A method to retrieve land surface temperature (LST) is proposed in the thermal infrared (8-12μm) from MODIS and ASTER data. Firstly, spectral mixture analysis was conducted between MODIS and ASTER VNIR channels and the result showed that the overall accuracy was about 5%. Secondly, the band average emissivities are calculated using spectral response function of MODIS sensor and published spectral data of terrestrial materials in wide rages of atmospheric and surface temperature conditions. Thirdly, LST was calculated using generalized split-window algorithm using the coefficients from regression analysis of radiative transfer simulations proposed by Z. Wan [Wan, 1996] . Comprehensive validation and error analysis has been made to evaluate the performance of the new LST algorithm using NASA/MODIS LST product (MOD11) and AMEDAS data. The maximum error in retrieved LST values were 1K. Results show that the new approach used with MODIS and ASTER data offers an improved retrieval of LST.
The Noise Reduction Filter (NRF) is improved from a Local Maximum Fitting filter (Sawada, et al., 2000) that has been developed to remove noise such as cloud and haze from time series NDVI images and extract vegetation information on cultivated fields. The time series data of SPOT/VEGETATION is processed by NRF. Then, the processed data are evaluated by two methods. One is comparison with an annual NDVI profile. Another one is statistic analysis based on the peak time and the peak value of the annual NDVI profile. Furthermore, an examination is conducted about the most suitable number of the time series data to enhance the processing accuracy. As the result, information on SPOT/VEGETATION is kept, and a noise element is removed. NRF is effective in the extraction of the time series property of NDVI and planting information from the image. As for the number of time series data, difference of the peak time is not shown clearly although one-year data are enough numbers to get the good correlation of peak values and four-years data are needed to extract smooth curves of NDVI changes.
Twelve Landsat Thematic Mapper (TM) indices were evaluated for their usefulness for monitoring logging and growth in forests. The 12 indices were 5 TM channels (TM2, TM3, TM4, TM5 and TM7), a linear combination of TM2, TM3 and TM7, three normalized indices using TM4 and TM3, TM5 or TM7, and three tasseled cap indices. The second principal component (PC2) was computed for each index using late summer TM images of 1986 and 1992. The usefulness of each index was evaluated by analyzing rlationships between PC2 values and the stand ages of Sugi cedar (Cryptomeria Japonica, D. Don.) by visual interpretation, scattergrams and regression analyses. TM4 and indices using TM4 did not differentiate logging areas from unchanged forests due to spectral changes of TM4 in the course of vegetation growth. Clear cutting areas within 5 years were distinguished from unchanged forests in PC2 of TM3 and TM7. An exponential relationship (γ2>0.8) was found between the stand ages and PC2 of TM3 or TM7 during the early regeneration stage. Changes due to growth could be distinguished for stands with ages up to about 12 years old.