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Crop Field Extraction Method using NDVI and Texture from Landsat TM Images
Shibasaki, Ryosuke,Suzaki, Junichi 대한원격탐사학회 1998 International Symposium on Remote Sensing Vol.14 No.1
Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover and land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize $quot;crop field$quot; in terms of NDVI value and textual information. Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by $quot;scale-space filtering$quot; method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.
A Design Method of a Model-following Control System
Hiroki Shibasaki,Rubiyah Yusof,Yoshihisa Ishida 제어·로봇·시스템학회 2015 International Journal of Control, Automation, and Vol.13 No.4
This paper describes and demonstrates a model-following control system that is based on a switching function where an optimal gain matrix is determined by the LQR method. In our proposed method, the optimal gain matrix is derived such that it does not depend on the plant parameters. Simulation results show various cases including the nominal plant and the plant with a modeling error. The experimental study is also performed using a DC motor. The simulation and experimental results show that the proposed method has superior effectiveness.
CLIMATE CHANGE IMPACT OVER INDIAN AGRICULTURE - A SPATIAL MODELING APPROACH
Priya, Satya,Shibasaki, Ryosuke 대한원격탐사학회 1999 International Symposium on Remote Sensing Vol.15 No.1
The large-scale distribution of crops is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher CO₂ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled CO₂ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.