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

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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

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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.

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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

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NDVI Data Preprocessing
BISE(Best Index Slope Extraction)
Noises caused by cloud
NDVI
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Climate Data Interpolation
Kriging Interpolation
Temperature
Meteorological Stations
Precipitation
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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)

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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. ?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.
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?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)
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?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.
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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
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Chang Pixels in Different Periods
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Change Pixel Distributions with Different Trends
NDVI Decreasing Trend
NDVI Increasing Trend
No Trend
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Grassland Monitoring
Xilinhot
Haibei
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Wetland Monitoring Habitats of Red-Crowned Cranes
and Oriental White Storks
Circles show the sites where birds stayed more
than 10 days.
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Thank you!