1 / 14

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

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.

raiden
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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

  7. 10/23/05 1/27/06 4/10/06 10/23/05 1/27/06 11/10/05 4/10/06 ASTER

  8. MODIS SR on 2006-4-10

  9. ASTER projected surface reflectance on 4/10/2006 (* used homogeneous samples and combined scenes from same acquisition dates)

  10. Landsat LAI MODIS LAI Landsat SR MODIS SR

  11. 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

  12. 5/24/01 (144) 6/4/01 (155) 7/4/01 (185) 7/11/01 (192) StarFM Algorithm

  13. 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

  14. 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+

More Related