# Presentation: 919459

## Trajectory Applications with Landsat

**Trajectory Applications with Landsat**

- Todd A. Schroeder
- Integrated Remote Sensing Studio
- Department of Forest Resources Management
- University of British Columbia

## Objectives :

**Objectives :**

- Why trajectories?
- Two examples
- 	1. Post-harvest forest regrowth rates – western OR
- 	2. Mt. Pine Beetle Infestation – British Columbia

## Why?

**Why?**

- Forest change is often a long term process which can be non-linear in nature.
- Multiple images are required to accurately characterize processes like time of  disturbance, rate of regrowth and gradual mortality from insect infestation.
- Given long term record, Landsat is a logical choice for trajectory analysis.
-                        3 advantages to using trajectories for vegetation change.......
-      1.  Spectral variations from atmosphere and phenology are minimized.
-      2.  Trajectory curves can be statistically compared using longitudinal analysis.
-      3.  Trajectories can be quantified through parameterization of the fitted mean curve (e.g., delay, time to canopy closure, maximum growth rate).

- Success hinges on successful radiometric calibration

## Radiometric Calibration :

-    “Absolute-normalization” approach – Multivariate Alteration       Detection (MAD)
-     Based on CCA – invariant to linear scaling (gain/bias, clouds)
-     Equally accurate as band-wise regression with hand-selected PIFs
-     Normalization most important step

**Radiometric Calibration :**

**Uncalibrated Trajectories**

**Calibrated Trajectories**

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

-   Schroeder et al., (2006). Radiometric calibration of Landsat data for characterization of early forest successional patterns in western Oregon.  *Remote Sensing of Environment. 103, 16-26.*

## Study Area :

**Study Area:**

**Data Cube:                  **

**Study Area :**

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

## Forest Regrowth Modeling :

- Image Normalization
- Build Initial Regression Model – % Tree Cover
- Extrapolate initial model to normalized images
- Temporal accuracy assessment – PI data
- Map clearcuts (3 periods)
- Spatially group pixels that have similar trajectories (ISODATA Clustering)
- Describe variation in regrowth trajectory classes using CART modeling

- CART

**Forest Regrowth Modeling :**

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

**Forest Regrowth Modeling :**

## Results :

- % of Disturbed Area

- Ownership

- Aspect

- Slope

- Elevation

**CRP**

**WCP**

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

**Results :**

**WCP**

## Forest Regrowth Maps :

- Rapid canopy closure
- Hardwood competition

- c. Logging damage (landing)
- d. Delayed succession

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

**Forest Regrowth Maps :**

## Significant Findings :

- Date-invariant regression improves on post-classification comparison technique through use of robust radiometric calibration technique, use of continuous (as opposed to class based) variables, and basing change on trajectory classes.
- Observed (re-confirmed) variable rates of post-harvest forest succession in western OR
- Extended forest regrowth trajectories from PI space to Landsat spectral space.
- CART modeling yielded ecologically interpretable results.
- Confirmed importance of plant relevant predictor variables – PRR.

***June 13, 2007 – Landsat Science Team meeting, Corvallis, OR ***

**Significant Findings :**

-   Schroeder et al., (2007). Patterns of forest regrowth following clearcutting as determined from a Landsat time-series.  *Forest Ecology and Management. *

##    Objective is to accurately map year of stand death, as well as better understand pre- and post-infestation forest characteristics.

-    Objective is to accurately map year of stand death, as well as better understand pre- and post-infestation forest characteristics.
-    Data cube is required as Mt. pine beetle attack can vary from severe to gradual and is a multi-staged process (green vs. red attack).