市井 和仁

Deforestation in Rondonia state in the south-western part of the Brazilian Legal Amazon was analysed using Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETMz), National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and hydrological data. The Landsat sensor data coverage was supplemented with Pathfinder AVHRR Land ( PAL) Normalized Difference Vegetation Index (NDVI) datasets. The results from the Landsat-based analysis show that more than 30% of the natural vegetation in the study area was subject to deforestation between 1973 and 1999, a finding reinforced by analysis of the PAL NDVI data. In addition, it was established that trends in the PAL NDVI datasets were coincident with the pattern of deforestation. Apart from imagery analysis, time variations in the hydrological data between 1982 and 1988 were used to estimate the evapotranspiration. A decreasing trend was identified in the rate of evapotranspiration, suggesting that deforestation has a significant impact on the local hydrological cycle.

A simple Earth system model, the Four-Spheres Cycle of Energy and Mass (4-SCEM) model, has been developed to simulate global warming due to anthropogenic CO2 emission. The model consists of the Atmosphere-Earth Heat Cycle (AEHC) model, the Four Spheres Carbon Cycle (4-SCC) model, and their feedback processes. The AEHC model is a one-dimensional radiative convective model, which includes the greenhouse effect of CO2 and H2O, and one cloud layer. The 4-SCC model is a box-type carbon cycle model, which includes biospheric CO2 fertilization, vegetation area variation, the vegetation light saturation effect and the HILDA oceanic carbon cycle model. The feedback processes between carbon cycle and climate considered in the model are temperature dependencies of water vapor content, soil decomposition and ocean surface chemistry. The future status of the global carbon cycle and climate was simulated up to the year 2100 based on the "business as usual" (IS92a) emission scenario, followed by a linear decline in emissions to zero in the year 2200. The atmospheric CO2 concentration reaches 645 ppmv in 2100 and a peak of 760 ppmv approximately in the year 2170, and becomes a steady state with 600 ppmv. The projected CO2 concentration was lower than those of the past carbon cycle studies, because we included the light saturation effect of vegetation. The sensitivity analysis showed that uncertainties derived from the light saturation effect of vegetation and land use CO2 emissions were the primary cause of uncertainties in projecting future CO2 concentrations. The climate feedback effects showed rather small sensitivities compared with the impacts of those two effects. Satellite-based net primary production trends analyses can somewhat decrease the uncertainty in quantifying CO2 emissions due to land use changes. On the other hand, as the estimated parameter in vegetation light saturation was poorly constrained, we have to quantify and constrain the effect more accurately.

The relationship between the Normalized Difference Vegetation Index (NDVI) and climatic variables was analysed on a global scale using the Pathfinder AVHRR Land NDVI data set, and observed climate data for the period 1982-1990. A significant correlation between interannual NDVI and temperature variation was recognized in the northern mid- to high latitude areas between spring and autumn. A significant correlation was also identified between the NDVI, temperature and precipitation in northern and southern semiarid regions. A comparison of global NDVI trends show that NDVI increases in the northern mid- and high latitudinal zones are related to temperature rise, and NDVI decreases in southern semiarid regions are due to a precipitation decrease in the survey period. Although the cause of NDVI increases in the equatorial regions remains unclear, the combined effects of forest regrowth, deforestation and fertilization may impact on the NDVI trend.

Interannual trends in annual and seasonal vegetation activities from 1982 to 1990 on a global scale were analysed using the Pathfinder AVHRR Land NDVI data set corrected by utilising desert and high NDVI areas. Climate effects on interannual variations in NDVI were also investigated using temperature and precipitation data compiled from stational observations. In the northern middlehigh latitudes, vegetation activities increased over broad regions because of a gradual rise in temperature. NDVI increases were also detected in the tropical regions, such as western Africa and south-eastern Asia. Plant photosynthetic activities on the other hand, decreased remarkably in some arid and semi-arid areas in the Southern Hemisphere, because annual rainfall decreased during this period. © 2001 Taylor &amp Francis Group, LLC.