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Importance of Geospatial Data for Monitoring and Managing Invasive Species for Sustainable Development: A Semantic Review
Invasive species pose significant threats to ecosystems, economies, and human well-being worldwide. Effective monitoring and management of invasive species are essential for mitigating their impacts and promoting sustainable development. An analysis of 617publications on invasive species using geospatial data and technologies revealed the critical role of geospatial analysis in understanding and managing invasive species. Based on the Latent Dirichlet Allocation analysis of the publications on invasive species six main categories of focus research areas were revealed, namely 1) Monitoring, Control and Invasion management; 2) Mapping, predictive modeling, and Climate Change Impact; 3) Risk assessment, and early detection; 4) Citizen Science and Community Engagement; 5) Impact Assessment and Ecological Interactions, and 6) Health and Disease Implications. The geographic concentration of research in the United States, China, Western Europe, and Australia underscores the need for broader global engagement. Predominant methods include remote sensing, aerial imagery, GIS, and spatial analysis, with a notable inclusion of citizen science indicating broader research participation. The focus on high-priority invasive species such as the Water Hyacinth, Zebra Mussel, and Emerald Ash Borer reveals a need for wider attention to other invasive species in scientific literature. The investigated research aligns with achieving several Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 13 (ClimateAction), emphasizing the importance of terrestrial and climate-related studies. Geospatial data utilization offers substantial benefits, including enhanced detection, efficient resource allocation, and informed decision-making. Despite challenges, ongoingadvancements in geospatial technologies and analytical methods promise to further improve capabilities in this field.
Keywords
Spatial Analysis, Satellite Images, SDGs,Machine Learning, Data Democratization, Alien Species
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