基于Landsat影像的柴达木盆地湖泊提取方法
收稿日期: 2021-08-30
修回日期: 2021-10-06
网络出版日期: 2022-05-30
基金资助
河南省科技攻关项目(212102310389);旱区地下水文与生态效应教育部重点实验室开放基金项目(2014G1502022);河南理工大学博士基金项目(B2020-54)
An automatic method for delineating lake surfaces in Qaidam Basin using Landsat images
Received date: 2021-08-30
Revised date: 2021-10-06
Online published: 2022-05-30
柴达木盆地内的湖泊对维系区域生态平衡、满足生产及生活用水、保护生态环境具有重要的作用。随着区域气候变化和人类活动影响的增强,盆地内湖泊格局发生了一系列变化,为了分析湖泊变化特征,查明变化原因,以快速、精确、适用性强为目标,基于Landsat系列遥感影像,通过分析可鲁克湖流域湖泊水体TOA（Top-of-atmosphere）反射率与其他地物TOA反射率的差异,提出了一种湖泊水体自动提取方法——湖泊水体差分模型。利用该模型处理了柴达木盆地的近百个Landsat图像场景,提取了不同时间节点、不同空间位置的湖泊水体,使用总体分类精度、Kappa系数及用户精度对其精度进行评价,并与NDWI（Normalized Difference Water Index）、MNDWI（Modified Normalized Difference Water Index）方法对该地区湖泊水体提取结果进行了比较分析。结果表明：（1） 利用TOA反射率差异,可区分目标与非目标地物;（2） 基于稳定的阈值,湖泊水体差分模型可实现湖泊水体信息快速提取,与NDWI、MNDWI等水体信息提取方法相比,能够更加有效抑制地表河流、冰雪、阴影、沼泽湿地等干扰因素,在模型应用的区域内,平均总体分类精度与用户精度均达到99%以上,Kappa达到0.9877;（3） 湖泊水体差分模型的输入数据既可以是TOA反射率,也可以是Landsat（Level-2）的地表反射率数据;（4） 湖泊水体差分模型适用于柴达木盆地内大范围区域的湖泊水体提取,能够为湖泊水体动态变化规律研究提供技术支持。
文广超,李兴,吴冰洁,王晓鹤,谢洪波 . 基于Landsat影像的柴达木盆地湖泊提取方法[J]. 干旱区研究, 2022 , 39(3) : 774 -786 . DOI: 10.13866/j.azr.2022.03.11
Lakes in the Qaidam Basin maintain regional ecological balance, supply production and domestic water consumption, and protect the ecological environment. These lakes have undergone a series of changes in response to enhanced regional climate change and human activities. To analyze characteristics of lake changes and investigate casual factors, an automatic method for lake surface delineation based on Landsat remote sensing images with a different series of sensors (the Lake Water Differential Model) was proposed by understanding TOA (top-of-atmosphere) reflectance differences between water and non-water surfaces in the Koruk Lake Basin. This model is applied to extract lake information at varying times or locations. Model performance is evaluated using overall accuracy, the Kappa coefficient, and user’s accuracy, and the derived lake surfaces were compared to those from the NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) methods. These results show the following: (1) target and non-target objects are distinguished by a TOA reflectance difference. (2) Based on a stable threshold, the Lake Water Differential Model can delineate lake surfaces. Compared with the NDWI, MNDWI, and other water body information extraction methods, the Lake Water Differential Model can more effectively suppress interference factors, such as surface rivers, ice, snow, shadows, swamps, and wetlands. In areas of model application, our proposed method can achieve a performance of 99.48% of the average OA, 99.66% of user accuracy, and 0.9877 of Kappa. (3) Input data of the model can be either TOA or Landsat (level-2) surface reflectance data. (4) The model is suitable for extracting lake water information in a large area of Qaidam Basin and provides technical support for the study of lake surface dynamics.
Key words： Landsat; lake; TOA reflectance; lake water differential model; Qaidam Basin
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