• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ecological indicators >Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling
【24h】

Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

机译：使用远程感测的植被指数和随机森林建模评估水坝对河岸和红细胞植被的影响

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

Riparian and deltaic areas exhibit a high biodiversity and offer a number of ecosystem services but are often degraded by human activities. Dams, for example, alter the hydrologic and sediment regimes of rivers and can negatively affect riparian areas and deltas. In order to sustainably manage these ecosystems, it is, therefore, essential to assess and monitor the impacts of dams. To this end, site-assessments and in-situ measurements have commonly been used in the past, but these can be laborious, resource demanding and time consuming. Here, we investigated the impact of three dams on the riparian forest of the Nestos River Delta in Greece by employing multi-temporal satellite data. We assessed the evolution in the values of eight vegetation indices over 27 years, derived from 14 dates of Landsat data. We also employed a modelling approach, using a machine learning Random Forests model, to investigate potential linkages between the observed changes in the indices and a host of climatic and terrestrial predictor variables. Our results show that low density vegetation (0-25%) is more affected by the construction of the dams due to its proximity to anthropogenic influences and the effects of hydrologic regime alteration. In contrast, higher density vegetation cover (50-75%) appears to be largely unaffected, or even improving, due to its proximity to the river, while vegetation with intermediate coverage (25-49%)exhibits no clear trend in the Landsat-derived indices. The Random Forests model found that the most important parameters for the riparian vegetation (based on the Mean Decrease Gini and the Mean Decrease Accuracy) were the distance to the dams, the sea and the river. Our results suggest that management plans of riparian and deltaic areas need to incorporate and take into consideration new innovative management practices and monitoring studies that employ multi-temporal satellite data archives.
机译：河岸和红叶地区具有高生物多样性，并提供了许多生态系统服务，但往往因人类活动而退化。例如，大坝改变了河流的水文和沉积物制度，可以对河岸地区和δ产生负面影响。为了可持续地管理这些生态系统，因此，评估和监控水坝的影响是至关重要的。为此，过去常常使用现场评估和原位测量，但这些可能是费力的，资源要求苛刻和耗时。在这里，我们通过采用多时间卫星数据来调查三个水坝对希腊巢河三角洲河岸森林的影响。我们评估了27年超过27年的八个植被指数的演变，得出了14个Landsat数据。我们还采用了一种使用机器学习随机森林模型的建模方法，调查观察到的指数变化与一系列气候和地面预测因子变量之间的潜在联系。我们的研究结果表明，由于其接近人为影响以及水文制度改变的影响，低密度植被（0-25％）受到坝体的建设的影响。相比之下，较高的密度植被覆盖（50-75％）似乎基本上不受影响，由于其对河流的邻近而甚至改善，而中间覆盖率（25-49％）的植被在Landsat中没有明确趋势 - 派生指数。随机森林模型发现，河岸植被的最重要参数（基于平均减少GINI和平均降低精度）是与水坝，海洋和河流的距离。我们的研究结果表明，河岸和红外地区的管理计划需要纳入并考虑新的创新管理实践和监测研究，雇用多时间卫星数据档案。

著录项

  • 来源
    《Ecological indicators》 |2019年第8期|630-641|共12页
  • 作者

    Zaimes George N.; Gounaridis Dimitrios; Symenonakis Elias;

  • 作者单位

    Eastern Macedonia & Thrace Inst Technol EMaTTech Dept Forestry & Nat Environm Management Lab Management & Control Mountainous Waters 1st Km Drama Microhoriou Drama 66100 Greece|Eastern Macedonia & Thrace Inst Technol EMaTTech UNESCO Chair Con E Ect Conservat & Ecotourism Riparian & Delta Ecosyst 1st Km Drama Microhoriou Drama 66100 Greece;

    Univ Michigan Sch Environm & Sustainabil Urban Sustainabil Res Grp 2544 Dana Bldg 440 Church St Ann Arbor MI 48109 USA;

    Manchester Metropolitan Univ Sch Sci & Environm Manchester M1 5GD Lancs England;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Vegetation alterations; Landsat; Anthropogenic impacts; Riparian forest; Random Forests;

    机译：植被改变;Landsat;人为影响;河岸森林;随机森林;

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