CLOUD DETECTION BASED ON DECISION TREE OVER TIBETAN PLATEAU WITH MODIS DATA

Abstract. Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the effect of the climate change. An unbelievable situation in mapping snow cover is the existence of clouds. Clouds can easily be found in any image from satellite, because clouds are bright and white in the visible wavelengths. But it is not the case when there is snow or ice in the background. It is similar spectral appearance of snow and clouds. Many cloud decision methods are built on decision trees. The decision trees were designed based on empirical studies and simulations. In this paper a classification trees were used to build the decision tree. And then with a great deal repeating scenes coming from the same area the cloud pixel can be replaced by "its" real surface types, such as snow pixel or vegetation or water. The effect of the cloud can be distinguished in the short wave infrared. The results show that most cloud coverage being removed. A validation was carried out for all subsequent steps. It led to the removal of all remaining cloud cover. The results show that the decision tree method performed satisfied.