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
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.
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.
Wehrwhein G S. The rural-urban fringe[J]. Economic Geography, 1942,18(3):217-228.
doi: 10.2307/141123
[2]
Adams J S. Residential structure of mid-western cities[J]. Annals of the Association of American Geographers, 1970,60(1):37-62.
doi: 10.1111/j.1467-8306.1970.tb00703.x
[3]
Puertas O L, Henriquez C, Meza F J. Assessing spatial dynamics of urban growth using an integrated land use model.Application in Santiago Metropolitan Area,2010—2045[J]. Land Use Policy, 2014,38:415-425.
doi: 10.1016/j.landusepol.2013.11.024
[4]
Jiao L, Liu J, Xu G, et al. Proximity expansion index:An improved approach to characterize evolution process of urban expansion[J]. Computers,Environment and Urban Systems, 2018,70:102-112.
doi: 10.1016/j.compenvurbsys.2018.02.005
[5]
Alberti M, Waddell P. An integrated urban development and ecological simulation model[J]. Integrated Assessment, 2000,1(3):215-227.
doi: 10.1023/A:1019140101212
[6]
Chen L, Yang X, Chen L, et al. Impact assessment of land use planning driving forces on environment[J]. Environmental Impact Assessment Review, 2015,55:126-135.
Yue W Z, Xue J H, Wu J W, et al. Remote sensing study on spatial pattern of urban old city transformation based on linear spectral analysis:A case study of Shanghai’s central city in 1997—2000[J]. Science Bulletin, 2006,51(8):966-974.
Zhou C L, Xu H Q. Spectral mixture analysis and recognition mapping of impervious surface in Fuzhou City[J]. Journal of Image and Graphics, 2007,12(5):875-881
Xie M M, Wang Y L, Li G C. Spatial differentiation measurement of impervious surface and vegetation cover based on sub-pixel decomposition:A case study of Shenzhen City[J]. Resources Science, 2009,31(2):83-90.
Xu H Q. Quantitative analysis of the relationship between urban impervious surface and related urban ecological factors[J]. Acta Ecologica Sinica, 2009,29(5):2456-2462.
Cao L Q, Li P X, Zhang L P, et al. Estimating impervious surfaces using the Fuzzy ARTMAP[J]. Geomatics and Information Science of Wuhan University, 2012,37(10):1236-1239.
[12]
Xu H Q. Rule-based impervious surface mapping using high spatial resolution imagery[J]. International Journal of Remote Sensing, 2013,34(1):27-44.
[13]
Singh P P, Garg R D. A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces[J]. International Journal of Remote Sensing, 2014,35(24):8074-8107.
Zhang L, Gao Z H, Liao M S, et al. Estimation of urban impervious surface coverage using multi-source remote sensing data[J]. Geomatics and Information Science of Wuhan University, 2010,35(10):1212-1216.
[16]
Pijanowski B C, Brown D G, Shellito B A, et al. Using neural networks and GIS to forecast land use changes:A land transformation model[J]. Computers Environment and Urban Systems, 2002,26(6):553-575.
[17]
Tayyebi A, Pijanowski B C, Tayyebi A H. An urban growth boundary model using neural networks,GIS and radial parameterization:An application to Tehran,Iran[J]. Landscape and Urban Planning, 2011,100(1-2):35-44.
[18]
Tian G, Ma B, Xu X, et al. Simulation of urban expansion and encroachment using cellular automata and multi-agent system model:A case study of Tianjin metropolitan region,China[J]. Ecological Indicators, 2016,70:439-450.
[19]
Mohammady S, Delavar M R. Urban sprawl assessment and modeling using landsat images and GIS[J]. Modeling Earth Systems and Environment, 2016,2(3):155.
[20]
Batty M, Xie Y, Sun Z. Modeling urban dynamics through GIS-based cellular automata[J]. Computers,Environment and Urban Systems, 1999,23(3):205-233.
[21]
Vaz E D N, Nijkamp P, Painho M, et al. A multi-scenario forecast of urban change:A study on urban growth in the Algarve[J]. Landscape and Urban Planning, 2012,104(2):201-211.
[22]
Feng Y, Liu Y, Batty M. Modeling urban growth with GIS based cellular automata and least squares SVM rules:A case study in Qingpu-Songjiang area of Shanghai,China[J]. Stochastic Environmental Research and Risk Assessment, 2016,30(5):1387-1400.
[23]
Shirzadi Babakan A, Taleai M. Impacts of transport development on residence choice of renter households:An agent-based evaluation[J]. Habitat International, 2015,49:275-285.
[24]
Hosseinali F, Alesheikh A A, Nourian F. Agent-based modeling of urban land-use development, case study:Simulating future scenarios of Qazvin City[J]. Cities, 2013,31:105-113.
[25]
Murray-Rust D, Rieser V, Robinson D T, et al. Agent-based modelling of land use dynamics and residential quality of life for future scenarios[J]. Environmental Modelling and Software, 2013,6(46):75-89.
Wang H, Lu S L, Wu B F, et al. Research progress in remote sensing extraction and application of impervious surface[J]. Advances in Earth Science, 2013,28(3):327-336.
[27]
Musa S I, Hashim M, Reba M N M. A review of geospatial-based urban growth models and modelling initiatives[J]. Geocarto International, 2017,32(8):813-833.
[28]
Karimi F, Sultana S, Babakan A S, et al. An enhanced support vector machine model for urban expansion prediction[J]. Computers,Environment and Urban Systems, 2019,75:61-75.
[29]
Munoz-Mari J, Bocolo F, Gmez-Chova L, et al. Semisupervised one-class support vector machines for classification of remote sensing data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(8):3188-3197.
Cui L L, Luo Y, Bao A M. Study on SVM land cover classification method based on NWFE texture features[J]. Remote Sensing for Land and Resources, 2012,24(1):36-42.doi: 10.6046/gtzyyg.2012.01.07.
Deng Z, Li D, Ke Y H, et al. An improved SVM algorithm for high spatial resolution remote sensing image classification[J]. Remote Sensing for Land and Resources, 2016,28(3):12-18.doi: 10.6046/gtzyyg.2016.03.03.
Dong Y L, Yu H, Wang Z M, et al. Analysis of land cover change and driving forces in North Korea from 1990 to 2015[J]. Journal of Natural Resources, 2019,34(2):70-82.
Guan X L, Fang C L, Zhou M, et al. Analysis of spatial and temporal characteristics of urban land use in Wuhan Urban Agglomeration[J]. Journal of Natural Resources, 2012,27(9):1447-1459.
doi: 10.11849/zrzyxb.2012.09.002
[34]
Ridd K M. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing:Comparative anatomy for cities[J]. International Journal of Remote Sensing, 1995,6(12):2165-2185.
[35]
Vapnik V. Pattern recognition using generalized portrait method[J]. Automation and Remote Control, 1963,24:774-780.
[36]
Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers[C]// Proceedings of the Fifth Annual Workshop on Computational Learning Theory.ACM, 1992:144-152.
[37]
Foody G M, Mathur A. A relative evaluation of multiclass image classification by support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(6):1335-1343.
doi: 10.1109/TGRS.2004.827257
Li N, Zhu X F, Pan Y Z, et al. SVM remote sensing image classification optimized by artificial bee colony algorithm[J]. Journal of Remote Sensing, 2018,22(4):29-39.
[39]
Chapelle O, Vapnik V, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002,46(1-3):131-159.
doi: 10.1023/A:1012450327387
[40]
Seto K C, Woodcock C E, Song C, et al. Monitoring land-use change in the Pearl River Delta using Landsat TM[J]. International Journal of Remote Sensing, 2002,23(10):1985-2004.
doi: 10.1080/01431160110075532
He C Y, Chen J, Chen Y H, et al. Study on the hybrid dynamic monitoring method of land use/cover change[J]. Journal of Natural Resources, 2001,16(3):255-262.
doi: 10.11849/zrzyxb.2001.03.010
Zhang Y H, Li X, Liu X P, et al. Simulation of urban expansion by coupling remote sensing observation and cellular automata[J]. Journal of Remote Sensing, 2013,17(4):872-886.
doi: 10.11834/jrs.20132169
[43]
Li X, Yeh A G O. Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta[J]. International Journal of Remote Sensing, 1998,19(8):1501-1518.
doi: 10.1080/014311698215315
[44]
Chang C C, Lin C J. LIBSVM:A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology(TIST), 2011,2(3):27.
Zhang J S, He C Y, Pan Y Z, et al. SVM based classification of high spatial resolution remote sensing data with multi-source information integration[J]. Journal of Remote Sensing, 2006,10(1):49-57.
doi: 10.11834/jrs.20060108
[46]
Pan T, Deng S L, Chi Z, et al. Urban land-cover dynamics in arid China based on high-resolution urban land mapping products[J]. Remote Sensing, 2017,9(7):730.
doi: 10.3390/rs9070730
[47]
Mcgaridal K, Marks B J. FRAGSTATS:Spatial pattern analysis program for quantifying landscape structure[J]. General Technical Report PNW, 1995,351.
[48]
Fei W, Zhao S. Urban land expansion in China’s six megacities from 1978 to 2015[J]. Science of the Total Environment, 2019,664:60-71.
[49]
Zhao S, Zhou D, Zhu C, et al. Rates and patterns of urban expansion in China’s 32 major cities over the past three decades[J]. Landscape Ecology, 2015,30(8):1541-1559.
Yu S S, Sun Z C, Guo D H, et al. Remote sensing monitoring and analysis of spatial expansion of mega cities along the maritime Silk Road[J]. Journal of Remote Sensing, 2017,21(2):169-181.