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RISS 인기검색어
- 검색키워드
- 저자 : Kenlo Nishida Nasahara (검색결과 3 건)
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Shin Nagai,Taku M. Saitoh,Rikie Suzuki,Kenlo Nishida Nasahara,이우균,손요환,Hiroyuki Muraoka 한국산림과학회 2011 Forest Science And Technology Vol.7 No.4
General, global, long-term, and comprehensive phenological observations are required to evaluate the variability of photosynthetic activities due to environmental changes in terrestrial ecosystems. The observation of seasonal changes and detection of interannual variation in canopy phenology over regional and global scales require satellite data with high temporal resolution (i.e. a daily time step). However, satellite data often include noise caused by snow cover on vegetation, cloud contamination, and atmospheric aerosols. To accurately detect the timing of leaf-expansion and leaf-fall, which occur rapidly, and their rates, it is necessary to examine the observational frequency of noise-free satellite-observed vegetation index data during each phenological period. In this study, we investigated the spatiotemporal distribution of the number of observational days (NUMdays) in the Terra/MODIS (Moderate Resolution Imaging Spectroradiometer)-observed daily high-quality normalized difference vegetation index (NDVIhigh) data with no effects of snow cover, cloud contamination, or atmospheric noise. These data were examined for each month over 10 years in the various ecosystems of East Asia. To ground-truth the relationship between the Terra/MODISobserved daily NDVIhigh data and canopy surface images, we performed a long-term continuous field study in a cooltemperate deciduous broad-leaved forest in central Japan. During the leaf-expansion and leaf-fall periods, the NUMdays for NDVIhigh data in southern Russia, northeastern China, the Tibetan Plateau, Korea, and maritime Japan was about 3–7 for each month. The NUMdays for NDVIhigh data exceeded 10 for each month in arid regions during the growing season and in the subtropical region including northeastern India, Myanmar, and southwestern China during the dry season. In contrast, the NUMdays for NDVIhigh data was almost 0 for each month in southeastern China throughout the year and in the subtropical region during the southeastern monsoon season (July and August). By considering observations from both the Terra/MODIS and Aqua/MODIS satellites, the NUMdays for NDVIhigh data in the deciduous broad-leaved forest in Japan was increased by 40% compared with only Terra/MODIS satellite observations. Our findings indicate that daily NDVI data from multiple satellites detect the seasonal changes in the various ecosystems of East Asia more accurately than 8-day or biweekly composite NDVI data.
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Taku M. Saitoh,Shin Nagai,Hibiki M. Noda,Hiroyuki Muraoka,Kenlo Nishida Nasahara 한국산림과학회 2012 Forest Science And Technology Vol.8 No.2
Leaf area index (LAI) is a crucial ecological parameter that represents canopy structure and controls many ecosystem functions and processes, but direct measurement and long-term monitoring of LAI are difficult, especially in forests. An indirect method to estimate the seasonal pattern of LAI in a given forest is to measure the attenuation of photosynthetically active radiation (PAR) by the canopy and then calculate LAI by the Beer–Lambert law. Use of this method requires an estimate of the PAR extinction coefficient (k), a parameter needed to calculate PAR attenuation. However, the determination of k itself requires direct measurement of LAI over seasons. Our goals were to determine (1) the best way to model k values that may vary seasonally in a forest, and (2) the sensitivity of estimates of canopy ecosystem functions to the errors in estimated LAI. We first analyzed the seasonal pattern of the ‘‘true’’ k (k_p) under cloudy and sunny conditions in a Japanese deciduous broadleaved forest by using the inverted form of the Beer–Lambert law with the true LAI and PAR. We next calculated the errors of PAR-based LAIs estimated with an assumed constant k (LAI_pred) and determined under what conditions we should expect k to be approximately constant during the growing period. Finally, we examined the effect of errors in LAI_pred on estimates of gross primary production (GPP), net ecosystem production (NEP), and latent heat flux (LE) calculated with a land-surface model using LAI_pred as an input parameter. During the growing period, cloudy kp varied from 0.47 to 1.12 and sunny kp from 0.45 to 1.59. Results suggest that the value of LAI_pred was adequately estimated with the k_p obtained under cloudy conditions during the fully-leaved period (0.53–0.57). However, LAI_pred was overestimated by up to 0.6 m2 m–2 inMay and November. The errors in LAIpred propagated to errors in modeled carbon and latent heat fluxes of –0.21 to 0.32 g C m^–2 day^–1 in GPP, –0.09 to 0.19 g C m^–2 day^–1 in NEP, and –3.2 to 3.9 Wm^–2 in LE, which is close to the measurement errors recognized in the tower flux measurement. LAI_pred estimated with an assumed constant k can be useful for some ecosystem studies as a second-best alternative if k is equated to the value of k_p measured under cloudy conditions especially during the fully-leaved period.
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