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首页> 外文期刊>Journal of Applied Remote Sensing >Land cover dynamic change in the Napahai Basin using the optimized random forest model
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Land cover dynamic change in the Napahai Basin using the optimized random forest model

机译：利用优化随机林模型，纳帕佩盆地的土地覆盖动态变化

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摘要

Based on the optimized random forest (ORF) model proposed in our research, data of the Landsat-OLI, Landsat-TM images, and digital elevation model were used by us to obtain the land cover maps during several periods in the Napahai Basin. Model performance testing results show that producer accuracy and kappa coefficient of the ORF approach are 0.916 and 0.903, which are higher than the traditional maximum likelihood method. Furthermore, we analyzed the characteristic of land cover dynamic change and seasonal variation of wetlands landscape in 2002 and 2017, and revealed their driving mechanisms and ecological response process. The conclusions are as follows: (1) Lake area expanded in the past 15 years because of increased glacial runoff generated from global warming. Construction land and farmland replaced wetland areas in the middle and downstream regions. Moreover, due to the limitation of hydrothermal conditions in the canyon, few forests in the northeastern basin transferred to shrubs. (2) Dam, which was constructed in northern Napahai lake, controls water storage and made the lake area stable after 2013, so, area change percentage in 2017 from wet to dry season is lower than 2002. The wetland region in the central and downstream basin was synthetically affected by climate change and drainage projects, so its environment condition becomes dryer, and vegetation communities' succession shows a reversed process. (3) Ecological problems in the Napahai Basin were mainly reflected at aspects of agricultural nonpoint source pollution, overgrazing, and urbanization. These factors affected lake, soil, and groundwater in the Napahai Basin and destroyed the ecological health of wetland landscapes. Therefore, in order to realize sustainable development environmental resources, local government should propose the effective policies to restore the degraded ecosystem and protect natural wetland. (C) 2019 Society of Photo Optical Instrumentation Engineers (SPIE)
机译：根据我们的研究中提出的优化随机森林（ORF）模型，我们使用LANDAT-OLI，LANDSAT-TM图像和数字海拔模型的数据，以便在纳帕佩盆地的几个时期获得陆地覆盖地图。模型性能测试结果表明，ORF方法的生产者准确性和κ系数为0.916和0.903，高于传统的最大似然方法。此外，我们分析了2002年和2017年湿地景观湿地景观的陆地覆盖动态变化和季节变化的特点，揭示了他们的驾驶机制和生态反应过程。结论如下：（1）由于全球变暖产生的冰川径流增加，湖区延长了15年。建筑用地和农田在中下游的湿地地区取代。此外，由于峡谷中水热条件的限制，东北盆地的森林几次转移到灌木上。 （2）在纳帕瓦北部建造的大坝控制储水，并在2013年后使湖泊面积稳定，因此，2017年从潮湿到旱季的区域变化百分比低于2002年。中部和下游的湿地地区盆地综合受气候变化和排水项目的影响，因此其环境状况变为烘干机，植被社区的继承表明了逆转过程。 （3）纳帕佩盆地的生态问题主要反映在农业非点源污染，过度广泛和城市化方面。这些因素受到纳帕佩盆地的湖泊，土壤和地下水，并摧毁了湿地景观的生态健康。因此，为了实现可持续发展的环境资源，地方政府应该提出恢复退化生态系统的有效政策，保护天然湿地。 （c）2019年照片光学仪表工程师（SPIE）

著录项

  • 来源
    《Journal of Applied Remote Sensing》 |2019年第4期|共18页
  • 作者

    Bai Chunyu; He Hongming; Li Yu; He Wenming; Zhao Hongfei; Mokhtar Ali; Keo Soksamnang; Zhang Chuangjuan; Ma Yue; He Qilin;

  • 作者单位

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Chinese Acad Sci Inst Geog Sci &

    Nat Resources Beijing Peoples R China;

    Raying Univ Sch Chem &

    Environm Meizhou Guangdong Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Northwest Agr &

    Forestry Univ Inst Soil &

    Water Conservat State Key Lab Soil Eros &

    Dry Land Farming Loess Yangling Shaanxi Peoples R China;

    Chinese Acad Sci &

    Minist Water Resources Inst Soil &

    Water Conservat Yangling Shaanxi Peoples R China;

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

    remote sensing interpretation; optimized random forest model; land cover; wetland landscape;

    机译：遥感解释;优化随机林模型;陆地覆盖;湿地景观;

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