Monitoramento da biodiversidade em áreas de restauração florestal: uma abordagem integrada de sensoriamento remoto

Abstract

Floristic biodiversity plays a fundamental role in maintaining ecosystem services and enhancing forest resilience against environmental degradation. In the current context of environmental degradation, Remote Sensing (RS) technologies have emerged as effective alternatives for the assessment and monitoring of floristic biodiversity. Remote monitoring represents an innovative and promising methodology. This study discusses the use of RS data for spatial analysis of floristic biodiversity in forest restoration environments. The methodology is based on the use of the biodivMapR package, which utilizes satellite images and data from remote sensors. The method is capable of generating alpha and beta diversity maps to estimate floristic species diversity (alpha) and ecosystem heterogeneity (beta) in a multi-temporal manner. The results showed that this method effectively evaluates alpha and beta diversity in native vegetation environments, specifically in High Conservation Value Areas (AAVCs) owned by Suzano S.A., located on flat terrains. When forest inventory data is used to calibrate the algorithm, a correlation of 0.7 is observed. The optimal parameters for estimating biodiversity were determined to be a window size of 2 and a cluster size of 60, which served as references for measuring alpha and beta diversity in ecological restoration environments. Between 2014 and 2024, ecological restoration areas demonstrated an increase in species diversity, and the monitoring methodology (biodivMapR) proved effective in measuring gains in floristic biodiversity in these environments. However, this study aims to evaluate the effectiveness of the package for monitoring floristic biodiversity in ecological restoration environments.