A Multicriteria Approach to Selecting Methods for Multispectral Earth Remote Sensing Data Analysis

Abstract

This article presents a novel approach utilizing qualimetry methods for selecting models and polymodel complexes to automate the process of calculating Earth remote sensing (ERS) data, particularly in the context of analyzing complex natural and technical systems. The proposed methodology is applied to the task of selecting calculation methods for forest sustainability indicators. In scenarios where multiple alternative methods and models can be applied at each stage of data processing, the approach employs multicriteria comparative analysis based on a set of key indicators. These indicators include cost, efficiency (calculation duration), and accuracy (the quality of the calculation result). The solution algorithm is demonstrated through the selection of a method to assess the consequences of forest fires. The results are presented in a table, allowing users to assess the trade-offs between different methods based on partial indicators. This algorithmic approach facilitates the automation of the selection process, simplifying the application of complex ERS data processing methods for end-users. Additionally, the approach expands the potential for scaling ERS data results from smaller to larger forest areas, offering greater flexibility and applicability.