Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands

Abstract Islands are an important part of the marine ecosystem. Increasing impervious surfaces in the Zhoushan Islands due to new development and increased population have an ecological impact on the runoff and water quality. Based on time-series classification and the complement of vegetation fraction in urban regions, Landsat thematic mapper and other high-resolution satellite images were applied to monitor the dynamics of impervious surface area (ISA) in the Zhoushan Islands from 1986 to 2011. Landsat-derived ISA results were validated by the high-resolution Worldview-2 and aerial photographs. The validation shows that mean relative errors of these ISA maps are < 15   % . The results reveal that the ISA in the Zhoushan Islands increased from 19.2     km 2 in 1986 to 86.5     km 2 in 2011, and the period from 2006 to 2011 had the fastest expansion rate of 5.59     km 2 per year. The major land conversions to high densities of ISA were from the tidal zone and arable lands. The expansions of ISA were unevenly distributed and most of them were located along the periphery of these islands. Time-series maps revealed that ISA expansions happened continuously over the last 25 years. Our analysis indicated that the policy and the topography were the dominant factors controlling the spatial patterns of ISA and its expansions in the Zhoushan Islands. With continuous urbanization processes, the rapid ISA expansions may not be stopped in the near feature.

[1]  Lalit Kumar,et al.  Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques , 2012 .

[2]  Randel L. Dymond,et al.  Evaluation of Impervious Surface Estimates in a Rapidly Urbanizing Watershed , 2004, Photogrammetric Engineering &amp; Remote Sensing.

[3]  Limin Yang,et al.  Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data , 2003 .

[4]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[5]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[6]  舟山市统计局 舟山统计年鉴 = Zhoushan statistical yearbook , 1997 .

[7]  Qihao Weng,et al.  A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[8]  C. Goksel,et al.  REMOTE SENSING AND GIS INTEGRATION FOR LAND COVER ANALYSIS , A CASE STUDY : GOKCEADA ISLAND , 2004 .

[9]  Jürgen Symanzik,et al.  Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective , 2003 .

[10]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[11]  Robert R. Gillies Growth of Impervious Surface Coverage and Aquatic Fauna , 2007 .

[12]  M. Alberti,et al.  Using NDVI to Assess Vegetative Land Cover Change in Central Puget Sound , 2006, Environmental monitoring and assessment.

[13]  N. Nakagoshi,et al.  An ecosystem service value assessment of land-use change on Chongming Island, China. , 2004 .

[14]  Huang Ming Estimating Reclamation Level of Saline Soil Using Laboratory Spectra , 2004 .

[15]  George Xian,et al.  Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions , 2008 .

[16]  M. Bauer,et al.  Estimating and Mapping Impervious Surface Area by Regression Analysis of Landsat Imagery , 2007 .

[17]  J. A. Tullis,et al.  Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness , 2003 .

[18]  Nathan J. Heinert,et al.  Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery , 2003 .

[19]  P. Gong,et al.  Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama , 2004 .

[20]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[21]  Fenglei Fan,et al.  Extraction and Analysis of Impervious Surfaces Based on a Spectral Un-Mixing Method Using Pearl River Delta of China Landsat TM/ETM+ Imagery from 1998 to 2008 , 2012, Sensors.

[22]  Yan Bai,et al.  Study of coastal water zone ecosystem health in Zhejiang Province based on remote sensing data and GIS , 2010 .

[23]  Yuyu Zhou,et al.  Photogrammetric Engineering & Remote Sensing Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification , 2022 .

[24]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[25]  C. Deguchi,et al.  Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery , 1994 .

[26]  M. Ridd Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[27]  C. Jacobson Identification and quantification of the hydrological impacts of imperviousness in urban catchments: a review. , 2011, Journal of environmental management.

[28]  Arlen D Feldman,et al.  Hydrologic Land Use Classification Using LANDSAT. , 1979 .

[29]  T. Schueler The importance of imperviousness , 1995 .

[30]  Qiuwen Chen,et al.  Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces. , 2011, Journal of environmental sciences.

[31]  Delu Pan,et al.  Land-Cover Reconstruction and Change Analysis Using Multisource Remotely Sensed Imageries in Zhoushan Islands since 1970 , 2014 .

[32]  T. Esch,et al.  Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data , 2009 .

[33]  Xuefei Hu,et al.  Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method , 2011 .

[34]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[35]  Zhihua Mao,et al.  Land-cover change and its time-series reconstructed using remotely sensed imageries in the Zhoushan islands , 2009, Remote Sensing.

[36]  F Bektas,et al.  Remote sensing and GIS integration for land cover analysis, a case study: Bozcaada Island. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[37]  Toby N. Carlson,et al.  The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective , 2000 .

[38]  T. Carlson,et al.  Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models , 1995 .

[39]  T. Webster,et al.  Object-oriented land cover classification of lidar-derived surfaces , 2006 .