High Resolution Change Detection

Efforts

Efforts describe the work of workgroups

Involved Workgroups:
Template:USDA

• Knowledge Creation
  • Adaptive Management
  • Arts and Crafts
  • Lessons
  • Monitoring
  • Research
• Protection
  • Acquisition
  • Green Infrastructure
  • Regulation
• Restoration
  • Beach Nourishment
  • Engineered Log Jams
  • Fish Passage
  • Revegetation
• Social Change
  • Advocacy
  • Coordination
  • Education
  • Funding
  • Integrated Ecosystem Management
  • Legislation
  • Planning

Effort References

• RCO's PRISM Project Search
• Salmon Recovery Portal
• Ecology's Administration of Grants and Loans
• USACE Regulatory In-lieu Fee and Bank information Tracking System

Wiki Rules

• Wiki text does not reflect the policy or opinion of any agency or organization
• Please adhere to our Social Contract and Style Guide
• Complain here, and be nice.

​The Washington Department of Fish and Wildlife's (WDFW) High Resolution Change Detection (HRCD) program monitors land cover changes across Washington State, particularly in the Puget Sound region. Initiated in 2006, HRCD employs 1-meter resolution aerial imagery from the National Agriculture Inventory Program to detect transitions from forested areas to developed Land Uses. This high-resolution approach enables precise tracking of changes in critical zones like riparian corridors and marine Shoreline Management. The program supports conservation efforts by providing accurate data for assessing habitat alterations, informing land management decisions, and aiding in the restoration of ecosystems within the Salish Sea watershed.

Notes

• https://hrcd-wdfw.hub.arcgis.com/ - GIS portal hosted by WDFW
• File:Pierce 2011 high resolution aerial imagery change detection.pdf is a final report for the inital wave of grants.
• On-line data viewer provides ability to observe change in forest cover at an event by event scale within your area of interest.
• Canopy polygons includes canopy height.
• Developed in response to low resolution products being unable to describe riparian zone condition.
• Combines NAIP including RGB, Infra-red, which combine to provide NVDI. This was combined with a WDNR height model using photogrammetry (not LIDAR).
• Building rather than just developed as useful once height was included.
• Starting to experiment with deep learning (for example road extraction). State-wide surface water may be used as training data.
• 2011, 2017, 2019, 2021, 2023 available, with change detection developed as a "bi-temporal product".
• six-inch NAIP photos results in 17 TB of data for statewide coverage.