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国土资源遥感  2020, Vol. 32 Issue (4): 163-171    DOI: 10.6046/gtzyyg.2020.04.21
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
利用SVM分类Landsat影像的朝鲜主要城市建设用地时空特征分析
王小龙1,2,3(), 闫浩文1,2,3(), 周亮1,2,3, 张黎明1,2,3, 党雪薇1,2,3
1.兰州交通大学测绘与地理信息学院,兰州 730070
2.地理国情监测技术应用国家地方联合工程研究中心,兰州 730070
3.甘肃省地理国情监测工程实验室,兰州 730070
Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea
WANG Xiaolong1,2,3(), YAN Haowen1,2,3(), ZHOU Liang1,2,3, ZHANG Liming1,2,3, DANG Xuewei1,2,3
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
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摘要 

朝鲜主要城市建设用地在广域时空尺度上的变化研究几乎处于空白,为弥补这一空白,基于长时间序列的Landsat TM/ETM+/OLI数据,采用面向对象的支持向量机(support vector machine,SVM)分类方法,提取1990—2018年间朝鲜6个主要城市的建设用地,并结合景观格局指数以及年增长与年增长率定量分析建设用地变化。研究结果表明,基于面向对象的SVM方法能够有效提取建设用地,平均总精度高于90%,Kappa系数在0.87以上。1990—2018年之间,各个城市的面积扩张达到1.2~1.4倍,且处于持续增长。平壤的年增长达到了1.15 km2,是6个主要城市中增长最多的,而元山的增长率波动幅度较小,最近时期内咸兴的年增长率最大,其值达到2.74%。朝鲜6个主要城市的扩张都集中于地势平坦之处且主城区沿河(海)分布,扩张模式为填充式和飞跃式2种; 总体来看,其城市化进程处于上升期。本研究为朝鲜的生态环境保护和新型城镇扩张奠定基础,同时为朝鲜的相关科学研究提供参考。

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关键词 SVM城市化建设用地景观格局指数朝鲜    
Abstract

The study of the change of main urban construction land that is almost blank in the wide area space-time scale can make up for the blank in the wide area space-time scale in the study area. The construction land of six major cities was extracted by using SVM classification method based on the Landsat TM/ETM+/OLI data of long time series from 1990 to 2018 in the study area. The quantitative analysis was made on the landscape metric as well as annual increase and annual growth rate urban development mode. The results show that the SVM method can effectively extract the construction land, with the average of overall accuracy higher than 90% and Kappa more than 0.87. The area expansion of each urban area had reached 1.2~1.4 times and was growing continuously from 1990 to 2018. The annual growth that the largest among the six cities of Pyongyang has reached 1.15 km2, while the growth rate of Wosan has a small fluctuation range. And the growth rate that the largest among the six cities of Humhang has reached 2.74% in the recent period. The expansion of six cities in the study area is concentrated in the flat terrain,and the main urban area is distributed along the river or the coast, with the expansion mode of filling type and filling type. In general, its urbanization process is on the rise. This study lays the foundation for the ecological environment protection and the urban expansion and provides reference for the relevant scientific research in the study area.

Key wordsSVM    urbanization    construction land    landscape metrics    Democratic People’s Republic of Korea
收稿日期: 2019-12-10      出版日期: 2020-12-23
:  TP79  
基金资助:国家重点研发计划项目“国土资源与生态环境安全常态监测系统研发”(2017YFB0504203);国家自然科学基金项目“干旱区城镇扩张对绿洲耕地多尺度影响与情景模拟”(41961027);甘肃高等学校产业支撑引导项目“地理空间数据数字指纹系统及应用示范”(2019c-04)
通讯作者: 闫浩文
作者简介: 王小龙(1996-),男,硕士研究生,研究方向为地理可视化和空间分析。Email:1724812353@qq.com
引用本文:   
王小龙, 闫浩文, 周亮, 张黎明, 党雪薇. 利用SVM分类Landsat影像的朝鲜主要城市建设用地时空特征分析[J]. 国土资源遥感, 2020, 32(4): 163-171.
WANG Xiaolong, YAN Haowen, ZHOU Liang, ZHANG Liming, DANG Xuewei. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea. Remote Sensing for Land & Resources, 2020, 32(4): 163-171.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.04.21      或      https://www.gtzyyg.com/CN/Y2020/V32/I4/163
Fig.1  研究区域示意图
城市 1990年 2000年 2010年 2018年
清津 TM,1991 ETM+ TM OLI
咸兴 TM ETM+,2002 TM,2011 OLI
江界 TM ETM+ TM OLI
平壤 TM ETM+,2001 TM OLI
沙里院 TM ETM+,2001 TM OLI
元山 TM ETM+ TM OLI
Tab.1  本文采用的主要研究数据
指数 表达式 描述
NP NP=ni
式中nii类型斑块数量
各类型的斑块总数量,本文为建设用地这一类型斑块数量
PD PD=niA(1000)(100)
式中A为景观总面积,m2
表示每个单位面积的斑块数量,便于在不同大小景观之间进行比较
LPI LPI=max(aij)j=1nA(100)
式中aij为斑块ij的面积,m2
表示最大斑块对所有景观的影响程度,表示建设用地在城市中的主导地位
PLAND PLAND=j=1naijA(100) 计算某一斑块类型占整个景观面积的相对比例
LSI LSI=0.25k=1meik*Aor=0.25E*A
式中eik*E*为边缘总长度,m
对调整景观大小的总边缘或边缘密度提供一个标准度量
Tab.2  Fragstats 4.2软件中计算景观指数的详细信息
城市 1990年 2000年 2010年 2018年
OA/% Kappa OA/% Kappa OA/% Kappa OA/% Kappa
清津 92.23 0.88 91.07 0.87 92.15 0.89 92.65 0.88
咸兴 90.20 0.88 92.80 0.89 92.58 0.91 92.88 0.90
江界 90.76 0.89 92.36 0.89 92.20 0.90 91.76 0.90
平壤 91.61 0.90 91.42 0.89 93.08 0.91 93.00 0.91
沙里院 93.46 0.88 91.48 0.88 91.08 0.87 92.99 0.91
元山 93.36 0.90 90.46 0.87 90.39 0.88 93.30 0.90
平均值 91.94 0.89 91.60 0.88 91.91 0.89 92.76 0.90
Tab.3  研究区主要城市分类精度评价
指标 城市 1990—
2000年
2000—
2010年
2010—
2018年
1990—
2018年
平均值
AI/km2 清津 0.02 0.63 0.04 0.17 0.22
咸兴 0.23 0.01 0.58 0.20 0.26
江界 0.07 0.09 0.05 0.51 0.18
平壤 0.01 0.86 1.15 0.03 0.51
沙里院 0.02 0.07 0.04 0.06 0.05
元山 0.08 0.04 0.10 0.06 0.07
AGR/% 清津 0.13 3.18 0.17 0.86 1.09
咸兴 1.30 0.04 2.74 1.02 1.28
江界 1.24 2.89 0.33 1.85 1.58
平壤 3.18 2.97 1.27 1.00 2.11
沙里院 0.03 1.89 2.08 1.08 1.27
元山 0.82 2.31 1.20 0.82 1.29
Tab.4  1990—2018年建设用地的年增长和年增长率
Fig.2  研究区主要城市面积扩张率
Fig.3  1990—2018年研究区主要城市扩张空间分布
Fig.4  1990—2018年清津、咸兴、江界、平壤、沙里院和元山的建设用地景观格局指数
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