Quantitative remote sensing research on the vegetation 3-D visual simulation based on object oriented technique

Abstract

In the field of remote sensing, it is important to understand interaction between light and vegetation. The interrelation of them has been addressed in many works, and many different radiant models of vegetation have been proposed, such as: geometrical optical models, turbid medium models, hybrid models and computer simulation models. With developing of quantitative remote sensing research, computer simulation models, for example, Monte Carlo simulation model and Radiosity show their importance in analyzing the experimental data. In order to continue calculating the reflectivity from the vegetation by using a computer simulation model, it is essential to build the 3D structure of the vegetation. Therefore, many 3D structure data and optical parameters about the real winter wheat were measured firstly, i.e. height of stem, positions and sizes of the leaves, distributions on the field of wheat. Because these data are numerous and discrete, it is very difficult to simulate the virtual scene with them directly. To cope with it, we arranged all data and parameters in several layers based on the object oriented technique. Moreover, in order to simplify and deduce the structural variables that will be applied to build the 3D visual winter wheat model, we analyzed experimental data statistically in the process of realistic structural model. Several geometric and logical relations about structural variables were developed subsequently, and some variables varying with season were summarized to get the simple regulation with the purpose of simulating growing process of the winter wheat. The extended Lindenmayer system (L-system) method is then used to simulate the virtual scene of winter wheat by giving a few structural variables simplified before. Once the simulation is correct, scattering and reflectance from the 3D structural scene can be calculated using the Monte Carlo simulation model or Radiosity and so on. Our results show that (a) our lighting simulation system efficiently provides the required information at the desired level of accuracy, and (b) the plant growth model is extremely well calibrated against real plants. Furthermore, the method and the relations developed in this paper can be used in other subjects, such as computer graphics.