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

Key Info
URL - the landing page for the learning resource: 
https://arset.gsfc.nasa.gov/land/webinars/un-biodiversity-2020?utm_source=social…
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

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.
 

Authoring Organization(s) Name: 
NASA Applied Remote Sensing Training Program (ARSET)
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 2.0 Generic - CC BY 2.0
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
Also available in - other languages in which the learning resource has been translated or made available other than the primary: 
French
Spanish
More info about
Keywords - short phrases describing what the learning resource is about: 
Aichi Biodiversity Targets (ABTs)
Biodiversity data
Capacity building
Conservation
Data access
Environmental management
Land management
Remote sensing
Satellite imagery
Sustainable Development Goals (SDGs)
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Education: Science and Mathematics Education
Physical Sciences and Mathematics: Earth Sciences
Physical Sciences and Mathematics: Environmental Sciences
Published / Broadcast: 
Tuesday, March 24, 2020
Publisher - organization credited with publishing or broadcasting the learning resource: 
NASA Applied Remote Sensing Training Program (ARSET)
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Contributor Name: 
Name: 
Amber McCullum
Type: 
Co-presenter
Contributor Organization(s): 
Name: 
UN Development Programme (UNDP)
Type: 
Collaborator
Name: 
United Nations Environment Programme (UNEP)
Type: 
Collaborator
Name: 
Global Environment Facility (GEF)
Type: 
Funding and sponsorship
Name: 
Convention on Biological Diversity (CBD)
Type: 
Collaborator
Contact Person(s): 
Brock Blevins
Contact Organization(s): 
NASA Applied Remote Sensing Training Program (ARSET)
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data manager
Data policymaker
Early-career research scientist
Mid-career research scientist
Research scientist
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)