# Presentation: 869757

## LandTrendr and TimeSync:Update on new tools to tap the archive

- LandTrendr: Robert E. Kennedy, Zhiqiang Yang, Warren B. Cohen
- TimeSync: Warren B. Cohen, Zhiqiang Yang, Robert E. Kennedy

- Landsat Science Team Meeting January 2009

## LandTrendr:  Landsat-based Detection of Trends in Disturbance and Recovery

- LandTrendr:  Landsat-based Detection of Trends in Disturbance and Recovery

- Extract spectral trajectories for pixels

- Prepare stack of yearly imagery

- Extract summary information from segments

- Statistically identify and fit segments with consistent trends

- Steps in the LandTrendr process

- Evaluate veracity of selected events: TimeSync

**Notes:**
This is a slide they’ve sort of seen before -- basic talking points:
-- Stack of imagery can include MORE THAN ONE IMAGE PER YEAR,  including cloudy images. This is key for later slides
-- Change information comes from summarizing the timing and slope of the segments
-- New change information requires new approaches for validation

## Outputs – Diversity of Disturbance and Regrowth Processes

- More subtle events (thinning, partial burns) due to increased signal-to-noise ratio and use of trajectory approach

- Dramatic events (clearcuts, fires) more often -- commensurate with climatic, economic, & ecological cycles

- Slow processes such as growth and recovery as well as insect and drought-related mortality, through use of trajectory approach

- High intensity, short duration

- Low intensity, short duration

- Low intensity, long duration

- Annual resolution

**Notes:**
Talking points:
This is in the Wenatchee NF in Washingon state, wilderness area on left, with big areas of insect outbreak (corroborated by FHM polygons visual interp) and fire, as well as recovery from past fire, and managed state and federal forest on the right (boxes are ownership boundaries). 

Main point here is that the change detection gets events and PROCESSES, and as such is setting the stage for being able to view the landscape for the dynamic place we know it to be.  These data are now being introduced into a variety fo projects (you know the ones). 

## Current LandTrendr Mapping

- LandTrendr mapping:
- Northwest Forest Plan (NWFP) update - western WA and OR (Jan 2009)
- ORCA II -- Carbon modeling and integration - Southern California (Spring 2009)
- National Parks (2008-2009)
- Idaho and Colorado insect outbreaks (Spring 2009)
- OWEB - link disturbance to coupled forest/hydrological models (Spring 2009)
- All of CA, WA, OR to follow

## TimeSync: Synching Automated and Human Interpretations of Landsat Time Series

- Use good, old-fashioned photo-interpretation skills, but applied to “plots” within Landsat time series
- Examine spectral trajectories and identify temporal vertices & segments

## TimeSync

- Any band or index
- Populate database with observations
- Sample design for “validation”

## TimeSync

- Reality check against recent Google Earth image
- Compare with other databases (harvest records, fire catalogs), as available

## TimeSync

- Complicated, but intuitive, effective process
- Applicable to any Landsat time series
- Variety of applications (exploration, calibration, validation)

## TimeSync

- Summarize agreement with LandTrendr map output...

- e.g., disturbance agreement (%) as a function of TimeSync- observed disturbance agent and relative intensity.

## LandTrendr: Beyond change detection

- NBR

- Brightness

- Greenness

- Wetness

- Use segmentation of one band to identify “vertices” in time series
- Smooth between vertices in other bands
- Result: “Pseudo-images” with year-to-year noise removed, but actual change retained

- 1985

- 1991

- 1995

- 2007

**Notes:**
I used NBR (top righ tplot) to do the change detection, and find the vertices. The segmentation is the white line.

Then I took the vertex years (the x-axis where there was a change in segment direction) and re-fit the other bands (B, G, and W).

Note that the change information is consistent across NBR, G, and W, but that B is noisy.  However, within the B signal, you can see the change “once you know to look for it”, which is what this segmentation process does. 
Images are extremely stable across time, except where there are disturbances -- here, fires in Kings Canyon National Park. 

New NOTE:  For reasons I won’t even get into, the images on the right and on the bottom are not from the same place.  The point is just to illustrate the concept.  The ones on the right are from a clearcut in 46/29.  

## Stable cross-time classification

- Stable spectral space allows application of consistent models, classification rules across time

- 1984

- 1986

- 1987

- 1988

- 1999

- 2002

- 2006

**Notes:**
Key point here:  One of the standard problems with delta-classification based change detection is that you can’t apply the same model across time without getting a 
Abunch of false signals.  Now we can 

## Stable cross-time classification

- Stable spectral space allows application of consistent models, classification rules across time

- 1999

- 2002

- 2006

- Area of detail

- Loss of vegetative cover

**Notes:**
Thus, stable areas on the landscape retain the same classification over time, as we want them, and the changed areas (here, a fire in Zion national park) have labels for 
Change associated with them. 

## Extension: L7 Gap-filling, cloud filling

- Multiple images ingested per year
- Best image (closest to median date) chosen if cloud-, gap-free
- Otherwise use secondary images
- Resultant image without gaps, clouds
- Result of both standard mosaicking + fitting

- Landsat 5
- August 10,
- 2006

- Landsat 7
- August 18,
- 2006

- Landsat 7
- September 4,
- 2006

- 2006
- Median
- Date

**Notes:**
Opportunity to produce standardized output images after throwing all possible images into the hopper.  We’re currently working with as many as 45 images for a 
Single scene to create a stack of 24 years.  

## LandTrendr & TimeSync

- Opening of the archive = paradigm shift in Landsat change detection
- Automated algorithms (LandTrendr - Kennedy, Vegetation Change Tracker – Huang) already exploiting the archive regionally, nationwide, as well as internationally (plus: exploring MSS-ETM+)
- LandTrendr composites to remove clouds and SLC-off gaps
- Smooths time series between change vertices enabling functional linkage between spectral properties and map classifications through time
- Given temporal and spatial richness of change information from algorithms, paucity of independent data to conduct validation
- TimeSync fills this void, and is backed up with more traditional reference data, where available.