基于随机森林的黄土地貌分类研究

doi:10.12082/dqxxkx.2020.190247

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (3): 452-463.doi: 10.12082/dqxxkx.2020.190247

• “数字地形分析”专栏 • 上一篇下一篇

基于随机森林的黄土地貌分类研究

曹泽涛^1,^2,³, 方子东^1,^2,³, 姚瑾⁴, 熊礼阳^1,^2,^3,^*()

^1. 南京师范大学地理科学学院,南京 210023
^2. 虚拟地理环境教育部重点实验室（南京师范大学）,南京 210023
^3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
^4. 自然资源部第一地理信息制图院,西安 710054

收稿日期:2019-05-23 修回日期:2019-12-09 出版日期:2020-03-25 发布日期:2020-05-18
通讯作者: 熊礼阳 E-mail:xiongliyang@163.com
作者简介:曹泽涛（1997— ）,男,江苏扬州人,硕士生,主要从事研究DEM数字地形分析研究。E-mail:zetao_cao_1997@163.com
基金资助:
国家自然科学基金项目(41601411);国家自然科学基金项目(41671389);江苏高校优势学科建设工程资助项目

Loess Landform Classification based on Random Forest

CAO Zetao^1,^2,³, FANG Zidong^1,^2,³, YAO Jin⁴, XIONG Liyang^1,^2,^3,^*()

^1. School of Geography, Nanjing Normal University, Nanjing 210023, China
^2. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Nanjing 210023, China
^3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
^4. The First Institute of Geoinformation Mapping, Ministry of Natural Resources of the People's Republic of China, Xi'an 710054, China

Received:2019-05-23 Revised:2019-12-09 Online:2020-03-25 Published:2020-05-18
Contact: XIONG Liyang E-mail:xiongliyang@163.com
Supported by:
National Natural Science Foundation of China(41601411);National Natural Science Foundation of China(41671389);Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

地貌分类在指导人类建设活动的规模与布局中有着重要的意义。然而,传统的基于数字高程模型（DEM）的地貌分类方法使用的地形因子和考虑到的地貌特征往往比较单一。本文提出了一种基于流域单元的地貌分类方法,该方法考虑了流域单元的多方面特征,包括基本地形因子统计量、地形特征点线统计量、小流域特征和纹理特征。本研究首先基于DEM进行水文分析将研究区域划分成不同的小流域。然后利用数字地形分析提取29个不同方面的特征来表征流域的形态,并基于随机森林（RF）算法进行了特征选择和参数标定。RF是一种基于决策树算法的集成分类器,能有效地处理高维数据,分类精度高。最后选择训练集小流域对RF分类器进行训练,使用训练完成的分类器对整个研究区域的地貌进行分类,研究地貌分异的规律。该实验在我国陕北黄土高原典型黄土地貌区域的地貌分类中取得了较好的结果,结果表明不同的地貌之间存在明显的区域界线,特定的地貌类型在空间上表现出明显的聚集性。通过人工判读进行验证的分类精度达到了85%,Kappa系数为0.83。

关键词: 地貌, 随机森林, 黄土高原, 地形特征, 特征选择, 地貌分类, DEM, 小流域

Abstract:

Landform classification is one of the most important steps tor eveal the mechanisms of surface matter flows and energy conversion, which could inform the scale and layout of human construction activities. However, traditional landform classification methods based on Digital Elevation Model (DEM) often use a small number of topographical derivatives or landform characteristics, resulting in insufficiently precise classification results. However, object-oriented landform classification performs better in that reliable classification can be achieved by maximizing the homogeneity within and between objects. But how to set conditions in object segmentation remains a challenge. In this paper, a geomorphological classification method based on watershed unitwas proposed, by accounting for many characteristics of watershed unit including statistics of basic topographic factors, feature point and feature line, basin and texture characteristics. Firstly, hydrological analysis based on DEM divided the study area into different small basins as the experimental units. Then, 29 features were extracted within each unit to represent watershed morphology using digital terrain analysis; feature selection and parameter calibration were carried out based on Random Forest (RF) algorithm. RF is a supervised integrated learning model aggregating different outputs of a single decision tree to reduce variances that may lead to classification errors in the decision tree. Finally, the watersheds in training set were selected to train the RF classifier, and the trained classifier was used to classify the landform of the whole study area, based on which we studied the landform spatial differentiation pattern. This experiment achieved good results in the landform classification of the Loess Plateau in northern Shaanxi Province. It is one of the areas with the most serious soil erosion and the most fragile eco-environment in the world. Most of them are covered by thick loess, and the topography is fluctuant. Result shows: (1) Compared with manual interpretation, excellent classification results based on small watershed in the study area were obtained, with the classification accuracy reaching 85% and the Kappa coefficient 0.83. (2)All small watersheds were divided into eight types of landforms. The same type of landforms showed obvious spatial aggregation. There were boundaries and transitional zones between different types of landforms. (3) Different geomorphological regions explained different situations of loess deposition and runoff erosion in different regions. Our findings suggest that the combination of RF algorithm and DEM data can achieve better classification results.

Key words: Landform, random forest, loess plateau, terrain feature, feature selection, geomorphological classification, DEM, watershed

曹泽涛, 方子东, 姚瑾, 熊礼阳. 基于随机森林的黄土地貌分类研究[J]. 地球信息科学学报, 2020, 22(3): 452-463.DOI:10.12082/dqxxkx.2020.190247

CAO Zetao, FANG Zidong, YAO Jin, XIONG Liyang. Loess Landform Classification based on Random Forest[J]. Journal of Geo-information Science, 2020, 22(3): 452-463.DOI:10.12082/dqxxkx.2020.190247

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		RF分类结果
		T1	T2	T2/T1	T3	T4	T4/T3	T5	T6	T7	T8
人工判读结果	T1	13	1	0	0	0	0	0	0	0	0
	T2	0	10	0	0	0	0	0	0	0	0
	T2/T1	1	2	0	0	0	0	0	0	0	0
	T3	0	1	0	12	0	0	0	0	0	0
	T4	0	1	0	0	10	0	0	0	0	0
	T4/T3	0	0	0	3	1	0	0	0	0	0
	T5	0	0	0	0	0	0	13	0	1	0
	T6	0	0	0	0	1	0	2	13	0	0
	T7	0	0	0	0	0	0	1	0	9	0
	T8	0	0	0	0	0	0	0	0	0	5

地貌类型	正确分类数量/个	错误分类数量/个	分类精度/%
T1	13	1	92.9
T2	10	5	66.7
T3	12	3	80.0
T4	10	2	83.3
T5	13	3	81.3
T6	13	0	100.0
T7	9	1	90.0
T8	5	0	100.0
汇总	85	15	85.0

基于随机森林的黄土地貌分类研究

Loess Landform Classification based on Random Forest

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类型	小流域特征	数量/个
基本地形因子统计量	平均高程、高程标准差、平均坡度、坡度标准差、平均起伏度、起伏度标准差、平均切割深度、切割深度标准差、平均平面曲率、平面曲率标准差、平均剖面曲率、剖面曲率标准差、平均坡向、坡向标准差	14
地形特征点线统计量	山顶点密度、山顶点高程标准差、沟沿线密度、沟沿线平均高程、割裂度、沟谷线密度、沟谷线平均高程	7
小流域特征	相对高程差、沟谷深度、面积高程积分、坡谱信息熵	4
纹理特征	纹理对比度、纹理角二阶矩、纹理信息熵、纹理逆差矩	4

地貌类型	样区小流域数量/个
沙丘草滩盆地	30
黄土低丘	30
黄土峁状丘陵沟壑	30
黄土梁状丘陵沟壑	30
黄土残塬丘陵沟壑	30
石质山地	30
黄土塬	16
黄土台塬	14
汇总	210

[1]	赵鹏军, 曹毓书. 基于多源地理大数据与机器学习的地铁乘客出行目的识别方法[J]. 地球信息科学学报, 2020, 22(9): 1753-1765.
[2]	蒲东川, 王桂周, 张兆明, 牛雪峰, 何国金, 龙腾飞, 尹然宇, 江威, 孙嘉悦. 基于独立成分分析和随机森林算法的城镇用地提取研究[J]. 地球信息科学学报, 2020, 22(8): 1597-1606.
[3]	朱守杰, 杜世宏, 李军, 商硕硕, 杜守基. 融合多源空间数据的城镇人口分布估算[J]. 地球信息科学学报, 2020, 22(8): 1607-1616.
[4]	李婉, 牛陆, 陈虹, 吴骅. 基于随机森林算法的地表温度鲁棒降尺度方法[J]. 地球信息科学学报, 2020, 22(8): 1666-1678.
[5]	毛亚萍, 房世峰. 基于机器学习的参考作物蒸散量估算研究[J]. 地球信息科学学报, 2020, 22(8): 1692-1701.
[6]	王学文, 赵庆展, 韩峰, 马永建, 龙翔, 江萍. 机载多光谱影像语义分割模型在农田防护林提取中的应用[J]. 地球信息科学学报, 2020, 22(8): 1702-1713.
[7]	赵青松, 兰安军, 范泽孟, 杨青. 贵州省不同地貌形态类型土壤侵蚀强度变化的定量分析[J]. 地球信息科学学报, 2020, 22(7): 1555-1566.
[8]	罗竹, 刘凯, 张春亢, 邓心远, 马荣华, 宋春桥. DEM在湖泊水文变化研究中的应用进展[J]. 地球信息科学学报, 2020, 22(7): 1510-1521.
[9]	崔成, 任红艳, 赵璐, 庄大方. 基于街景影像多特征融合的广州市越秀区街道空间品质评估[J]. 地球信息科学学报, 2020, 22(6): 1330-1338.
[10]	杨建思, 柳帅, 王艳东, 廖明生. 融合多源大数据的武汉城市空置区域评估与分析[J]. 地球信息科学学报, 2020, 22(5): 997-1007.
[11]	程维明. 现代地貌制图学的发展与展望——纪念陈述彭先生诞辰100周年[J]. 地球信息科学学报, 2020, 22(4): 688-696.
[12]	熊礼阳, 汤国安. 黄土高原沟谷地貌发育演化研究进展与展望[J]. 地球信息科学学报, 2020, 22(4): 816-826.
[13]	李思进, 代文, 熊礼阳, 汤国安. DEM分辨率对黄土侵蚀沟形态特征表达的不确定性分析[J]. 地球信息科学学报, 2020, 22(3): 338-350.
[14]	王春, 徐燕, 江岭, 赵明伟. 规则格网DEM局地坡面凸凹性精度分析[J]. 地球信息科学学报, 2020, 22(3): 361-369.
[15]	赵尚民, 程维明, 蒋经天, 沙文娟. 资源三号卫星DEM数据与全球开放DEM数据的误差对比[J]. 地球信息科学学报, 2020, 22(3): 370-378.