Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (1) : 83-89     DOI: 10.6046/gtzyyg.2014.01.15
Technology Application |
TM image and nighttime light data to monitoring regional urban expansion:A case study of Zhejiang Province
CHEN Zheng1, HU Deyong1, ZENG Wenhua2, DENG Lei1
1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
2. Zhejiang Surveying and Mapping Data Archives, Hangzhou 310012, China
Download: PDF(1011 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Acquiring urban information and dynamically monitoring urban expansion forms constitute important parts of remote sensing technique in the field of resource and environment application. As a coastal province, Zhejiang has experienced a rapid economic development in the past 20 years. At the same time, its urban expansion phenomenon is significant. With Zhejiang Province as the study area, the authors obtained the spatial distribution information of urban land from Landsat TM data by using the CART algorithm. Under the condition of getting accurate classification results, the urban expansion spatial-temporal features of Zhejiang Province from 1995 to 2010 were analyzed.Applying CART to extracting urban information from TM data and optimized with NTL is an effective and adaptable method for monitoring regional urban expansion.During the past 15 years, the expansion acceleration of most cities is greater than zero, except for Dongtou, Qingyuan, Wencheng, Yunhe, Lanxi, Longquan and Shaoxing. From 1995 to 2010, Xiaoshan, Yuhang, Ningbo remained the first three cities in this aspect.It is shown that the urbanization levels of various cities are significantly different from each other. The urbanization level of coastal cities and cities in the terrain flat area is higher than that of the non-coastal cities and cities in the complicated topography area. As a result, the first three large coastal cities, which are Hangzhou, Shaoxing and Wenzhou, and the zone of small and medium sized cities in the non-costal area around Jinhua have been developed.

Keywords impervious surface      classification and regression tree      variable precision rough sets      temperature vegetation dryness index      plain river network region     
:  TP79  
Issue Date: 08 January 2014
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LI Xiaoning
ZHANG Youjing
SHE Yuanjian
CHEN Liwen
CHEN Jingxin
Cite this article:   
LI Xiaoning,ZHANG Youjing,SHE Yuanjian, et al. TM image and nighttime light data to monitoring regional urban expansion:A case study of Zhejiang Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 83-89.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.01.15     OR     https://www.gtzyyg.com/EN/Y2014/V26/I1/83

[1] 胡德勇, 李京, 陈云浩, 等.基于多时相Landsat数据的城市扩张及其驱动力分析[J].国土资源遥感, 2006, 18(4):46-49, 54. Hu D Y, Li J, Chen Y H, et al.An analysis of urban expansion and its dynamics based on multi-temporal Landsat data[J].Remote Sensing for Land and Resources, 2006, 18(4):46-49, 54.

[2] 查勇, 倪绍祥, 杨山.一种利用TM图像自动提取城镇用地信息的有效方法[J].遥感学报, 2003, 7(1):37-40. Zha Y, Ni S X, Yang S.An effective approach to automatically extract urban land-use from TM imagery[J].Journal of Remote Sensing, 2003, 7(1):37-40.

[3] 徐涵秋.基于谱间特征和归一化指数分析的城市建筑用地信息提取[J].地理研究, 2005, 24(2):311-319. Xu H Q.Fast information extraction of urban built-up land based on the analysis of spectral signature and normalized difference index[J].Geographical Research, 2005, 24(2):311-319.

[4] 潘卫华, 徐涵秋.泉州市城市扩展的遥感监测及其城市化核分析[J].国土资源遥感, 2004, 16(4):36-40. Pan W H, Xu H Q.A study of urban spatial expansion of Quanzhou City on the basis of remote sensing technology and urbanization core analysis[J].Remote Sensing for Land and Resources, 2004, 16(4):36-40.

[5] 卓莉, 史培军, 陈晋, 等.20世纪90年代中国城市时空变化特征——基于灯光指数CNLI方法的探讨[J].地理学报, 2003, 58(6):893-901. Zhuo L, Shi P J, Chen J, et al.Application of compound night light index derived from DMSP/OLS data to urbanization analysis in China in the 1990s[J].ACTA Geographica Sinica, 2003, 58(6):893-901.

[6] 徐梦洁, 陈黎, 刘焕金, 等.基于DMSP/OLS夜间灯光数据的长江三角洲地区城市化格局与过程研究[J].国土资源遥感, 2011, 23(3):106-112. Xu M J, Chen L, Liu H J, et al.Pattern and process of urbanization in the Yangtze Delta based on DMSP/OLS data[J].Remote Sensing for Land and Resources, 2011, 23(3):106-112.

[7] Liu Z F, He C Y, Zhang Q F, et al.Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008[J].Landscape and Urban Planning, 2012, 106(1):62-72.

[8] Zhang Q L, Seto K C.Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J].Remote Sensing of Environment, 2011, 115(9):2320-2329.

[9] Breiman L, Friedman J, Olshen R, et al.Classification and regression trees[M].New York:Chapman and Hall, 1984:358.

[10] 赵哲远, 马奇, 华元春, 等.浙江省1996—2005年土地利用变化分析[J].中国土地科学, 2009, 23(11):55-60, 54. Zhao Z Y, Ma Q, Hua Y C, et al.Analysis on land use changes from 1996 to 2005 in Zhejiang Province[J].China Land Science, 2009, 23(11):55-60, 54.

[11] 王翠平, 王豪伟, 李春明, 等.基于DMSP/OLS影像的我国主要城市群空间扩张特征分析[J].生态学报, 2012, 32(3):942-954. Wang C P, Wang H W, Li C M, et al.Analysis of the spatial expansion characteristics of major urban agglomerations in China using DMSP/OLS images[J].Acta Ecologica Sinica, 2012, 32(3):942-954.

[12] Alphan H, Derse M A.Change detection in southern Turkey using normalized difference vegetation index(NDVI)[J].Journal of Environmental Engineering and Landscape Management, 2013, 21(1):12-18.

[13] Epting J, Verbyla D, Sorbel B.Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+[J].Remote Sensing of Environment, 2005, 96(3-4):328-339.

[14] Yang L M, Huang C Q, Homer C G, et al.An approach for mapping large-area impervious surfaces:Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery[J].Canadian Journal of Remote Sensing, 2003, 29(2):230-240.

[15] 刘勇洪, 牛铮, 王长耀.基于MODIS数据的决策树分类方法研究与应用[J].遥感学报, 2005, 9(4):405-411. Liu Y H, Niu Z, Wang C Y.Research and application of the decision tree classification using MODIS data[J].Journal of Remote Sensing, 2005, 9(4):405-411.

[16] Huang C, Townshend J R G.A stepwise regression tree for nonlinear approximation:Applications to estimating subpixel and cover[J].International Journal of Remote Sensing, 2003, 24(1):75-90.

[17] 舒松, 余柏蒗, 吴健平, 等.基于夜间灯光数据的城市建成区提取方法评价与应用[J].遥感技术与应用, 2011, 26(2):169-175. Shu S, Yu B L, Wu J P, et al.Methods for deriving urban built-up area using night-light data:Assessment and application[J].Remote Sensing Technology and Application, 2011, 26(2):169-175.

[18] Fan F L, Wang Y P, Qiu M H, et al.Evaluating the temporal and spatial urban expansion patterns of Guangzhou from 1979 to 2003 by remote sensing and GIS methods[J].International Journal of Geographical Information Science, 2009, 23(11):1371-1388.

[19] 林目轩, 师迎春, 陈秧分, 等.长沙市区建设用地扩张的时空特征[J].地理研究, 2007, 26(2):265-274. Lin M X, Shi Y C, Chen Y F, et al.A study on spatial-temporal features of construction land expansion in Changsha urban area[J].Geographical Research, 2007, 26(2):265-274.

[20] 穆晓东, 刘慧平, 薛晓娟.基于遥感监测的北京1984—2007年城市扩展研究[J].北京师范大学学报:自然科学版, 2012, 48(1):81-85. Mu X D, Liu H P, Xue X J.Urban growth in Beijing from 1984 to 2007 as gauged by remote sensing[J].Journal of Beijing Normal University:Natural Science, 2012, 48(1):81-85.

[1] ZHAO Yi, XU Jianhui, ZHONG Kaiwen, WANG Yunpeng, HU Hongda, WU Pinghao. Impervious surface extraction based on Sentinel-2A and Landsat8[J]. Remote Sensing for Land & Resources, 2021, 33(2): 40-47.
[2] CAO Yong, TAO Yuxiang, DENG Lu, LUO Xiaobo. An impervious surface index construction for restraining bare land[J]. Remote Sensing for Land & Resources, 2020, 32(3): 71-79.
[3] Yuting YANG, Hailan CHEN, Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.
[4] Ru WANG, Yanfang ZHANG, Hongmin ZHANG, Yun LI. Study on the relationship between impervious surface coverage and artificial heat in new urban districts: A case study of Xixian New District, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(1): 247-254.
[5] Jiaqi ZUO, Zegen WANG, Jinhu BIAN, Ainong LI, Guangbin LEI, Zhengjian ZHANG. A review of research on remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(3): 20-28.
[6] Chang LIU, Kang YANG, Liang CHENG, Manchun LI, Ziyan GUO. Comparison of Landsat8 impervious surface extraction methods[J]. Remote Sensing for Land & Resources, 2019, 31(3): 148-156.
[7] Jiasi YI, Xiangyun HU. Extracting impervious surfaces from multi-source remote sensing data based on Grabcut[J]. Remote Sensing for Land & Resources, 2018, 30(3): 174-180.
[8] Xiaoping ZHANG, Ying LYU, Huaguo ZHANG, Chaokui LI. Remote sensing analysis of impervious surface changes in Zhoushan Islands during 1990—2011[J]. Remote Sensing for Land & Resources, 2018, 30(2): 178-185.
[9] ZHAO Feifei, BAO Nisha, WU Lixin, SUN Rui. Retrieving land surface temperature and soil moisture from HJ-1B data: A case study of Yimin open-cast coal mine region in Hulunbeier grassland[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 1-9.
[10] LI Xinyu, DU Peijun, ALIM Samat. Spatial-temporal analysis of urban heat island effect and surface parameters variation in Nanjing City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 177-183.
[11] ZHU Honglei, LI Ying, LIU Zhaoli, FU Bolin. Estimation of impervious surface based on semi-constrained spectral mixture analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 48-53.
[12] YANG Keming, ZHOU Yujie, QI Jianwei, WANG Linwei, LIU Shiwen. Remote sensing estimating of urban impervious surface area and land surface temperature[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 134-139.
[13] LI Xiaoning, ZHANG Youjing, SHE Yuanjian, CHEN Liwen, CHEN Jingxin. Estimation of impervious surface percentage of river network regions using an ensemble leaning of CART analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 174-179.
[14] LUO Hao, WANG Hong, SHI Changhui. Retrieving groundwater in Yellow River Delta area using remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 145-152.
[15] LI Weina, YANG Jiansheng, LI Xiao, ZHANG Jilong, LI Shiwei. Extraction of urban impervious surface information from TM image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 66-70.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech