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Luego aprenderás cómo instalar y trabajar con MALLET, una caja de herramientas para procesamiento de lenguajes naturales (PLN) que sirve para realizar este tipo de análisis. MALLET requiere que se modifique una variable de entorno (esto es, configurar un atajo para que la computadora sepa en todo momento dónde encontrar el programa MALLET) y que se trabaje con la línea de comandos (es decir, tecleando comandos manualmente en vez de hacer clic en íconos o menús).", "abstract_format": "filtered_html", "subject": "Arts and Humanities", "keywords": [ "Data access methods", "Data modeling", "Data skills education", "Digital humanities", "Humanities data", "Programming", "Software management" ], "license": "", "usage_info": "", "citation": "Shawn Graham, Scott Weingart, y Ian Milligan, \"Introducci\u00f3n a Topic Modeling y MALLET\", traducido por Ulrike Henny-Krahmer, The Programming Historian en espa\u00f1ol 2 (2018), https://doi.org/10.46430/phes0040.", "locator_data": "10.46430/phes0040", "locator_type": "DOI", "publisher": "The Programming Historian", "version": "", "created": "2021-06-01T17:00:29", "published": "2012-09-02T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Research faculty", "Research scientist", "Software engineer", "Technology expert group", "Undergraduate student" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "af3793fd-2008-315f-9f10-4a79adef3b9f", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Content-based Identifiers for Iterative Forecasts: A Proposal", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:55Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://www.dataone.org/webinars/iterative-forecasts/", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Carl", "familyName": "Boettiger", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Carl Boettiger" ], "author_org": { "name": "University of California, Berkeley", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "Iterative forecasts pose particular challenges for archival data storage and retrieval. In an iterative forecast, data about the past and present must be downloaded and fed into an algorithm that will output a forecast data product. Previous forecasts must also be scored against the realized values in the latest observations. Content-based identifiers provide a convenient way to consistently identify input and outputs and associated scripts. These identifiers are:
\r\n(1) location-agnostic – they don’t depend on a URL or other location-based authority (like DOI)
\r\n(2) reproducible – the same data file always has the same identifier
\r\n(3) frictionless – cheap and easy to generate with widely available software, no authentication or network connection
\r\n(4) sticky – the identifier cannot become unstuck or separated from the content
\r\n(5) compatible – most existing infrastructure, including DataONE, can quite readily use these identifiers.
\r\n
\r\nIn this webinar, the speaker will illustrate an example iterative forecasting workflow. In the process, he will highlight some newly developed R packages for making this easier.", "abstract_format": "filtered_html", "subject": "Physical Sciences and Mathematics: Environmental Sciences", "keywords": [ "Data archiving", "Data skills education", "Data storage", "Persistent Identifiers (PID)", "Programming", "R software" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "DataONE", "version": "", "created": "2020-11-12T10:35:13", "published": "2020-10-13T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data professional", "Data supporter", "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Repository manager", "Research scientist", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "38de3dde-104f-3cff-a0a9-7036e7c53cb9", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Fundamentals of Remote Sensing [Introductory]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:47Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/join-mission/training/english/arset-fundamentals-remote-sensing", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [], "author_names": [], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "
These webinars are available for viewing at any time. They provide basic information about the fundamentals of remote sensing and are often a prerequisite for other ARSET training.
\n
\nOBJECTIVE
\nParticipants will become familiar with satellite orbits, types, resolutions, sensors, and processing levels. In addition to a conceptual understanding of remote sensing, attendees will also be able to articulate their advantages and disadvantages. Participants will also have a basic understanding of NASA satellites, sensors, data, tools, portals, and applications to environmental monitoring and management.
\n
\nSESSIONS
\nSession 1: Fundamentals of Remote Sensing
\nA general overview of remote sensing and its application to disasters, health & air quality, land, water resource, and wildfire management.
\nSession 1A: NASA's Earth Observing Fleet
\nGet familiar with\u00a0Earth-observing satellites in NASA's fleet, sensors that collect data you can use in ARSET training, and potential applications.\u00a0
\nSession 2A: Satellites, Sensors, Data and Tools for Land Management and Wildfire Applications
\nSpecific satellites, sensors, and resources for remote sensing in land management and wildfires. This includes land cover mapping and products, fire detection products, detecting land cover change, and NDVI and EVI.\u00a0
\nSession 2B: Satellites, Sensors, and Earth Systems Models for Water Resources Management
\nWater resources management, an overview of relevant satellites and sensors, an overview of relevant Earth system models, and data and tools for water resources management.\u00a0
\nSession 2C: Fundamentals of Aquatic Remote Sensing
\nOverview of relevant satellites and sensors, and data and tools for aquatic environmental management.\u00a0
\n", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Data collection", "Environmental management", "Land management", "Landcover applications", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-10-02T11:39:12", "published": "", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data policymaker", "Data professional", "Early-career research scientist", "Graduate student", "Mid-career research scientist", "Research scientist", "Technology expert group", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "85900db7-74e9-3310-8822-4f73d9fa8be9", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Mapeo y Monitoreo de los Bosques con Datos SAR [Avanzado]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:46Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://arset.gsfc.nasa.gov/land/webinars/bosques-sar?utm_source=social&utm_medium=ext&utm_campaign=SAR-Landcover-ES", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Erika", "familyName": "Podes", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Amber", "familyName": "McCullum", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Juan", "familyName": "Luis Torres Perez", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sean", "familyName": "McCartney", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Erika Podes", "Amber McCullum", "Juan Luis Torres Perez", "Sean McCartney" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "Esta capacitaci\u00f3n avanzada cubrir\u00e1 los siguientes temas 1) an\u00e1lisis del cambio en los bosques con datos SAR multi-temporales utilizando Google Earth Engine (GEE); 2) la clasificaci\u00f3n de la cobertura terrestre con datos SAR y \u00f3pticos utilizando GEE; 3) el mapeo de manglares con SAR; 4) y la estimaci\u00f3n de la altura de los bosques utilizando SAR. Cada sesi\u00f3n incluir\u00e1 una porci\u00f3n te\u00f3rica describiendo el uso de SAR para el mapeo de la cobertura relevante el\u00a0enfoque de la sesi\u00f3n, seguida por una demostraci\u00f3n de c\u00f3mo acceder, descargar y analizar datos SAR para el mapeo y monitoreo del bosque. Estas demostraciones utilizan datos y software de\u00a0libre acceso y de fuente abierta.
\n
\nObjetivos de Aprendizaje:\u00a0 \n \nFormato del\u00a0Curso:\u00a0 \n \n \n
\nPrerequisitos:\u00a0
\nCompletar los\u00a0Fundamentos de la Percepci\u00f3n Remota (Teledetecci\u00f3n),\u00a0Introducci\u00f3n al Radar de Apertura Sint\u00e9tica\u00a0y\u00a0SAR y sus Aplicaciones para la Cobertura Terrestre\u00a0o tener experiencia equivalente. Los participantes que no completen los prerrequisitos podr\u00edan no estar lo suficientemente preparados para el ritmo de la capacitaci\u00f3n.
\nSoftware instrucciones
\nPuede seguir las demostraciones utilizando el software enumerado a continuaci\u00f3n. Las grabaciones de cada parte estar\u00e1n disponibles en YouTube dentro de 24 horas despu\u00e9s de cada demostraci\u00f3n para que usted pueda repasarlas a su propio ritmo.
\nPrimera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
\nGoogle Earth Engine
\nSegunda Parte: SAR Interferom\u00e9trico para la Observaci\u00f3n de Derrumbes
\nTercera Parte: Generaci\u00f3n de un Modelo de Elevaci\u00f3n Digital (Digital Elevation Model o DEM)
\nPara ambas partes, los presentadores utilizar\u00e1n el\u00a0Sentinel-1 Toolbox
\n
\nPrimera Parte: An\u00e1lisis del Cambio en los Bosques con Datos SAR Multi-Temporales
\n\u0095 Introducci\u00f3n al an\u00e1lisis e interpretaci\u00f3n de datos SAR para el mapeo de los bosques\u00a0
\n\u0095 Ejercicio:\u00a0Datos SAR multi-temporales para el an\u00e1lisis del cambio en los bosques usando GEE\u00a0
\n\u0095 Sesi\u00f3n de preguntas y respuestas
\n
\nSegunda Parte: Clasificaci\u00f3n de la Cobertura Terrestre con Datos SAR y \u00d3pticos
\n\u0095 Repaso de las caracteristicas de los datos SAR y \u00f3pticos relevantes al mapeo de bosques y c\u00f3mo se pueden complementar entre s\u00ed
\n\u0095 Algoritmos para la clasificaci\u00f3n con im\u00e1genes \u00f3pticas\u00a0
\n\u0095 Ejercicio: Clasificaci\u00f3n de la cobertura terrestre con datos SAR y opticos usando GEE\u00a0
\n\u0095 Sesi\u00f3n de preguntas y respuestas
\n
\nTercera Parte: Mapeo de Manglares
\n\u0095 Introducci\u00f3n al an\u00e1lisis e interpretaci\u00f3n de datos SAR para el mapeo de manglares\u00a0
\n\u0095 Ejercicio: El mapeo de\u00a0Manglares con el Sentinel Toolbox\u00a0
\n\u0095 Sesi\u00f3n de preguntas y respuestas
\n
\nCuarta Parte: Estimaci\u00f3n de la Altura de los Bosques con SAR (Presentador Invitado el Dr. Paul Siqueira)
\n\u0095 Introducci\u00f3n al uso de datos SAR para estimar la altura de los bosques
\n\u0095 Aplicaciones y a la espera de NISAR en el 2022\u00a0
\n\u0095 Demo: Estimacion de la altura de los bosques\u00a0
\n\u0095 Sesi\u00f3n de preguntas y respuestas", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Agriculture data", "Conservation", "Data access", "Environmental change records", "Environmental management", "Forest mapping applications", "Land management", "Landcover applications", "Open access", "Open data", "Remote sensing", "Satellite imagery", "Sustainable cities", "Sustainable Development Goals (SDGs)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-18T14:17:25", "published": "2020-05-12T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "396bb7ab-a914-34b2-903c-aefa350138e4", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Using Earth Observations to Monitor Water Budgets for River Basin Management II [Advanced] ", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:46Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://arset.gsfc.nasa.gov/water/webinars/water-budgets-river-basin?utm_source=social&utm_medium=ext&utm_campaign=River-Basin-2020", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Amita", "familyName": "Mehta", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sean", "familyName": "McCartney", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Amita Mehta", "Sean McCartney" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "
Rivers are a major source of fresh water. They support aquatic and terrestrial ecosystems, provide transportation, generate hydropower, and when treated, provide drinking and agricultural water. Estimating and monitoring water budgets within a river basin is required for sustainable management of water resources and flooding within watersheds. This advanced-level webinar series will focus on the use of NASA Earth observations and Earth system-modeled data for estimating water budgets in river basins.
\nPast ARSET training on monitoring water budgets for river basins focused on data sources relevant for river basin monitoring and management and provided case studies for estimating the water budget of a watershed using remote sensing products. This advanced webinar will include lectures and hands-on exercises for participants to estimate water budgets for a given river basin.
\n
\nLearning Objectives:
\n\u00a0By the end of this training, attendees will be able to:
\n \n \n \n
Course Format:\u00a0
\n \n \n \n

\nPrerequisites:\u00a0Attendees who have not completed the following may not be prepared for the pace of the training:
\n \n \n \n
Portions of the series will include data import to QGIS. If you wish to follow along with those steps, please install using the instructions\u00a0here:
\n \n \n \n
Part 1: Review and Access of Earth Observations and Earth System-Modeled Data for River Basin Monitoring and Management
\n \n
This session will provide an overview of data sources relevant to estimating water budgets for a river basin. There will be a demonstration and guided exercise to download water budget component data to estimate the water budget of a given watershed using remote sensing products.
\n \n
Part 2: Water Budget Estimation using Remote Sensing Observations
\nThis session will include a demonstration and step-by-step exercise to estimate an integrated water budget over a river basin using Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation data, Atmosphere Land Exchange Inverse (ALEXI) evapotranspiration data, and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data, all analyzed with QGIS.
\nPart 3: Water Budget Estimation using the Global Land Data Assimilation Model
\nThe final session will include a demonstration and step-by-step exercise to estimate water budgets at a sub-watershed level within a river basin using water budget components from the latest version of the Global Land Data Assimilation System (GLDAS v2.2), which includes assimilation of groundwater data.
\n\u00a0
\n \n
Each part of 3 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.
\n\u00a0
\n", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Conservation", "Environmental management", "Flood mapping applications", "Freshwater habitat applications", "Geographic Information System (GIS)", "Land management", "Remote sensing", "River basin management", "Satellite imagery", "Sustainable Development Goals (SDGs)", "Water quality" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-23T13:16:03", "published": "2020-09-21T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "e2bde7bc-911f-3f78-8698-de12bc3924c2", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "SAR for Disasters and Hydrological Applications [Advanced] ", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:45Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://arset.gsfc.nasa.gov/disasters/webinars/2019-SAR-Disasters?utm_source=social&utm_medium=ext&utm_campaign=SAR-Disasters", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Erika", "familyName": "Podes", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sean", "familyName": "McCartney", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Eric", "familyName": "Jameson Fielding", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Alexander", "familyName": "Louis Handwerger", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Nicol\u00e1s A.", "familyName": "Grunfeld Brook", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Erika Podes", "Sean McCartney", "Eric Jameson Fielding", "Alexander Louis Handwerger", "Nicol\u00e1s A. Grunfeld Brook" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "This training builds on the skills taught from previous ARSET SAR training in terms of the use of Google Earth Engine for flood mapping of radar data. This\u00a0training presents two new topics;\u00a0the use of InSAR for characterizing landslides and the generation of a digital elevation model (DEM).
\nLearning Objectives:\u00a0By the end of this training, attendees will be able to: \n \nCourse Format:\u00a0 \n \n \n
\nPrerequisites:\u00a0
\nPrerequisites are not required for this training, but attendees that do not complete them may not be adequately prepared for the pace of the training.\u00a0 \n \n
\nPart One: SAR for Flood Mapping Using Google Earth Engine
\nThis session will focus on the use of the Google Earth Engine (GEE) to generate a flood map utilizing SAR images from Sentinel-1. The first part of this session will cover the basic principles of radar remote sensing related to flooding. The remaining time in the session will be dedicated to a demonstration on how to use GEE to generate flood extent products with Sentinel-1 and how to integrate socioeconomic data into the flood map to identify areas at risk.
\nPart Two: Interferometric SAR for Landslide Observations
\nFeaturing guest speaker Dr. Eric Fielding from JPL,\u00a0this session is focused on landslide observations utilizing and building on InSAR skills from the previous three SAR webinar series. The first part of the session will cover the physics of InSAR as related to landslides. The remainder will be focused on how to generate and interpret the derived landslide product.
\n\u00a0Part Three: Generating a Digital Elevation Model (DEM)
\nFeaturing guest speaker Nicol\u00e1s Grunfeld Brook, from Argentina\u0092s CONAE, participants will learn how to generate a digital elevation model (DEM) through InSAR techniques. The first part of the session will cover the physics behind using two SAR phase images to generate a DEM. The remainder of the time will focus on how to generate a DEM.
\n
\nEach part of 3 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.
\n\u00a0", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Agriculture data", "Disaster applications", "Elevation data", "Environmental management", "Flood mapping applications", "Hydrologic data", "Land management", "Landcover applications", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)", "Synthetic aperture radar data (SAR)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-17T10:18:49", "published": "2019-12-03T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [ "es" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "ea6e6b19-18c8-340a-8fad-ffa67cbf968e", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "SAR para Desastres y Aplicaciones Hidrol\u00f3gicas [Avanzado]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:45Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Erika", "familyName": "Podes", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sean", "familyName": "McCartney", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Eric", "familyName": "Jameson Fielding", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Alexander", "familyName": "Louis Handwerger", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Nicol\u00e1s A.", "familyName": "Grunfeld Brook", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Erika Podes", "Sean McCartney", "Eric Jameson Fielding", "Alexander Louis Handwerger", "Nicol\u00e1s A. Grunfeld Brook" ], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "Esta capacitaci\u00f3n se basar\u00e1 en las capacidades de utilizar Google Earth Engine para el mapeo de inundaciones a partir de datos de radar ense\u00f1adas en capacitaciones ARSET de SAR anteriores. Esta capacitaci\u00f3n presenta dos temas nuevos; el uso de InSAR para la caracterizaci\u00f3n de derrumbes y la generaci\u00f3n de un modelo de elevaci\u00f3n digital (digital elevation model o DEM).
\n
\nObjetivos de Aprendizaje:\u00a0Para la conclusi\u00f3n de esta capacitaci\u00f3n, los participantes podr\u00e1n: \n \nFormato del Curso:\u00a0 \n \n \nPrerrequisitos:
\nLos prerrequisitos no son obligatorios para esta capacitaci\u00f3n, pero quienes no los completen podr\u00edan no estar lo suficientemente preparados para esta.\u00a0 \n \nPrimera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
\nEsta sesi\u00f3n estar\u00e1 enfocada en el uso de of Google Earth Engine (GEE) para generar un mapa de inundaci\u00f3n utilizando im\u00e1genes SAR de Sentinel-1. \u00a0La primera parte de la sesi\u00f3n cubrir\u00e1 los principios b\u00e1sicos de SAR relacionados con las inundaciones. El resto de la sesi\u00f3n ser\u00e1 dedicada a una demostraci\u00f3n de c\u00f3mo utilizar GEE para generar productos relevantes a la extensi\u00f3n de inundaciones y c\u00f3mo integrar datos socioecon\u00f3micos al mapeo de inundaciones para identificar \u00e1reas en peligro.\u00a0
\n
\nSegunda Parte: SAR Interferom\u00e9trico para la Observaci\u00f3n de Derrumbes
\nDirigida por el presentador invitado, el Dr. Eric Fielding\u00a0de JPL, esta sesi\u00f3n se enfocar\u00e1 en la observaci\u00f3n de derrumbes. Desarrollar\u00e1 las capacidades con InSAR ense\u00f1adas en las tres anteriores series de webinars de SAR. La primera parte de la sesi\u00f3n cubrir\u00e1 la f\u00edsica de InSAR relacionada con los derrumbes.\u00a0El resto se enfocar\u00e1 en c\u00f3mo generar e interpretar el producto derrumbes derivado.
\n
\nTercera Parte: Generaci\u00f3n de un Modelo de Elevaci\u00f3n Digital (Digital Elevation Model o DEM)
\nA cargo de un presentador invitado de la agencia espacial argentina, CONAE, los participantes aprender\u00e1n c\u00f3mo generar un modelo de elevaci\u00f3n digital (DEM) a trav\u00e9s de t\u00e9cnicas de InSAR.\u00a0 La primera parte de la sesi\u00f3n cubrir\u00e1 la f\u00edsica de utilizar dos im\u00e1genes de fase de SAR para generar un DEM. El resto del tiempo se enfocar\u00e1 en c\u00f3mo generar un DEM. \n
\u00a0
\n", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Agriculture data", "Disaster applications", "Elevation data", "Environmental management", "Flood mapping applications", "Hydrologic data", "Land management", "Landcover applications", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)", "Synthetic aperture radar data (SAR)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-17T10:47:48", "published": "2019-12-03T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "1db9163a-2379-3160-95f4-5587852c4f5c", "contributors": [], "contributor_orgs": [], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Using the UN Biodiversity Lab to Support National Conservation and Sustainable Development Goals [Introductory]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:45Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://arset.gsfc.nasa.gov/land/webinars/un-biodiversity-2020?utm_source=social&utm_medium=ext&utm_campaign=UN-BIO", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [], "author_names": [], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "As we enter the fourth industrial revolution, technology is revolutionizing our ability to map nature. Satellite data provide a bird\u0092s eye, yet incredibly detailed view of the Earth\u0092s surface in real-time, while drones and mobile apps enable local communities and indigenous peoples to map their knowledge of local ecosystems.\u00a0To support policymakers to develop data-driven sustainable development solutions, UNDP, the United Nations Environment Programme (UNEP), and the Secretariat of the Convention on Biological Diversity (CBD) \u00a0launched\u00a0UN Biodiversity Lab, with funding from the GEF and support from MapX, UNEP World Conservation Monitoring Centre, Global Resource Information Database - Geneva, and NASA. The UN Biodiversity Lab is an online platform that allows policymakers and other stakeholders to access global data layers, upload national datasets, and analyze these datasets in combination to provide key information on the CBD\u0092s Aichi\u00a0Biodiversity Targets and on the nature-based Sustainable Development Goals. Already in use by over 50 countries, as well as utilized as the key decision support system for two NASA-funded applied science projects, the UN Biodiversity Lab has high potential to be scaled up to reach new ministries and countries and stakeholder groups.
\n
\nThere is a global demand for more NASA ARSET training focused on biodiversity, conservation, the UN Sustainable Development Goals (SDGs),\u00a0and how to link NASA satellite data to ecological and human-influenced systems. This training aims to fill that gap by extending the influence of this NASA-supported tool and increasing its dissemination, use, and overall success. UN Biodiversity Lab makes global datasets on biodiversity and sustainable development easily accessible, supporting our broad audience.
\n
\nLearning Objectives:\u00a0By the end of this training, attendees will: \n \nCourse Format:\u00a0 \n \n \nPrerequisites:
\n\u00a0Attendees that do not complete the required prerequisites may not be adequately prepared for the pace of the training. \n \nPart One: Introduction to Spatial Data and Policies for Biodiversity (
\nPart Two: UN Biodiversity Lab: Introduction and Training\u00a0
\nPart Three: How are Countries Using Spatial Data to Support Conservation of Nature?\u00a0
\n
\nEach part of 3 includes links to the recordings, presentation slides, and Question & Answer Transcripts.
\n\u00a0", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Aichi Biodiversity Targets (ABTs)", "Biodiversity data", "Capacity building", "Conservation", "Data access", "Environmental management", "Land management", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-17T12:17:11", "published": "2020-03-24T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [ "fr", "es" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "7190acff-3386-351e-8185-5e100c038bc9", "contributors": [ { "familyName": "McCullum", "givenName": "Amber", "type": "Co-presenter" } ], "contributor_orgs": [ { "name": "UN Development Programme (UNDP)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "United Nations Environment Programme (UNEP)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Global Environment Facility (GEF)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" }, { "name": "Convention on Biological Diversity (CBD)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" } ], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Utiliser le UN Biodiversity Lab pour soutenir les objectifs nationaux de conservation et de d\u00e9veloppement durable [d\u0092introduction]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:45Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [], "author_names": [], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "
Alors que nous entrons dans la quatri\u00e8me r\u00e9volution industrielle, la technologie r\u00e9volutionne notre capacit\u00e9 \u00e0 cartographier la nature. Les donn\u00e9es spatiales fournissent une vue d\u0092ensemble, mais \u00e9galement une vue incroyablement d\u00e9taill\u00e9e de la surface de la Terre en temps r\u00e9el, tandis que les drones et les applications mobiles permettent aux communaut\u00e9s locales et aux peuples autochtones de cartographier leurs connaissances des \u00e9cosyst\u00e8mes locaux.\u00a0Pour aider les d\u00e9cideurs \u00e0 \u00e9laborer des solutions de d\u00e9veloppement durable fond\u00e9es sur des donn\u00e9es, le PNUD, le Programme des Nations Unies pour l'environnement (PNUE) et le Secr\u00e9tariat de la Convention sur la diversit\u00e9 biologique (CDB) ont lanc\u00e9 le UN Biodiversity Lab, avec un financement du FEM et le soutien de MapX, le Centre mondial de surveillance de la conservation du PNUE (UNEP-WCMC), le Global Resource Information Database \u0096 Geneva et la NASA. Le UN Biodiversity Lab est une plateforme en ligne qui permet aux d\u00e9cideurs et autres parties prenantes d'acc\u00e9der aux couches de donn\u00e9es mondiales, de t\u00e9l\u00e9charger des ensembles de donn\u00e9es nationaux et d'analyser ces ensembles de donn\u00e9es en combinaison pour fournir des informations cl\u00e9s sur les objectifs d'Aichi pour la biodiversit\u00e9 de la CDB et sur les objectifs de d\u00e9veloppement durable fond\u00e9s sur la nature. D\u00e9j\u00e0 utilis\u00e9 par plus de 50 pays, et utilis\u00e9 comme syst\u00e8me cl\u00e9 d'aide \u00e0 la d\u00e9cision pour deux projets de science appliqu\u00e9e financ\u00e9s par la NASA, le UN Biodiversity Lab a un fort potentiel d'\u00eatre \u00e9tendu pour atteindre de nouveaux minist\u00e8res et pays et groupes de parties prenantes.
\n
\nIl existe une demande mondiale pour plus de formations ARSET de la NASA ax\u00e9es sur la biodiversit\u00e9, la conservation, les objectifs de d\u00e9veloppement durable (ODD) des Nations Unies et la fa\u00e7on de relier les donn\u00e9es spatiales de la NASA \u00e0 des syst\u00e8mes \u00e9cologiques et influenc\u00e9s par l'homme. Cette formation vise \u00e0 combler cette lacune en \u00e9tendant l'influence de cet outil soutenu par la NASA et en augmentant sa diffusion, son utilisation et son succ\u00e8s global. Le UN Biodiversity Lab rend les ensembles de donn\u00e9es mondiaux sur la biodiversit\u00e9 et le d\u00e9veloppement durable facilement accessibles, soutenant notre large public.
\n
\nObjectifs d\u0092apprentissage:\u00a0\u00c0 la fin de cette formation, les participants: \n \n \nFormat du cours:
\nTrois sessions de une heure et demie, dispens\u00e9es en anglais, fran\u00e7ais et espagnol
\n
\nPr\u00e9-requis:
\nLes participants qui ne remplissent pas les conditions pr\u00e9alables requises peuvent ne pas \u00eatre convenablement pr\u00e9par\u00e9s au rythme de la formation.
\nPrincipes fondamentaux des donn\u00e9es\u00a0spatiales\u00a0(en anglais)\u00a0\u00bb\u00a0
\n
\nPartie 1: Introduction aux donn\u00e9es spatiales et aux politiques de biodiversit\u00e9\u00a0
\nPartie 2: UN Biodiversity Lab: Introduction et formation\u00a0\u00a0
\nPartie 3: Comment les pays utilisent-ils les donn\u00e9es spatiales pour soutenir la conservation de la nature ?\u00a0", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Aichi Biodiversity Targets (ABTs)", "Biodiversity data", "Capacity building", "Conservation", "Data access", "Environmental management", "Land management", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-17T12:34:34", "published": "2020-03-24T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "fr", "languages_secondary": [ "en", "es" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "a3c0ba20-ba3a-3698-a1c2-3b04fa6dedb6", "contributors": [ { "familyName": "McCullum", "givenName": "Amber", "type": "Co-presenter" } ], "contributor_orgs": [ { "name": "Programme des Nations Unies pour le d\u00e9veloppement (PNUD)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Programme des Nations Unies pour l'environnement", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Convention sur la diversit\u00e9 biologique", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Fonds pour l'environnement mondial", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Utilizando el UN Biodiversity Lab para Apoyar los Objetivos Nacionales de Conservaci\u00f3n y Desarrollo Sostenible [Introductoria]", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:45Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [], "author_names": [], "author_org": { "name": "NASA Applied Remote Sensing Training Program (ARSET)", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Brock Blevins", "org": "NASA Applied Remote Sensing Training Program (ARSET)", "email": "" }, "abstract_data": "
A inicios de la cuarta revoluci\u00f3n industrial, la tecnolog\u00eda est\u00e1 revolucionando nuestra capacidad de mapear la naturaleza. Los datos satelitales proporcionan una vista panor\u00e1mica pero a la vez incre\u00edblemente detallada de la superficie de la Tierra en tiempo real mientras que los drones y las aplicaciones m\u00f3viles permiten que las comunidades locales y los pueblos ind\u00edgenas mapeen su conocimiento de ecosistemas locales.\u00a0Para poder ayudar a los formuladores de pol\u00edticas a desarrollar soluciones para el desarrollo sostenible basadas en datos y pol\u00edticas enfocadas, el UNDP, el Programa de las Naciones Unidas para el Medio Ambiente (UNEP por sus siglas en ingl\u00e9s) y la Secretar\u00eda del Convenio sobre la Diversidad Biol\u00f3gica (CDB) lanzaron el UN Biodiversity Lab con financiaci\u00f3n del GEF y apoyo de MapX, el Centro de Monitoreo de la Conservaci\u00f3n Mundial del UNEP,\u00a0la Base de Datos Mundial sobre Recursos de Informaci\u00f3n \u0096 Ginebra y la NASA.\u00a0El UN Biodiversity Lab\u00a0es una plataforma en l\u00ednea que permite a los formuladores de pol\u00edticas y otras partes interesadas acceder a capas de datos a nivel mundial, cargar conjuntos de datos nacionales y analizar estos conjuntos de datos en combinaci\u00f3n para brindar informaci\u00f3n clave sobre los Objetivos Aichi para la Biodiversidad del CDB y sobre los Objetivos de Desarrollo Sostenible relacionados con la naturaleza. Ya lo est\u00e1n utilizando en m\u00e1s de 50 pa\u00edses, incluso como el principal sistema de apoyo a la toma de decisiones para dos proyectos de ciencias aplicadas financiados por la NASA. El UN Biodiversity Lab tiene un alto potencial de ser escalado para llegar a nuevos ministerios y pa\u00edses y grupos de partes interesadas.\u00a0
\n \n
Existe una demanda a nivel mundial de m\u00e1s capacitaciones NASA ARSET enfocadas en la biodiversidad, conservaci\u00f3n, los Objetivos de Desarrollo Sostenible (ODS) de la ONU y sobre c\u00f3mo conectar datos de sat\u00e9lites de la NASA con sistemas ecol\u00f3gicos y aquellos que han sido influidos por la actividad humana. Esta capacitaci\u00f3n pretende llenar este vac\u00edo extendiendo la influencia de esta herramienta apoyada por la NASA y fomentando su diseminaci\u00f3n, utilizaci\u00f3n y \u00e9xito general. El \u00a0UN Biodiversity Lab hace conjuntos de datos mundiales sobre la biodiversidad y el desarrollo sostenible f\u00e1cilmente accesibles, apoyando a nuestro p\u00fablico variado.
\n
\nObjetivos de Aprendizaje:\u00a0Para la conclusi\u00f3n de esta capacitaci\u00f3n, los/las participantes podr\u00e1n: \n \n \nFormato del Curso: \n \n \nPrerrequisitos:\u00a0
\nLos participantes que no completen los prerrequisitos podr\u00edan no estar lo suficientemente preparados para el ritmo de la capacitaci\u00f3n.
\n
\nFundamentos de la Percepci\u00f3n Remota (Teledetecci\u00f3n)\u00a0Diapositivas de la Presentaci\u00f3n \u00bb
\n
\nPrimera Parte: Introducci\u00f3n a Datos Espaciales y Pol\u00edticas para la Diversidad Biol\u00f3gica
\nSegunda Parte: El UN Biodiversity Lab\u00a0
\nTercera Parte: Casos de Uso por Pa\u00edses\u00a0
\n\u00a0", "abstract_format": "filtered_html", "subject": "Education: Science and Mathematics Education", "keywords": [ "Aichi Biodiversity Targets (ABTs)", "Capacity building", "Conservation", "Data access", "Environmental management", "Land management", "Remote sensing", "Satellite imagery", "Sustainable Development Goals (SDGs)" ], "license": "Creative Commons Attribution 2.0 Generic - CC BY 2.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "NASA Applied Remote Sensing Training Program (ARSET)", "version": "", "created": "2020-09-17T12:43:43", "published": "2020-03-24T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en", "fr" ], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data policymaker", "Early-career research scientist", "Mid-career research scientist", "Research scientist", "Technology expert group" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "2b758b17-e587-3e73-a4fa-a4c18156f446", "contributors": [ { "familyName": "McCullum", "givenName": "Amber", "type": "Co-presenter" } ], "contributor_orgs": [ { "name": "Programa de la ONU para el Desarrollo", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Programa de las Naciones Unidas para el Medio Ambiente", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "convenio sobre la diversidad biol\u00f3gica", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" }, { "name": "Fondo para el Medio Ambiente Mundial", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 2.772948, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] } ] }