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Developing a Consistent Landsat Data Set from MSS, TM/ETM+, and International Data Sources for Land Cover Change Detecti PowerPoint Presentation
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Landsat Science Team Meeting, USGS EROS, January 9-11, 2007. Developing a Consistent Landsat Data Set from MSS, TM/ETM+, and International Data Sources for Land Cover Change Detection. Feng Gao Earth Resources Technology, Inc. NASA Goddard Space Flight Center. Goals.

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slide1

Landsat Science Team Meeting, USGS EROS, January 9-11, 2007

Developing a Consistent Landsat Data Set from MSS, TM/ETM+, and International Data Sources for Land Cover Change Detection

Feng Gao

Earth Resources Technology, Inc.

NASA Goddard Space Flight Center

goals
Goals
  • Process Landsat (esp. MSS) and international Landsat-like data sources into a consistent orthorectified surface reflectance data set.
  • Use consistent surface reflectance data set for land cover change detection.
  • Simulate Landsat surface reflectance using MODIS and Landsat data.
  • Evaluate international Landsat-like data for possible Landsat gap and reduce LDCM program risk.
background
Background
  • This project is built upon on several existed projects and have strong relations with
    • NASA LDCM data blender project (Jeff Masek) – data fusion algorithms
    • LEDAPS (Jeff Masek) – Landsat surface reflectance
    • MODIS MOD09 product (Eric Vermote) – MODIS surface reflectance
    • NASA ACCESS LC-ComPS (Jeff Masek) – share some basic modules
progress 1 orthorectification
Progress (1) - Orthorectification

Orthorectification module was initially developed for the NASA LDCM data blender project and has been extended for uses in this project.

  • Uses GeoCover data as orthorectified ground truth
  • Searches control points automatically
  • Combines orthorectification and precise registration together (only resample once)
  • Implemented preliminary registration to correct large geolocation errors (such as in the early MSS scenes)
  • Tested on TM, ETM+, MSS, CBERS/CCD, and TERRA/ASTER data (IRS/AWiFS data next) using 30m and 90m SRTM DEM
progress 2 international data
Progress (2) – International Data
  • Orthorectified international data using GeoCover Landsat data as references
  • Developed model to convert DN to surface reflectance using MODIS surface reflectance as reference

(Note that not all international data sources can be atmospherically corrected using physical models)

  • Corrected BRDF/Phenology effects and thus allows seamless mosaic from different acquisition dates and paths
slide6

ASTER L1B DN in

coarse resolution

MODIS SR

(nadir view)

Filtering & sampling

surface homogeneity

from fine res. ASTER

MODIS aerosol

on ASTER date

multispectral samples

training

ASTER projected

SR in fine resolution

ASTER L1B DN

in fine resolution

neural network or

regression tree

DN correction

based on NN

slide7

10/23/05

1/27/06

4/10/06

10/23/05

1/27/06

11/10/05

4/10/06

ASTER

slide9

ASTER projected

surface reflectance

on 4/10/2006

(* used homogeneous samples and combined scenes from same acquisition dates)

slide10

Landsat LAI

MODIS LAI

Landsat SR

MODIS SR

progress 3 data fusion spatial and temporal adaptive reflectance fusion model starfm
Progress (3) – Data FusionSpatial and Temporal Adaptive Reflectance Fusion Model (StarFM)
  • Objectives:

fuse high-frequency temporal information from MODIS and high spatial resolution information from Landsat to produce “daily” Landsat-like surface reflectance

  • Input:
      • MODIS surface reflectance M(xi,yj,tk) at tk
      • Landsat surface reflectance L(xi,yj,tk) at tk
      • MODIS surface reflectance M(xi,yj,t0) at t0
  • Predict:

Landsat surface reflectanceL(xi,yj,t0) at t0

MODIS SR

Landsat SR

1

2

3

4

1

?

?

4

slide12

5/24/01 (144)

6/4/01 (155)

7/4/01 (185)

7/11/01 (192)

StarFM Algorithm

mapping wildland fire scar using fused landsat and modis data
Mapping Wildland Fire Scar Using Fused Landsat and MODIS Data

(a) MODIS, 9/14/2000 (b) MODIS, 6/13/2002 (c) MODIS, 7/8/2002

(e) Fused from (a), (b) and (d) (f) Fused from (a), (c) and (d)

(d) Landsat ETM+, 9/14/2000

near term goals
Near Term Goals
  • Develop a general empirical relation model (GERM) to compute surface reflectance or biophysical parameters for international data sources based on high quality MODIS products (assume no land cover change)
  • Run GERM model on AWiFS data
  • Convert MSS DN to surface reflectance at TM/ETM+ acquisition dates using “stable” targets approach (land cover changed)
  • Do change detection based on the projected surface reflectance of MSS and TM/ETM+