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首页> 外文学位 >Use of multi-spectral imagery and LiDAR data to quantify compositional and structural characteristics of vegetation in red-cockaded woodpecker ( Picoides borealis) habitat in North Carolina.
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Use of multi-spectral imagery and LiDAR data to quantify compositional and structural characteristics of vegetation in red-cockaded woodpecker ( Picoides borealis) habitat in North Carolina.

机译：利用多光谱图像和LiDAR数据量化北卡罗莱纳州红red啄木鸟（Picoides borealis）生境中植被的组成和结构特征。

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摘要

This study evaluated habitat parameters for the red-cockaded woodpecker (RCW; Picoides borealis) on three tracts in Hoke County, North Carolina. Multi-spectral imagery was used to classify shadow, non-vegetation, herbaceous, hardwoods, and loblolly and longleaf pine trees. Field data were collected for image classification training and validation. Overall classification accuracy for separating hardwood from pine trees, was 80.8%. When separating longleaf (Pinus palustris Mill.) and loblolly (Pinus taeda L.) pine from hardwoods the accuracy was 73.7%. Field-based height/diameter relationships were applied to LiDAR-identified trees to predict diameter classes. Due to differences in management regimes and site conditions, each tract had different majority pine diameter classes. Average height, diameter, basal area, and stem density per plot were reported from matched, unmatched, and total LiDAR trees to field trees. Differences between the height, diameter, basal area, and stem density values occurred between the matched and unmatched LiDAR- and field-identified trees.
机译：这项研究评估了北卡罗来纳州霍克县三个地区的红冠啄木鸟（RCW; Picoidesboalis）的栖息地参数。多光谱图像用于对阴影，非植被，草本，硬木以及火炬树和长叶松树进行分类。收集现场数据以进行图像分类训练和验证。从松树中分离硬木的总分类精度为80.8％。当从硬木中分离长叶（Pinus palustris Mill。）和火炬松（Pinus taeda L.）松树时，准确度为73.7％。将基于场的高度/直径关系应用于LiDAR识别的树木，以预测直径等级。由于管理制度和场地条件的差异，每个道的松树多数直径类别不同。从匹配的，未匹配的和总的LiDAR树到田间树，报告了每个样地的平均高度，直径，基面积和茎密度。匹配和未匹配的LiDAR和田野识别的树木之间出现了高度，直径，基础面积和茎密度值之间的差异。

著录项

  • 作者

    Carney, Joelle Marie.;

  • 作者单位

    Mississippi State University.;

  • 授予单位 Mississippi State University.;
  • 学科 Agriculture Forestry and Wildlife.;Remote Sensing.
  • 学位 M.S.
  • 年度 2009
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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