Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (4) : 103-107     DOI: 10.6046/gtzyyg.2010.04.21
Technology Application |
Research on the Relationship Between Urban Landscape Pattern and Hydrological Effect Based on Remote Sensing and GIS
QI Xiao-ming 1,2, DU Pei-jun 1, ZHOU Yu-liang 3, WU Zhi-yong 4
1. Department of Remote Sensing and Geographical Information Science, China University of Mining and Technology, Xuzhou 221116, China; 2. Department of Computer Science and Technology, Bengbu College, Bengbu
233000, China; 3. College of Civil and Conservancy Engineering, HeFei University of Technology, Hefei 230009, China; 4. Research Institute of Water Problems, Hohai University, Nanjing 210098, China
Download: PDF(883 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

With Hefei City as the study area, the authors investigated the relationship between urban landscape pattern and hydrological effect. The landscape classification maps were obtained from Landsat TM/ETM+ images taken in 1995, 1999, 2003 and 2007 with the support vector machine classifier. The thermal radiance transfer equation was estimated by mono-window algorithm designed by Qin Zhihao. The relationship between urban landscape pattern and hydrological effect was analyzed using landscape indices derived from the land cover classification maps. The result shows that urban hydrology and landscape structure are highly correlated with each other in Hefei City. With the increase and centralization of the built-up land, the surface runoff and urban water supply capacity have also increased, but the per capita water consumption has relatively decreased and the water quality is relatively low. It is concluded that adjusting the pattern of the urban landscape is effective for the improvement of the urban hydrologic effect.

Keywords PCA      SAM      RM      Quantitative measure of anomaly slicing      Thresolding     
: 

 

 
  X 820  
  X 87

 
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHANG Yu-jun
ZENG Zhao-ming
CHEN Wei
Cite this article:   
ZHANG Yu-jun,ZENG Zhao-ming,CHEN Wei. Research on the Relationship Between Urban Landscape Pattern and Hydrological Effect Based on Remote Sensing and GIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(4): 103-107.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.04.21     OR     https://www.gtzyyg.com/EN/Y2010/V22/I4/103

[1]傅国斌,刘昌明.遥感技术在水文学中的应用与研究进展[J].水科学进展,2001,12(4):547-55.

[2]Schultz G A. Remote Sensing in Hydrology [J]. Journal of Hydrology, 1988(100):239-265.

[3]Smith L C. Satellite Remote Sensing of River Inundation Area, Stage, and Discharge:A review [J]. Hydrological Processes, 1997(11):1427-1439.

[4]王裕德.试论城市水文效应及城市水环境[J].贵州师范大学学报(自然科学版),1995,14(2):34-36.

[5]http://glovis.usgs.gov/ImgViewer/Java1ImgViewer.html.

[6]李崇巍,刘世荣,孙鹏森,等.岷江上游景观格局及生态水文特征分析[J].生态学报,2005,25(4):691-698.

[7]徐丽华,岳文泽,曹宇.上海市城市土地利用景观的空间尺度效应[J].应用生态学报,2007,18(12):2827-2834.

[8]Yue W Z,Xu J H,Tan W Q.Spatial Analysis of the Urban Landscape Pattern[J].Ecologic Science,2005,24(2):102-106.

[9]覃志豪,李文娟,徐斌,等.利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法[J]. 海洋科学进展,2004,22(B10):129-137

[10]邬建国.景观生态学——格局、过程、尺度与等级[M].北京:高等教育出版社,2000:96-97.

[11]Wu J G.Efects of Changing Scale on Landscape Pattern Analysis:Scaling Relations[J].Landscape Ecology,2004,19(2):125-130.

[1] YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[2] JIANG Na, CHEN Chao, HAN Haifeng. An optimization method of DEM resolution for land type statistical model of coastal zones[J]. Remote Sensing for Natural Resources, 2022, 34(1): 34-42.
[3] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[4] LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.
[5] XIAO Yehui, SONG Nidi, MENG Panpan, WANG Peijun, FAN Shenglong. Prediction of lead content in soil based on model population analysis coupled with ELM algorithm[J]. Remote Sensing for Natural Resources, 2021, 33(4): 143-152.
[6] LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[7] FAN Tianyi, ZHANG Xiang, HUANG Bing, QIAN Zhan, JIANG Heng. Downscaling of TRMM precipitation products and its application in Xiangjiang River basin[J]. Remote Sensing for Natural Resources, 2021, 33(4): 209-218.
[8] LAI Peiyu, HUANG Jing, HAN Xujun, MA Mingguo. An analysis of impacts from water impoundment in Three Gorges Dam Project on surface water in Chongqing area base on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2021, 33(4): 227-234.
[9] ZHOU Chaofan, GONG Huili, CHEN Beibei, LEI Kunchao, SHI Liyuan, ZHAO Yu. Prediction of land subsidence along Tianjin-Baoding high-speed railway using WT-RF method[J]. Remote Sensing for Natural Resources, 2021, 33(4): 34-42.
[10] JIANG Xiao, ZHONG Chang, LIAN Zheng, WU Liangting, SHAO Zhitao. Research progress on classification criterion of geological information products based on satellite remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 279-283.
[11] WEI Yingjuan, LIU Huan. Remote sensing-based mineralized alteration information extraction and prospecting prediction of the Beiya gold deposit, Yunnan Province[J]. Remote Sensing for Natural Resources, 2021, 33(3): 156-163.
[12] SHU Huiqin, FANG Junyong, LU Peng, GU Wanfa, WANG Xiao, ZHANG Xiaohong, LIU Xue, DING Lanpo. Research on fine recognition of site spatial archaeology based on multisource high-resolution data[J]. Remote Sensing for Land & Resources, 2021, 33(2): 162-171.
[13] LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[14] SONG Chengyun, HU Guangcheng, WANG Yanli, TANG Chao. Downscaling FY-3B soil moisture based on apparent thermal inertia and temperature vegetation index[J]. Remote Sensing for Land & Resources, 2021, 33(2): 20-26.
[15] LONG Zehao, ZHANG Tianyuan, XU Wei, QIN Qiming. Development of farmland drought remote sensing dynamic monitoring system based on Android[J]. Remote Sensing for Land & Resources, 2021, 33(2): 256-261.
Viewed
Full text


Abstract

Cited

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