Remote sensing data management for large-area forest mapping projects

Project Title: Remote sensing data management for large-area forest mapping projects
Program:
CAREERS (323)

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Tags:
api (394), data-management (260), data-sharing (732), gis (275), python (69), sql (424)

Status: Complete
Project Leader
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Project Leader:
Kasey Legaard

Email: kasey.legaard@maine.edu
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Project Personnel
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Mentor(s):
Kenneth Bundy (15806)

Student-facilitator(s):
Charles Brush (3846)

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Project Information
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Project Description:
Freely available satellite imagery has enormous potential to deliver low-cost data to support forest management. Yet forest management has been slow to adopt satellite derived maps for management planning, arguably because satellite remote sensing has not yet delivered sufficient value over traditional sources of information. Researchers at UMaine have been working to develop scalable remote sensing and machine learning workflows to map tree species, aboveground carbon, and other forest attributes at high resolution over large areas. Workflows rely on satellite imagery collected throughout the spring, summer, and fall, typically over multiple years. Cloud cover and other considerations make it difficult to select the best available images and to coordinate image processing tasks over large areas. Additional difficulty arises when projects span multiple sites or longer periods of time, and when processed imagery needs to be shared across multiple projects. 

This project is funded through the NSF Center for Advanced Forestry Systems with the goal of creating and implementing improved procedures and tools for managing, visualizing, and utilizing satellite imagery and other remote sensing data for large forest mapping projects. Project contributions will include the design and implementation of a spatial database containing large quantities of remote sensing imagery and related metadata. Data will be made accessible through a server and API for on-demand identification and delivery of best-available data to forest mapping applications. 


Project Information Subsection
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Project Deliverables:
- Geospatial database designed to meet project requirements (likely PostGIS or MongoDB)
- API to enable programmatic access via HTTP/S, SSH, or other communication modality
- Python client to integrate into existing image processing workflows
- Deployment to UMaine private cloud resources
- API and client documentation

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Project Institution: University of Maine
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Start as soon as possible.: Yes
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 7
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Project Milestones:
- Milestone Title: Geospatial database API
  Milestone Description: Complete development of an HTTP/S API to enable programmatic access a geospatial database suitable for project requirements.
  Completion Date Goal: 2023-07-14
- Milestone Title: Python client
  Milestone Description: Complete development of a Python client to integrate with existing image processing workflows.
  Completion Date Goal: 2023-09-01
- Milestone Title: Deployment
  Milestone Description: Deployment on UMaine private cloud resources; completion of documentation.
  Completion Date Goal: 2023-11-17

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Final Report
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