地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 657-672.doi: 10.12082/dqxxkx.2022.210449
林偲蔚1,2(), 陈楠1,2,*(
), 刘奇祺1,2, 贺卓文1,2
收稿日期:
2021-08-03
修回日期:
2021-09-07
出版日期:
2022-04-25
发布日期:
2022-06-25
通讯作者:
*陈 楠(1975— ),男,福建厦门人,博士,研究员,主要从事数字地形分析研究。E-mail: chennan@fzu.edu.cn作者简介:
林偲蔚(1996— ),男,福建漳州人,硕士生,主要从数字地形分析研究。E-mail: n195520011@fzu.edu.cn
基金资助:
LIN Siwei1,2(), CHEN Nan1,2,*(
), LIU Qiqi1,2, HE Zhuowen1,2
Received:
2021-08-03
Revised:
2021-09-07
Online:
2022-04-25
Published:
2022-06-25
Contact:
CHEN Nan
Supported by:
摘要:
地貌识别,对于人类建设,地质构造研究,环境治理等相关领域都有着重要意义。传统的基于像素单元或面向对象的地貌识别方法存在局限性。由于流域小单元具有表面形态的完整性,在地貌演化中具有明确的地理意义,基于流域小单元的地貌识别成为了该领域的一个新热点。然而,基于传统地形因子的地貌识别方法使用的因子往往较为单一或者在地学描述上存在重复性,目前尚无针对流域小单元进行空间结构描述和拓扑关系特征量化的地貌识别研究。基于此,本文基于DEM进行水文分析并通过坡谱方法解决了小流域稳定面积难以确定的问题,在黄土高原样区提取了181个稳定小流域。根据复杂网络理论和地貌学原理提出了流域加权复杂网络的概念和相应的8个定量指标用于流域空间结构的模拟和量化描述。最后采用了基于决策树的XGBoost机器学习算法进行地貌识别,实验对于黄土高原主要地貌类型的识别显现出较好的效果,Kappa系数为86.00%,总体精度达到了88.33%。对于地貌形态特征明显的地貌,复杂网络方法其顾及空间结构和拓扑特征的特性导致了其较高的识别性能,精度和召回率都在90%~100%之间。通过与前人的研究进行对比,其识别结果亦呈现出较高的精度,这些都验证了流域加权复杂网络是一种基于流域小单元地貌识别的高精度且有效的方法。
林偲蔚, 陈楠, 刘奇祺, 贺卓文. 基于DEM小流域复杂网络的黄土高原地貌自动识别研究[J]. 地球信息科学学报, 2022, 24(4): 657-672.DOI:10.12082/dqxxkx.2022.210449
LIN Siwei, CHEN Nan, LIU Qiqi, HE Zhuowen. Geomorphological Automatic Recognition of Loess Plateau based on Complex Network of Small Watershed from DEM[J]. Journal of Geo-information Science, 2022, 24(4): 657-672.DOI:10.12082/dqxxkx.2022.210449
表1
流域加权复杂网络定量指标和相应的计算方法
指标分类 | 指标名称 | 公式 | 说明 | 公式编号 |
---|---|---|---|---|
点及其 派生指标 | 点强度 | | | (4) |
网络密度 | | m为网络中实际拥有的连接数;n为网络节点总数 | (5) | |
线及其 派生指标 | 平均路径长度 | | | (6) |
沟谷线数目 | 通过对流域的沟谷进行统计得到 | |||
网络直径 | | | (7) | |
网络及其 派生指标 | 结构熵 | | | (8) |
计盒维数 | | 对于任意一个沟谷网络,采用边长为r的正方形盒子覆盖时,会出现其中一些盒子是包含图形的,而另一些盒子却是空的,若逐渐增大盒子的尺寸,则包含图形的盒子数目就会越来越少。对不同的盒子边长(r=1,2,3,4,…,M),求取覆盖沟谷网络所需的非空盒子数N(r)。在适当的范围内,对r选取一系列不同的值,以lnr为横坐标,lnN(r)为纵坐标,利用最小二乘法对其进行线性回归,对其斜率取负即为沟谷网络的计盒维数 | (9) | |
模块度 | | | (10) |
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