...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Biogeography >Random Forest characterization of upland vegetation and management burning from aerial imagery
【24h】

Random Forest characterization of upland vegetation and management burning from aerial imagery

机译：航空影像对山地植被的随机森林特征及其管理的影响

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The upland moorlands of Great Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create gamebird habitat and there is concern over whether this is driving long-term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high-resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors. Peak District National Park, England, UK. Colour and infrared aerial photographs were classified into seven dominant land-cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of the vegetation classes and managed burning using detrended correspondence analysis. Classification accuracy was c. 95% and produced a 5-m resolution map for 514 kmpo of moorland. Cover classes were highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt. Random Forest classification of aerial imagery is an efficient method for producing high-resolution maps of upland vegetation. These may be used to monitor long-term changes in vegetation and management burning and infer species-environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.
机译：英国的高地沼泽地形成了具有国际保护意义的独特景观，包括荒地，酸性草原，沼泽沼泽和蕨菜的马赛克。这种景观的大部分通过旋转燃烧来管理，从而形成了野鸟栖息地，并且人们担心这是否会导致高地植被群落的长期变化。然而，高地的不可及性和规模意味着难以监测植被和燃烧方式的变化。我们旨在通过开发将航空影像分类为主要植被覆盖的高分辨率地图的方法来克服这一问题，包括在管理的松鸡沼地上烧伤的分布。峰区国家公园，英国，英国。使用随机森林集成机器学习算法将彩色和红外航空照片分为七个主要的土地覆盖类别。此外，石南花（Calluna vulgaris）进一步分化为生长期，包括新烧的部位。然后，我们分析了植被类别的分布，并使用去趋势对应分析来管理燃烧。分类精度为c。 95％的人制作了514 kmpo的高沼地5米分辨率地图。覆盖类是高度聚集的，在构造高地植被群落的过程中，高程和坡度的强非线性效应以及纵横比和基岩类型的弱效应明显。分类显示了受控燃烧的空间分布，并表明相对陡峭的区域可能燃烧得不成比例。航空影像的随机森林分类是一种产生高地植被的高分辨率地图的有效方法。这些可用于监测植被和管理燃烧的长期变化并推断物种与环境的关系，因此可为在景观尺度上有效保护提供重要工具。

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