Title: Identification of land-use and land-cover changes in East-Asia
1Identification of land-use and land-cover
changes in East-Asia
- Masayuki Tamura, Jin Chen,
- Hiroya Yamano, and Hiroto Shimazaki
- National Institute for Environmental Studies
2Objectives
- 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.
3Data 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
4NDVI Data Preprocessing
BISE(Best Index Slope Extraction)
Noises caused by cloud
NDVI
5Climate Data Interpolation
Kriging Interpolation
Temperature
Meteorological Stations
Precipitation
6Method
- 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)
7Normalization 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. ?T, and ?W account for the
effects of temperature and precipitation
differences from the optimum conditions
respectively. ?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 NDVIo directly.
?other can be moved off through pre-processing of
original NDVI data, which includes sensor
calibration, atmospheric correction and cloud
filter. ?T, ?W can be estimated according to the
relationship between vegetation growth and
seasonal climate condition.
8?T Estimation
- ?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) , ?T 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?w Estimation
?w describes the effect of water stress to plant
growth. By considering the lag effect of
precipitation, it is calculated by When Sum
(PPT) lt Sum (PET) When Sum (PPT) gt Sum (PET)
where PET is potential evapotranspiration
and determined by Thornthwaite method, PPT is
precipitation for calculating period.
10Change 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
11Chang Pixels in Different Periods
12Change Pixel Distributions with Different Trends
NDVI Decreasing Trend
NDVI Increasing Trend
No Trend
13Grassland Monitoring
Xilinhot
Haibei
14Wetland Monitoring Habitats of Red-Crowned Cranes
and Oriental White Storks
Circles show the sites where birds stayed more
than 10 days.
15Thank you!