ConservationTwin: A Digital Twin Framework for Dynamic Simulation of Wildlife Habitat Changes and Restoration Planning

Abstract

Habitats for wildlife in various locations around the globe face rapid degradation owing to climate change, land use changes, and human expansion; however, conservation plans still rely extensively on surveys and delayed field analysis. This paper presents ConservationTwin, which is an innovative framework for building a digital twin of the targeted habitat to facilitate real-time monitoring, prediction of future scenarios, and estimation of the restoration outcomes. The presented solution includes remote sensing data, data from IoT-enabled sensors, and ecological data that are used to build a virtual representation of the habitat to be updated constantly based on the current status. Unlike conventional GIS-based frameworks, ConservationTwin uses a data/model synchronization module that makes it possible to ensure the evolution of the virtual environment along with the evolution of the habitat. A prototype implementation of the framework was performed using a representative dataset of a habitat to assess the ability of the system to identify trends in habitat degradation and predict the effectiveness of multiple scenarios for habitat restoration. As shown by the obtained results, the proposed approach allows improving the efficiency of restoration decisions compared to conventional modeling techniques.