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Use of remotely sensed imagery to map Sudden Oak Death (Phytophthora ramorum) in the Santa Cruz Mountains.

机译:使用遥感图像绘制圣克鲁斯山中的突然橡树死亡(Phytophthora ramorum)。

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摘要

This project sought a method to map Sudden Oak Death distribution in the Santa Cruz Mountains of California, a coastal mountain range and one of the locations where this disease was first observed. The project researched a method to identify forest affected by SOD using 30 m multi-spectral Landsat satellite imagery to classify tree mortality at the canopy-level throughout the study area, and applied that method to a time series of data to show pattern of spread. A successful methodology would be of interest to scientists trying to identify areas which escaped disease contagion, environmentalists attempting to quantify damage, and land managers evaluating the health of their forests. The more we can learn about the disease, the more chance we have to prevent further spread and damage to existing wild lands.;The primary data source for this research was springtime Landsat Climate Data Record surface reflectance data. Non-forest areas were masked out using data produced by the National Land Cover Database and supplemental land cover classification from the Landsat 2011 Climate Data Record image. Areas with other known causes of tree death, as identified by Fire and Resource Assessment Program fire perimeter polygons, and US Department of Agriculture Forest Health Monitoring Program Aerial Detection Survey polygons, were also masked out. Within the remaining forested study area, manually-created points were classified based on the land cover contained by the corresponding Landsat 2011 pixel. These were used to extract value ranges from the Landsat bands and calculated vegetation indices. The range and index which best differentiated healthy from dead trees, SWIR/NIR, was applied to each Landsat scene in the time series to map tree mortality. Results Validation Points, classified using Google Earth high-resolution aerial imagery, were created to evaluate the accuracy of the mapping methodology for the 2011 data.
机译:该项目寻求一种方法来绘制加利福尼亚圣克鲁斯山脉,沿海山脉以及首次发现这种疾病的地点之一的突然橡树死亡分布图。该项目研究了一种方法,该方法使用30 m多光谱Landsat卫星图像来识别受SOD影响的森林,从而在整个研究区域的冠层水平上对树木死亡率进行分类,并将该方法应用于时间序列数据以显示扩散模式。一个成功的方法论将对试图找出逃脱疾病蔓延的地区的科学家,试图量化破坏的环保主义者以及评估森林健康的土地管理者感兴趣。我们越了解这种疾病,就越有机会防止进一步扩散和破坏现有的野生土地。;该研究的主要数据来源是春季Landsat气候数据记录的地面反射率数据。使用国家土地覆盖数据库产生的数据以及Landsat 2011气候数据记录影像的补充土地覆盖分类,将非森林地区掩盖了。由火灾和资源评估计划的火灾周边多边形和美国农业部森林健康监测计划的空中探测调查多边形所确定的具有其他已知树木死亡原因的区域也被掩盖了。在剩余的森林研究区域内,基于对应的Landsat 2011像素所包含的土地覆盖物对手动创建的点进行分类。这些被用来从Landsat波段中提取值范围并计算植被指数。在时间序列中对每个Landsat场景应用了最能区分健康树和死树的范围和指数,即SWIR / NIR,以绘制树木死亡率。创建了使用Google地球高分辨率航空影像分类的结果验证点,以评估2011年数据的制图方法的准确性。

著录项

  • 作者

    Gillis, Trinka.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Geographic information science and geodesy.;Forestry.;Remote sensing.
  • 学位 M.S.
  • 年度 2014
  • 页码 126 p.
  • 总页数 126
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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