Fractal Analysis and Remote Sensing Data Processing: From Paradigm to Practice

This paper provides an overview of the current state and prospects for applying fractal analysis to Earth remote sensing (ERS) data processing. It examines the evolution of the scientific paradigm–the transition from the concept of smooth, differentiable functions to a fractal paradigm that describes complex natural forms. The theoretical foundations of the fractal approach are analyzed in detail, including the concepts of fractal dimension, self-similarity, and anomalous stochastic processes. Particular attention is paid to the methodology for calculating the fractal dimension of natural objects using ERS data: the variogram method, the Box Counting algorithm, and triangulation methods. Applied aspects of fractal analysis for land cover classification, including polarimetric radar data and multispectral optical imagery, are also considered. The relationship between fractal characteristics, forest inventory parameters, and underlying surface structural features is analyzed. Further development prospects are discussed, including multifractal analysis and consideration of distorting factors during surveying