Research Issues:
Prior Works:
Environmental analysis, management and modelling require detailed and precise land-use/land-cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image-based and physically-based correction method (Integrated Radiometric Correction (IRC) method) simultaneously considering both the atmospheric and the topographic effects, whilst additionally using the information deduced from the satellite images and 5m resolution DEM data. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method was evaluated visually and statistically. Furthermore, we showed that the IRC method deals with the shortcomings of conventional semi-empirical models and also that most of the well-established atmospheric and/or topographic correction approaches can be considered as formulations of particular case studies of the IRC.
Moreover, we assessed the performance of image-based radiometric correction methods for optical satellite imageries by comparing variants of the traditional semi-empirical C-correction and the physically-based Integrated Radiometric Correction (IRC) methods, and firmly established the superiority of its performance.
The research results constitute an essential contribution to the effective utilization of satellite data, not only for Landsat data but also other optical remote sensing imageries. It should be stressed here that by using the radio-metrically corrected data for discriminating land surface conditions (albedo and land-use/land-cover etc.), more precise environmental analysis and climatic modeling can be accomplished.
Future Works:
The fundamental research area mentioned above is indeed of crucial importance for scholarly study. However, in order to return the research fruits to society, I really wish to deepen my knowledge on application research related with environment analysis and climatic change based on “fundamentals in remote sensing data” and “skills for GIS analysis”.
In this G-COE Program (Initiative 3), my research task is monitoring the state and changes of planted Acacia forests in the south of Sumatra Island using GIS and Satellite Remote Sensing. Through a comprehensive analysis of remotely sensed satellite data, on-site survey data and many other ancillary data, the estimation of the biomass and growth of the forest will be aimed at. Furthermore, the ultimate aim of this research is to provide sound analysis outputs for the decision makers involved in sustainable forest management.