of look angle, aerosols and water vapour (Brent N. HOLBEN, 1986).
Several authors, among them, Justice et al (1984), Hiernaux et al (1986), Huete et al (2002), David et al, (2004) have demonstrated the performance, but also the limitations of this index in the monitoring and characterisation of vegetation on a global scale. Given the limitations of NDVI, the Enhanced Vegetation Index (EVI) was proposed (Liu and Huete, 1995). The comparative performance study of the indices, NDVI and EVI, shows that the first saturates rapidly in densely vegetated areas while the latter maintains good sensitivity to vegetation cover characteristics (Huet et al, 2002; Brun, 2003).
Furthermore, several other indices using, the difference or ratio between red and near-infrared bands such as: Ratio Vegetation Index (RVI), Difference Vegetation index (DVI), Atmospherically Resistant Vegetation Index (ARVI), Perpendicular Vegetation Index (PVI), and the Soil-Adjusted Vegetation Index (SAVI) have been developed to better detect the density of vegetation cover, in order to alleviate the limitations of NDVI (Junishi et al, 2007). Comparison of a series of Vegetation Index (VI) images of a maize crop (NDVI, EVI, LAI and FAPAR) derived from MODIS TERRA and SPOT VEGETATION in Mexico certainly shows the limitations of VI images in monitoring vegetation dynamics, but also shows that the vegetation indices from SPOT VEGETATION are better. The MODIS images at 250 m resolution do not provide any more accurate information on vegetation dynamics than those of 500 and 1000 m (PEI-Yu Chen et al, 2006).
Temporal series of NDVI images are used for the study of vegetation dynamics, particularly in the context of the qualitative and quantitative monitoring and evaluation of agricultural and pastoral production; that is to estimate agricultural yields and biomass production of rangelands. Thus, the time series of NDVI images and rainfall estimates are analysed in the context of rainfall variability according to different agroecological zones.
Spanish to English: Sample English to Spanish - earth observation and cloud processing General field: Science Detailed field: IT (Information Technology)
Source text - Spanish Google Earth Engine
Cloud platforms are online repositories of data collocated with software and immense computing resources. Cloud-based Earth observation systems are evolving rapidly and offer significant advantages when they include access to application-ready (pre-processed) satellite data archives as well as image processing tools. These include the removal of any requirements for high capacity parallel processing server infrastructure or massive data storage. They also reduce the magnitude of data to be download (by a factor of 10 or more) to just the volumes associated with processed end products.
Google Earth Engine (GEE) claims to be the most advanced cloud-based geospatial processing platform in the world. It:
• allows highly-interactive algorithm development at global scale;
• pushes the edge of the envelope for big data in remote sensing;
• enables high-impact, data-driven science;
• makes substantive progress on global challenges that involve large geospatial datasets.
The main components of GEE are:
• Datasets: a petabyte-scale archive of publicly available remotely sensed imagery and other data.
• Compute power: Google’s computational infrastructure optimized for parallel processing of geospatial data.
• APIs: for JavaScript (and Python, hosted on GitHub) for making requests to the Earth Engine servers.
• Code Editor: an online Integrated Development Environment (IDE) for rapid prototyping and visualization of complex spatial analyses using the JavaScript API.
GEE allows users to run algorithms on georeferenced imagery and vectors stored on Google's infrastructure. The Google Earth Engine API provides a library of functions which may be applied to imagery for display and analysis. Earth Engine's public data catalogue contains a large amount of publicly available imagery, and vector datasets are available through Google Fusion Tables. Developers can access existing vector datasets or create their own. The Code Editor is an interactive environment for developing Earth Engine applications.
The power of GEE makes it possible to pick up and process an entire archive of Sentinel data to answer analysis questions in minutes: the equivalent job of downloading data and processing them offline could take weeks or months.
Edge removal
Some Sentinel-1 data held in Google Earth Engine have spurious values at the swath edges that cause problems when adjacent swaths are mosaicked. The problem is partially addressed by Google’s pre-processing, but incidence angle values can be used to completely mask these swath extremities.
The parameterisation of this step can be further constrained to minimise swath overlap, thereby reducing areas covered by multiple swaths where revisit frequency is higher and high and low incidence angles (near and far range) are interleaved. Note that swath overlap varies with latitude.
Translation - English Google Earth Engine
Las plataformas en la nube son repositorios en línea de datos ubicados juntos con software e inmensos recursos de computación. Los sistemas de observación de la Tierra basados en la nube evolucionan rápidamente y ofrecen ventajas significativas cuando incluyen acceso a archivos de datos satelitales listos para la aplicación (procesados previamente), así como a herramientas de procesamiento de imágenes. Estos eliminan cualquier requisito para la infraestructura del servidor de procesamiento en paralelo de alta capacidad o el almacenamiento masivo de datos. También reducen la magnitud de los datos que se descargarán (por un factor de 10 o más) a solo los volúmenes asociados con los productos finales procesados.
Google Earth Engine (GEE) afirma ser la plataforma de procesamiento geoespacial en el ámbito de la nube más avanzada del mundo. Eso:
• permite el desarrollo de algoritmos altamente interactivos a escala global;
• expande los límites de los datos masivos (big data) en la teledetección;
• permite una ciencia de alto impacto y basada en datos;
• hace un progreso sustancial en los desafíos globales que involucran grandes conjuntos de datos geoespaciales.
Los componentes principales de GEE son:
• Conjuntos de datos: un archivo a escala de petabytes de imágenes de teledetección disponible públicamente y otros datos.
• Potencia de cálculo: infraestructura computacional de Google optimizada para procesamiento paralelo de datos geoespaciales.
• API: para JavaScript (y Python, alojado en GitHub) para realizar solicitudes a los servidores de Earth Engine.
• Code Editor: el editor de código - un entorno de desarrollo integrado (Integrated Development Environment - IDE) en línea para la creación rápida de prototipos y la visualización de análisis espaciales complejos utilizando la API de Javascript.
GEE les permite a los usuarios ejecutar algoritmos en imágenes georreferenciadas y vectores almacenados en la infraestructura de Google. La API de Google Earth Engine proporciona una biblioteca de funciones que se pueden aplicar a las imágenes para su visualización y análisis. El catálogo de datos públicos de Earth Engine contiene una gran cantidad de imágenes disponibles públicamente, y los conjuntos de datos vectoriales están disponibles a través de Google Fusion Tables. Los desarrolladores pueden acceder a conjuntos de datos vectoriales existentes o crear los suyos propios. El editor de código (Code Editor) es un entorno interactivo para desarrollar aplicaciones de Earth Engine.
El poder de GEE hace posible recoger y procesar un archivo completo de datos de Sentinel para responder a preguntas de análisis en minutos: el trabajo equivalente de descargar datos y procesarlos sin conexión podría llevar semanas o incluso meses.
Eliminación de bordes
Algunos datos Sentinel-1 que se encuentran en Google Earth Engine tienen valores espurios en los bordes de los barridos que causan problemas cuando se realizan mosaicos con los barridos adyacentes. El problema se aborda parcialmente mediante el procesamiento previo de Google, pero se pueden utilizar los valores de ángulo de incidencia para enmascarar completamente estas extremidades del barrido.
La parametrización de este paso puede ser más restringida para minimizar la superposición del barrido, reduciendo así las áreas cubiertas por múltiples barridos donde la frecuencia de revisita es mayor y los ángulos de incidencia alta y baja (de alcance cercana y lejana) se intercalan. Observe que el solapamiento del barrido varía con la latitud.
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Translation education
Other - Alliance Francaise/Instituto Cervantes
Experience
Registered at ProZ.com: Sep 2018. Became a member: May 2019.
I am a native English speaker from the United Kingdom, based in northern Spain, and I translate from Spanish and French into English. My specialist fields are the environment, agriculture and international cooperation or development. I have specialist knowledge of the technologies of satellite earth observation and geographic information, which I do consultancy work in.
My expertise in this field is based on a long international career working in agricultural and environmental monitoring projects in Europe, Africa and South America; and my degrees – a BSc in Environmental Sciences and an MSc in Applied Remote Sensing.
I am qualified to the European C1 level Spanish (DELE - Diploma de Español como Lengua Extranjera, Instituto Cervantes) and French (DALF - Diplôme approfondi de langue française, Alliance Française). I also translate in general texts in my two source languages.
- scientific documents with a Spanish translation agency
- technical translation of project materials from English into Spanish for earth observation projects with technical consultancies.
- freelance translator with Translated S.r.l.: website localisation, software manuals, general documents.
- tasks for Translators without Borders: documents for NGOs working in international cooperation (e.g. Action Contre le Faim), I am a Kato verified translator for both my language pairs. See my TWB profile here:
Member since May '19
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