测绘通报 ›› 2023, Vol. 0 ›› Issue (8): 7-13.doi: 10.13474/j.cnki.11-2246.2023.0223

• 生态环境动态监测 • 上一篇    下一篇

基于高分卫星数据的藏北高寒湿地监测模型研究——以麦地卡湿地为例

白玛仁增1,2, 益西多吉3, 顿玉多吉1, 边多1,2, 边巴次仁1   

  1. 1. 西藏自治区气候中心, 西藏 拉萨 850000;
    2. 中国气象局成都高原气象研究所拉萨分部, 西藏 拉萨 850000;
    3. 西藏自治区气象局, 西藏 拉萨 850000
  • 收稿日期:2022-10-13 修回日期:2023-05-30 发布日期:2023-09-01
  • 通讯作者: 边多。E-mail:1213098351@qq.com
  • 作者简介:白玛仁增(1994-),男,硕士,工程师,主要从事遥感应用和作物模型研究。E-mail:466797027@qq.com
  • 基金资助:
    第二次青藏高原综合科学考察研究项目(2019QZKK0304;2019QZKK020809;2019QZKK0106);国家自然科学基金(41465006);西藏自治区科技厅重点项目(XZ201703-GA-01)

Research on the monitoring model of alpine wetlands in the northern Tibetan Plateau based on Gaofen satellite data:taking Mcdika Wetlands as an example

Pema Rigzin1,2, Yeshe Dorji3, Dhonyo Dorji1, Bendor1,2, Penpa Tsring1   

  1. 1. Climate Center of Tibet Autonomous Region, Lhasa 850000, China;
    2. Lhasa Branch of Chengdu Plateau Meteorological Research Institute of China Meteorological Administration, Lhasa 850000, China;
    3. Tibet Meteorological Bureau, Lhasa 850000, China
  • Received:2022-10-13 Revised:2023-05-30 Published:2023-09-01

摘要: 藏北高原独特的高寒湿地为西藏乃至全国提供了优势资源和环境,而人类活动和自然因素导致湿地退化越来越严重。本文选取高分一号遥感影像为基础数据,综合考虑湿地光谱特征和纹理特点,结合适当尺度的分割影像技术和各类主要湿地信息识别方法,经多次试验形成分层分类决策树,最终建立适用于藏北高原的基于高分卫星数据的高寒湿地监测模型。该模型能够实现湿地信息自动提取和分类,为湿地退化后的生态恢复研究奠定了基础。利用模型对2021年麦地卡自然保护区湿地进行监测,湿地总面积为319.02 km2,占自然保护区面积的36.26%,其中草本沼泽面积最大,其次分别为湖泊湿地、河流湿地、冰川积雪、泥炭沼泽、洪泛湿地。通过随机生成样点,采用混淆矩阵的方法对监测结果进行精度评价,总分类精度达86.83%,Kappa系数为0.827 5,模型使用效果佳。

关键词: 高寒湿地, 高分卫星数据, 分类, 麦地卡, 监测模型

Abstract: The unique alpine wetlands of the northern Tibetan Plateau provide advantageous resources and environment for Tibet and the whole country. Wetland degradation is becoming more and more serious due to human activities and natural causes. So far, this paper uses GF-1 remote sensing data to conduct multiple experiments to complete the establishment of a hierarchical classification decision tree, comprehensively considers the spectral characteristics and texture characteristics of wetlands, and combines appropriate scale segmentation images and various major wetland information identification methods. Finally, an alpine wetland monitoring model based on high-resolution satellite data is established for the northern Tibetan Plateau. This model can realize automatic extraction and classification of wetland information. The results lay a foundation for the research on wetland degradation and its ecological restoration. Using the model to monitor the wetlands of the Mcdika Nature Reserve in 2021, the total area of wetlands is 319.02 km2, and the wetland area accounts for 36.26% of the total area of nature reserves. The areas of various types of wetlands from large to small are herbaceous swamps, lake wetlands, river wetlands, glacial snow, peat swamps, and floodplain wetlands. By randomly selecting test points and adopting the method of confusion matrix, the monitoring accuracy is evaluated. It is found that the total classification accuracy is 86.83%, and the classification accuracy Kappa coefficient is 0.827 5, so the model has achieved a good use effect.

Key words: alpine wetlands, high-resolution satellite data, classification, Mcdika, monitoring model

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