Adv Search

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

SERASINGHE PATHIRANAGE Inoka Sandamali,  Lakshmi N. KANTAKUMAR,  SUNDARAMOORTHY Sivanantharajah

downloadPDF
文章导航 > Chinese Geographical Science  > 2018 >  28(2): 274-286
SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. 中国地理科学, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
引用本文: SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. 中国地理科学, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
shu
SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. Chinese Geographical Science, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
Citation: SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. Chinese Geographical Science, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
shu

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

doi: 10.1007/s11769-018-0946-6
  • 1.

    Mahaweli Authority of Sri Lanka, Colombo 10, Sri Lanka;

  • 2.

    Institute of Human Resource Advancement, University of Colombo, Colombo 7, Sri Lanka;

  • 3.

    Institute of Environment Education and Research, Bharati Vidyapeeth Deemed University, Pune 411043, India;

  • 4.

    District Survey Office, The Sri Lanka Survey Department, Jaffna 40000, Sri Lanka

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

  • 1.

    Mahaweli Authority of Sri Lanka, Colombo 10, Sri Lanka;

  • 2.

    Institute of Human Resource Advancement, University of Colombo, Colombo 7, Sri Lanka;

  • 3.

    Institute of Environment Education and Research, Bharati Vidyapeeth Deemed University, Pune 411043, India;

  • 4.

    District Survey Office, The Sri Lanka Survey Department, Jaffna 40000, Sri Lanka

  • 摘要: Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.
    Abstract: Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.
  • [1] Abd-Allah M M A, 2007. Modelling Urban Dynamics using Geographic Information Systems, Remote Sensing and Urban Growth Models. Cairo:Cairo University.
    [2] Al-shalabi M, Billa L, Pradhan B et al., 2013. Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models:the case of Sana'a metropolitan city, Yemen. Environmental Earth Sciences, 70(1):425-437. doi: 10.1007/s12665-012-2137-6
    [3] Ayazli I E, Kilic F, Demir H, 2014. A simulation model of urban growth driven by the bosphorus bridges. In:Popovich V et al.(eds). Information Fusion and Geographic Information Systems. Berlin Heidelberg:Springer, 237-248.
    [4] Batty M, Xie Y C, Sun Z L, 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3):205-233. doi:10.1016/S 0198-9715(99)00015-0
    [5] Bhatta B, 2009. Analysis of urban growth pattern using remote sensing and GIS:a case study of Kolkata, India. International Journal of Remote Sensing, 30(18):4733-4746. doi: 10.1080/01431160802651967
    [6] Birch E L, Wachter S M, 2011. Global Urbanization. Philadelphia, PA:University of Pennsylvania Press.
    [7] Butsch C, Kumar S, Wagner P Det al., 2017. Growing ‘smart’? Urbanization processes in the pune urban agglomeration. Sustainability, 9:2335. https://doi.org/10.3390/su9122335
    [8] Chaudhuri G, Clarke K, 2013. The SLEUTH land use change model:a review. Environmental Resources Research, 1(1):88-105. doi: 10.22069/IJERR.2013.1688
    [9] Clarke K C, Hoppen S, Gaydos L, 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environment and Planning B:Planning and Design, 24(2):247-261. doi: 10.1068/b240247
    [10] Clarke K C, Gaydos L J, 1998. Loose-coupling a cellular automaton model and GIS:long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7):699-714. doi: 10.1080/136588198241617
    [11] Coppin P, Jonckheere I, Nackaerts K et al., 2004. Digital change detection methods in ecosystem monitoring:a review. International Journal of Remote sensing, 25:1565-1596. https://doi.org/10.1080/0143116031000101675
    [12] Dietzel C, Clarke K C, 2007. Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 11(1):29-45. doi: 10.1111/j.1467-9671.2007.01031.x
    [13] El-Hattab M M, 2016. Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay). The Egyptian Journal of Remote Sensing and Space Science, 19:23-36. https://doi.org/10.1016/j.ejrs.2016.02.002
    [14] Feng H H, Liu H P, Lü Y, 2012. Scenario prediction and analysis of urban growth using SLEUTH model. Pedosphere, 22, 206-216. https://doi.org/10.1016/S1002-0160(12)60007-1
    [15] Gandhi S I, Suresh V M, 2012. Prediction of urban sprawl in Hyderabad City using spatial model, remote sensing and GIS techniques. International Journal of Scientific Research, 1(2):80-81. doi: 10.15373/22778179
    [16] Goers L, Lawson J, Garen E, 2012. Economic drivers of tropical deforestation for agriculture. Managing Forest Carbon in a Changing Climate. Dordrecht:Springer, 305-320. https://doi.org/10.1007/978-94-007-2232-3_14
    [17] Goldstein N C, 2004. Brains versus brawn-comparative strategies for the calibration of a cellular automata-based urban growth model. In:Atkinson P M et al. (eds). Citation Information. Boca Ration, FL:CRC Press, 249-272. doi:10.1201/97814 20038101.ch18
    [18] Goodarzi M S, Sakieh Y, Navardi S, 2017. Scenario-based urban growth allocation in a rapidly developing area:a modeling approach for sustainability analysis of an urban-coastal coupled system. Environment Development and Sustainability, 19, 1103-1126. https://doi.org/10.1007/s10668-016-9784-9
    [19] Grimm N B, Faeth S H, Golubiewski N E et al., 2008. Global change and the ecology of cities. Science, 319(5864):756-760. doi: 10.1126/science.1150195
    [20] Jafarnezhad J, Salmanmahiny A, Sakieh Y, 2016. Subjectivity versus objectivity:comparative study between Brute Force method and Genetic Algorithm for calibrating the SLEUTH urban growth model. Journal of Urban Planning and Development, 142(3):05015015. doi: 10.1061/(ASCE)UP.1943-5444.0000307
    [21] Jantz C A, Goetz S J, Shelley M K, 2004. Using the sleuth urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B:Planning and Design, 31(2):251-271. doi: 10.1068/b2983
    [22] Jantz C A, Goetz S J, Donato D et al., 2010. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems, 34(1):1-16. doi:10.1016/j.compenvurbsys. 2009.08.003
    [23] Jat M K, Choudhary M, Saxena A, 2017. Urban growth assessment and prediction using RS, GIS and SLEUTH model for a heterogeneous urban fringe. The Egyptian Journal of Remote Sensing and Space Science. doi: 10.1016/j.ejrs.2017.02.002
    [24] Kantakumar L N, Neelamsetti P, 2015. Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Science, 18(2):289-295, doi:10. 1016/j.ejrs.2015.09.003
    [25] Kantakumar L N, Kumar S, Schneider K, 2016. Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat International, 51:11-22. doi:10.1016/j. habitatint.2015.10.007
    [26] KantaKumar N L, Sawant N G, Kumar S, 2011. Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area. International Journal of Geomatics and Geosciences, 2(2):568-579.
    [27] Kuang Wenhui, 2011. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area. Journal of Geographical Sciences, 21(2):317-330. doi:10. 1007/s11442-011-0847-4
    [28] Kuang Wenhui, 2012. Spatio-temporal patterns of intra-urban land use change in Beijing, China between 1984 and 2008. Chinese Geographical Science, 22(2):210-220. doi:10.1007/s 11769-012-0529-x
    [29] Lambin E F, Geist H J, Lepers E, 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28(1):205-241. doi: 10.1146/annurev.energy.28.050302.105459
    [30] Li X, Yeh A G O, 2000. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2):131-152. doi: 10.1080/136588100240886
    [31] Li X M, Zhou W Q, Ouyang Z Y, 2013. Forty years of urban expansion in Beijing:what is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38:1-10. doi: 10.1016/j.apgeog.2012.11.004
    [32] Osman T, Divigalpitiya P, Arima T, 2016. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on land use in the Giza Governorate, Greater Cairo Metropolitan region. International Journal of Urban Sciences, 20(3):407-426. doi: 10.1080/12265934.2016.1216327
    [33] Sacchi L V, Gasparri N I, 2016. Impacts of the deforestation driven by agribusiness on urban population and economic activity in the Dry Chaco of Argentina. Journal of Land Use Science, 11:523-537. https://doi.org/10.1080/1747423X.2015.1098739
    [34] Sakieh Y, Amiri B J, Danekar A et al., 2015. Scenario-based evaluation of urban development sustainability:an integrative modeling approach to compromise between urbanization suitability index and landscape pattern. Environment, Development and Sustainability, 17(6):1343-1365. doi:10.1007/s 10668-014-9609-7
    [35] Sangawongse S, Sun C H, Tsai B W, 2005. Urban growth and land cover change in Chiang Mai and Taipei:results from the SLEUTH model. Proceedings of the MODSIM 05 Conference. Melbourne, Australia, 2622-2628.
    [36] Shalaby Adel, Moghanm Farahat Saad, 2015. Assessment of urban sprawl on agricultural soil of northern Nile Delta of Egypt using RS and GIS. Chinese Geographical Science, 25(3):274-282. doi:10.1007/s 11769-015-0748-z
    [37] Silva E A, Clarke K C, 2002. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6):525-552. doi:10. 1016/S0198-9715(01)00014-X
    [38] Singh A, 1989. Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6):989-1003. doi: 10.1080/01431168908903939
    [39] Sohl T L, Claggett P R, 2013. Clarity versus complexity:land-use modeling as a practical tool for decision-makers. Journal of Environmental Management, 129:235-243. doi: 10.1016/j.jenvman.2013.07.027
    [40] Solecki W D, Oliveri C, 2004. Downscaling climate change scenarios in an urban land use change model. Journal of Environmental Management, 72(1), 105-115. https://doi.org/10.1016/j.jenvman.2004.03.014
    [41] Stevens D, Dragicevic S, Rothley K, 2007. iCity:a GIS-CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22(6):761-773. doi:10.1016/j. envsoft.2006.02.004
    [42] Su D Z, 1998. GIS-based urban modelling:practices, problems, and prospects. International Journal of Geographical Information Science, 12(7):651-671. doi: 10.1080/136588198241581
    [43] World Bank, 2012. Turning Sri Lanka's Urban Vision into Policy and Action. The International Bank for Reconstruction and Development, the World Bank.
    [44] Wagner P D, Bhallamudi S M, Narasimhan B et al., 2016. Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment. Science of the Total Environment, 539:153-164. doi:10.1016/j.scitotenv. 2015.08.148
    [45] Wu X Q, Hu Y M, He H S et al., 2009. Performance evaluation of the SLEUTH Model in the Shenyang metropolitan area of Northeastern China. Environmental Modeling & Assessment, 14(2):221-230. doi: 10.1007/s10666-008-9154-6
    [46] Yang X J, 2010. Integration of remote sensing with GIS for urban growth characterization. In:Jiang B and Yao X (eds). Geospatial Analysis and Modelling of Urban Structure and Dynamics. Dordrecht:Springer, 223-250. doi: 10.1007/978-90-481-8572-6_12
    [47] Yuan F, Sawaya K E, Loeffelholz B C et al., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment. 98:317-328. https://doi.org/10.1016/j.rse.2005.08.006
  • [1] LUAN Qingzu, JIANG Wei, LIU Shuo, GUO Hongxiang.  Impact of Urban 3D Morphology on Particulate Matter 2.5 (PM2.5) Concentrations: Case Study of Beijing, China . Chinese Geographical Science, 2020, 30(2): 294-308. doi: 10.1007/s11769-020-1112-5
    [2] ZHANG Zengxiang, LIU Fang, ZHAO Xiaoli, WANG Xiao, SHI Lifeng, XU Jinyong, YU Sisi, WEN Qingke, ZUO Lijun, YI Ling, HU Shunguang, LIU Bin.  Urban Expansion in China Based on Remote Sensing Technology: A Review . Chinese Geographical Science, 2018, 28(5): 727-743. doi: 10.1007/s11769-018-0988-9
    [3] ZHU Jiang, YU Yanna, ZHOU Shenglu, WANG Xiang, LV Ligang.  Simulating Sustainable Urban Development by Incorporating So-cial-ecological Risks into a Constrained CA Model . Chinese Geographical Science, 2018, 28(4): 600-611. doi: 10.1007/s11769-018-0977-z
    [4] LI Hao, Richard M CRUSE, LIU Xiaobing, ZHANG Xingyi.  Effects of Topography and Land Use Change on Gully Development in Typical Mollisol Region of Northeast China . Chinese Geographical Science, 2016, 26(6): 779-788. doi: 10.1007/s11769-016-0837-7
    [5] ZHAO Rongqin, HUANG Xianjin, LIU Ying, ZHONG Taiyang, DING Minglei, CHUAI Xiaowei.  Carbon Emission of Regional Land Use and Its Decomposition Analysis: Case Study of Nanjing City, China . Chinese Geographical Science, 2015, 25(2): 198-212. doi: 10.1007/s11769-014-0714-1
    [6] FANG Xiangmin, WANG Qingli, ZHOU Wangming, ZHAO Wei, WEI Yawei, NIU Lijun, DAI Limin.  Land Use Effects on Soil Organic Carbon, Microbial Biomass and Microbial Activity in Changbai Mountains of Northeast China . Chinese Geographical Science, 2014, 0(3): 297-306. doi: 10.1007/s11769-014-0670-9
    [7] XUE Desheng, HUANG Gengzhi, GUAN Jingwen, LIN Jiarong.  Changing Concepts of City and Urban Planning Practices in Guangzhou (1949-2010)：An Approach to Sustainable Urban Development . Chinese Geographical Science, 2014, 0(5): 607-619. doi: 10.1007/s11769-014-0711-4
    [8] HU Yecui, ZHENG Yunmei, ZHENG Xinqi.  Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models . Chinese Geographical Science, 2013, 23(1): 92-100.
    [9] CAI Yuanbin, ZHANG Hao, PAN Wenbin, et al..  Urban Expansion and Its Influencing Factors in Natural Wetland Distribution Area in Fuzhou City, China . Chinese Geographical Science, 2012, 22(5): 568-577.
    [10] KUANG Wenhui.  Spatio-temporal Patterns of Intra-urban Land Use Change in Beijing, China Between 1984 and 2008 . Chinese Geographical Science, 2012, 22(2): 210-220.
    [11] XIU Chunliang, CHENG Lin, SONG Wei, WU Wei.  Vulnerability of Large City and Its Implication in Urban Planning: A Perspective of Intra-urban Structure . Chinese Geographical Science, 2011, 21(2): 204-210.
    [12] CAO Kang, GUAN Hua.  Brownfield Redevelopment Toward Sustainable Urban Land Use in China . Chinese Geographical Science, 2007, 17(2): 127-134. doi: 10.1007/s11769-007-0127-5
    [13] LIU Ji-yuan, DENG Xiang-zheng, LIU Ming-liang, ZHANG Shu-wen.  STUDY ON THE SPATIAL PATTERNS OF LAND—USE CHANGE AND ANALYSES OF DRIVING FORCES IN NORTHEASTERN CHINA DURING 1990-2000 . Chinese Geographical Science, 2002, 12(4): 299-308.
    [14] XU Han-qiu.  AN ASSESSMENT OF LAND USE CHANGES IN FUQING COUNTY OF CHINA USING REMOTE SENSING TECHNOLOGY . Chinese Geographical Science, 2002, 12(2): 126-135.
    [15] HU Yuan-man, JIANG Yan, CHANG Yu, BU Ren-cang, LI Yue-hui, XU Chong-gang.  THE DYNAMIC MONITORING OF HORQIN SAND LAND USING REMOTE SENSING . Chinese Geographical Science, 2002, 12(3): 238-243.
    [16] LIU Ming-liang, ZHUANG Da-fang, LIU Ji-yuan.  FARMLAND AND URBAN AREA DYNAMICS MONITORING IN CHINA USING REMOTE SENSING AND SPATIAL STATISTICS METHODOLOGY . Chinese Geographical Science, 2001, 11(1): 42-49.
    [17] WANG Lei, LATHAM Michael.  THE ROLE OF RAILROAD IN THE DEVELOPMENT OF THE AMERICAN WEST— Railroad, Migration and Urban Growth . Chinese Geographical Science, 2001, 11(3): 223-232.
    [18] 黄铁青, 刘兆礼, 潘瑜春, 张养贞.  LAND COVER SURVEY IN NORTHEAST CHINA USING REMOTE SENSING AND GIS . Chinese Geographical Science, 1998, 8(3): 264-270.
    [19] 张养贞, 常丽萍, 张柏, 张树文, 黄铁青, 刘雅琴.  LAND RESOURCES SURVEY BY REMOTE SENSING AND ANALYSIS OF LAND CARRYING CAPACITY FOR POPULATION IN TUMEN RIVER REGION . Chinese Geographical Science, 1996, 6(4): 342-350.
    [20] 孙鸿烈, 胡鞍钢, 傅伯杰.  THE EXPERIENCES AND MODELS OF LAND RESOURCES USE IN CHINA . Chinese Geographical Science, 1991, 1(4): 293-305.
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  438
  • HTML全文浏览量:  20
  • PDF下载量:  930
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-05-02
  • 修回日期:  2017-08-29
  • 刊出日期:  2018-04-27

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

doi: 10.1007/s11769-018-0946-6
  • 1. Mahaweli Authority of Sri Lanka, Colombo 10, Sri Lanka;
  • 2. Institute of Human Resource Advancement, University of Colombo, Colombo 7, Sri Lanka;
  • 3. Institute of Environment Education and Research, Bharati Vidyapeeth Deemed University, Pune 411043, India;
  • 4. District Survey Office, The Sri Lanka Survey Department, Jaffna 40000, Sri Lanka
  • 收稿日期: 2017-05-02
  • 修回日期: 2017-08-29
  • 刊出日期: 2018-04-27

摘要: Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.

English Abstract

SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. 中国地理科学, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
引用本文: SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. 中国地理科学, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
shu
SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. Chinese Geographical Science, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
Citation: SERASINGHE PATHIRANAGE Inoka Sandamali, Lakshmi N. KANTAKUMAR, SUNDARAMOORTHY Sivanantharajah. Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning[J]. Chinese Geographical Science, 2018, 28(2): 274-286. doi: 10.1007/s11769-018-0946-6
shu

    全文HTML

参考文献 (47)

目录

    /

    返回文章
    返回

    Copyright © Editorial office of Chinese Geographical Science

    Tel: +86-0431-85542219   Email: egeoscien@neigae.ac.cn