Modeling vegetation complexity through remote sensing: key concepts and alternative approaches

In this paper, we review the role of remote sensing in the study of vegetation structure and its complexity. Starting from the definitions of vegetation structure and structural complexity, we first analyze concepts related to the application of remote sensing in ecosystem studies. Next, we review the physical foundations of remote sensing, the different types of resolution (spatial, spectral, and temporal) involved in this type of research, and the influence of the instantaneous conditions inherent in data acquisition processes. Additionally, we explore the use of indices that synthesize the information contained in different bands, both those that have been used for many years and others that have been recently developed. The previous sections summarize the knowledge underlying the process of modeling vegetation attributes using remote sensing inputs. In the final section of this paper, we review the two main modeling approaches, namely physical and empirical, contrasting their characteristics, scope, and limitations. Although historically conceived as alternative approaches, there is now a growing trend toward their integration, giving rise to a novel approach known as hybrid modeling. This integration represents a promising strategy that optimizes ecosystem assessment and monitoring, ensuring a balance between efficacy and accuracy in remote sensing-based studies.