Item talk:Q269269

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Revision as of 23:58, 21 August 2024 by Sky (talk | contribs) (Created page with "{ "USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Fine-resolution land cover mapping over large and mountainous areas for L\u0101na\u2018i, Hawaii using posterior probabilities, and expert knowledge", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70252213", "url": "htt...")
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   "name": "Fine-resolution land cover mapping over large and mountainous areas for L\u0101na\u2018i, Hawaii using posterior probabilities, and expert knowledge",
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   "abstract": "The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawai\u02bbi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of L\u0101na\u2018i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for L\u0101na\u2018i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on L\u0101na\u2018i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.",
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