# Presentation: 497212

## LEDAPS Overview and Status

**Jeff Masek, Robert Wolfe, Forrest Hall, NASA GSFC**
**Chengquan Huang, Samuel Goward, UMD**
**Sean Healey, Scott Powell, USFS**
**October 17, 2006**

***Landsat Ecosystem Disturbance Adaptive Processing System***

## NACP

**The central objective of the **_**North American Carbon Program**_** is to measure and understand the sources and sinks of Carbon Dioxide (CO2), Methane (CH4), and Carbon Monoxide (CO) in North America and in adjacent ocean regions.**

**Specific Program Goals**
- Develop quantitative scientific knowledge, robust observations, and models to determine the emissions and uptake of CO2, CH4, and CO, changes in carbon stocks, and the factors regulating these processes for North America and adjacent ocean basins.
- Develop the scientific basis to implement full carbon accounting on regional and continental scales.
- Support long-term quantitative measurements of fluxes, sources, and sinks of atmospheric CO2 and CH4, and develop forecasts for future trends.

**http://www.nacarbon.org**

**NACP**

## Background

-  Forest disturbance (fire, harvest, insect damage) and recovery critical for carbon cycling
-    - direct emissions
-    - recovery ~ age distribution ~ NEE
-  Patch size often small – requires Landsat-type data analysis
-  NACP Science Plan calls for analysis of disturbance from satellite data

**Background**

## LEDAPS Goals

*** *****Generate surface reflectance (SR) products for North America from Landsat GeoCover archive (1975-2000)**
* **apply lessons from MODIS processing*
-  **Generate ****decadal, wall-to-wall**** maps of forest disturbance, recovery, and conversion for ****North America**
*high-resolution (30m) scene-based products*
*coarse-resolution (0.05 deg) modeling products*
-  **Develop automated approaches to Landsat processing that can be adapted for other applications**
-  *we do this for AVHRR, MODIS, VIIRS... why not Landsat?*
** ****Work with representatives of USDA Forest Service to evaluate applications utility of SR and disturbance products for carbon management and forest monitoring**.

**LEDAPS Goals**

## LEDAPS Processing Overview

**Landsat TM, ETM+**

**Landsat MSS**

-  Calibration
-  Atmospheric Correction
- Cloud/Snow masking

-  Radiometric Normalization

**Radiometrically Consistent Surface Reflectance Dataset (1975-2000)**

- Preprocessing

- Analysis

**QA/**
**Validation**

-  Disturbance Rate via Disturbance Index
-  Biophysical change from canopy reflectance model

**Disturbance/Recovery **
**Products for Carbon Assessments**

- Aggregation

- MODIS Products

## Atmospheric Correction

**Based on MODIS/6S radiative transfer approach**
- 	water vapor from NCEP re-analysis data
- 	ozone from TOMS, EP-TOMS
- 	topographic-dependent Rayleigh correction
**Aerosol optical thickness estimated from imagery using the Kaufmann et al (1997) “Dense, dark vegetation” approach**
- 	- estimate blue reflectance based on TOA SWIR 2
- 	- difference between TOAblue and SRblue gives AOT
- 	- interpolate valid targets across image

## Atmospheric Correction

- 1990’s Landsat-5 mosaic

**100 km**

- TOA reflectance

- Surface reflectance

- BOREAS Study Region

**100 km**

## Effect of Atmospheric Correction

-  (%)

- (MOD9A surface reflectance) – (ETM+ reflectance), 8/3/00

## Reflectance Validation

- TM TOA

- MODIS surface

- TM surface

- Blue

- Red

- MIR

- NIR

- 1000

- 700

- 200

- 100

- Units: Reflectance (x 10000)

- 2400

- 1000

- 0

- 0

## TM TOA

**TM TOA**

## TM SR

**TM SR**

## ETM+ Comparison with MODIS

**ETM+ Comparison with MODIS**

## ETM+ SR Mosaic

## Forest Disturbance Mapping

- Initial Goal:  stand-clearing disturbances (harvest, fire) and secular changes in forest cover
- Two approaches to mapping disturbance:
- 1.**  ****“Disturbance Index”**: semi-empirical spectral index developed by Sean Healey and Warren Cohen, USDA Forest Service.
- Matching **spectral trajectories** from canopy reflectance models to retrieve physical canopy parameters (D. Peddle/F. Hall/F. Huemmrich)

## Disturbance Index:  Brightnessrescaled – (Greennessrescaled+Wetnessrescaled)

**Disturbance Index: ** Brightnessrescaled – (Greennessrescaled+Wetnessrescaled)

- Brightnessrescaled = (B – forest)/forest

## Disturbance Index Example

- 1988

- 2000

- Disturbance
- Index Change

- Map

**Olympic Peninsula**

**Disturbance Index Example**

*5km*

## Slide 17

## Slide 18

## Forest Disturbance 

**Forest Disturbance **

## <20 Years

- >20 Years

- FIA Stand Age

## Sampling Approach

- ~25 Sample Sites

- Annual or Biennial Image Time Series
- “Data Cubes” (1972-2004)

- Disturbance history / stand age
- + regrowth rate
- (~ biomass?)

- S.N. Goward, “*North American Forest Disturbance and Regrowth since 1972*“

**Sampling Approach**

## Time Series Analysis

**Time Series Analysis**

## Landsat mapped forest change between 1987 and 2005 in western Oregon along the Clackamas County-Wasco County border (left) 

- Landsat mapped forest change between 1987 and 2005 in western Oregon along the Clackamas County-Wasco County border (left)
- Temporal variation of the percentage of change area (below)

## LEDAPS SCIENCE MODULES

- lndcal

- lndsr

- lndcsm

- Calibration,
- TOA reflectance

- Cloud/snow/
- shadow mask

- lndrr

- Precision registration and orthorectification

- lndcom

- lndpacom

- lnddm

- Direct and BRDF/phenology adjusted compositing

- Disturbance index change

**LEDAPS SCIENCE MODULES**

- STAR-FM

- Synthetic “daily” Landsat SR

**MODIS SR**

**MODIS VCF**

***I.  Image preprocessing***

***II.  Compositing/***
***        ******fusion***

***III.  Science ***
***    ******analysis***

- lndreg, ortho

- 6S Atmospheric Correction to SR

- Radiometric
- rectification

**NCEP Water Vapor**
**TOMS Ozone**
**1km DEM**

## System Performance

-  MODAPS architecture allows rapid processing of large data volumes
-  Uses commodity PC’s – one scene per processor
-  PGE (product generation executables) are C/C++ modules designed to work with standard library routines (HDF, geographic)

**Wall-clock time (hr)**

***Processing a 30-year North American surface reflectance dataset should take < 4 days***

**Decadal NAM**

**One-time NAM**

**Time to process 1 scene, 1 CPU**

**Time to process N scenes, 16 CPUs**

## Summary and Current Status 

**Current LEDAPS project funding 2003-2007**
**Two pending proposals to continue LEDAPS capability at GSFC through 2010**
**Vermote et al project funded through Landsat Science Team to continue development of atmospheric correction work**

**LEDAPS offers one way to move toward:**
-  automated processing chains for Landsat data
-  higher level Landsat products (reflectance, biophysics, LC/LC)
-  reflectance-based analyses of land cover condition
-  merging of multiple RS sources (Landsat, MODIS, IRS, etc)

## Thank You

## Slide 28

## Automated GCP selection to calculate Xtransl

- Select candidate points along nadir
- Subpixel cross-correlation within window
- Apply Xtransl to target image
- Calculate topographic displacement using DEM and
- LOS calculation
- Resample image (cubic convolution or nearest-neighbor)
- Check RMS via GCP selection across whole scene
- If RMS is high, go back to (1) and include rotation term
**NOTE:  Currently assumes UTM projection**

**+**

**+**

**+**

**+**

**+**

**+**

**+**

**+**

- X’ = X + Xtransl + Xtopo(x,y) {+ ROT}

**LEDAPS Orthorectification Algorithm**

## Landsat 1Gs (2)

## Orthorectified (2)

## S. Olympic Peninsula

**S. Olympic Peninsula**
**2.6% disturbed / yr**
**Turnover = 38 Yr**

**W. Montana**
**1.5% disturbed / yr**
**Turnover = 69 Yr**

**S. Virginia**
**2.2% disturbed / yr**
**Turnover = 44 Yr**

**NW Colorado**
**0.7% disturbed / yr**
**Turnover = 145 Yr**

**N. Louisiana**
**3.4% disturbed / yr**
**Turnover = 29 Yr**

**W. Pennsylvania**
**0.2% disturbed / yr**
**Turnover = 550 Yr**