# Presentation: 538924

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

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

- 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*

***Warren B. Cohen***
***USDA Forest Service, PNW Research Station, Corvallis, OR***

## Two Main Studies

**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

## 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**

## Each sample scene consists of ~ 2-year interval data cube

**1972**

**2006**

**Each sample scene consists of ~ 2-year interval data cube**

## FIA plot data; e.g., biomass

**Arizona**

**FIA plot data; e.g., biomass**

- Observed

- Predicted

## 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

**Notes:**
Again, the information is in the shape itself – once the best model is found, the fitting parameters that describe it are the things we’re interested in – so year of disturbance, intensity of disturbance, rate of recovery all fall out from a single act of curve-fitting.  

## Slide 7

**Notes:**
The segmentation that best fits the trajectory is picked as the winner, using the p-value from the f-statistic again as the arbiter of the choice.  It is critical to note that, again, p-values are penalized for each addition segment (in terms of degrees of freedom), so a simpler segmentation over a more complicated one

## One disturbance two recovery segments (both subtle)

**One disturbance two recovery segments (both subtle)**

**Notes:**
Another example here shows an area that was increasing at a higher rate initially, then was affected by a biomass loss (perhaps more thinning, but it occurred over several years, and then resumption of biomass increase.  The loss percentage for this pixel is 42% (hard to tell on the stretch of the loss% map). 

## 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

**Studies, cont.**

## NPS I&M network research

**NPS I&M network research**

## Baseline map updating over n 2-date intervals

**Time 1 baseline map**

**Time 1 spectral data**

## Idealized Distributions of Basic Physiognomic Types in Landsat Spectral Space

- Snow and ice

- Mixed

- Open: Dark

- Water/Deep shade

- Closed-canopy conifer

- Dense broadleaf/ grass

- Broadleaf tree/shrub

- Conifer/Broad-leaf Mix

***Increasing TC Brightness***

***Increasing TC Greenness***

- Open: Bright

**Idealized Distributions of Basic Physiognomic Types in Landsat Spectral Space**

## Fuzzy classification—Probability of Membership

## Change detection: 2-date change in POM

- Snow and ice

- Mixed

- Open: Dark

- Water/Deep shade

- Closed-canopy conifer

- Dense broadleaf/ grass

- Broadleaf tree/shrub

- Conifer/Broad-leaf Mix

*Increasing TC Brightness*

*Increasing TC Greenness*

- Change detection: *2*-date change in POM

- We measure high POM difference & (1) have confidence meaningful change has occurred and (2) be relatively certain what type of change has occurred

## More subtle change in POM

- Snow and ice

- Mixed

- Open: Dark

- Water/Deep shade

- Closed-canopy conifer

- Dense broadleaf/ grass

- Broadleaf tree/shrub

- Conifer/Broad-leaf Mix

*Increasing TC Brightness*

*Increasing TC Greenness*

- Open: Bright

- 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)

## 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**