1 / 16

Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization

Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization. Warren B. Cohen USDA Forest Service, PNW Research Station, Corvallis, OR. Collaborators: S. Goward, C. Haung, S. Healey, R. Kennedy, J. Masek, G. Moisen, S. Powell, T. Schroeder, Z. Yang.

charlsiee
Download Presentation

Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization Warren B. Cohen USDA Forest Service, PNW Research Station, Corvallis, OR Collaborators: S. Goward, C. Haung, S. Healey, R. Kennedy, J. Masek, G. Moisen, S. Powell, T. Schroeder, Z. Yang LDCM Science Team Meeting, 9-11 January 2007

  2. Two Main Studies • 1. NACP/USFS FIA integration • Initial focus on providing sample-based estimation of forest disturbance and succession dynamics for the entire conterminous US since 1972 • Accelerate the operational use of Landsat data by the Forest Service in the nation’s ongoing forest census

  3. Chosen run Probability of inclusion National-level estimation of forest dynamics • Random sample constrained by several criteria, e.g., • Dispersion of scenes • Capture of all major forest types • Preference for high forest-area in each selected scene • Inclusion of certain fixed scenes

  4. Take advantage of temporal richness of Landsat data Longer-term trends emerge above the noise of year-to-year variation and may be the most reliable signal Each sample scene consists of ~ 2-year interval data cube 1972 2006

  5. 1972 LEDAPS processing Continuous change in biomass Biomass (lbs/acre) 2006 0 300,000 Arizona Predicted Observed FIA plot data; e.g., biomass

  6. Fit Curves to Trends Parameters of best-fitting trajectory define its shape, e.g. using spectral band • P(0) Year of disturbance • P(2) Intensity of disturbance • P(3) Rate of recovery Kennedy, Cohen et al., in review

  7. Regrowth Disturbance Intensity of disturbance = biomass at beginning of disturbance segment – biomass at the end Recovery rate = slope of regrowth segment in units of biomass/yr; 2 disturbance segments Regrowth Disturbance Disturbance

  8. 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 One disturbance two recovery segments (both subtle)

  9. Studies, cont. • 2. NPS Inventory & Monitoring • I&M protocols, based on integration of limited field data and low cost remote sensing (i.e., Landsat, MODIS, …) • Broad array of vegetation types, change agents, and questions • Diversity in emphasis and modest funding requires robust, generally applicable strategy

  10. NCCN SWAN NCPN/SCPN NPS I&M network research

  11. Time 2 spectral data Spectral contrast data Time 2 baseline / updated map Change Map Baseline map updating over n2-date intervals Time 1 spectral data Time 1 baseline map

  12. Idealized Distributions of Basic Physiognomic Types in Landsat Spectral Space Dense broadleaf/ grass Conifer/Broad-leaf Mix Closed-canopy conifer Broadleaf tree/shrub Water/Deep shade Increasing TC Greenness Mixed Open: Bright Snow and ice Open: Dark Increasing TC Brightness

  13. For each class a Gaussian probability surface is calculated: probability of membership (POM) Class 1 Class 2 Class n Etc. Fuzzy classification—Probability of Membership

  14. Pixel starts here… … and ends here Open: Bright Change detection: 2-date change in POM Dense broadleaf/ grass Conifer/Broad-leaf Mix Closed-canopy conifer Broadleaf tree/shrub Water/Deep shade Increasing TC Greenness Mixed Snow and ice Open: Dark Increasing TC Brightness We measure high POM difference & (1) have confidence meaningful change has occurred and (2) be relatively certain what type of change has occurred

  15. … and ends here Pixel starts here… More subtle change in POM Dense broadleaf/ grass Conifer/Broad-leaf Mix Closed-canopy conifer Broadleaf tree/shrub Water/Deep shade Increasing TC Greenness Mixed Open: Bright Snow and ice Open: Dark Increasing TC Brightness Low POM difference—we have lower confidence that change has occurred & also not certain what type of change (e.g., class change or within class change)

  16. Summary • Focus on two research projects relying on Landsat for change detection • One uses Landsat for time-series analyses to quantify historic disturbance and succession processes nationally • Other establishes protocols for long-term, interval-based monitoring of all vegetation types across several park networks

More Related