• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geosciences >How Do Continuous High-Resolution Models of Patchy Seabed Habitats Enhance Classification Schemes?
【24h】

How Do Continuous High-Resolution Models of Patchy Seabed Habitats Enhance Classification Schemes?

机译：斑驳海床生境的连续高分辨率模型如何增强分类方案？

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Predefined classification schemes and fixed geographic scales are often used to simplify and cost-effectively map the spatial complexity of nature. These simplifications can however limit the usefulness of the mapping effort for users who need information across a different range of thematic and spatial resolutions. We demonstrate how substrate and biological information from point samples and photos, combined with continuous multibeam data, can be modeled to predictively map percentage cover conforming with multiple existing classification schemes (i.e., HELCOM HUB; Natura 2000), while also providing high-resolution (5 m) maps of individual substrate and biological components across a 1344 km 2 offshore bank in the Baltic Sea. Data for substrate and epibenthic organisms were obtained from high-resolution photo mosaics, sediment grab samples, legacy data and expert annotations. Environmental variables included pixel and object based metrics at multiple scales (0.5 m–2 km), which improved the accuracy of models. We found that using Boosted Regression Trees (BRTs) to predict continuous models of substrate and biological components provided additional detail for each component without losing accuracy in the classified maps, compared with a thematic model. Results demonstrate the sensitivity of habitat maps to the effects of spatial and thematic resolution and the importance of high-resolution maps to management applications.
机译：预定义的分类方案和固定的地理比例通常用于简化和成本有效地绘制自然的空间复杂性。但是，这些简化可能会限制映射工作对需要跨不同主题和空间分辨率范围的信息的用户的有用性。我们演示了如何对来自点样本和照片的底物和生物学信息以及连续的多束数据进行建模，以预测性地绘制符合多种现有分类方案（即HELCOM HUB; Natura 2000）的覆盖率百分比，同时还提供高分辨率（ 5 m）横跨波罗的海1344 km 2的近岸河岸的单个底物和生物成分的地图。底物和表皮生物的数据来自高分辨率的照片马赛克，沉积物采集样本，遗留数据和专家注释。环境变量包括多个尺度（0.5 m–2 km）的基于像素和对象的度量，从而提高了模型的准确性。我们发现，与主题模型相比，使用增强回归树（BRT）预测底物和生物成分的连续模型可为每种成分提供更多详细信息，而不会降低分类图中的准确性。结果证明了栖息地图对空间和主题分辨率的影响的敏感性，以及高分辨率图对管理应用程序的重要性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号