Predictive Analytics and Remote Sensing for Biodiversity Loss Assessment in Urban Green Zones

Urban green zones are crucial for maintaining ecological balance and biodiversity, as well as enhancing living standards. Still, growing metropolitan areas and land use alterations undermine biodiversity within these zones. The research creates a remote sensing predictive analytics model to analyze and track biodiversity loss in open spaces and urban parks. The model predicts areas of potential hazard using high-resolution satellite images, vegetation indices, species occurrence data, and machine learning techniques. Temporal analysis reveals ecological patterns and drivers that are anthropogenic, influencing species diversity over time. The model also maintains proactive biodiversity loss warning systems, enabling city planners to prioritize conservation efforts. A case study in a fast-urbanizing urban area also illustrates it, where the model is trained and tested on the multi-temporal satellite-derived imagery and field derived species data, which spatially confirms that the model can sufficiently explain spatial patterns in changes over time in biodiversity-key fluctuations, to capture the landscape-ecological processes. The enhanced resilience of urban ecosystems demonstrates the power of informed policy and management strategies possible with data-driven methodologies.