Audiencia:

Las personas que se beneficiarán de este curso incluyen:

1. Especialista en teledetección en países que reportan a REDD +

2. Funcionarios que trabajan en análisis de datos e incertidumbre para la contabilidad del carbono

3. Estudiantes de teledetección en aplicaciones forestales

Resumen y objetivos del curso:

La reducción de las emisiones derivadas de la deforestación y la degradación forestal y el papel de la conservación, la gestión sostenible de los bosques y la mejora de las reservas de carbono forestal en los países en desarrollo (REDD +) es el contexto de la Convención Marco de las Naciones Unidas sobre el Cambio Climático (CMNUCC) con el objetivo de crear cooperación para lograr la mitigación climática de los bosques.

El elemento central de REDD + MRV es el monitoreo y seguimiento de conversiones de bosques a otras tierras debido a actividades humanas. La información satelital es la fuente de datos con suficiente periodicidad y cobertura para proporcionar información sobre el uso de la tierra, el tipo y la intensidad de los cambios en la tierra, la deforestación y la degradación forestal. Para cumplir con los criterios de reportes de REDD + de cuantificación de imparcialidad e incertidumbre, es necesario hacer enfoques basados ​​en muestreo para la estimación de la precisión del área y del mapa. Este curso presenta a los participantes el concepto, la justificación y los procedimientos para la estimación basada en las técnicas de muestreo.

 Requisitos del sistema

 La versión en línea de este curso se ejecuta en las últimas versiones de Chrome y Safari. Actualmente no hay una versión descargable del curso.

Duración: Aproximadamente 2 horas.

Please Note:  This course is available in different languages.  Please select the best one for you:

• English:  Course: Sampling-Based Estimation of Area and Map Accuracy (doi.gov)
• French:  Course: Sampling-based Estimation of Area and Map Accuracy--French Version (doi.gov)
• Spanish:  Course: Sampling-based Estimation of Area and Map Accuracy--Spanish Version (doi.gov)
• Vietnamese:  Course: Sampling-based Estimation of Area and Map Accuracy--Vietnamese Version (doi.gov)

Comience haciendo clic en el enlace ("Muestreo") debajo del tema "Curso de aprendizaje electrónico".

Audience:

Individuals who will benefit from this course include: 

1.     Remote sensing specialist in countries reporting to REDD+ 

2.     Officials working on uncertainty and data analysis for carbon accounting 

3.     Students of remote sensing in forestry applications 

Course Summary and Objectives:

Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+) is a framework under the United Nations Framework Convention on Climate Change (UNFCCC) with the goal of building international cooperation to achieve climate mitigation from forests.    

A core element of REDD+ MRV is the monitoring and tracking of forest conversions to other lands due to human activities.  Satellite information is the data source with enough periodicity and coverage to provide information on land use, type and intensity of land changes, deforestation and forest degradation. In order to meet REDD+ reporting criteria of unbiasedness and uncertainty quantification, sampling-based approaches for the estimation of area and map accuracy are necessary. This e-learning course will introduce participants to the concept, rationale and how-to of sampling-based estimation. 

Please Note:  This course is available in different languages.  Please select the best one for you:

• English:  Course: Sampling-Based Estimation of Area and Map Accuracy (doi.gov)
• French:  Course: Sampling-based Estimation of Area and Map Accuracy--French Version (doi.gov)
• Spanish:  Course: Sampling-based Estimation of Area and Map Accuracy--Spanish Version (doi.gov)
• Vietnamese:  Course: Sampling-based Estimation of Area and Map Accuracy--Vietnamese Version (doi.gov)

System Requirements 

The online version of this course runs on the latest versions of Chrome and Safari. There is no downloadable version of the course currently. 

Duration:  Approximately 2 hours.  

Audience:

Individuals who will benefit from this course include: 

1.     Remote sensing specialist in countries reporting to REDD+ 

2.     Officials working on uncertainty and data analysis for carbon accounting 

3.     Students of remote sensing in forestry applications 

Course Summary and Objectives:

Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+) is a framework under the United Nations Framework Convention on Climate Change (UNFCCC) with the goal of building international cooperation to achieve climate mitigation from forests.    

A core element of REDD+ MRV is the monitoring and tracking of forest conversions to other lands due to human activities.  Satellite information is the data source with enough periodicity and coverage to provide information on land use, type and intensity of land changes, deforestation and forest degradation. In order to meet REDD+ reporting criteria of unbiasedness and uncertainty quantification, sampling-based approaches for the estimation of area and map accuracy are necessary. This e-learning course will introduce participants to the concept, rationale and how-to of sampling-based estimation. 

Please Note:  This course is available in different languages.  Please select the best one for you:

• English:  Course: Sampling-Based Estimation of Area and Map Accuracy (doi.gov)
• French:  Course: Sampling-based Estimation of Area and Map Accuracy--French Version (doi.gov)
• Spanish:  Course: Sampling-based Estimation of Area and Map Accuracy--Spanish Version (doi.gov)
• Vietnamese:  Course: Sampling-based Estimation of Area and Map Accuracy--Vietnamese Version (doi.gov)

System Requirements 

The online version of this course runs on the latest versions of Chrome and Safari. There is no downloadable version of the course currently. 

Duration:  Approximately 2 hours.  

Introduction:

This e-learning course will introduce you to the idea of continuous degradation detection, or CODED, and how it can be used to monitor and forecast forest health. By taking this course you will learn about:

1. The challenges of forest degradation monitoring

2. The methodology and purpose of CODED algorithm

3. Spectral un-mixing and the Normalized Difference Fraction Index (NDFI)

4. How degradation and natural disturbances are distinguished from deforestation

Audience:

Who will benefit from this course include:

1. Remote sensing specialist in countries reporting to REDD+

2. Officials working on understanding the role of forest degradation on carbon emission

3. Students of remote sensing in forestry applications

4. Individuals interested in estimation of land cover change using earth observation

Duration:

Approximately 2 hours

Course No.:  USGS-GSP-21-135

Target Audience:  Anyone that needs to collect suspended-sediment data.

Summary and Objectives:  This is the self-certification for tracking purposes.  The course was originally on a CD-ROM. For your convenience, we have created a zip file to download.  This course presents an introduction to methods currently used by the USGS to sample suspended-sediment concentrations and particle-size distribution in streams. By the end of this course, the student will be able to: sample suspended-sediment concentrations and particle size distribution in streams

Course Contact:  Molly Wood, mswood@usgs.gov

Duration:  2 Hours

Introducción:

Este curso de aprendizaje electrónico le presentará la idea de la detección continua de la degradación, o codificación y cómo se puede utilizar para monitorear y pronosticar el estado de los bosques.

Al realizar este curso, aprenderá sobre:

1. Los desafíos del seguimiento de la degradación forestal

2. La metodología y el propósito del algoritmo CODED

3. Espectral sin mezclas y índice de fracción de diferencia normalizada (NDFI)

4. Cómo se distinguen la degradación y las perturbaciones naturales de la deforestación

Audiencia:

Quienes se beneficiarán de este curso:

1. Especialista en teledetección en países que reportan a REDD +

2. Funcionarios que trabajan para comprender el papel de la degradación forestal en las emisiones de carbono.

3. Estudiantes de teledetección en aplicaciones forestales

4. Personas interesadas en estimar el cambio de la cobertura terrestre mediante la observación de la tierra.

Recursos y lenguajes de codificación sugeridos antes de comenzar este e-learning - Descripción general de Earth Engine - Earth Engine 101 - JavaScript, imágenes, colecciones, mapa y reducción

Duración:

Aproximadamente 2 horas.

Introduction:

This e-learning course will introduce you to the idea of continuous degradation detection, or CODED, and how it can be used to monitor and forecast forest health. By taking this course you will learn about:

1. The challenges of forest degradation monitoring

2. The methodology and purpose of CODED algorithm

3. Spectral un-mixing and the Normalized Difference Fraction Index (NDFI)

4. How degradation and natural disturbances are distinguished from deforestation

Audience:

Who will benefit from this course include:

1. Remote sensing specialist in countries reporting to REDD+

2. Officials working on understanding the role of forest degradation on carbon emission

3. Students of remote sensing in forestry applications

4. Individuals interested in estimation of land cover change using earth observation

Duration:

Approximately 2 hours

Introduction:

This e-learning course will introduce you to the idea of continuous degradation detection, or CODED, and how it can be used to monitor and forecast forest health. By taking this course you will learn about:

1. The challenges of forest degradation monitoring

2. The methodology and purpose of CODED algorithm

3. Spectral un-mixing and the Normalized Difference Fraction Index (NDFI)

4. How degradation and natural disturbances are distinguished from deforestation

Audience:

Who will benefit from this course include:

1. Remote sensing specialist in countries reporting to REDD+

2. Officials working on understanding the role of forest degradation on carbon emission

3. Students of remote sensing in forestry applications

4. Individuals interested in estimation of land cover change using earth observation

Duration:

Approximately 2 hours

Target Audience:  

1. Remote sensing specialist in countries reporting to REDD+
2. Officials working on understanding the role of forest degradation on carbon emission
3. Students of remote sensing in forestry applications
4. Individuals interested in estimation of land cover change using earth observation

Summary and Objectives:  This e-learning course will introduce you to the implementation of Landsat-based detection of Trends in Disturbance and Recovery (LT) Algorithms in Google Earth Engine (LT GEE), and how it can be used to monitor and forecast forest health.

By the end of this course, you will have learned about:

1. How LandTrendr algorithms distinguish the disturbances and changes in the forest in a different time series.
2. The methodology and purpose of the LandTrendr algorithms (LT-GEE).
3. Explore and identify the requirements to run each LT algorithm in GEE.

Course Contact:  Monica Jeada  mjeada@contractor.usgs.gov or Sylvia Wilson snwilson@usgs.gov

Target Audience:  REDD+ reporting managers

Summary and Objectives:  This e- learning module has been developed as a collaboration between the US SilvaCarbon program as one of GFOl's capacity building leads together with their partners at The Nature Conservancy and Conservation International.

The purposes of this e-learning module are to provide background and context for REDD+ through the analysis of the key decisions adopted by the United Nations Framework Convention on Climate Change's COP. Review the range of REDD+ standards and financing options that already exist, and the major reporting differences between these different standards. It also highlights, guess, emerging developments that may impact REDD+ standards, financing and reporting going forward.

And finally, it provides a comprehensive list of resources for participants to consult, particularly countries in helping them to consider which REDD+ scheme may best suit their own unique national needs.

Goals 

1. To provide background and context of the UN Climate Change initiative, Reducing Emissions from Deforestation and Forest Degradation plus the role of enhancing forest carbon stocks through conservation, protection, and sustainable forest management (or REDD+) through the major decisions adopted at the UNFCCC COPs (reiterate the importance of these decisions in (1) driving the standards designs and (2) financing needs/options available).
2. To review the range of REDD+ standards that exist, the financing options that currently exist, and the major reporting differences that exist amongst the standards.
   
3. To provide an overview of selected REDD+ standards for which methodologies and financing exist.
   
4. To highlight the emerging developments that may impact REDD+ standards, financing and reporting.
   
5. To provide a comprehensive list of additional resources for the -learning participants to consult.
   

Course Contact:  Monica Jeada (202) 394-5059 mjeada@contractor.usgs.gov or Sylvia Wilson (571) 286-0680 snwilson@usgs.gov 

Target Audience:  Remote sensing specialists in countries reporting to REDD+, officials working on uncertainty and data analysis for carbon accounting, and students of remote sensing in forestry applications

Summary and Objectives:  Google Earth Engine (GEE) developed a robust visualization and analysis tool that help to perform a complex geospatial analysis in both local and global scale. Earth engine is a useful tool for scientists, researchers, and non-traditional users around the globe in monitoring and forecasting forest health. And it became a container or environment for developing and applying different applications and tools that help in monitoring and observing changes and trends in forest in different time series.

To meet REDD+ reporting criteria of unbiasedness and uncertainty quantification, google earth engine platforms are necessary and indispensable for reporting and monitoring forest carbon stock and providing information on land use, deforestation and forest degradation.

To introduce participants to the concept, platforms and applications of google earth engine (GEE).

You will learn about

1. What the google earth engine and how it works 
2. Accessing google earth engine.
3. Google earth engine platforms and applications
4. Geospatial data available in google earth catalog
5. The strength of google earth engine

Course Structure

• E-learning: Google earth engine
• Concepts
• Platforms 
• Applications
• Course Self-assessment Questions

Course Contact:  Monica Jeada mjeada@contractor.usgs.gov or Sylvia Wilson snwilson@usgs.gov

Target Audience:  Individuals who will benefit from this course include:

1. Remote sensing specialists in countries reporting to REDD+
2. Officials working on uncertainty and data analysis for carbon accounting
3. Students of remote sensing in forestry applications

Summary and Objectives:  This e-Learning course will introduce you to the implementation of the BULC algorithm in Google Earth Engine, or BULC and how it can be used to monitor and forecast forest health. By the end of this course, you will learn about:

1. How BULC algorithms distinguish the disturbances and changes in the forest in a different time series.
2. The methodology and purpose of the Bayesian Updating of Land-Cover algorithm.
3. Explore the requirements to run the BULC algorithm.

This course requires a basic understanding of the use of the Google Earth Engine platform.

System Requirements:  The online version of this course runs on the latest version of Microsoft edge, Firefox, Chrome and Safari. There is no downloadable version of the course currently.

Course Contact:  Monica Jeada mjeada@contractor.usgs.gov or Sylvia Wilson snwilson@usgs.gov

Target Audience:  All financial disclosure filers and other covered employees as designated by the Bureau or Office Ethics Counselor. 

Summary and Objectives:   An online presentation including PowerPoint video and interactive questions that will remind DOI employees the Executive Ethics Rules including criminal conflict of interest statutes, Employee Standards of Conduct, and the DOI supplemental ethics regulations.

Target Audience:  New DOI employees who were directed to complete this training prior to onboarding.

Summary and Objectives:  The Controlled Unclassified Information (CUI) Module is an introduction to the Federal CUI Program that was authorized by 32 CFR Part 2002, Controlled Unclassified Information (CUI), released September 14, 2016 and went into effect November 14, 2016. This course covers:

• Purpose, goals and objectives
• Introduces key CUI terminology and concepts
• Covers CUI practices (designating, handing, marking, safeguarding and decontrol)
• Summarizes how the CUI relates to other information and information management programs
• Introduces the CUI Programs driving authorities
• Covers the Federal CUI Program implementation requirements, compliance timeline and Interior's Implementation Compliance Plan.

Target Audience:  USGS Water Science Center Employees

Summary and Objectives:  The use of the index-velocity method has benefitted the USGS stream gaging program by providing a means to obtain more accurate streamflow data in challenging measurement environments. The goal of this online course is to provide students with the basic knowledge needed to attend the SW1319 Streamflow Record Computation Using ADVMs and Index-Velocity Methods training course, including a familiarity of:

• • The index-velocity method and when it should be used
  • Acoustic measurement theory
  • Reconnaissance techniques and site selection
  • Types of ADVMs
  • ADVM mounts and installation options
  • Basic ADVM configuration settings

The course also is a valuable refresher for students who are already familiar with the index-velocity method.

Course Contact:  Travis Knight (239) 579-6068 tknight@usgs.gov