CLASSIFICATION AND IDENTIFICATION OF ARTIFICIAL LAKES BASED ON NATIONAL GEOGRAPHIC-CONDITIONS DATA

Abstract. In recent years, the issue of violations or over-exploitation in artificial lake projects have attracted gradual concern. In the initial stage, in order to determine whether there are violations or over-exploitation in the artificial lake project, it is necessary to accurately identify the status of artificial lakes. Because of the relative lack of water resources along the Yellow River, issues such as the contradiction between supply and demand as well as the protection of ecological environment have become increasingly prominent. Besides, the situation of development of artificial lakes along the Yellow River has become serious, increasing the pressure of utilizing water resources along the Yellow River. Therefore, the identification of status of artificial lake is of great significance in ecological supervision of the Yellow River.

Under this context, this paper takes the Yellow River as the research area. Based on the land cover data in the multi-temporal geographic national conditions, using spatial analysis, automatic identification and manual differentiation etc., to develop a rapid classification system of artificial lakes to classify the artificial lakes along the Yellow River, tracking and analyzing its annual changes, and finally establishing an artificial lake classification database and an artificial lake change database. Realizing the effective identification and supervision of changes in the artificial lake. Through experiments and analysis of existing data, this paper summarizes a set of indicators for determining the characteristics of artificial lakes, and proposes a set of effective artificial lake identification approaches. It provides an important basis for the follow-up supervision of artificial lake, and also establishes a basic framework for identifying other surface geographic-conditions, which has important theoretical and practical significance in the application of national geographic-conditions data.