Takahiro Sasai

A comparison of simulation results from two terrestrial carbon cycle models using three climate data sets

This study addressed how different climate data sets influence simulations of the global terrestrial carbon cycle. For the period 1982–2001, we compared the results of simulations based on three climate data sets (NCEP/NCAR, NCEP/DOE AMIP-II and ERA40) employed in meteorological, ecological and biogeochemical studies and two different models (BEAMS and Sim-CYCLE). The models differed in their parameterizations of photosynthetic and phenological processes but used the same surface climate (e.g. shortwave radiation, temperature and precipitation), vegetation, soil and topography data. The three data sets give different climatic conditions, especially for shortwave radiation, in terms of long-term means, linear trends and interannual variability. Consequently, the simulation results for global net primary productivity varied by 16%–43% only from differences in the climate data sets, especially in these regions where the shortwave radiation data differed markedly: differences in the climate data set can strongly influence simulation results. The differences among the climate data set and between the two models resulted in slightly different spatial distribution and interannual variability in the net ecosystem carbon budget. To minimize uncertainty, we should pay attention to the specific climate data used. We recommend developing an accurate standard climate data set for simulation studies.

Coupling and Fusion in Modern Geoscience

We summarize two projects representative of a developing movement in modern geosciences. By establishing a linkage between the developed coupling simulator and fusion prototype, we successfully incorporate geosciences with informatics, computer technologies and mathematics in the field of data processing. A challenging coupled inversion scheme is shining under such an integration.

Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

An accurate estimation of the leaf area index (LAI) by satellite remote sensing is essential for studying the spatial variation of ecosystem structure. The goal of this study was to estimate the spatial variation of LAI over a forested catchment in a mountainous landscape (ca. 60 km2) in central Japan. We used a simple model to estimate LAI using spectral reflectance by adapting the Monsi-Saeki light attenuation theory for satellite remote sensing. First, we applied the model to Landsat Operational Land Imager (OLI) imagery to estimate the spatial variation of LAI in spring and summer. Second, we validated the model’s performance with in situ LAI estimates at four study plots that included deciduous broadleaf, deciduous coniferous, and evergreen coniferous forest types. Pre-processing of the Landsat OLI imagery, including atmospheric correction by elevation-dependent dark object subtraction and Minnaert topographic correction, together with application of the simple model, enabled a satisfactory 30-m spatial resolution estimation of forest LAI with a maximum of 5.5 ± 0.2 for deciduous broadleaf and 5.3 ± 0.2―for evergreen coniferous forest areas. The LAI variation in May (spring) suggested an altitudinal gradient in the degree of leaf expansion, whereas the LAI variation in August (mid-summer) suggested an altitudinal gradient of yearly maximum forest foliage density. This study demonstrated the importance of an accurate estimation of fine-resolution spatial LAI variations for ecological studies in mountainous landscapes, which are characterized by complex terrain and high vegetative heterogeneity.