Please Visit the NWCG Training Catalog for the most current course description and requirements.

NOTE: Individuals instructing NWCG aviation courses are required to meet instructor qualifications within the NWCG Field Managers Course Guide or individual course instructor guides.

Course Summary: 

This course guides you through key considerations for rebuilding after natural disasters with an eye on the climate's changing patterns. You'll explore how to protect our natural and cultural heritage, grasp the impacts of climate change, and develop strategies for a resilient future. It's about collaborating to create a smarter, stronger recovery plan.

Course Learning Objectives:

1. Explain the relationship between climate change and natural disasters and why a climate-smart approach is critical for recovery efforts.
2. Identify major factors influencing climate change and impacts on cultural and natural resources.
3. Describe a range of possible climate-smart recovery strategies for cultural and natural resources.
4. Connect Department of Interior guidance and other federal resources to climate-smart recovery efforts for cultural and natural resources.
5. Identify elements of a climate-informed recovery plan incorporating effective communication principles and scientifically valid climate change data sources.

Target Audience: Anyone involved in Riparian & Wetland AIM monitoring data collection, interpretation, or application, including monitoring field crews (BLM seasonal employees and current Riparian & Wetland AIM contractors/agreement holders), BLM resource specialists, and other agencies. Any other interested parties not involved in AIM data collection must first obtain permission from the national Riparian & Wetland AIM team to enroll in this course and an enrollment fee may apply, including non-government contractors and local community members. 

Prerequisites: 

1. You must be a DOI employee, an active AIM contractor, or actively collecting AIM data through an agreement.  Please refer to the Riparian & Wetland AIM Data Management Protocol for more details on who should enroll and how often. Any other interested parties not involved in AIM data collection must first obtain permission from the national Riparian & Wetland AIM team to enroll in this course and an enrollment fee may apply, including non-government contractors and local community members.
2. Once enrolled in the class, you must complete the mandatory pre-training material prior to attending the in-person, 6-day field training component.

Course Summary: Riparian & Wetland AIM monitoring data collection, interpretation, or application, including monitoring field crews (BLM seasonal employees and current Riparian & Wetland AIM contractors/agreement holders), BLM resource specialists, and other agencies. This course shows/instructs employees how to create a spatially balanced random sample design (if applicable). Instructs and assists in data collection following all Riparian & Wetland AIM core, contingent, covariate, and annual use methods, which focus on: Biodiversity, vegetation cover and composition, and habitat quality (e.g., species inventory, vegetation cover and height, woody structure), Assists in the understanding of water quality (e.g., pH, conductance, temperature, nutrients) and hummocks. Calculates annual use (e.g., stubble height, soil alteration, riparian woody species use) by wildlife and livestock. Helps understand the collection of riparian or wetland covariate data (e.g., plot classification, hydrology, photos, soil description, and disturbances). Explains and uses electronic data capture applications (Survey123 and Field Maps). Is geared toward data quality assurance and quality control procedures for field going employees.

Course Objectives: To ensure standard, consistent, and proficient implementation of: 

• Spatially balanced random sample design (if applicable). 

• Data collection following all Riparian & Wetland AIM core, contingent, covariate, and annual use methods, which focus on: o Biodiversity, vegetation cover and composition, and habitat quality (e.g., species inventory, vegetation cover and height, woody structure), o Water quality (e.g., pH, conductance, temperature, nutrients) and hummocks, o Annual use (e.g., stubble height, soil alteration, riparian woody species use), o Riparian or wetland covariate data (e.g., plot classification, hydrology, photos, soil description, and disturbances). 

• Electronic data capture applications (Survey123 and Field Maps). • Data quality assurance and quality control procedures.

Duration: 40 hours

Special Requirements: Requests for interpreters or other special requirements must be received at the NTC no later than 45 days prior to the start of the class.  Form can be accessed at:  https://www.ntc.blm.gov/krc/uploads/982/Reasonable_Accommodation_Request_Form.pdf

Target Audience:

Objectives:  Using landscape and site level characteristics, the participant will use a process to plan, implement, evaluate and maintain a restoration project.

Special Requirements: Requests for interpreters or other special requirements must be received at the NTC no later than 45 days prior to the start of the class. Form can be accessed at: https://www.ntc.blm.gov/krc/uploads/982/Reasonable_Accommodation_Request_Form.pdf NOTE: NUMBER OF SEATS AS LISTED ISN'T ACCURATE. PLEASE REGISTER IF YOU'RE INTERESTED IN ATTENDING. Upon approval, all names will be put on a Waiting List. Notifications of selection will be sent prior to the course start date.

Keyword: 1730-60

Target Audience: Anyone involved in terrestrial monitoring data collection, interpretation or application; including monitoring field crews (BLM seasonal employees and partners), BLM resource specialists, other agencies, local community members. 

Prerequisites:  You must complete the pre-work before you are able to request to attend a specific location and time for an in-person class. First you must enroll in this course to find and compete the prerequisite training.  

This class provides the framework and skills to understand the condition and trends of terrestrial resources through implementing a monitoring program which is consistent with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. 

Objective:  

• • Measure and calculate the AIM terrestrial quantitative indicators using standard field methods 
  • Ensure monitoring data quality through calibration, electronic data capture and data management 
  • Implement monitoring in a structured and consistent manner. 

Special Requirements: Requests for interpreters or other special requirements must be received at the NTC no later than 45 days prior to the start of the class.  Form can be accessed at:  https://www.ntc.blm.gov/krc/uploads/982/Reasonable_Accommodation_Request_Form.pdf

Target Audience:  Materials will be used by the thousands of participants already registered as users of Nature’s Notebook, including USGS, NPS, BLM and USFWS professionals, as well as new participants from a variety of disciplines and backgrounds.

Course Summary and Objectives:  A presentation to help you use Nature's Notebook for recording species (plants and animals), phenology, and other biological/environmental activities within a site you choose to observe.

This course will provide you with step-by-step instructions for getting started with tracking plant and/or animal phenology using Nature’s Notebook. You will learn a standardized approach to observing phenology; these methods have been broadly vetted, published in the peer-reviewed literature, and can be adapted to a variety of questions, sampling designs and ecosystems.

Performance Objectives:  At the conclusion of this course, you will be able to:

• Navigate through the Nature’s Notebook program.
• Recognize relationships between plant and animal seasonal life cycle events (phenology) and environmental variation including climate change.
• Design and implement a phenology monitoring program in a variety of settings to enhance existing science, management and outreach/education goals.
• Recognize the value of phenology to research, natural resource management, and decision-making.
  

Intended Audience:  Any scientist who performs surface-water data collection activities. 

Course Summary and Objectives:  This is an introductory course.This course is designed to introduce and review basic procedures that underlie much of what is done in the field and office when producing daily and instantaneous values of streamflow discharge.

• Demonstrate basic procedures used to collect surface-water data.
  
• Explain why we use those procedures.
  
• Define why USGS employs these procedures where we do.
  
• Demonstrate safe conduct of these procedures.
  

Duration:  6 hours

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