LAUSR

Abstract Fuzziness is an inherent property of geographical phenomena and the processes of data acquisition, processing, and analysis often introduce uncertainty. Existing methods predominantly use fuzzy set (FS) theory to capture the fuzziness of geographical phenomena as fuzzy spatial objects. However, this approach has a conceptual confusion regarding fuzziness, uncertainty, and vagueness, and the membership degree is expressed using accurate values that ignore uncertainty. Furthermore, FS-based methods lack a vague temporal descriptor. Herein, a vague-spatiotemporal-object model based on the interval type-2 FS theory is proposed to express the vagueness of spatiotemporal objects. To verify the feasibility and superiority of the proposed method, the fuzzy and vague clustering algorithm was used to classify the vegetation cover types on Poyang Lake Plain, China. Furthermore, the classification accuracy was validated via field investigations, and its ability to identify the wet season of the area was verified via the annual vague water area changes of Poyang Lake. The results indicate that, compared with the spatial object model based on FSs, the proposed method increases the ability to measure membership error and express spatiotemporal vagueness.