Identification of land-use and land-cover changes in East-Asia

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

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

5. Climate Data Interpolation Kriging Interpolation Temperature Meteorological Stations Precipitation

6. Method • 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)

7. Normalization for Climate Conditions • An observed NDVI (NDVIo) for a pixel can be expressed as: • where NDVI * is a potential NDVI in an optimum climate condition. ɛT, and ɛW account for the effects of temperature and precipitation differences from the optimum conditions respectively. ɛother 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 NDVIo directly. ɛother can be moved off through pre-processing of original NDVI data, which includes sensor calibration, atmospheric correction and cloud filter. ɛT, ɛW can be estimated according to the relationship between vegetation growth and seasonal climate condition.

8. Topt ɛTEstimation • ɛT reflects theconcept 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) , ɛT has an asymmetric bell shape that falls off more quickly at high than at low temperature. Topt is optimum temperature, defined as the air temperature when the NDVI reaches its maximum for a long period. (Hamlyn G. Jones, 1992)

9. ɛwEstimation ɛwdescribes 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 Change Pixel Detection Flow NDVI dataset in 1983 Base Dataset NDVI dataset in 1984 Change Vector Calculation Threshold Applying …… NDVI dataset in 1999 Time Series Filtering Change Pixels

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

12. Special Modification by Forest Fire Change Pixel Distributions with Different Trends NDVI Decreasing Trend NDVI Increasing Trend 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!