# Presentation: 077589

## Trajectory-based change detection in forests:Tapping the latent informationof the TM/ETM+ archive

- Robert E. Kennedy & Warren B. Cohen
- USDA Forest Service PNW Research Station
-  3200 SW Jefferson Way / Corvallis OR 97331

## Change detection in the Woodcockian era 

- Evolving context:
- Expanding data access
- Increasingly cheap data storage and analysis
- Emerging synergies in sensors, institutions, etc.
- Change detection methods must move toward:
**Automation** (thresholding and labeling)
**Extension** (site specific solutions are out)
**Richness** (leverage sensor and temporal depth)

## Test case: Forests

- Forests

- Soil

- Carbon

- Habitat

- Society

- Economics

- Climate

- Water

## Changes of interest in forests

- Where?

- How intense?

- How fast?

- Where?

- How intense?

- Regrowth

- Establishment

- Growth

- How fast?

- Where?

## Trajectory-based approach*

- Rather than look for disturbance EVENTS, look for disturbance TRAJECTORIES

- Thresholds of change are statistical rather than data-specific
- Goodness of fit to idealized curves

- Characteristic spectral patterns before and after disturbance
- Develop _idealized curves_ and describe with simple functions

## Trajectory modeling

- a single pixel
- from a stack
-  of normalized
- Landsat TM images

- Estimate initial
- parameters for
- four types

- Determine best fit;
- Calculate f-stat
- and p-of-f

- Select model with
- lowest p-of-f; write
- parameter values and
- derived values to
- growing image

- Extract SWIR band from yearly stack of Landsat imagery
- For each pixel, compare four idealized trajectories to the observed trajectory
- Select model with lowest p-of-f, write parameter values to file

## Parameters describing function also describe disturbance
Outputs: 
Year of disturb. 
Pre-dist. mean
Intensity
Recovery rate

- Outputs:
- Year of disturb.
- Pre-dist. mean
- Intensity
- Recovery rate

- Idealized trajectories

## Testing: Landsat Path 46 Row 29

## Clear-cuts: Year of disturbance captured 

- Clear-cuts: Year of disturbance captured

## Year of disturbance captured 

- Year of disturbance captured

## Subtle disturbance captured 

- Subtle disturbance captured

## Key result

- Trajectory approach detected and labels subtle forest disturbance well
**Increased signal-to-noise ratio of dense time series data**

## Forest change within agricultural context

- Forest change within agricultural context

- No need for *a priori* determination of forest population
- Year-to-year variation in agriculture is noise; coherent trajectory in forests in signal

## Ongoing work

- Expansion:
- Move beyond small number of idealized curves
- Segmenting time series
- Improve speed of processing
- Apply to NAFD project sample scenes

## Trajectory segmentation

## Trajectory segmentation

## Hurricane Hugo

## Science/Applications

- Track evolution of forest management practices over time (more thinning?)
- Quantify riparian forest management in agricultural matrix
- Understand drivers of landscape-level patterns in forest recovery rate, insect outbreak intensity
- Understand ecological precursors to and trends after fire

## Key points

- Change detection:  Look for the change process, not the change event
- Discrete and continuous-variable phenomena “fall out” of fitting
- Increased SNR  detection of subtle phenomena, screen out noisy phenomena
- No need for pre-screening

## Key point

- All of this is possible
- only because of the
***quality, depth, and continuity ***
- of the Landsat
- TM/ETM+ archive!

## The End

## Example applications of method

- Douglas-fir, high-elevation mixed, ponderosa, and juniper forests

- Oregon Ecoregions

- Study area

*Oregon*

## Diverse forest ownership

- Private, USDA Forest Service, USDI Bureau of Land Management, Tribal

## Outputs: Year of disturbance

## Slide 25

## Slide 26

**Notes:**
Spruce budworm outbreak

## Disturbance rates by owner

- Areal rates highest on private lands
- Year-to-year variability

## Low intensity disturbance across management regimes

## Impacts of policy

## Rates normalized to 1991

## Impacts of economics

## Spatial variation in recovery rates

- Landscape-wide estimates of initial revegetation
- Planned analyses:  contrast pixel vs. stand level drivers of recovery rate, including management, climate, soil, etc.