1. Identification of land-use and land-cover changes in East-Asia Masayuki Tamura, Jin Chen, Hiroya Yamano, and Hiroto Shimazaki National Institute for Environmental Studies

2. Objectives  To develop a robust and reliable algorithm for detecting land use/cover changes using coarse spatial resolution data (MODIS, NOAA/AVHRR, SPOT/VEGETATION).  To analyze land use/cover changes in China during 1982-1999 using the Pathfinder 8km NDVI and climate data.

3. 1)Satellite data: Pathfinder 8km NDVI data  Spatial resolution: 8 x 8 km  Temporal resolution: 10-day. 20 years of data (1981-2000).  Preprocessing 2)Climate data: China National Meteorological Bureau.  620 meteorological stations  10-day mean temperatures and precipitations from 1980-1999.  Preprocessing Data Sources

4. NDVI Data Preprocessing NDVI Noises caused by cloud BISE(Best Index Slope Extraction)

5. Kriging Interpolation Climate Data Interpolation Meteorological Stations Temperature Precipitation

6.  NDVI profile differences are used to detect land cover changes between two years.  Normalization and correction of NDVI data Calibration of sensor degradation. Atmospheric correction Normalization of climate conditions (T, P) Method

7.  An observed NDVI (NDVI o ) for a pixel can be expressed as: where NDVI * is a potential NDVI in an optimum climate condition. ɛ T, and ɛ W account for the effects of temperature and precipitation differences from the optimum conditions respectively. ɛ other accounts for the effects of sensor degradation and atmospheric condition changes.  Land cover change detection should be performed by comparing NDVI * differences between two years rather than NDVI o directly. Normalization for Climate Conditions ɛ other can be moved off through pre-processing of original NDVI data, which includes sensor calibration, atmospheric correction and cloud filter. ɛ T, ɛ W can be estimated according to the relationship between vegetation growth and seasonal climate condition.

8. ɛ T Estimation  ɛ T reflects the concept that plant growth is depressed when plant is growing at a temperature displaced from its optimum temperature.  According to existing study (Potter, 1993; Hamlyn G. Jones, 1992), ɛ T has an asymmetric bell shape that falls off more quickly at high than at low temperature. T opt (Hamlyn G. Jones, 1992) T opt is optimum temperature, defined as the air temperature when the NDVI reaches its maximum for a long period.

9. ɛ w Estimation ɛ w describes the effect of water stress to plant growth. By considering the lag effect of precipitation, it is calculated by When Sum (PPT) < Sum (PET) When Sum (PPT) > Sum (PET) where PET is potential evapotranspiration and determined by Thornthwaite method, PPT is precipitation for calculating period.

10. 1 2 3 NDVI dataset in 1983 Change Vector Calculation Change Vector Calculation NDVI dataset in 1984 NDVI dataset in 1999 …… Threshold Applying Threshold Applying Time Series Filtering Time Series Filtering Change Pixels Change Pixel Detection Flow Base Dataset

11. Change pixels during 1984-1988 Change pixels during 1989-1993 Change pixels during 1994-1997 Chang Pixels in Different Periods

12. NDVI Decreasing Trend NDVI Increasing Trend Change Pixel Distributions with Different Trends Special Modification by Forest Fire No Trend

13. Grassland Monitoring Xilinhot Haibei

14. Wetland Monitoring Habitats of Red-Crowned Cranes and Oriental White Storks Circles show the sites where birds stayed more than 10 days.

15. Thank you!