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首页> 外文期刊>International journal of remote sensing >Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information
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Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information

机译：利用ALOS PALSAR-，RADARSAT-2和激光雷达衍生的信息来区分高山地区的植被类型

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摘要

Natural vegetation monitoring in the alpine mountain range is a priority in the European Union in view of climate change effects. Many potential monitoring tools, based on advanced remote sensing sensors, are still not fully integrated in operational activities, such as those exploiting very high-resolution synthetic aperture radar (SAR) or light detection and ranging (lidar) data. Their testing is important for possible incorporation in routine monitoring and to increase the quantity and quality of environmental information. In this study the potential of ALOS PALSAR and RADARSAT-2 SAR scenes' synergic use for discrimination of different vegetation types was tested in an alpine heterogeneous and fragmented landscape. The integration of a lidar-based canopy height model (CHM) with SAR data was also tested. A SPOT image was used as a benchmark to evaluate the results obtained with different input data. Discrimination of vegetation types was performed with maximum likelihood classification and neural networks. Six tested data combinations obtained more than 85% overall accuracy, and the most complex input which integrates the two SARs with lidar CHM outperformed the result based on SPOT. Neural network algorithms provided the best results. This study highlights the advantages of integrating SAR sensors with lidar CHM for vegetation monitoring in a changing environment.
机译：考虑到气候变化的影响，对高山山脉进行自然植被监测是欧洲联盟的优先事项。许多基于高级遥感传感器的潜在监视工具仍未完全集成到操作活动中，例如那些利用超高分辨率合成孔径雷达（SAR）或光检测和测距（激光）数据的工具。他们的测试对于可能纳入常规监测并增加环境信息的数量和质量非常重要。在这项研究中，在高山异质和破碎的景观中测试了ALOS PALSAR和RADARSAT-2 SAR场景协同鉴别不同植被类型的潜力。还测试了基于激光雷达的机盖高度模型（CHM）与SAR数据的集成。 SPOT图像用作基准，以评估使用不同输入数据获得的结果。通过最大似然分类和神经网络对植被类型进行区分。六个测试数据组合获得了超过85％的整体精度，最复杂的输入将两个SAR与激光雷达CHM集成在一起，其性能优于基于SPOT的结果。神经网络算法提供了最佳结果。这项研究突出了将SAR传感器与激光雷达CHM集成在一起的优势，可以在不断变化的环境中进行植被监测。

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  • 来源
    《International journal of remote sensing》 |2013年第20期|6898-6913|共16页
  • 作者

    Gaia Vaglio Laurin; Fabio Del Frate; Luca Pasolli; Claudia Notamicola; Leila Guerriero; Riccardo Valentini;

  • 作者单位

    Department of Civil Engineering and Computer Science Engineering, Tor Vergata University of Rome 00133, Italy,CMCC - Centra Euro-Mediterraneo per i Cambiamenti Climatici (Euro-Mediterranean Center for Climate Change), via Augusto Imperatore, Lecce 73100, Italy;

    Department of Civil Engineering and Computer Science Engineering, Tor Vergata University of Rome 00133, Italy;

    EURAC Research Institute for Applied Remote Sensing, Viale Druso, 1 I-39100 Bolzano, Italy;

    EURAC Research Institute for Applied Remote Sensing, Viale Druso, 1 I-39100 Bolzano, Italy;

    Department of Civil Engineering and Computer Science Engineering, Tor Vergata University of Rome 00133, Italy;

    Department of Forest Resources and Environment, University of Tuscia, Viterbo I-01100, Italy,CMCC - Centra Euro-Mediterraneo per i Cambiamenti Climatici (Euro-Mediterranean Center for Climate Change), via Augusto Imperatore, Lecce 73100, Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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