The sea breeze has thermal moderation effects on leeward areas. Because evapotranspiration from leaf surfaces is functionally similar to that from water surfaces in relation to thermal budgets on earth surface, the existence of a temperature fall in proportion to the amount of evapotranspiration in a forest and of the earth surface temperature moderation phenomena influencing the earth surface of a leeward area can be expected. Let us assume that the temperature difference ((c-Tf)) between the intercept c of the temperature axis and the average forest temperature Tf in the expression of relationship between Tf and the normalized vegetation index NVI means evapotranspiration, and the gradient ((c-Tf)) /NVI, which Nemani et al. once tried to relate to the canopy resistance, is assumed to represent the normalized evapotranspiration excluding the effect of NVI. Then, factors influencing the gradient ((c-Tf))/NVI and the temperature difference ((c-Tf)) has been analyzed using Matsue Weather Bureau data. Following results were obtained. Forest evapotranspiration ((c-Tf)) and normalized evapotranspiration of forests ((c-Tf))/NVI are mainly controled by the canopy temperature and the product u(qs-q) of the wind velocity u and the saturation deficit (qs-q). Solar radiation and air temperature are not governing factors for evapotranspiration of forests on nine days of fine weather condition.
Monthly mean air temperature has influence on evapotranspiration and vegetation growth, and plays an essential part of hydrological process on the land surface. The target of this study is to estimate the monthly mean air temperature over the Asian Continent where meteorological observing stations are quite few. NOAA-11 AVHRR has split window channels at 10.2-11.5, μm and 11.3-12.5 μm. These data are used for the atmospheric correction for estimating not only the sea surface temperature, but also the land surface temperature. Though the split window method also gives good estimations for deriving monthly mean air temperature during April to October in the case study, the appropriate estimation is not obtained through a year. The reasons are considered that the emissivity on the land surface and the relation between the derived brightness temperatures and air temperature change with a season, respectively. Therefore, a new type of the regression equation is proposed in this paper. The equation is consisted of explanatory variables which are split window channels data and a cosine of the zenith distance related to extra-terrestrial radiation. The equation is powerful for estimating the monthly mean air temperature through a year. The regression analyses were carried out at 37 stations for conventinal meteorological observation over the Asian Continent. As a result, many fine regression equations were obtained. Multiple correlation coefficients of derived regression equations were more than 0.95 and standard deviation errors are within 2 degrees Celsius.
A new technique to classify remotely sensed data was developed on the basis of the fuzzy theory. The classification was conducted to the cells which consist of some hundreds of pixels. A membership function was derived from the histogram, i.e. frequencies of the occurrence of each of the digital numbers (DN) within a cell, since the hitogram is a probability function itself with regard to each of DN. Similarity, distance and likelihood between cells were defined using the membership function. A supervised classification was carried out by applying this tequnique to LANDSAT TM data. An unsupervised classification was also performed using this technique with a merging process of cells to obtain clusters. The classification results were displayed taking the 1st and the 2nd largest likelihoods into account.
In the JAROS/NASA JERS-1 Simulation Project, the NASA/JPL DC-8 SAR (AIRSAR) was flown over six test sites in the U.S.A. The AIRSAR operates at three frequencies (L, C, P-band) and four polarization realizations (HH, HV, VH, VV) and also works as a polarimetric SAR. This paper describes feasibility of vegetation analysis with SAR data derived from AIRSAR data analysis. Ten targets with vegetation and without vegetation cover are selected in the calibrated data, Then radar backscatter intensity at three frequencies and like and cross polarizations and polarimetry of these targets are investigated. Radar backcatter at P and L-band is much affected by vegetation. Vegetation indicates a smaller difference of radar backscatter intensity between like and cross polarizations. The difference at C-band is small for all targets with greater than 20-percent vegetation cover. The differences at P and L-band decrease with an increase in vegetation density and height. Using these characteristics, vegetation volume classification was made for three test sites. Polarimetric signatures give us an additional information about scatterers. They allow us to discriminate corn from short grass, which was impossible with only like and cross polarizations of P, L, C-band. Multifrequency polarimetric SAR can be a more useful tool for vegetation analysis than single or multi polarization and multifrequency SAR.
The importance of green plants in cities, especially that of the urban forest is recognized in recent years to provide the peaceful and calm city life. In the urban areas, the forests are not only playing the environment conservation function such as wind arresting, dust prevention and sound isolation, and disaster provention function such as the prevention of landslide disaster, but also playing psychological functions to provide the scenic spots and place of comfort to the citizens. However, the urban forest is in declining trend along with the expansion of urban area. From these point of views, it will be quite important to grasp the present state and characteristics of forest in the cities for the urban planning and open space planning in the future. In this study, we tried to grasp the present state and characteristics of the urban forests by the overlay processing of raster data and vector data, taking the forests in Kokubunji-City as an example. We extracted the forest areas from aerial color photograph, overlaid there on the forest areas obtained from each kind of geographical imformation, and eliminated the errors by automatic classification. Next, these raster data were converted into vector data; and we grasped the distribution characteristics of forest by overlaying the vector data with the geographical information such as topographic conditions and land use classifications. We obtained the following results from this study. (1) By applying the present study method, it has become possible to obtain forest information with attributes which are necessary for the open space planning from the "green cover" extracted from the remote sensing data. (2) We were able to grasp the characteristics and functions of the forests in urban area by overlaying the forest areas thus obtained and natural and social factors which characterize these forests.