Document Type

Other

Publication Date

2-2025

Abstract

The spread of invasive species is a concrete problem, threatening biodiversity, ecosystem integrity, and the functional status of wetlands. As these species continue to advance, it is crucial to assess their spread as well as management success through the more sophisticated invasive monitoring methodologies. This research examines the change in vegetation communities and the spread of invasive plants in the Water Conservation Area 3A (WCA-3A) using Object-Based Image Analysis (OBIA) and Support Vector Machine (SVM) classifiers on time series Landsat images. Unlike traditional pixel-based methods, OBIA enhances classification accuracy by incorporating spatial, spectral, and contextual information, enabling precise differentiation between native vegetation (sawgrass, open marsh, shrubland) and invasive species (Cattail). The study area, WCA-3A, is a critical component of the Florida Everglades, a globally significant wetland undergoing extensive restoration efforts, making it imperative to assess whether these initiatives effectively control invasive species and restore native plant communities.

By integrating Markov Chain Analysis, this research further projects future vegetation dynamics, providing data-driven insights for ecosystem management and conservation planning. The findings will inform wetland restoration strategies at the regional and state levels, particularly in guiding the allocation of resources within the Comprehensive Everglades Restoration Plan (CERP). Additionally, the methodological framework developed in this study can be adapted for other wetland ecosystems worldwide, offering a scalable approach to invasive species monitoring and long-term environmental sustainability.

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