A Universal Method for Quantitatively Measuring Land Surface Anomaly Intensity Using Multiscale Remote Sensing Features

Land surface anomalies refer to various activities on the Earth’s surface that consist of short-term and sudden changes due to external disturbances. These anomalies are closely related to the safety of human life and property. Remote sensing offers irreplaceable advantages such as broad coverage, high temporal dynamics, and comprehensive observations, so it is the most effective tool for monitoring land surface anomalies and measuring their intensities. However, existing studies have limitations such as unclear sensitivity features, uncertain applicability, and a lack of quantitative expression at different scales. Therefore, this study develops a quantitative assessment framework for land surface anomaly intensity across four scales: the pixel scale, structure scale, object scale, and scene scale. This framework enables an adaptive and flexible weight determination of the intensity of land surface anomalies from a satellite perspective. Using the Chongqing fire as an example of a land surface anomaly, this study evaluates its land surface anomaly intensity. Moreover, we demonstrate the method’s applicability to other land surface anomaly events, such as floods and earthquakes. The experiments reveal that the land surface anomaly intensity evaluation framework, which is constructed based on pixel-scale, structure-scale, object-scale, and scene-scale features, can quantitatively express the land surface anomaly intensity with an accuracy of 75.25% and more effectively represent severely affected areas. The weights of the features at the four scales sequentially decrease: structure scale (0.2974), pixel scale (0.3225), object scale (0.1867), and scene scale (0.1932). The extensive application of this method to other land surface anomaly events provides accurate quantitative expressions of the land surface anomaly intensity. This remote sensing-based multiscale feature assessment method is adaptable and applicable to various land surface anomalies and offers critical decision support for land surface anomaly intensity warning systems.