Linear mixture modeling for quantifying vegetation cover using time series NDVI data
Lin Zhu
Center for Environment Remote Sensing (CEReS)
Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522
Tel: (81) -43-290-3850
Fax: (81)-43-290-3857
E-mail: zhulin@ceres.cr.chiba-u.ac.jp
Japan
Ryutaro Tateishi
Center for Environment Remote Sensing (CEReS)
Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522
Tel: (81) -43-290-3850
Fax: (81)-43-290-3857
E-mail: zhulin@ceres.cr.chiba-u.ac.jp
Japan
Abstract
Linear mixture modeling technique has been applied to time series NOAA AVHRR and
SPOT VEGETATION monthly composite NDVI data in order to determine its effectiveness as a mapping
tool. Fraction images of forest, farmland and steppe were extracted using Constrained Least Squares
(CLS) method over a test area. Classification results of multi-temporal Landsat TM data were used to
validate the performance of linear mixture modeling. Also, land cover change of the test area between
1992 and 2000 was detected. Then, linear mixture modeling technique was extended to vegetation cover
mapping of Asia. Selection of appropriate endmembers was based on Global Land Cover Ground Truth
(GLCGT) database. Percentage images of vegetation cover of Asia were obtained from SPOT
VEGETATION monthly composite NDVI data of 2000. This study suggests that with improved estimates of
endmember values and more sophisticated methods to fully utilize the included information, linear mixture
modeling technique can have great potential when applied to coarse spatial time series NDVI data for
global studies.
Introduction
Study on geosphere- biophere -atmosphere interaction depends on reliable and unambiguous definition of
the existing terrestrial vegetation. Vegetation characteristics, including land cover and phenology, affect
processes such as water cycle, absorption and re-emission of solar radiation, momentum transfer, carbon
cycle, and latent and sensible heat fluxes (Maselli et al., 1998). Variations in the composition and
distribution of vegetation represent one of the main sources of systematic change on local, regional, or
global scale, and the ability to detect these variations using remotely sensed data is of utmost importance
for both environmental researches and management activities (Hall et al., 1995). Traditionally, vegetation
monitoring by remotely sensed data has been carried out using vegetation indices, which are mainly
derived from mathemati cal transformations of reflectance data in red (R) and near-infrared (NIR) channels.
One of the most widely used indices is the well-known normalized difference vegetation index (NDVI).
Vegetation monitoring demands high temporal frequency information to follow the rapid vegetation
phenological change. This can be realized by using satellite data such as NOAA AVHRR data and SPOT
VEGETATION data for their daily coverage and synoptic overview. NOAA AVHRR and SPOT
VEGETATION data have been widely utilized for global, continental and regional land use and land cover
studies. However, one major problem when using these data is their volume and noise. Fortunately, these
can be solved through monthly maximum composite processing. The use of monthly composite data
represents a compromise between temporal frequency and the need for cloud-free data (Moody and
Strahler, 1994). It also provides a means to reduce data volume while maintaining vegetation phenological
information. Actually, most vegetation mapping applications at broad spatial scales have been based on
time series NDVI data (Tucker et al. 1985, Loveland et al. 1991), it is also indicated that global and regional
land cover classification derived from remotely sensed data has to date generally relied on a single year.time series to characterize phenology of the vegetation (Defries et al., 2000). Another major problem when
using these data is their poor spatial resolution. Usually, the large ground area of each AVHRR or
VEGETATION pixel is a mixture of several land cover types. Conventional classification approaches that
categorize each pixel into a discrete vegetation type do not fully utilize the rich information content of the
data to describe gradients and mosaics in the landscape, and also variation in vegetation characteristics
within a cover type is obscured (Defries et al., 2000). Efforts to address the problem of mixed pixels are of
increasing importance as emphasis is being placed in providing global-scale monitoring.
A number of researches have been taken to depict pixel heterogeneity in land cover from remotely sensed
data. Linear mixture modeling to deconvolve proportional cover based on spectral reflectance of
endmembers or pure pixels is a main one. In general, linear mixture modeling techniques have been
mainly applied at local or regional scales over limited areas using single date or multi- temporal spectral
reflectance data. Clearly, there is a need for operational methods permitting the application of linear
mixture modeling directly at NDVI l evel (i.e., the mixed NDVI is the sum of components’ NDVIs weighted by
the components’ surface proportions), especially for global and regional studies. Only a few researches
have addressed this issue in view of improving crop monitoring and yield predicti on through estimation of
pure crop NDVI profiles (Kerdiles and Grondona, 1995), and almost no research has addressed
application of linear mixture modeling for NOAA AVHRR or SPOT VEGETATION monthly composite NDVI
data, neither at local scales, nor regional and global scales. Therefore, a research in this area should be a
meaningful challenge.
In this study, firstly, linear mixture modeling technique was applied to time series AVHRR monthly
composite NDVI data ranging from April 1992 to March 1993. The percentages of forest, farmland and
steppe in each pixel were determined by the CLS method (Shimabukuro and Smith, 1991) over a typical
agricultural area in the northeast region of China. Also, multi- temporal Landsat TM data and NOAA
AVHRR monthly composite NDVI data were classified using conventional methods. A good agreement
was found between fraction images and classification results of Landsat TM data. Then, in the same way,
fraction images were obtained from time series SPOT VEGETATION monthly composite NDVI data
ranging from January to December of 2000, and land cover change between 1992 and 2000 was detected.
Finally, linear mixture modeling technique was extended to vegetation cover mapping of Asia.
