Please wait a minute...
 
Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 178-185     DOI: 10.6046/gtzyyg.2018.02.24
|
Remote sensing analysis of impervious surface changes in Zhoushan Islands during 1990—2011
Xiaoping ZHANG1,2,3(), Ying LYU1, Huaguo ZHANG2, Chaokui LI3
1. School of Land and Tourism, Luoyang Normal University, Luoyang 471934, China
2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
3. National-Local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
Download: PDF(3979 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The expansion of impervious surfaces(IS)exacerbates the pollution of water resources in the island city, which is one of the important human factors affecting the vulnerability of island ecosystem. Landsat images acquired in three days of the same season were applied to monitor the dynamics of IS in Zhoushan Islands during 1990―2011. Firstly, the non-IS region was masked by the land use data set of the study area by supervised classification. Then, the complement of vegetation coverage was used to extract IS in 1990, 2000 and 2011. The results show that the IS expansions have occurred continuously over the past 20 years. The IS area in Zhoushan Islands increased from 47.96 km 2 (accounting for 6.28% of the total study area) in 1990 to 114.40 km 2 in 2011 (16.27%), and the increased IS were mostly located around the old city of Zhoushan Islands and along the periphery of surrounding islands. It is observed that the height of new IS was gradually changing to greater depth with time. The analysis indicates that the topography and the policy as well as the functions and transportation convenience are the dominant factors controlling the spatial patterns of IS and its expansions in Zhoushan Islands.

Keywords impervious surfaces(IS)      remote sensing      spatial analysis      Zhoushan Islands     
:  TP79  
Issue Date: 30 May 2018
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xiaoping ZHANG
Ying LYU
Huaguo ZHANG
Chaokui LI
Cite this article:   
Xiaoping ZHANG,Ying LYU,Huaguo ZHANG, et al. Remote sensing analysis of impervious surface changes in Zhoushan Islands during 1990—2011[J]. Remote Sensing for Land & Resources, 2018, 30(2): 178-185.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.24     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/178
Fig.1  Location of study area
海岛名称 所属行政区 岛陆面积/km2 距本岛距离/km 人口数量/万人 跨海大桥(是否连通舟山本岛) 功能定位
舟山岛 定海/普陀区 503.84 4.70 综合开发
金塘岛 定海区 81.11 6.26 4.20 综合开发
秀山岛 岱山县 25.47 2.50 0.83 海洋旅游
册子岛 定海区 15.01 1.88 0.40 港口物流
长白岛 定海区 13.32 1.10 0.52 城区拓展
长峙岛 定海区 7.51 0.36 0.47 城区拓展/海洋科研
大猫岛 定海区 6.60 3.01 0.14 港口物流
岙山岛 定海区 5.15 2.60 0.19 港口物流
盘峙岛 定海区 4.23 0.87 0.18 修造船舶基地
小干岛 普陀区 5.24 0.33 0.16 临港工业
鲁家峙 普陀区 3.77 0.19 0.52 城区拓展
Tab.1  Basic information of major islands in study area
Fig.2  Land use/land cover map of Zhoushan Islands from 1990 to 2011
Fig.3  ISA maps of Zhoushan Islands from 1990 to 2011
年份 统计指标
MRE/% R
1990年 15.05 0.671
2000年 11.95 0.605
2011年 8.00 0.733
Tab.2  Accuracy assessment for results of ISA extraction
Fig.4-1  Changes of IS area in Zhoushan Islands from 1990 to 2011
Fig.4-2  Changes of IS area in Zhoushan Islands from 1990 to 2011
Fig.5  Spatial distribution of increased IS in different time periods
Fig.6  Changes of height for new increased IS during different periods
时期 高程值≤0 高程值>0
最小值 最大值 平均值 标准差 最小值 最大值 平均值 标准差
1990—2000年 -18.84 0 -1.21 1.96 0 458.80 13.37 17.86
2000―2011年 -20.88 0 -1.59 1.98 0 456.78 12.97 23.55
Tab.3  Height of new increased IS during different periods(m)
Fig.7  Changes of population density in Zhoushan Islands during different periods
[1] Weng Q H . Remote sensing of impervious surfaces in the urban areas:Requirements,methods,and trends[J]. Remote Sensing of Environment, 2012,117:34-49.
doi: 10.1016/j.rse.2011.02.030 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425711002811
[2] 刘珍环, 王仰麟, 彭建 . 不透水表面遥感监测及其应用研究进展[J]. 地理科学学报, 2010,29(9):1143-1152.
doi: 10.11820/dlkxjz.2010.09.018 url: http://d.wanfangdata.com.cn/Periodical/dlkxjz201009018
[2] Liu Z H, Wang Y L, Peng J . Remote sensing of impervious surface and its applications:A review[J]. Progress in Geography, 2010,29(9):1143-1152.
[3] Chen J Y, Pan D L, Mao Z H , et al.Land-cover reconstruction and change analysis using multi-source remotely sensed imageries in Zhoushan Islands since 1970[J]. Journal of Coastal Research, 2014,30(2):272-282.
doi: 10.2112/JCOASTRES-D-13-00027.1 url: http://www.jstor.org/stable/43290053
[4] Carlson T N, Arthur S T . The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology:A satellite perspective[J]. Global and Planetary Change, 2000,25(1/2):49-65.
doi: 10.1016/S0921-8181(00)00021-7 url: http://linkinghub.elsevier.com/retrieve/pii/S0921818100000217
[5] Xian G, Crane M . Assessments of urban growth in the Tampa Bay watershed using remote sensing data[J]. Remote Sensing of Environment, 2005,97(2):203-215.
doi: 10.1016/j.rse.2005.04.017 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425705001367
[6] Powell R L, Roberts D A, Dennison P E , et al. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis:Manaus,Brazil[J]. Remote Sensing of Environment, 2007,106(2):253-267.
doi: 10.1016/j.rse.2006.09.005 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425706003142
[7] 徐涵秋 . 近30 a来福州盆地中心的城市扩张进程[J]. 地理科学, 2011,31(3):351-357.
url: http://www.cqvip.com/QK/95809X/201103/37015102.html
[7] Xu H Q .Urban expansion process in the center of the Fuzhou Basin,Southeast China in 1976―2006[J]. Scientia Geographica Sinica, 2011,31(3):351-357.
[8] Zhang L, Weng Q H.Annual dynamics of impervious surface in the Pearl River Delta,China,from 1988 to 2013,using time series Landsat imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,113:86-96.
doi: 10.1016/j.isprsjprs.2016.01.003 url: http://linkinghub.elsevier.com/retrieve/pii/S0924271616000113
[9] Im J, Lu Z Y, Rhee J , et al. Impervious surface quantification using a synjournal of artificial immune networks and decision/regression trees from multi-sensor data[J]. Remote Sensing of Environment, 2012,117:102-113.
doi: 10.1016/j.rse.2011.06.024 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425711002847
[10] Zhao T, Wang J F . Use of Iidar-derived NDTI and intensity for rule-based object-oriented extraction of building footprints[J]. International Journal of Remote Sensing, 2014,35(2):578-597.
doi: 10.1080/01431161.2013.871394 url: http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.871394
[11] Yan W Y, Shaker A, El-Ashmawy N . Urban land cover classification using airborne LiDAR data:A review[J]. Remote Sensing of Environment, 2015,158:295-310.
doi: 10.1016/j.rse.2014.11.001 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425714004374
[12] 李德仁, 罗晖, 邵振峰 . 遥感技术在不透水层提取中的应用与展望[J]. 武汉大学学报(信息科学版), 2016,41(5):569-577.
[12] Li D R, Luo H, Shao Z F . Review of impervious surface mapping using remote sensing technology and its application[J]. Geomatics and Information Science of Wuhan University, 2016,41(5):569-577.
[13] Lu D S, Li G Y, Kuang W H , et al. Methods to extract impervious surface areas from satellite images[J]. International Journal of Digital Earth, 2014,7(2):93-112.
doi: 10.1080/17538947.2013.866173 url: http://www.tandfonline.com/doi/abs/10.1080/17538947.2013.866173
[14] 黄建波.中国海岛.沙洲. 瑚礁遥感监测应用中的典型问题研究[D]. 青岛:中国海洋大学, 2006.
[14] Huang J B . Research on Typical Questions of China Islands,Sand Banks and Coral Reefs Remote Sensing Monitoring Application[D]. Qingdao:Ocean University of China, 2006.
[15] Zhao B, Kreuter U, Li B , et al. An ecosystem service value assessment of land-use change on Chongming Island,China[J]. Land Use Policy, 2004,21(2):139-148.
doi: 10.1016/j.landusepol.2003.10.003 url: http://linkinghub.elsevier.com/retrieve/pii/S0264837703000814
[16] Quan B, Chen J F, Qiu H L , et al. Spatial-temporal pattern and driving forces of land use changes in Xiamen[J]. Pedosphere, 2006,16(4):477-488.
doi: 10.1016/S1002-0160(06)60078-7 url: http://linkinghub.elsevier.com/retrieve/pii/S1002016006600787
[17] Zhang X P, Pan D L, Chen J Y , et al. Using long time series of Landsat data to monitor impervious surface dynamics:A case study in the Zhoushan Islands[J]. Journal of Applied Remote Sensing, 2013,7(1):073515.
doi: 10.1117/1.JRS.7.073515 url: http://remotesensing.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JRS.7.073515
[18] Gillies R R, Box J B, Symanzik J , et al. Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta-a satellite perspective[J]. Remote Sensing of Environment, 2003,86(3):411-422.
doi: 10.1016/S0034-4257(03)00082-8 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425703000828
[19] Carlson T N, Ripley D A . On the relation between NDVI,fractional vegetation cover,and leaf area index[J]. Remote Sensing of Environment, 1997,62(3):241-252.
doi: 10.1016/S0034-4257(97)00104-1 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425797001041
[20] 王浩, 吴炳方, 李晓松 , 等. 流域尺度的不透水面遥感提取[J]. 遥感学报, 2011,15(2):388-400.
[20] Wang H, Wu B F, Li X S , et al. Extraction of impervious surface in Hai Basin using remote sensing[J]. Journal of Remote Sensing, 2011,15(2):388-400.
[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[3] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[4] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[5] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[6] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[7] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[8] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[9] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[10] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[11] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[12] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[13] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[14] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[15] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
Viewed
Full text


Abstract

Cited

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