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Efficacy of using remotely sensed data for early detection of invasive plants in the Chihuahuan Desert.

机译:使用遥感数据对奇瓦瓦沙漠中的入侵植物进行早期检测的功效。

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摘要

Invasive plants have the ability to displace native species, disrupt nutrient and fire cycles, and alter the character of the plant community by enhancing additional invasions. Early detection of invasive plants can circumvent negative ecological impacts to communities and ecosystems. This study evaluated the utility of using remotely sensed and presence-only datasets as a tool for the early detection of invasive plants in the Chihuahuan Desert.;Thirteen invasive plant species were selected in Big Bend National Park (BIBE) and Holloman Air Force Base (HAFB). Locations of selected species were obtained from BIBE and HAFB and during field surveys conducted in 2006--2007. Predictive habitat models were created using a presence-only data modeling approach. Seven Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) datasets representing three seasons (fall, summer, spring) and five years (1999--2003) of spectral data were used for analyses on BIBE. Quickbird imagery was used on HAFB, and corresponded to the season (spring/summer 2007) of field data collection. Spectral bands, vegetation indices, digital elevation model, and land cover map represented environmental variables. Risk assessments were conducted by incorporating locations of known plants, suitable habitats, and vectors and pathways. Model performance was evaluated using the area under curve (AUC) metric from receiver operating characteristic curves (threshold independent), and the testing omission rate (threshold dependent).;All plant predictive habitat models had high discriminate ability (AUC > 0.90) to differentiate suitable invasive plant habitat from random background locations. Predictive models yielded omission rates ≤ 19%, but were typically 5--15%. Quickbird imagery may be more desirable for invasive plant habitat modeling as habitat models derived from Quickbird imagery yielded fewer errors of omission than Landsat 7 ETM+ derived models.;Habitat models and risk analysis were an effective approach to assess landscapes for the potential of invasive plant incursion. Models can inform managers regarding the potential introduction of invasive species and allocation of resources for invasive plant control. In addition to prioritizing areas to monitor or control invasive plants, spatial models can be used to evaluate the effects of invasive species occurrences on Threatened and Endangered species, species of concern, recreational activities, and management actions.
机译:入侵植物具有取代本地物种,破坏养分和火循环以及通过增强其他入侵来改变植物群落特征的能力。早期发现入侵植物可以避免对社区和生态系统的负面生态影响。这项研究评估了使用遥感数据和仅存在数据集作为早期发现奇瓦瓦沙漠入侵植物的工具的实用性;在大弯国家公园(BIBE)和Holloman空军基地选择了13种入侵植物物种( HAFB)。选定物种的位置是从BIBE和HAFB以及2006--2007年进行的实地调查中获得的。使用仅存在数据建模方法创建了预测性栖息地模型。使用七个代表三个季节(秋季,夏季,春季)和五年(1999--2003年)光谱数据的Landsat增强型专题制图仪增强版(Landsat 7 ETM +)数据集进行BIBE分析。在HAFB上使用了快鸟图像,它对应于现场数据收集的季节(2007年春/夏)。光谱带,植被指数,数字高程模型和土地覆盖图代表环境变量。风险评估是通过结合已知植物的位置,合适的栖息地以及媒介和途径进行的。使用接收器工作特征曲线下的曲线下面积(AUC)度量标准来评估模型性能(阈值独立),测试遗漏率(阈值相关).;所有植物预测生境模型具有很高的区分能力(AUC> 0.90)来自随机背景位置的合适入侵植物栖息地。预测模型的遗漏率≤19%,但通常为5--15%。 Quickbird影像可能更适合于侵入性植物栖息地建模,因为从Quickbird影像获得的栖息地模型比Landsat 7 ETM +衍生的模型产生的遗漏错误更少。人居模型和风险分析是评估景观是否具有侵入性植物入侵潜力的有效方法。 。这些模型可以为管理人员提供有关入侵物种的潜在引进和入侵植物控制资源分配的信息。除了优先监视或控制入侵植物的区域外,空间模型还可用于评估入侵物种的出现对受威胁和濒危物种,相关物种,娱乐活动和管理行为的影响。

著录项

  • 作者

    Young, Kendal Eugene.;

  • 作者单位

    New Mexico State University.;

  • 授予单位 New Mexico State University.;
  • 学科 Natural Resource Management.;Agriculture Range Management.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 348 p.
  • 总页数 348
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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