Full text: Proceedings, XXth congress (Part 8)

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 
The empirical models to link the spectral reflectance to the 
field measured LAI were developed separately for each forest 
type as well as for all forest types. In this study, we tried to use 
several vegetation indices (SVI) as independent variables to the 
multiple regression model. Three other spectral indices of 
brightness (BR), greenness (GN), and wetness (WT) were also 
created by the tasseled cap (TC) transformation. 
To build the optimal statistical regression model to estimate 
LAI for entire study area, we compared several sets of 
independent variables that are subset of a few spectral 
vegetation indices. Two multiple regression models were built 
for each of two forest types. In addition to the forest cover map, 
we also need a land cover map to validate the empirical LAI 
estimation algorithm. A land cover map, in which the class 
categorization is comparable to the MODIS Land cover type 3- 
scheme (9 classes), was obtained by ordinary maximum 
likelihood classification method. Although the major portion of 
the study area is forest, it also includes small and segmented 
agricultural areas. The LAI values for the grass and croplands 
(mostly rice paddy) were adapted from the previous study by 
Hong et al. (1998). 
Quality assessments of MODIS LAI product 
As shown in Figure 3, the operational algorithm for producing 
MODIS LAI uses two MODIS land products of the surface 
reflectance (MODO09) and land cover (MODI4). The 1km 
resolution MODIS LAI products are produced every 8 days, 
which corresponds to the maximum value composition interval 
to remove cloud cover. LAI values are calculated by 
mathematical inversion of a rather sophisticated canopy 
reflectance (CR) model that uses MODO09 and MOD 14. If the 
CR model-based main algorithm fails, a backup algorithm 
based on the empirical relationship with vegetation index is 
triggered to estimate LAL 
The MODIS LAI image that corresponded to the date of the 
reference LAI map was obtained. Since the MODIS LAI value 
is separately calculated by cover type, the MODIS land cover 
products of were also acquired. MODIS land products can be 
directly obtained from the Earth Observing System Data 
Gateway (EOS, 2003). The MODIS LAI and land cover 
products supplied by the EOSDG is originally referenced by 
the sinusoidal map projection. To compare with the reference 
LAI map of the study site, the MODIS products were geo- 
referenced to the Transverse Mercator map projection by using 
the MODIS reprojection tool (MRT) software provided by 
NASA. To compare the reference LAI surface with MODIS 
LAI product, the reference LAI map having 28.5m pixel size 
was rescaled to Ikm pixel size. 
For the quality assessment of MODIS LAI data, we applied 
three phases of 1) quality of input datasets, 2) the MODIS LAI 
estimation algorithms, and 3) LAI value by land cover type 
(Figure 3). The accuracy of the MODIS land cover product was 
assessed by the reference land cover map. We also analyzed the 
effects of cloud cover at each pixel location for the MODIS 
reflectance data. After the validation of input datasets, we 
compared the reference LAI map with the MODIS LAI data by 
the estimation algorithms. Within the scene, some pixels had 
LAI value from the main CR-based algorithm and the other 
pixels had LAI value from the NDVI-based backup algorithm. 
As LAI value is also very sensitive to vegetation types, we tried 
to analyze the MODIS LAI value by different vegetation type 
(forest vegetation and grass /cropland). 
assessment of Estimation of LAI 
For Land 
Cover types Y 
MODIS LAI product 
Quality | 
Assessment | : 
of Input data | MSS MODIS 
- Estimation | Reflectance Land cover 
classified accuracy | (MOD09) (MOD14) 
of MODIS Land | 
cover ] 
- Assessmentof | 
Cloud effect in | 
reflectance | 
| Y 
7 | MODIS LAI Main 
Quality | Algorithm 
assessment of | - 3D Canopy Radiative 
MODIS LAI | Transfer Modeling 
for Algorithm | 
type i: Back-up 
| Fal, Algorithm 
| - - Empirical 
| Success modeling used 
Quality | " 
Figure 3. Flowchart of producing MODIS LAI and 
schemes of quality assessment of MODIS LAI 
Two separate multiple regression models to predict LAI for the 
reference map were developed for each of coniferous and 
deciduous forest. To avoid over-fitting problem of too many 
independent variables (five SVIs), we imposed the rule that 
only three variables can be selected for each model. Table 1 
shows the selected independent variables, R^ value, and root 
mean squared error of each of two models developed. Although 
the use of two separate estimation models requires additional 
effort of classifying the forest into two species groups, it should 
be a better approach to obtain more reliable LAI map. We 
generated the high-resolution reference LAI map by applying 
the best regression models to the radiometrically corrected 
reflectance data. Using the reference land cover type map, the 
coniferous and deciduous forests were extracted prior to 
applying the models and the grass and croplands were given the 
same LAI value that was measured in September. 
Table 1. Regression models to estimate LAI over the study area 
Selected independent 2 ; 
Type variables (SVI) R RMSE 
Coniferous RSR, BR, GN 0.8018 | 0.6289 
Deciduous NDVI, RSR, WT 0.3413 | 0.4504 

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