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首页> 外文期刊>Environmental earth sciences >Spatiotemporally monitoring forest landscape for giant panda habitat through a high learning-sensitive neural network in Guanyinshan Nature Reserve in the Qinling Mountains, China
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Spatiotemporally monitoring forest landscape for giant panda habitat through a high learning-sensitive neural network in Guanyinshan Nature Reserve in the Qinling Mountains, China

机译：通过高敏感神经网络对秦岭观音山自然保护区的大熊猫栖息地森林景观进行时空监测

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摘要

During the 1970s and 1990s, Guanyinshan Nature Reserve (GNR), a giant panda (Ailuropoda melanoleuca) distribution area historically, had experienced periodic commercial logging. After officially logging stopping in 1998 and converting to a giant panda nature reserve in 2002, GNR got the chance on forest restoration. It is very necessary to monitor the spatiotemporal change of its forest habitat. It is also widely known that it is difficult to make accurate mapping the mountainous area based on images through traditional classification algorithm. So, this study aims to monitor the spatiotemporal change of mountainous habitat in GNR in order to provide proper suggestions for giant panda conservation. The research applied a multilayer perceptron model, a high learning-sensitive algorithm, to classify the land cover types and monitor habitat change in GNR by using Landsat images acquired in 1978, 1988, 1997 and 2007, respectively. Our results showed that: (1) three types of forests composed the main landscape of the GNR, and an increase of 7.7% forest coverage occurred within 30 years. (2) Due to logging, there were many forest clearing-cutting areas in 1997 and swaths of shrub-grass in 1978 and 1988. However, these two types of landscape were strongly reduced by 2007 due to more attention and protection. (3) A decrease in the number of patches, an increase in the mean patch size, and an over-time decreasing in the mean nearest neighbor distance all revealed a decreasing on habitat fragmentation. Therefore, reduction in detrimental human activities has helped enhance and expand giant panda habitat toward a healthier and more stable ecosystem.
机译：在1970年代和1990年代，观音山自然保护区（GNR）历史上是大熊猫（Ailuropoda melanoleuca）的分布地区，曾经历过定期的商业采伐。在1998年正式停止采伐并于2002年转变为大熊猫自然保护区后，GNR获得了恢复森林的机会。监测其森林栖息地的时空变化非常必要。众所周知，通过传统的分类算法难以基于图像对山区进行准确的测绘。因此，本研究旨在监测GNR山区生境的时空变化，为大熊猫保护提供适当的建议。该研究应用了多层感知器模型（一种对学习有高度敏感性的算法），分别使用1978年，1988年，1997年和2007年获得的Landsat图像对GNR的土地覆盖类型进行分类并监测栖息地的变化。我们的结果表明：（1）三种类型的森林构成了GNR的主要景观，在30年内森林覆盖率增加了7.7％。 （2）由于采伐，1997年有许多森林砍伐区，1978年和1988年有许多灌木丛。但是，由于受到更多关注和保护，这两种类型的景观到2007年已大大减少。 （3）斑块数量的减少，斑块平均大小的增加以及平均最近邻居距离的随时间的减小都表明栖息地破碎化的减少。因此，减少有害的人类活动已帮助改善和扩大大熊猫栖息地，使其朝着更健康，更稳定的生态系统发展。

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  • 来源
    《Environmental earth sciences》 |2017年第17期|589.1-589.12|共12页
  • 作者

    Liu Xuehua; Wu Pengfeng; Shao Xiaoming; Songer Melissa; Cai Qiong; Zhu Yun; He Xiangbo;

  • 作者单位

    Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100084, Peoples R China;

    China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China|Shenyang Normal Univ, Coll Life Sci, Shenyang 110034, Liaoning, Peoples R China;

    China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China;

    Smithsonian Conservat Biol Inst, Conservat Ecol Ctr, Front Royal, VA 22630 USA;

    Shaanxi Guanyinshan Nat Reserve, Foping County 723400, Shaanxi, Peoples R China;

    Shaanxi Guanyinshan Nat Reserve, Foping County 723400, Shaanxi, Peoples R China;

    Shaanxi Foping Nat Reserve, Foping County 723400, Shaanxi, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Guanyinshan Nature Reserve (GNR); Multilayer perceptron model (MLP); Forest landscape; Dynamic change; Giant panda habitat;

    机译：观音山自然保护区;多层感知器模型;森林景观;动态变化;大熊猫栖息地;自然保护区;

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