基于DEM小流域复杂网络的黄土高原地貌自动识别研究

doi:10.12082/dqxxkx.2022.210449

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 657-672.doi: 10.12082/dqxxkx.2022.210449

• 地球信息科学理论与方法 • 上一篇下一篇

基于DEM小流域复杂网络的黄土高原地貌自动识别研究

林偲蔚¹^,²(), 陈楠¹^,²^,^*(), 刘奇祺¹^,², 贺卓文¹^,²

1.福州大学空间数据挖掘与信息共享教育部重点实验室,福州 350108
2.福州大学数字中国研究院（福建）,福州 350108

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
^*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:
国家自然科学基金项目(41771423)

Geomorphological Automatic Recognition of Loess Plateau based on Complex Network of Small Watershed from DEM

LIN Siwei¹^,²(), CHEN Nan¹^,²^,^*(), LIU Qiqi¹^,², HE Zhuowen¹^,²

1. Key Lab for Spatial Data Mining and Information Sharing of Education Ministry, Fuzhou University, Fuzhou 350108, China
2. The Academy of Digital China, Fuzhou University, Fuzhou 350108, China

Received:2021-08-03 Revised:2021-09-07 Online:2022-04-25 Published:2022-06-25
Contact: CHEN Nan
Supported by:
National Natural Science Foundation of China(41771423)

摘要/Abstract

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。

关键词: 地貌识别, 流域, 数字地形分析, 黄土高原, 地形特征, DEM

Abstract:

Landform recognition is of great significance to human construction, geological structure research, environmental governance and other related fields. Traditional recognition methodology is mainly based on pixel unit or object-oriented recgnition, which existed limitations. Landform recognition based on the watershed unit has become a new hotspot in this field because of its surface morphology integrity and clear geographical significance. However, the traditional methods of landform recognition based on terrain factors are often simple or repeatable in the geological description, which cannot be used to describe the spatial structure and quantify the topological relationship characteristics of the watershed unit. The slope spectrum method was used to solve the problem that it was difficult to determine the stable area of watershed unit, and 181 small watersheds were extracted through hydrological analysis. Based on the theory of complex network and geomorphology, the concept of watershed weighted complex network and 8 quantitative indexes were put forward to simulate and quantify the spatial structure of the watershed. Finally, XGBoost machine learning algorithm is adopted for landform recognition. XGBoost machine learning algorithm based on decision tree is used for landform recognition. The experiment shows a well performance on the landform recognition of the main landform types on the Loess Plateau, with the Kappa coefficient of 86.00% and the overall accuracy of 88.33%. Compared with the landforms having obvious morphological features, the complex network method considers the characteristics of spatial structure and topological features, resulting in higher recognition accuracy and kappa coefficient of 90%~100%. Compared with previous studies, the recognition results show high accuracy, which verifies that the method based on watershed weighted complex network is an effective method with high accuracy for landform recognition based on watershed.

Key words: landform recognition, watershed, digital terrain analysis, Loess Plateau, terrain feature, DEM

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

图/表 14

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图2

图3

图4

图5

图6

图7

图8

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	$N S i = ∑ j ∈ N i a ij w ij$	$N i$ 是节点i的近邻集合; $w ij$ 为节点i和节点j的连边权重,即为节点i和节点j之间的高程差; $a ij$ 为网络邻接矩阵元素; $S i$ 为节点i的点强度	（4）
点及其派生指标	网络密度	$d (G) = 2 m / [n (n - 1)]$	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	$AL = 1 12 n (n - 1) ∑ i > j d ij$	$n$ 为网络中包含的节点总数; $d ij$ 为节点i和j之间的距离;即2个节点最短路径的边数	（6）
	沟谷线数目		通过对流域的沟谷进行统计得到
	网络直径	$ND = ∑ i, j max (d ij)$	$d ij$ 为2个节点i和j之间的距离	（7）
网络及其派生指标	结构熵	$SE = - ∑ i = 1 n (d i ∑ i = 1 n d i ln H i)$	$SE 是结构熵; H i$ 表示 $i$ 个节点的重要度; $n$ 为网络节点的总数; $d (i)$ 表示第 $i$ 节点的度	（8）
	计盒维数	$d = - lim ln N r ln r$	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	$M = 1 2 m ∑ i, j (A ij - k i k j / (2 m) δ (C i, C j)$	$m$ 为网络的总边数; $k i$ 是节点i的权重和; $A ij$ r表示节点i和j之间的边的权值; $δ (a, b)$ 为令a=b时δ=1,否则δ=0的函数; $C i$ 是节点i的社区。当模块度值为0时,表示网络中没有集聚结构	（10）

表1

表2

图9

表3

表4

图10

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样区	地貌类型	平均坡度/°	稳定面积/km²
神木	风沙-黄土过渡区	9	13.11
绥德	黄土峁状丘陵沟壑区	29	8.60
延川	黄土墚峁状丘陵沟壑区	31	10.13
富县	黄土墚峁状丘陵沟壑区	30	10.25
宜君	黄土残塬区	19	13.65
淳化	黄土塬区	12	34.28

	XGBoost识别结果
	黄土塬	黄土梁	黄土峁	石质山地	风积沙丘	河谷平原	精度/%	召回率/%	F1值
黄土塬	9	0	0	1	0	0	90	90	90
黄土梁	0	9	1	0	0	0	100	90	95
黄土峁	0	0	10	0	0	0	91	100	95.5
石质山地	0	0	0	9	0	1	82	90	86
风积沙丘	0	0	0	0	9	1	81	90	85.5
河谷平原	1	0	0	1	1	7	78	70	74
总体精度/%	88.33
Kappa系数/%	86.00

	XGBoost识别结果
	黄土塬	黄土梁	黄土峁	石质山地	风积沙丘	河谷平原	精度/%	召回率/%	F1值/%
黄土塬	8	0	0	0	1	1	73	80	76.5
黄土梁	0	8	1	1	0	0	80	80	80.0
黄土峁	1	2	7	0	0	0	88	70	79.0
石质山地	0	0	0	8	1	1	80	80	80.0
风沙丘陵	1	0	0	1	7	1	70	70	70.0
河谷平原	1	0	0	0	1	8	73	80	76.5
总体精度/%	76.70
Kappa系数/%	72.04

基于DEM小流域复杂网络的黄土高原地貌自动识别研究

Geomorphological Automatic Recognition of Loess Plateau based on Complex Network of Small Watershed from DEM

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