This paper examines the relationship between the seasonal land cover change and microclimate formed in a local small city of paddy field areas using airborne remote sensing data and CFD (Computational Fluid Dynamics) simulation. The land cover maps for three seasons, the 3D urban district model and the 3D surface temperature images are made using the airborne MSS (Multi-Spectral Scanner) data obtained in each season and GIS data in Tonami city, Toyama prefecture. These data are applied to the boundary conditions for the CFD simulation, and air current and air temperature distributions are simulated for three seasons taking into account the seasonal land cover change. The simulation results are compared with the field measurement results for the microclimates in the site. These results quantitatively indicate that the control of air temperature by the paddy fields changes seasonally as its land cover changes through the year. In the summertime, the cooling effect of the paddy fields and the cool air current from the area contribute to the decrease in air temperature in the urbanized area.
A method has been developed for mapping mangrove forests using two unique characteristics of mangroves. (1) Reflectance of mangrove forests in short-wave-infrared bands is lower than that of non-mangrove vegetation. (2) Mangroves can form forests only in the intertidal zones between the mean and the highest sea levels. A Landsat ETM+ image and digital elevation model were used to map mangrove forests in Iriomote Island, Okinawa, Japan. The delineated extent of mangrove forests agreed fairly well with ground truth data with the producer’s accuracy of 64.2∼80.0 %, the user’s accuracy of 60.8∼84.3 %, and the kappa coefficients of 0.61∼0.79. The method was also applied to Trang Province, Thailand for detecting area changes of mangrove forests using a Landsat TM image of 31 January 1990, an ASTER image of 27 January 2003, and the C-band SRTM DEM. The potential of the method has been shown for investigating area changes of mangrove forests.
The possibility of remote sensing for precision management of grassland resources through the collection of site specific data detailing vegetation and soil conditions, has been advocated during the previous 40 years. Farmers need such information at early growth stages for optimizing fertilizer supply. In this review, we discuss the role of remote sensing in precision grassland management, putting emphasis on advances made in the last two decades. We describe the use of hyperspectral sensors, application of GPS, and the use of newly developed sensing devices that monitor livestock grazing behavior.