All Learning Resources

  • R Data Curation Primer

    The purpose of this primer is to guide a data curator through the curation process for text files with a “.R” extension that contain code for executing programs in the R language.
    Key questions for curation review
    -What is the purpose of the file?
    -Are any data associated with the file?
    -Are the referenced data present at the indicated location? 
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • Tableau Data Curation Primer

    Tableau Software is a proprietary suite of products for data exploration, analysis, and visualization with an initial concentration in business intelligence. This primer focuses on the Tableau workbook files – .twb and .twbx – produced using Tableau Desktop. Like Microsoft Excel, Tableau Desktop uses a workbook and sheet file structure. Workbooks can contain worksheets, dashboards, and stories.
    Key questions for curation review
    ● Can the Tableau workbook file be opened?
    ● If the Tableau workbook is provided as a .twb file, is there an accompanying data source file or data extract?
    ● Is there documentation for how to navigate and work with the Tableau workbook?
    ● Is there an accompanying snapshot to show how a workbook, dashboard, or story view should be rendered?

    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers

  • PDF Data Curation Primer

    The purpose of this primer is to guide a data curator through the curation process for Portable Document Format (PDF) files. As a highly-used document publication format, PDF documents represent considerable bodies of important information globally and have become commonly used for publishing data and related files.
    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.

    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers
     

  • Atlas.ti Data Curation Primer

    Altas.ti is a software application that allows researchers to analyze qualitative data in a systematic and transparent way, increasing the validity of results (Friese 2019). ATLAS.ti handles different types of data that are kept in a project. The project files can contain text documents, images, audio recordings, videos, pdf files, geodata, Twitter data, citations from Evernote and reference managers, and survey data. The purpose of this primer is to guide a data curator through the curation process for Altas.ti files.
    Key questions for curation review
    -What ATLAS.ti version was used?
    -Can other researchers open the project without the ATLAS.ti?
    -Does the project include metadata/documentation/codebook?
    -Are there consent forms/participation agreements? Is there sensitive information that can compromise human subjects’ rights?
    -Are there associated data that has been exported (i.e. result reports, codebook) outside the project?

    This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #2 held at Johns Hopkins University on April 17-18, 2019.
    The full set of Data Curation Primers can be found at:https://conservancy.umn.edu/handle/11299/202810
    Interactive primers available for download and derivatives at:https://github.com/DataCurationNetwork/data-primers

  • Data Management and Reporting: BCO-DMO Data Management Services and Best Practices

    The University-National Oceanographic Laboratory System (UNOLS) hosted an Early Career Chief Scientist Training Workshop in June 2019. The goal of this workshop was to help early-career marine scientists plan and write effective cruise proposals, develop collaborative sampling strategies and plans, become familiar with shipboard equipment and sampling at sea, and communicate major findings through the writing of manuscripts and cruise reports. This presentation provides information on data management and reporting best practices for chief scientists. It includes information on the National Science Foundation (NSF) data policy requirements, writing a Data Management Plan (DMP), the data lifecycle, data publication, and shipboard data management recommendations.

  • Fundamentals of Remote Sensing [Introductory]

    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.

    OBJECTIVE
    Participants 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.

    SESSIONS
    Session 1: Fundamentals of Remote Sensing
    A general overview of remote sensing and its application to disasters, health & air quality, land, water resource, and wildfire management.
    Session 1A: NASA's Earth Observing Fleet
    Get familiar with Earth-observing satellites in NASA's fleet, sensors that collect data you can use in ARSET training, and potential applications. 
    Session 2A: Satellites, Sensors, Data and Tools for Land Management and Wildfire Applications
    Specific 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. 
    Session 2B: Satellites, Sensors, and Earth Systems Models for Water Resources Management
    Water resources management, an overview of relevant satellites and sensors, an overview of relevant Earth system models, and data and tools for water resources management. 
    Session 2C: Fundamentals of Aquatic Remote Sensing
    Overview of relevant satellites and sensors, and data and tools for aquatic environmental management. 

  • Remote Sensing of Coastal Ecosystems [Introductory]

    Coastal and marine ecosystems serve key roles for carbon storage, nutrients, and materials cycling, as well as reservoirs of biodiversity. They also provide ecosystem services such as sustenance for millions of people, coastal protection against wave action, and recreational activities. Remote sensing of coastal and marine ecosystems is particularly challenging. Up to 90% of the signal received by the sensors in orbit comes from the atmosphere. Additionally, dissolved and suspended constituents in the water column attenuate most of the light received through absorption or scattering. When it comes to retrieving information about shallow-water ecosystems, even in the clearest waters under the clearest skies, less than 10% of the signal originates from the water and its bottom surface. Users, particularly those with little remote sensing experience, stand to benefit from this training covering some of the difficulties associated with remote sensing of coastal ecosystems, particularly beaches and benthic communities such as coral reefs and seagrass.

    OBJECTIVES
    by the end of this training, attendees will be able to:

    • Identify the different water column components and how they affect the remote sensing signal of shallow-water ecosystems
    • Outline existing satellite sensors used for ocean color and shallow-water ecosystem characterization
    • Understand the interaction between water constituents, the electromagnetic spectrum, and the remote sensing signal
    • Recognize the different processes used to remove the water column attenuation from the remotely-sensed signal to characterize benthic components
    • Summarize techniques for characterizing shoreline beach environments with remotely-sensed data and field methods for beach profiling
    COURSE FORMAT
    • Three one-hour sessions with presentations in English and Spanish
    • One Google Form homework
    • Spanish sessions 
    PREREQUISITES
    Part One: Overview of Coastal Ecosystems and Remote Sensing
    • Introduction to coastal and marine ecosystems
    • Overview of sensors for remote sensing of coastal areas
    • Q&A
    Part Two: Penetration of Light in the Water Column
    • Apparent and inherent optical properties 
    • Field bio-optical measurements 
    • Water column corrections 
    • Deriving bathymetry and benthic characterization from multispectral data 
    • Validation and calibration of ocean color data 
    • Q&A
    Part Three: Remote Sensing of Shorelines
    • Geophysical components of shorelines 
    • The parts of a beach 
    • Field-based measurements in shorelines for image validation 
    • Image processing and analysis for shoreline characterization 
    • Q&A
    Each part of 3 includes links to the recordings, presentation slides,  and Question & Answer Transcripts. 
  • Teledetección de Ecosistemas Costeros

    Los ecosistemas marinos y costeros tienen roles vitales en el almacenamiento de carbono, reciclaje de nutrientes y otros materiales, al igual que sirven de reservorios de biodiversidad. Además, proveen servicios ecosistémicos tales como comida para millones de personas, protección costera contra el oleaje, y actividades recreativas. La teledetección de los ecosistemas costeros y marinos es particularmente difícil. Hasta el 80% de la señal recibida por los sensores en órbita proviene de la atmósfera. Además, los componentes de la columna de agua (disueltos y suspendidos) atenúan la mayor parte de la luz mediante absorción o dispersión. Cuando se trata de recuperar información del fondo del océano, incluso en las aguas más claras, solo menos del 10% de la señal proviene de el fondo marino. Los usuarios, particularmente aquellos con poca experiencia en teledetección, pueden beneficiarse de esta capacitación que cubre algunas de las dificultades asociadas con la teledetección de ecosistemas costeros, particularmente playas y comunidades bentónicas tales como arrecifes de coral y yerbas marinas.

    OBJETIVOS DE APRENDIZAJEAl
    final de esta capacitación, los asistentes podrán:

    • Identificar los diferentes componentes de la columna de agua y cómo afectan la señal de teledetección remota de los ecosistemas de aguas poco profundas.
    • Describir los sensores satelitales existentes utilizados para analizar el color del océano y en la caracterización de ecosistemas de aguas poco profundas.
    • Comprender la interacción entre los componentes del agua, el espectro electromagnético y la señal de detección remota.
    • Reconocer los diferentes procesos utilizados para eliminar la atenuación de la columna de agua de la señal de teledetección remota para caracterizar los componentes bentónicos.
    • Resumir las técnicas para caracterizar los entornos de playas costeras con datos de teledetección remota y métodos de campo para el perfil de playas.

    FORMATO DEL CURSO

    • Tres sesiones de una hora cada una con presentaciones en inglés y español
    • Una tarea a someter usando Google Forms 
    • English

    Parte Uno: Una Mirada a los Ecosistemas Costeros y la Teledetección

    • Introducción a ecosistemas costeros 
    • Un resumen de los sensores más utilizados para la teledetección de áreas costeras 
    • Preguntas y Respuestas

    Parte Dos: Penetración de la Luz en la Columna de Agua

    • Propiedades Aparentes e Inherentes 
    • Medidas de Campo Bio-ópticas 
    • Correcciones de la Columna de Agua 
    • Derivación de Batimetría y Caracterización Béntica Usando Datos Multiespectrales 
    • Calibración y Validación de Datos de Color del Océano 
    • Preguntas y Respuestas

    Parte Tres: Teledetección de Componentes de la Línea de Costa

    • Componentes Geofísicos de la Línea de Costa
    • Las Partes de una Playa
    • Medidas de Campo en la Línea de Costa Necesarias para Validar Imágenes
    • Procesamiento y Análisis de Imágenes para la Caracterización de la Línea de Costa
    • Preguntas y Respuestas
    Materiales:
    • Ver Grabación
    • Diapositivas de la Presentación
    • Tarea 
    • Transcripción de Preguntas y Respuestas
  • Remote Sensing for Freshwater Habitats [Intermediate]

    Freshwater habitats play an important role in ecological function and biodiversity. Remote sensing of these ecosystems is primarily tied to observations of the drivers of biodiversity and ecosystem health. Remote sensing can be used to understand things like land use and land cover change in a watershed, habitat connectivity along a water body, water body location and extent, and water quality parameters. This webinar series will guide participants through using NASA Earth observations for habitat monitoring, specifically for freshwater fish and other species. The training will also provide a conceptual overview, as well as the tools and techniques for applying landscape environmental variables to genetic and habitat diversity in species. 

    Learning Objectives: By the end of this training, attendees will: 


    • understand the limitations of using remote sensing for freshwater habitats
    • find data and models that can be used in their landscape genetics and habitat monitoring work
    • see how remote sensing can be used for habitat restoration, ecological assessments, and climate change assessments relating to freshwater systems
    • be able to use the Riverscape Analysis Project decision support system
    • be familiar with the Freshwater Health Index


    Course Format: 


    • Three, one-hour parts that include lectures, demonstrations, and question & answer sessions
    • This training will only be broadcast in English
    • A certificate of completion will be available to participants who attend all parts and complete all homework assignments. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.


    Prerequisites: Please complete ARSET's Fundamentals of Remote Sensing or have equivalent knowledge. Attendees that do not complete the prerequisite may not be prepared for the pace of the training. 

    Part One: Review of Aquatic Remote Sensing & Freshwater Habitats
    As a result of this part of the webinar series, attendees will be able to: 


    • identify which NASA satellites & sensors can be used for freshwater monitoring
    • understand the limitations of remote sensing of freshwater habitats
    • find data and models they can use in landscape genetics and habitat monitoring work


    Part Two: Overview of the Riverscape Analysis Project (RAP)
    As a result of this part of the webinar series, attendees will be able to: 


    • understand using remote sensing for habitat restoration, ecological assessments, and climate change assessments relating to freshwater systems through case studies
    • use the RAP decision-support system for accessing, downloading, and applying remote sensing data


    Part Three: Overview of the Freshwater Health Index (FHI)
    As a result of this part of the webinar series, attendees will be able to: 


    • understand how to evaluate freshwater ecosystem health
    • have the ability to use the FHI data and tools to assess freshwater ecosystem health
    • identify potential uses of the FHI for their work and decision-making
    • Use the FHI to identify vulnerabilities to degradation and/or climate change, as well as opportunities for improvement of infrastructure development within a basin


    Each part of 3 includes links to the recordings, presentation slides, and Question & Answer Transcripts.
     

  • 'Good Enough' Research Data Management: A Brief Guide for Busy People

    This brief guide presents a set of good data management practices that researchers can adopt, regardless of their data management skills and levels of expertise.

  • Remote Sensing for Monitoring Land Degradation and Sustainable Cities Sustainable Development Goals (SDGs) [Advanced]

    The Sustainable Development Goals (SDGs) are an urgent call for action by countries to preserve our oceans and forests, reduce inequality, and spur economic growth. The land management SDGs call for consistent tracking of land cover metrics. These metrics include productivity, land cover, soil carbon, urban expansion, and more. This webinar series will highlight a tool that uses NASA Earth Observations to track land degradation and urban development that meet the appropriate SDG targets. 

    SDGs 11 and 15 relate to sustainable urbanization and land use and cover change. SDG 11 aims to "make cities and human settlements inclusive, safe, resilient, and sustainable." SDG 15 aims to "combat desertification, drought, and floods, and strive to achieve a land degradation neutral world." To assess progress towards these goals, indicators have been established, many of which can be monitored using remote sensing. 

    In this training, attendees will learn to use a freely-available QGIS plugin, Trends.Earth, created by Conservation International (CI) and have special guest speakers from the United Nations Convention to Combat Desertification (UNCCD) and UN Habitat. Trends.Earth allows users to plot time series of key land change indicators. Attendees will learn to produce maps and figures to support monitoring and reporting on land degradation, improvement, and urbanization for SDG indicators 15.3.1 and 11.3.1. Each part of the webinar series will feature a presentation, hands-on exercise, and time for the speaker to answer live questions. 

    Learning Objectives: By the end of this training, attendees will: 

    • Become familiar with SDG Indicators 15.3.1 and 11.3.1
    • Understand the basics on how to compute sub indicators of SDG 15.3.1 such as: productivity, land cover, and soil carbon 
    • Understand how to use the Trends.Earth Urban Mapper web interface
    • Learn the basics of the Trends.Earth toolkit including: 
      • Plotting time series 
      • Downloading data
      • Use default or custom data for productivity, land cover, and soil organic carbon
      • Calculate a SDG 15.3.1 spatial layers and summary table 
      • Calculate urban change metrics
      • Create urban change summary tables



    Course Format: This training has been developed in partnership with Conservation International, United Nations Convention to Combat Desertification (UNCCD), and UN Habitat. 

    • Three, 1.5-hour sessions that include lectures, hands-on exercises, and a question and answer session
    • The first session will be broadcast in English, and the second session will contain the same content, broadcast in Spanish (see separate record for Spanish version at:  https://dmtclearinghouse.esipfed.org/node/10935 


    ​Prerequisites: 


    Each part of 3 includes links to the recordings, presentation slides, exercises and Question & Answer Transcripts.   

  • Teledetección para el Monitoreo de los ODS sobre la Degradación de Tierras y Ciudades Sostenibles

    Los Objetivos de Desarrollo Sostenible (ODS) son un llamado urgente a la acción a todos los países para preservar nuestros océanos y bosques, reducir la desigualdad y fomentar el crecimiento económico. Los ODS sobre la gestión de tierras exigen un seguimiento consistente de las métricas de la cobertura terrestre. Estas métricas incluyen productividad, cobertura terrestre, carbono en el suelo, expansión urbana y más. Esta serie de webinars resaltará una herramienta que utiliza observaciones de la tierra de la NASA para monitorear la degradación de las tierras y el desarrollo urbano que cumplen las metas de los ODS apropiados.  

    Los ODS 11 y 15 tratan la urbanización sostenible así como el uso y los cambios en la cobertura terrestre. El ODS anhela “lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles.” El ODS 15 promueve “luchar contra la desertificación, la sequía y las inundaciones y procurar lograr un mundo con una degradación neutra del suelo." Para evaluar el progreso hacia estos fines, hay indicadores establecidos, muchos de los cuales se pueden monitorear mediante la teledetección.

    En esta capacitación los/las participantes aprenderán a utilizar un plugin de QGIS libremente disponible, Trends.Earth, creado por Conservation International (CI). Trends.Earth permite a los usuarios diagramar series temporales de indicadores clave de cambios en la cobertura terrestre. Los/las participantes aprenderán a producir mapas y figuras para apoyar el seguimiento y la información sobre la degradación de tierras, mejoras y urbanización para los indicadores de los ODS 15.3.1 y 11.3.1. Cada parte de esta serie contendrá una presentación, un ejercicio práctico y tiempo para hacerle preguntas en vivo al presentador/ la presentadora.  

    Objetivos de Aprendizaje:

    Durante esta capacitación, usted hará lo siguiente:

    • Se familiarizará con los Indicadores de los ODS 15.3.1 y 11.3.1
    • Llegará a entender lo básico de cómo computar los sub-indicadores del ODS 15.3.1 como productividad, cobertura terrestre y carbono del suelo
    • Aprenderá a utilizar la interfaz en línea Trends.Earth Urban Mapper
    • Aprenderá lo básico del conjunto de herramientas (Toolkit) de Trends.Earth incluyendo:
      • Diagramación de series temporales
      • Descarga de datos
      • Cómo utilizar los datos preconfigurados o personalizados para productividad, cobertura terrestre y carbono orgánico del suelo
      • Cómo calcular capas espaciales y una tabla de resumen para el ODS 15.3.1
      • Cómo calcular métricas de cambios urbanos
      • Cómo crear tablas de resumen para cambios urbanos


    Formato del Curso:

    • Esta capacitación ha sido desarrollada en colaboración con Conservation International
    • Tres sesiones de una hora y media cada una que incluyen presentaciones, ejercicios prácticos y una sesión de preguntas y respuestas
    • La primera sesión se transmitirá en inglés y la segunda sesión tendrá el mismo contenido transmitido en español.
    • Habrá un certificado de finalización disponible para quienes asistan a las tres sesiones y completen la tarea asignada, la cual se basará en las presentaciones del webinar. Nota: los certificados de finalización sólo indican que el poseedor participó en todos los aspectos de la capacitación, no implican proficiencia en el material de esta, ni se deben ver como una certificación profesional.


    Prima Parte

    En esta sesión aprenderán acerca del marco de los ODS y la coordinación entre agencias a nivel mundial; se familiarizarán con el ODS 15, Meta 15.3 e Indicador 15.3.1; aprenderán sobre el concepto de la productividad primaria neta y cómo monitorear esa métrica con datos por teledetección; también aprenderemos cómo visualizar e interpretar datos por teledetección asociados con el ODS 15 dentro de una herramienta para QGIS desarrollada por Conservation International llamada Trends.Earth como un ejercicio práctico.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 1 (subindicadores) »
      • Ejercicio 1.2 (descargar resultados) »
      • Transcripción de preguntas y respuestas »


    Segunda Parte

    En esta sesión, aprenderán acerca de los cambios en la cobertura terrestre y el carbono orgánico del suelo y cómo monitorear esas métricas mediante la teledetección; aprenderán acerca de los requisitos en cuanto a la presentación de informes para el ODS 15; además,visualizarán e interpretarán datos por teledetección locales asociados con el ODS dentro de Trends.Earth.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 2 »
      • Transcripción de preguntas y respuestas »


    Tercera Parte

    En esta sesión aprenderán acerca del ODS 11, Meta 11.3 e Indicador 11.3.1; aprenderán acerca de las entradas necesarias para calcular el Indicador 11.3.1 y visualizarán e interpretarán el mapeo de áreas urbanas dentro de Trends.Earth.

    • Ver grabación »
      • Diapositivas de la Presentación »
      • Ejercicio 3 »
      • Tarea (completar hasta el 6 de agosto) »
      • Transcripción de preguntas y respuestas »

  • SAR for Landcover Applications [Advanced]

    This webinar series will build on the knowledge and skills previously developed in ARSET SAR training. Presentations and demonstrations will focus on agriculture and flood applications. Participants will learn to characterize floods with Google Earth Engine. Participants will also learn to analyze synthetic aperture radar (SAR) for agricultural applications, including retrieving soil moisture and identifying crop types.

    Learning Objectives: By the end of this training, attendees will be able to: 

    1. analyze SAR data in Google Earth Engine
    2. generate soil moisture analyses
    3. identify different types of crops   


    Course Format: 

    • This webinar series will consist of two, two-hour parts
    • Each part will include a presentation on the theory of the topic followed by a demonstration and exercise for attendees. 
    • This training is also available in Spanish. Please visit the Spanish page for more information.
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.


    Prerequisites: 
    Prerequisites are not required for this training, but attendees that do not complete them may not be adequately prepared for the pace of the training. 



    Part One: Monitoring Flood Extent with Google Earth Engine
    This session will focus on the use of Google Earth Engine (GEE) to generate flood extent products using SAR images from Sentinel-1. The first third of the session will cover the basic principles of radar remote sensing related to flooded vegetation. 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.
    Part Two: Exploiting SAR to Monitor Agriculture
    Featuring guest speaker Dr. Heather McNairn, from Agriculture and Agri-Food Canada, this session will focus on using SAR to monitor different agriculture-related topics, building on the skills learned in the SAR agriculture session from 2018. The first part of the session will cover the basics of radar remote sensing as related to agriculture. The remainder of the session will focus on the use of SAR to retrieve soil moisture, identify crop types, and map land cover.

    Each part of 2 includes links to the recordings, presentation slides, and Question & Answer Transcripts.
     

  • SAR y sus Aplicaciones para la Cobertura Terrestre [Avanzado]

    Esta capacitación se basará en los conocimientos y las habilidades adquiridas en capacitaciones anteriores de ARSET sobre radar de apertura sintética (synthetic aperture radar o SAR). Las presentaciones y demostraciones se enfocarán en aplicaciones para la agricultura y para desastres. Los participantes aprenderán a utilizar imágenes SAR 1) para caracterizar inundaciones con Google Earth Engine 2) y para aplicaciones en la agricultura incluyendo estimación de la humedad del suelo e identificación de cultivos.

    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los participantes podrán:

    1. analizar datos SAR en Google Earth Engine para el mapeo de inundaciones
    2. generar análisis de la humedad del suelo
    3. identificar diferentes tipos de cultivos


    Formato del Curso: Dos partes de dos horas cada una

    • Cada parte incluirá una
    • presentación teórica del tema seguida por una demostración
    • Esta capacitación también está disponible en inglés. Por favor visite la página de inscripciones en inglés para más información.
    • Habrá un certificado de finalización disponible para los participantes que asistan a las dos sesiones y completen la tarea, la cual estará basada en las sesiones del webinar.
    • Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.



    Prerequisites:

    Los prerrequisitos no son obligatorios para esta capacitación, pero quienes no los completen podrían no estar lo suficientemente preparados para esta capacitación


    Inscripciones:
    Debido a la demanda anticipada, por favor inscríbase solo para la sesión en español o la sesión en inglés.

    Parte Uno: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Esta parte estará enfocada en el uso de Google Earth Engine (GEE) para mapear inundaciones utilizando imágenes SAR de Sentinel-1. La primera parte de la sesión cubrirá los principios básicos de SAR relacionados a las inundaciones. El resto de la sesión estará enfocada en una demostración de cómo utilizar GEE para generar mapas de inundación con Sentinel-1.
    Parte Dos: SAR para el Monitoreo Agrícola
    Esta parte estará enfocada en el uso de SAR para monitorear diferentes aspectos relacionados con la agricultura, extendiendo los conocimientos adquiridos en la sesión de SAR para la agricultura del 2018. El resto de la sesión estará enfocada en el uso de SAR para estimar la humedad del suelo e identificar diferentes tipos de cultivos. La Dra. Heather McNairn, de Agriculture and Agri-Food Canadá, será la presentadora de esta sesión.
     

  • SAR for Disasters and Hydrological Applications [Advanced]

    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 training presents two new topics; the use of InSAR for characterizing landslides and the generation of a digital elevation model (DEM).
    Learning Objectives: By the end of this training, attendees will be able to:

    • Create a flood map using Google Earth Engine
    • Generate a map characterizing areas where landslides have occurred
    • Generate a digital elevation model (DEM)


    Course Format: 

    • This webinar series will consist of three, two-hour parts
    • Each part will include a presentation on the theory of the topic followed by a demonstration and exercise for attendees. 
    • This training is also available in Spanish. Please visit the Spanish page for more information.
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: 
    Prerequisites are not required for this training, but attendees that do not complete them may not be adequately prepared for the pace of the training. 



    Part One: SAR for Flood Mapping Using Google Earth Engine
    This 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.
    Part Two: Interferometric SAR for Landslide Observations
    Featuring guest speaker Dr. Eric Fielding from JPL, this 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.
     Part Three: Generating a Digital Elevation Model (DEM)
    Featuring guest speaker Nicolás Grunfeld Brook, from Argentina’s 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.

    Each part of 3 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.
     

  • SAR para Desastres y Aplicaciones Hidrológicas [Avanzado]

    Esta capacitación se basará en las capacidades de utilizar Google Earth Engine para el mapeo de inundaciones a partir de datos de radar enseñadas en capacitaciones ARSET de SAR anteriores. Esta capacitación presenta dos temas nuevos; el uso de InSAR para la caracterización de derrumbes y la generación de un modelo de elevación digital (digital elevation model o DEM).

    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los participantes podrán:

    • Crear un mapa de inundación utilizando Google Earth Engine
    • Generar un mapa que caracteriza las zonas donde ocurrieron derrumbes
    • Generar un modelo de elevación (digital elevation model o DEM)


    Formato del Curso: 

    • Tres partes de dos horas cada una
    • Cada parte incluirá una presentación teórica del tema seguida por una demostración y un ejercicio para quienes asistan. 
    • Esta página también está disponible en inglés. Por favor visite la página de inscripciones en inglés para más información. 
    • Habrá un certificado de finalización disponible para los participantes que asistan a todas las sesiones y completen la tarea, la cual estará basada en las sesiones del webinar. Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.


    Prerrequisitos:
    Los prerrequisitos no son obligatorios para esta capacitación, pero quienes no los completen podrían no estar lo suficientemente preparados para esta. 


    Primera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Esta sesión estará enfocada en el uso de of Google Earth Engine (GEE) para generar un mapa de inundación utilizando imágenes SAR de Sentinel-1.  La primera parte de la sesión cubrirá los principios básicos de SAR relacionados con las inundaciones. El resto de la sesión será dedicada a una demostración de cómo utilizar GEE para generar productos relevantes a la extensión de inundaciones y cómo integrar datos socioeconómicos al mapeo de inundaciones para identificar áreas en peligro. 

    Segunda Parte: SAR Interferométrico para la Observación de Derrumbes
    Dirigida por el presentador invitado, el Dr. Eric Fielding de JPL, esta sesión se enfocará en la observación de derrumbes. Desarrollará las capacidades con InSAR enseñadas en las tres anteriores series de webinars de SAR. La primera parte de la sesión cubrirá la física de InSAR relacionada con los derrumbes. El resto se enfocará en cómo generar e interpretar el producto derrumbes derivado.

    Tercera Parte: Generación de un Modelo de Elevación Digital (Digital Elevation Model o DEM)
    A cargo de un presentador invitado de la agencia espacial argentina, CONAE, los participantes aprenderán cómo generar un modelo de elevación digital (DEM) a través de técnicas de InSAR.  La primera parte de la sesión cubrirá la física de utilizar dos imágenes de fase de SAR para generar un DEM. El resto del tiempo se enfocará en cómo generar un DEM.

     

  • Using the UN Biodiversity Lab to Support National Conservation and Sustainable Development Goals [Introductory]

    As we enter the fourth industrial revolution, technology is revolutionizing our ability to map nature. Satellite data provide a bird’s eye, yet incredibly detailed view of the Earth’s surface in real-time, while drones and mobile apps enable local communities and indigenous peoples to map their knowledge of local ecosystems. To 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)  launched UN 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’s Aichi Biodiversity 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.

    There is a global demand for more NASA ARSET training focused on biodiversity, conservation, the UN Sustainable Development Goals (SDGs), and 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.

    Learning Objectives: By the end of this training, attendees will:

    • Understand key global biodiversity and sustainable development policy instruments (CBD, UN Framework Convention on Climate Change (UNFCCC), the 2030 Agenda for Sustainable Development) as they relate to conservation efforts
    • Have knowledge of spatial data on biodiversity and sustainable development, including data generated by NASA projects
    • Be familiar with the UN Biodiversity Lab structure, data, and tools
    • Have the ability to apply UN Biodiversity Lab tools to their region of interest
    • Utilize case study examples from multiple partner countries as a context for their work


    Course Format: 

    • Three, 1.5-hour sessions offered in English, French, and Spanish
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignments, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification. 


    Prerequisites:
     Attendees that do not complete the required prerequisites may not be adequately prepared for the pace of the training.


    Part One: Introduction to Spatial Data and Policies for Biodiversity (
    Part Two: UN Biodiversity Lab: Introduction and Training 
    Part Three: How are Countries Using Spatial Data to Support Conservation of Nature? 

    Each part of 3 includes links to the recordings, presentation slides, and Question & Answer Transcripts.
     

  • Utiliser le UN Biodiversity Lab pour soutenir les objectifs nationaux de conservation et de développement durable [d’introduction]

    Alors que nous entrons dans la quatrième révolution industrielle, la technologie révolutionne notre capacité à cartographier la nature. Les données spatiales fournissent une vue d’ensemble, mais également une vue incroyablement détaillée de la surface de la Terre en temps réel, tandis que les drones et les applications mobiles permettent aux communautés locales et aux peuples autochtones de cartographier leurs connaissances des écosystèmes locaux. Pour aider les décideurs à élaborer des solutions de développement durable fondées sur des données, le PNUD, le Programme des Nations Unies pour l'environnement (PNUE) et le Secrétariat de la Convention sur la diversité biologique (CDB) ont lancé 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 – Geneva et la NASA. Le UN Biodiversity Lab est une plateforme en ligne qui permet aux décideurs et autres parties prenantes d'accéder aux couches de données mondiales, de télécharger des ensembles de données nationaux et d'analyser ces ensembles de données en combinaison pour fournir des informations clés sur les objectifs d'Aichi pour la biodiversité de la CDB et sur les objectifs de développement durable fondés sur la nature. Déjà utilisé par plus de 50 pays, et utilisé comme système clé d'aide à la décision pour deux projets de science appliquée financés par la NASA, le UN Biodiversity Lab a un fort potentiel d'être étendu pour atteindre de nouveaux ministères et pays et groupes de parties prenantes.

    Il existe une demande mondiale pour plus de formations ARSET de la NASA axées sur la biodiversité, la conservation, les objectifs de développement durable (ODD) des Nations Unies et la façon de relier les données spatiales de la NASA à des systèmes écologiques et influencés par l'homme. Cette formation vise à combler cette lacune en étendant l'influence de cet outil soutenu par la NASA et en augmentant sa diffusion, son utilisation et son succès global. Le UN Biodiversity Lab rend les ensembles de données mondiaux sur la biodiversité et le développement durable facilement accessibles, soutenant notre large public.



    Objectifs d’apprentissage: À la fin de cette formation, les participants:

    • Comprendront les principaux instruments de politique mondiale sur la biodiversité et le développement durable (CDB, Convention-cadre des Nations Unies sur les changements climatiques (CCNUCC), le Programme de développement durable à l'horizon 2030) en ce qui concerne les efforts de conservation
    • Connaîtront les données spatiales sur la biodiversité et le développement durable, y compris les données générées par les projets de la NASA
    • Connaîtront la structure, les données et les outils du UN Biodiversity Lab
    • Auront la capacité d'appliquer les outils du UN Biodiversity Lab à leur région d'intérêt
    • Utiliseront des exemples d'études de cas de plusieurs pays partenaires comme contexte pour leur travail


    Format du cours:
    Trois sessions de une heure et demie, dispensées en anglais, français et espagnol

    Pré-requis:
    Les participants qui ne remplissent pas les conditions préalables requises peuvent ne pas être convenablement préparés au rythme de la formation.
    Principes fondamentaux des données spatiales (en anglais) » 

    Partie 1: Introduction aux données spatiales et aux politiques de biodiversité 
    Partie 2: UN Biodiversity Lab: Introduction et formation  
    Partie 3: Comment les pays utilisent-ils les données spatiales pour soutenir la conservation de la nature ? 

  • Utilizando el UN Biodiversity Lab para Apoyar los Objetivos Nacionales de Conservación y Desarrollo Sostenible [Introductoria]

    A inicios de la cuarta revolución industrial, la tecnología está revolucionando nuestra capacidad de mapear la naturaleza. Los datos satelitales proporcionan una vista panorámica pero a la vez increíblemente detallada de la superficie de la Tierra en tiempo real mientras que los drones y las aplicaciones móviles permiten que las comunidades locales y los pueblos indígenas mapeen su conocimiento de ecosistemas locales. Para poder ayudar a los formuladores de políticas a desarrollar soluciones para el desarrollo sostenible basadas en datos y políticas enfocadas, el UNDP, el Programa de las Naciones Unidas para el Medio Ambiente (UNEP por sus siglas en inglés) y la Secretaría del Convenio sobre la Diversidad Biológica (CDB) lanzaron el UN Biodiversity Lab con financiación del GEF y apoyo de MapX, el Centro de Monitoreo de la Conservación Mundial del UNEP, la Base de Datos Mundial sobre Recursos de Información – Ginebra y la NASA. El UN Biodiversity Lab es una plataforma en línea que permite a los formuladores de políticas y otras partes interesadas acceder a capas de datos a nivel mundial, cargar conjuntos de datos nacionales y analizar estos conjuntos de datos en combinación para brindar información clave sobre los Objetivos Aichi para la Biodiversidad del CDB y sobre los Objetivos de Desarrollo Sostenible relacionados con la naturaleza. Ya lo están utilizando en más de 50 países, 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 países y grupos de partes interesadas. 


    Existe una demanda a nivel mundial de más capacitaciones NASA ARSET enfocadas en la biodiversidad, conservación, los Objetivos de Desarrollo Sostenible (ODS) de la ONU y sobre cómo conectar datos de satélites de la NASA con sistemas ecológicos y aquellos que han sido influidos por la actividad humana. Esta capacitación pretende llenar este vacío extendiendo la influencia de esta herramienta apoyada por la NASA y fomentando su diseminación, utilización y éxito general. El  UN Biodiversity Lab hace conjuntos de datos mundiales sobre la biodiversidad y el desarrollo sostenible fácilmente accesibles, apoyando a nuestro público variado.



    Objetivos de Aprendizaje: Para la conclusión de esta capacitación, los/las participantes podrán:

    • Entender instrumentos políticos claves para la diversidad biológica global y el desarrollo sostenible (CDB, Convención Marco De Las Naciones Unidas Sobre el Cambio Climático (UNFCCC), la Agenda 2030 para el Desarrollo Sostenible) en lo que se refieren a campañas de conservación.
    • Adquirir conocimiento sobre datos espaciales sobre la diversidad biológica y el desarrollo sostenible, incluso datos generados por proyectos de la NASA
    • Estar familiarizados con la estructura, datos y herramientas del UN Biodiversity Lab
    • Tener la capacidad de aplicar las herramientas del UN Biodiversity Lab a su región de interés
    • Utilizar ejemplos de casos de estudio de múltiples países colaboradores como contexto para su trabajo


    Formato del Curso:

    • Tres sesiones de una hora y media cada una ofrecidas en inglés, francés y español
    • Habrá un certificado de finalización disponible para los participantes que asistan a todas las sesiones y completen las tareas, la cual estará basada en las sesiones del webinar. Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.


    Prerrequisitos: 
    Los participantes que no completen los prerrequisitos podrían no estar lo suficientemente preparados para el ritmo de la capacitación.

    Fundamentos de la Percepción Remota (Teledetección) Diapositivas de la Presentación »

    Primera Parte: Introducción a Datos Espaciales y Políticas para la Diversidad Biológica
    Segunda Parte: El UN Biodiversity Lab 
    Tercera Parte: Casos de Uso por Países 
     

  • Satellite Remote Sensing for Agricultural Applications[Introductory]

    Since the launch of NASA’s first Landsat mission in 1972, satellite imagery has been used for global agricultural monitoring, providing one of the longest operational applications for the Landsat program. Although satellite observations of land began with agricultural monitoring, only in recent years has agricultural remote sensing seen reinvigoration among space agencies, national ministries of agriculture, and global initiatives. To monitor agricultural systems, NASA utilizes satellite observations to assess a wide variety of geophysical and biophysical parameters, including precipitation, temperature, evapotranspiration, soil moisture, and vegetation health.

    Past ARSET webinars on land and water resources covered remote sensing-derived parameters relevant to agriculture within a broader scope. This 4-part introductory webinar will focus on data products, data access, and case-studies demonstrating how remote sensing data can be used for decision-making among the agriculture and food security communities.



    This training will address how to use remote sensing data for agriculture monitoring, specifically drought and crop monitoring. The webinar will also provide end-users the ability to evaluate which regions of the world agricultural productivity are above or below long-term trends. This informs decisions pertaining to market stability and humanitarian relief.

    Learning Objectives: By the end of this training, attendees will be able to:

    • Identify which satellites and sensors can be used for agricultural applications
    • Understand the limitations of remote sensing and modeled data for agriculture and food security
    • Acquire specific remote sensing data products that are appropriate for their work
    • Apply remote sensing techniques to crop monitoring, drought, and humanitarian relief


    course Format: 

    • Four online, 1.5-hour parts with sessions offered twice a day
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.


     Prerequisites:
     Attendees who have not completed the following may not be prepared for the pace of the training:
    Fundamentals of Remote Sensing  

    Part 1: Overview of Agricultural Remote Sensing
    This section will cover the ARSET Program and give a general overview of remote sensing as it pertains to agriculture. This part will include the history of Earth observations (EO) for agriculture, satellites and sensors that can be used, the limitations of satellite data, an introduction of NASA HARVEST, examples of current EO applications in agriculture, and a Q&A session.

    Supplementary Materials:
    NASA Satellites and Sensors Relevant for Agriculture »

    Fact Sheets:

    • Air Quality
    • Vegetation
    • Water Availability
    • Water Quality


    Part 2: Soil Moisture for Agricultural Applications
    This part of the training provides an overview of SMAP and case studies for agricultural applications and an overview of soil moisture and shallow groundwater from the Land Data Assimilation System (LDAS), as well as a Q&A session.

    Part 3: Earth Observations for Agricultural Monitoring
    This section will cover previous ARSET training that relates to agricultural monitoring and present case studies of EO being used for agricultural monitoring. There will also be a Q&A session.

    Part 4: Evapotranspiration (ET) & Evaporative Stress Index (ESI) for Agricultural Applications
    This section includes a presentation from guest speaker Dr. Christopher Hain, along with an overview and case studies of ET and ESI in agricultural applications. This section will conclude with a Q&A session.

    Each part of 4 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.
     

  • Forest Mapping and Monitoring with SAR Data [Advanced]

    Measurements of forest cover and change are vital to understanding the global carbon cycle and the contribution of forests to carbon sequestration. Many nations are engaged in international agreements, such as the Reducing Emissions from Deforestation and Degradation (REDD+) initiative, which includes tracking annual deforestation rates and developing early warning systems of forest loss. Remote sensing data are integral to data collection for these metrics, however, the use of optical remote sensing for monitoring forest health can be challenging in tropical, cloud-prone regions.
    Radar remote sensing overcomes these challenges because of its ability to “see” the surface through clouds or regardless of day or night conditions. In addition, the radar signal can penetrate through the vegetation canopy and provide information relevant to structure and density. Although the capabilities and benefits of SAR data for forest mapping and monitoring are known, it is underutilized operationally due to data complexities and limited user-friendly tutorials.


    This advanced webinar series will introduce participants to 1.) SAR time series analysis of forest change using Google Earth Engine (GEE), 2.) land cover classification with radar and optical data with GEE, 3.) mapping mangroves with SAR, and 4.) forest stand height estimation with SAR. Each training will include a theoretical portion describing the use of SAR for landcover mapping as related to the focus of the session followed by a demonstration that will show participants how to access, download, and analyze SAR data for forest mapping and monitoring. These demonstrations will use freely-available, open-source data, and software.

    Learning Objectives: By the end of this training, attendees will be able to:

    • Interpret radar data for forest mapping
    • Understand how radar data can be applied to land cover mapping
    • Become familiar with open source tools used to analyze radar data
    • Conduct a land cover classification with radar and optical data
    • Map mangrove forests with radar data
    • Understand how forest stand height can be mapped using radar data
    • Apply SAR time-series analysis to map forest change
    • Learn about upcoming radar missions at NASA


    Course Format: 

    • Four parts with sessions offered in English and Spanish
    • Four exercises
    • One Google Form homework


    prerequisites: Attendees who have not completed the following may not be prepared for the pace of this training:



    Part 1: Time Series Analysis of Forest Change

    • Introduction to analysis and interpretation of SAR data for forest mapping
    • Exercise: Time Series of Forest Change using GEE
    • Q&A


    Part 2: Land Cover Classification with Radar and Optical Data

    • Review of the unique attributes of radar and optical data as related to forest mapping and how they can be complementary
    • Classification algorithms and improvements with optical imagery
    • Exercise: Land Cover Classification with Radar and Optical using GEE
    • Q&A


    Part 3: Mangrove Mapping

    • Introduction to analysis and interpretation of SAR data for mangrove mapping
    • Exercise: Mapping Mangroves with the Sentinel Toolbox
    • Q&A


    Part 4: Forest Stand Height (with Guest Speaker Paul Siqueria)

    • Introduction to the use of SAR data for mapping forest stand height
    • Applications and looking forward to NISAR 2022
    • Demo: Estimating Forest Stand Height
    • Q&A


    ​Each part of 4 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.

  • Mapeo y Monitoreo de los Bosques con Datos SAR [Avanzado]

    Esta capacitación avanzada cubrirá los siguientes temas 1) análisis del cambio en los bosques con datos SAR multi-temporales utilizando Google Earth Engine (GEE); 2) la clasificación de la cobertura terrestre con datos SAR y ópticos utilizando GEE; 3) el mapeo de manglares con SAR; 4) y la estimación de la altura de los bosques utilizando SAR. Cada sesión incluirá una porción teórica describiendo el uso de SAR para el mapeo de la cobertura relevante el enfoque de la sesión, seguida por una demostración de cómo acceder, descargar y analizar datos SAR para el mapeo y monitoreo del bosque. Estas demostraciones utilizan datos y software de libre acceso y de fuente abierta.

    Objetivos de Aprendizaje: 

    • Para la conclusión de esta capacitación, los participantes podrán:
    • Interpretar datos radar para el mapeo de los bosques
    • Entender cómo se puede aplicar datos radar para el mapeo de la cobertura terrestre
    • Estar familiarizados con herramientas de fuente abierta para analizar datos radar
    • Realizar una clasificación de la cobertura terrestre con datos radar y ópticos
    • Mapear manglares con datos radar
    • Entender cómo la altura de los rodales de los bosques se puede mapear con datos radar
    • Aplicar análisis de series temporales SAR para mapear cambios en los bosques
    • Aprender sobre futuras misiones radar de la NASA


    Formato del Curso: 

    • Cuatro partes con sesiones disponibles en inglés y español
    • Cuatro ejercicios
    • Una tarea en Google Form
    • Habrá un certificado de finalización disponible para los participantes que asistan a todas las sesiones y completen las tareas, la cual estará basada en las sesiones del webinar. Nota: los certificados de finalización indican únicamente que el poseyente participó en todos los aspectos de la capacitación, no implican competencia en la temática ni se deben ver como una certificación profesional.



    Prerequisitos: 
    Completar los Fundamentos de la Percepción Remota (Teledetección), Introducción al Radar de Apertura Sintética y SAR y sus Aplicaciones para la Cobertura Terrestre o tener experiencia equivalente. Los participantes que no completen los prerrequisitos podrían no estar lo suficientemente preparados para el ritmo de la capacitación.
    Software instrucciones
    Puede seguir las demostraciones utilizando el software enumerado a continuación. Las grabaciones de cada parte estarán disponibles en YouTube dentro de 24 horas después de cada demostración para que usted pueda repasarlas a su propio ritmo.
    Primera Parte: SAR para el Mapeo de Inundaciones Utilizando Google Earth Engine
    Google Earth Engine
    Segunda Parte: SAR Interferométrico para la Observación de Derrumbes
    Tercera Parte: Generación de un Modelo de Elevación Digital (Digital Elevation Model o DEM)
    Para ambas partes, los presentadores utilizarán el Sentinel-1 Toolbox

    Primera Parte: Análisis del Cambio en los Bosques con Datos SAR Multi-Temporales
    • Introducción al análisis e interpretación de datos SAR para el mapeo de los bosques 
    • Ejercicio: Datos SAR multi-temporales para el análisis del cambio en los bosques usando GEE 
    • Sesión de preguntas y respuestas

    Segunda Parte: Clasificación de la Cobertura Terrestre con Datos SAR y Ópticos
    • Repaso de las caracteristicas de los datos SAR y ópticos relevantes al mapeo de bosques y cómo se pueden complementar entre sí
    • Algoritmos para la clasificación con imágenes ópticas 
    • Ejercicio: Clasificación de la cobertura terrestre con datos SAR y opticos usando GEE 
    • Sesión de preguntas y respuestas

    Tercera Parte: Mapeo de Manglares
    • Introducción al análisis e interpretación de datos SAR para el mapeo de manglares 
    • Ejercicio: El mapeo de Manglares con el Sentinel Toolbox 
    • Sesión de preguntas y respuestas

    Cuarta Parte: Estimación de la Altura de los Bosques con SAR (Presentador Invitado el Dr. Paul Siqueira)
    • Introducción al uso de datos SAR para estimar la altura de los bosques
    • Aplicaciones y a la espera de NISAR en el 2022 
    • Demo: Estimacion de la altura de los bosques 
    • Sesión de preguntas y respuestas

  • Groundwater Monitoring using Observations from NASA’s Gravity Recovery and Climate Experiment (GRACE) Missions [Introductory]

    Groundwater makes up roughly 30% of global freshwater. It also provides drinking water for the world’s population and irrigation for close to 1/3rd of global agricultural land. Because of this level of reliance, monitoring groundwater is crucial for water resources and land management. The Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) missions from NASA and the German Research Centre for Geosciences (GFZ) provide large-scale terrestrial water storage estimation from mid-2000 to present. The mission uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution.


     


    GRACE observations have been used for detecting groundwater depletion and for drought and flood predictions. This lightning-style training is designed to answer the demand and interest from the applications community in technologies that can be used to support water resources management. The webinar will provide an overview of the GRACE missions, groundwater data availability, and their applications in the monitoring and management of water resources. This lightning webinar will also serve as the foundation for the upcoming advanced webinar: Using Earth Observations to Monitor Water Budgets for River Basin Management II.

    Learning Objectives:
     By the end of this training, attendees will be able to:


    • Access GRACE data and analyze regional groundwater changes


    course Format: 


    • A single, 1.5-hour webinar that includes a lecture and a question & answer session
    • No certificate of completion will be available for this webinar


    Prerequisites:
     Fundamentals of Remote Sensing   

    Agenda:
    • Introduction to GRACE and GRACE-FO
    • Data Format, Variables, and Resolution
    • GRACE Data Access
    • Q&A Session

  • Understanding Phenology with Remote Sensing [Introductory]

    This training will focus on the use of remote sensing to understand phenology: the study of life-cycle events. Phenological patterns and processes can vary greatly across a range of spatial and temporal scales and can provide insights about ecological processes like invasive species encroachment, drought, wildlife habitat, and wildfire potential. This training will highlight NASA-funded tools to observe and study phenology across a range of scales. Attendees will be exposed to the latest in phenological observatory networks and science, and how these observations relate to ecosystem services, the carbon cycle, biodiversity, and conservation.

    Learning Objectives: 
    By the end of this training series, attendees will be able to:

    • Summarize NASA satellites and sensors that can be used for monitoring global phenology patterns
    • Outline the benefits and limitations of NASA data for phenology
    • Describe the multi-scalar approach to vegetation life cycle analyses
    • Compare and contrast data from multiple phenology networks
    • Evaluate various projects and case-study examples of phenological data


    Course Format: 

    • Three, one-hour sessions


    Prerequisites: Attendees who have not completed the course(s) below may be unprepared for the pace of this training.
    Fundamentals of Remote Sensing  

    Part 1: Overview of Phenology and Remote Sensing

    • Introduction to NASA data and Phenology
    • Land Surface Phenology from MODIS and VIIRS


    Part 2: Scales of Phenology

    • Resolving challenges associated with variability in space, time, and resolution for phenology research and applications
    • USA-National Phenology Network (NPN) and The National Ecological Observatory Network (NEON) 
    • Phenocam: Near-surface phenology
    • Conservation Science Partners


    Part 3: Utility and Advantage of Multi-Scale Analysis

    • Field-based phenology and gridded products
    • Case-study examples:
    • Integration of PhenoCam near-surface remote sensing and satellite phenological data
    • Greenwave modeling
    • Urbanization and plant phenology


    Each part of 3 includes links to the recordings, presentation slides, and Question & Answer Transcripts.
     

  • Using Earth Observations to Monitor Water Budgets for River Basin Management II [Advanced]

    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.
    Past 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.

    Learning Objectives:
     By the end of this training, attendees will be able to:


    • Identify and access remote sensing and Earth system-modeled data for estimating water budgets in a river basin
    • Explain the uncertainties involved in estimating water budgets for river basins
    • Replicate the steps for estimating water budgets for a river basin and sub-watersheds using remote sensing products and GIS


    Course Format: 


    • Three, two-hour webinars 
    • A certificate of completion will also be available to participants who attend all sessions and complete the homework assignment, which will be based on the webinar sessions.
    • NOTE: Certificates of completion only indicate participation in all aspects of the training.
    • They do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: Attendees who have not completed the following may not be prepared for the pace of the training:



    Portions of the series will include data import to QGIS. If you wish to follow along with those steps, please install using the instructions here:



    Part 1: Review and Access of Earth Observations and Earth System-Modeled Data for River Basin Monitoring and Management


    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.


    Part 2: Water Budget Estimation using Remote Sensing Observations
    This 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.
    Part 3: Water Budget Estimation using the Global Land Data Assimilation Model
    The 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.
     


    Each part of 3 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.
     

  • An Inside Look at how NASA Measures Air Pollution [Introductory]

    Would you like to learn how to access and visualize NASA satellite imagery? With the world’s eyes and media coverage turned to recent global changes in air pollution from the economic downturn, this two-part webinar series provides a primer for the novice and a good refresher course for all others. You will learn which pollutants can be measured from space, how satellites make these measurements, the do’s and don’ts in interpreting satellite data, and how to download and create your own visualizations.

    Learning Objectives: By the end of this training, attendees will be able to:

    • List the pollutants that can be observed by NASA satellites
    • Find and download imagery for NO2 and aerosols/particles
    • Describe the capabilities and limitations of NASA NO2 and aerosol measurements



    Prerequisites: Fundamentals of Remote Sensing (recommended but not required)

    Part One: Nitrogen Dioxide (NO2)
    • What is NO2?
    • NASA Remote Sensing Basics
    • Interpreting NO2 Imagery: Dos and Don’ts
    • Downloading Data and Creating Imagery

    Part Two: Particulate Matter (Aerosols)
    • What are Aerosols?
    • Interpreting Aerosol Imagery: Dos and Don’ts
    • A Tour of NASA Resources for Generating Your Own Visualizations

    Each part of 2 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.

  • Un Vistazo a Cómo la NASA Mide la Contaminación del Aire [Introductorio]

    ¿Le gustaría saber cómo acceder y visualizar imágenes satelitales de la NASA? La reciente disminución de la contaminación atmosférica a nivel mundial debido al bajón económico ha capturado la atención del mundo entero y recibido mucha cobertura mediática. Inspirándose en ello, esta serie de dos webinars imparte conocimiento fundamental para novatos y sirve de curso de repaso para los demás. Ud. aprenderá cuáles son los contaminantes que se pueden medir desde el espacio, cómo los satélites hacen estas mediciones, lo que se debe hacer y no se debe hacer al momento de interpretar datos satelitales y cómo descargar y crear sus propias visualizaciones. 

    Objetivos de Aprendizaje:Al finalizar esta capacitación, los/las participantes podrán:

    • Nombrar los contaminantes que pueden ser observados por satélites de la NASA
    • Encontrar y descargar imágenes para NO2 y aerosoles/partículas
    • Describir las capacidades y limitaciones de las mediciones de NO2 y aerosoles de la NASA



    Prerrequisitos: Fundamentos de la Teledetección (Percepción Remota) -  recomendado pero no obligatorio

    Parte 1: Dióxido de Nitrógeno (NO2)

    • ¿Qué es el NO2?
    • Conceptos Básicos de la Teledetección de la NASA 
    • Interpretación de Imágenes de NO2: Qué hacer y qué no hacer
    • Descargar Datos y Crear Imágenes


    Parte 2: Partículas (Aerosoles)

    • ¿Qué son los Aerosoles?
    • Interpretación de Imágenes de Aerosoles: Qué hacer y qué no hacer
    • Un Recorrido por los Recursos de la NASA para Generar sus Propias Visualizaciones

  • Earth Observations for Disaster Risk Assessment & Resilience [Introductory]

    According to a UN report, between 1998 and 2017, the U.S. alone lost $944.8 billion USD from disasters. Between 1878 and 2017, losses from extreme weather events rose by 251 percent. It is critical to developing disaster management strategies to reduce and mitigate disaster risks. A major factor in regional risk assessment is evaluating the vulnerability of lives and property to disasters. Environmental information about disasters, their spatial impact, and their temporal evolution can plan an important role as well.
    This webinar series will focus on Earth observation (EO) data useful for disaster risk assessment. The series will cover disasters including tropical cyclones, flooding, wildfires, and heat stress. The training will also include access to socioeconomic and disaster damage data. Sessions 3 & 4 will cover case studies and operational applications of EO for disaster risk assessment.

    Learning Objectives: By the end of this training, attendees will: 


    • learn about available NASA remote sensing and socioeconomic data and how to combine them for assessing risk
    • understand how to apply these data for assessing risk from floods and tropical cyclones in specific regions
    • learn how operational agencies are using NASA data for risk management



    Course Format:


    • Four, two-hour parts that include lectures, demonstrations, and question and answer sessions
    • Both Session A & B will be broadcast in English
    • A certificate of completion will also be available to participants who attend all four parts and complete all homework assignments. Note: certificates of completion only indicate the attendee participated in all aspects of the training, they do not imply proficiency on the subject matter, nor should they be seen as a professional certification.



    Prerequisites: 



    Part One: NASA Remote Sensing and Socioeconomic Data for Disaster Risk Assessment Attendees will learn basic concepts and definitions in disaster risk management. Attendees will also learn about the types of satellites and socioeconomic data available through NASA for disaster risk management.


    Part Two: Assessing the Risk of Floods and Cyclones Using NASA Data Attendees will learn a methodology for analyzing remote sensing and socioeconomic data to assess flood and cyclone risk. Examples will be shown for an urban area (Houston, TX, USA) and a country (Mozambique). These case studies will use both historical and forecast data.

    Part Three: Disaster Risk Assessment Case Studies Using Remote Sensing Data This will cover two case studies for using remote sensing data. One on how New York state is using NASA data for heatwave risk assessment, another on the freely available online tools from the World Resources Institute for visualizing NASA remote sensing and socioeconomic data.

    Part Four: Operational Application of Remote Sensing for Disaster Management The Pacific Disaster Center will describe the data, applications, and strategies they use for disaster risk reduction, response, and relief operations.




    Each part of 4 includes links to the recordings, presentation slides,  and Question & Answer Transcripts.

  • Remote Sensing for Conservation & Biodiversity [Introductory]

    The United Nations Millennium Ecosystem Assessment states: “ecosystems are critical to human well-being - to our health, our prosperity, our security, and to our social and cultural identity.” Conservation and biodiversity management play important roles in maintaining healthy ecosystems. Earth observations can help with these efforts. This online webinar series introduces participants to the use of satellite data for conservation and biodiversity applications. The series will highlight specific projects that have successfully used satellite data. Examples include:

    • monitoring chimpanzee habitat loss
    • decreasing whale mortality
    • detecting penguins
    • monitoring wildfires
    • biodiversity observation networks


    Learning Objectives: By the end of this training, attendees will: 

    • be able to outline uses of remote sensing for habitat suitability, species population dynamics, and monitoring wildfires
    • learn about the Group on Earth Observations Biodiversity Observation Network (GEOBON), Marine Biodiversity Observation Network (MBON), and essential biodiversity variables


    Course Format: 

    • Two, one hour sessions
    • The same session will be broadcast at both times, both in English


    Prerequisites: Fundamentals of Remote Sensing or equivalent knowledge
    If you do not complete the prerequisite, you may not be adequately prepared for the pace of the training.

    Session One: Remote Sensing for Conservation 
    This session will focus on remote sensing for habitat suitability, species population dynamics, and monitoring wildfires.

    Session Two: Remote Sensing for Biodiversity 
    This session will focus on the Group on Earth Observations Biodiversity Observation Network (GEOBON), Marine Biodiversity Observation Network (MBON), and essential biodiversity variables.

    Each part of 2 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts, in English and in Spanish.  There is no link to a landing page in Spanish for this resource.   

  • An Introduction to Humanities Data Curation

    This webpage is a compilation of articles that address aspects of data curation in the digital humanities. The goal of it is to direct readers to trusted resources with enough context from expert editors and the other members of the research community to indicate how these resources might help them with their own data curation challenges.
    Each article provides a short introduction to a topic and a list of linked resources. Structuring articles in this way acknowledges the many excellent resources that already exist to provide guidance on subjects relevant to curation such as data formats, legal policies, description, and more.
    The table of contents:
    -An Introduction to Humanities
    -Data Curation-Classics, “Digital Classics” and Issues for Data Curation
    -Data Representation
    -Digital Collections and Aggregations
    -Policy, Practice, and Law
    -Standards

  • Data Management using NEON Small Mammal Data

    Undergraduate STEM students are graduating into professions that require them to manage and work with data at many points of a data management lifecycle. Within ecology, students are presented not only with many opportunities to collect data themselves but increasingly to access and use public data collected by others. This activity introduces the basic concept of data management from the field through to data analysis. The accompanying presentation materials mention the importance of considering long-term data storage and data analysis using public data.

    Content page: ​https://github.com/NEONScience/NEON-Data-Skills/blob/master/tutorials/te...

  • Introduction To The Principles Of Linked Open Data

    This lesson offers a brief and concise introduction to Linked Open Data (LOD). No prior knowledge is assumed. Readers should gain a clear understanding of the concepts behind linked open data, how it is used, and how it is created. The tutorial is split into five parts, plus further reading:
    -Linked open data: what is it?
    -The role of the Uniform Resource Identifier (URI)
    -How LOD organizes knowledge: ontologies
    -The Resource Description Framework (RDF) and data formats
    -Querying linked open data with SPARQL
    -Further reading and resources
    The tutorial should take a couple of hours to complete, and you may find it helpful to re-read sections to solidify your understanding. Technical terms have been linked to their corresponding page on Wikipedia, and you are encouraged to pause and read about terms that you find challenging. After having learned some of the key principles of LOD, the best way to improve and solidify that knowledge is to practice. This tutorial provides opportunities to do so. By the end of the course, you should understand the basics of LOD, including key terms and concepts.
    In order to provide readers with a solid grounding in the basic principles of LOD, this tutorial will not be able to offer comprehensive coverage of all LOD concepts. The following two LOD concepts will not be the focus of this lesson:
    -The semantic web and semantic reasoning of datasets. A semantic reasoner would deduce that George VI is the brother or half-brother of Edward VIII, given the fact that a) Edward VIII is the son of George V and b) George VI is the son of George V. This tutorial does not focus on this type of task.
    -Creating and uploading linked open datasets to the linked data cloud. Sharing your LOD is an important principle, which is encouraged below. However, the practicalities of contributing your LOD to the linked data cloud are beyond the scope of this lesson. Some resources that can help you get started with this task are available at the end of this tutorial.

    This tutorial is also available in Spanish at:  https://programminghistorian.org/es/lecciones/introduccion-datos-abiertos-enlazados

  • From Hermeneutics To Data To Networks: Data Extraction And Network Visualization Of Historical Sources

    Network visualizations can help humanities scholars reveal hidden and complex patterns and structures in textual sources. This tutorial explains how to extract network data (people, institutions, places, etc) from historical sources through the use of non-technical methods developed in Qualitative Data Analysis (QDA) and Social Network Analysis (SNA), and how to visualize this data with the platform-independent and particularly easy-to-use Palladio.

    This tutorial will focus on data extraction from unstructured text and shows one way to visualize it using Palladio. It is purposefully designed to be as simple and robust as possible. For the limited scope of this tutorial it will suffice to say that an actor refers to the persons, institutions, etc. which are the object of study and which are connected by relations. Within the context of a network visualization or computation (also called graph), we call them nodes and we call the connections ties. In all cases it is important to remember that nodes and ties are drastically simplified models used to represent the complexities of past events, and in themselves do not always suffice to generate insight. But it is likely that the graph will highlight interesting aspects, challenge your hypothesis and/or lead you to generate new ones. Network diagrams become meaningful when they are part of a dialogue with data and other sources of information.

    Topics include:  

    • Introduction
    • About the case study
    • Developing a coding scheme
    • Visualize network data in Palladio
    • The added value of network visualizations
    • Other network visualization tools to consider

    This tutorial is also available in Spanish at:  https://programminghistorian.org/es/lecciones/creando-diagramas-de-redes-desde-fuentes-historicas.

  • Ocean Data Management for Researchers

    This training course is aimed at researchers at the post-graduate level and provides a comprehensive introduction to a variety of marine datasets and formats and the use of software for synthesis and analysis of marine data. The importance of good research data management practices and the role of researchers will also be highlighted. Personal projects are presented by the students at the end of the course. 

    To acquire Certificates of Participation, this course required an application and once approved, member login.  Guest access is available to review course slides, video presentations, exercises, class activities and supplementary materials.  

    Aims and Objectives
    -Provide an introduction to the use of software for synthesis and analysis of marine data
    -Introduction to the FAIR Guiding Principles for scientific data management and stewardship
    -Understand best practice for management and analysis of marine data
     
    The learning outcomes of this course include:
    -Knowledge and understanding of the importance of management of ocean data
    -Experience in the use of data analysis and visualization tools
    -Recognize the importance of good research data management practice
    -Awareness of European based marine research projects and data repositories

    Preparation 
    Participants must download the latest version of ODV (5.1.5) from https://odv.awi.de/software/download/ and install the software on their laptops. If not already done, participants must register as non-commercial users before getting access to the software.
    Participants also must download the course material package from https://drive.google.com/file/d/1SojaNEPE3uI5zUN2gib7SfPI8f319ILQ/view?u... unzip to the desktop.

  • Centre Of Excellence Ocean Data Management

    This course provides a comprehensive introduction to a wide variety of earth science datasets, formats and analysis software. Students will learn and practice methods using a common ocean area, and they are expected to create a personal project of data products for a marine region of their own choosing. Personal projects are presented by the students at the end of the course.  This course requires either guest or POGO Scholar login, and is hosted on a Moodle platform.

    Aims and Objectives:
    -Recognize the importance of good research data management practice
    -Provide an introduction to the use of free software for synthesis of marine data and analyses
    -Creation and use of multi-parameter marine data collections to prepare and publish standard data products
    -Develop marine data and products from multiple sources using selected software programs

    Course overview
    1. Course outline and summary
    2. Pre-course reading (optional)
    3. Introduction to IODE and data management
    4. Research Data Management
    5. Ocean Data Collections using Ocean Data View
    6. Introduction to Marine Metadata
    7. Managing Operational Data using Integrated Data Viewer
    8. Marine GIS operations using Saga
    9. Student Project - Marine data products for selected project areas

     

  • Train-the-Trainer Concept on Research Data Management

    Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design, and a range of didactic methods.
    After the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/Nestor Working Group Research Data (DINI/Nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials, and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.
    This document is primarily intended for trainers who want to conduct a Train-the-Trainer workshop on research data management. It contains background knowledge on the PowerPoint slides and teaching scripts as well as further information on the individual subject areas required for reuse and implementation of a two-day workshop of seven and a half hours a day.

    Each unit of this guide contains information about how to teach the unit including the unit's learning objectives, key aspects, contents, didactic methods and exercises, training materials, addiitional sources, template, and teaching scripts.

    Topics of the units inlclude orientation, didactic approach, digital research data, research data policies, data management plans, order and structure, documentation and metadata, storage and backup, long term archiving, access control, formal framework, data publication, re-use of research data, legal aspects, institutional infrastructure, training exercises, concept development and didactic methods.