Spatio-Temporal Variation Analysis of Landscape Pattern Response to Land Use Change from 1985 to 2015 in Xuzhou City, China

被引:15
作者
Xi, Yantao [1 ,2 ]
Nguyen Xuan Thinh [2 ]
Li, Cheng [2 ]
机构
[1] China Univ Min & Technol, Sch Resources & Geosci, Xuzhou 221116, Jiangsu, Peoples R China
[2] TU Dortmund, Sch Spatial Planning, D-44227 Dortmund, Germany
关键词
land use/land cover (LULC); nighttime light (NTL); Normalized Difference Enhanced Urban Index (NDEUI); landscape metrics; random forests; urban growth mode; IMPERVIOUS SURFACE-AREA; NIGHTTIME LIGHT DATA; URBAN-GROWTH; COMPOSITION INDEX; RANDOM FORESTS; BEIJING CITY; COVER CHANGE; BUILT-UP; URBANIZATION; DYNAMICS;
D O I
10.3390/su10114287
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rapid urbanization has dramatically spurred economic development since the 1980s, especially in China, but has had negative impacts on natural resources since it is an irreversible process. Thus, timely monitoring and quantitative analysis of the changes in land use over time and identification of landscape pattern variation related to growth modes in different periods are essential. This study aimed to inspect spatiotemporal characteristics of landscape pattern responses to land use changes in Xuzhou, China durfing the period of 1985-2015. In this context, we propose a new spectral index, called the Normalized Difference Enhanced Urban Index (NDEUI), which combines Nighttime light from the Defense Meteorological Satellite Program/Operational Linescan System with annual maximum Enhanced Vegetation Index to reduce the detection confusion between urban areas and barren land. The NDEUI-assisted random forests algorithm was implemented to obtain the land use/land cover maps of Xuzhou in 1985, 1995, 2005, and 2015, respectively. Four different periods (1985-1995, 1995-2005, 2005-2015, and 1985-2015) were chosen for the change analysis of land use and landscape patterns. The results indicate that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has occurred in a dual-core development mode throughout the urbanization process. We quantified the patch complexity, aggregation, connectivity, and diversity of the landscape, employing a number of landscape metrics to represent the changes in landscape patterns at both the class and landscape levels. The results show that with respect to the four aspects of landscape patterns, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation in landscape patterns was examined based on 900 x 900 m sub-grids. Combined with the land use changes and spatiotemporal variations in landscape patterns, urban growth mainly occurred in a leapfrog mode along both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period of 1995 to 2005, and the edge-expansion and leapfrog modes coexisted in the period from 2005 to 2015. The high value spatiotemporal information generated using remote sensing and geographic information system in this study could assist urban planners and policymakers to better understand urban dynamics and evaluate their spatiotemporal and environmental impacts at the local level to enable sustainable urban planning in the future.
引用
收藏
页数:24
相关论文
共 64 条
[1]  
[Anonymous], 2016, REP WORK XUZH MUN GO
[2]   Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area [J].
As-Syakur, Abd. Rahman ;
Adnyana, I. Wayan Sandi ;
Arthana, I. Wayan ;
Nuarsa, I. Wayan .
REMOTE SENSING, 2012, 4 (10) :2957-2970
[3]   Object-based Urban Change Detection Analyzing High Resolution Optical Satellite Images [J].
Boldt, Markus ;
Thiele, Antje ;
Schulz, Karsten .
EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS III, 2012, 8538
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   Effects of settlement size, urban heat island and habitat type on urban plant biodiversity [J].
Ceplova, Natalie ;
Kalusova, Veronika ;
Lososova, Zdenka .
LANDSCAPE AND URBAN PLANNING, 2017, 159 :15-22
[6]   Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery [J].
Chan, Jonathan Cheung-Wai ;
Paelinckx, Desire .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (06) :2999-3011
[7]   A landscape ecological perspective of the impacts of urbanization on urban green spaces in the Klang Valley [J].
Chan, Kar Men ;
Tuong Thuy Vu .
APPLIED GEOGRAPHY, 2017, 85 :89-100
[8]   Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors [J].
Chander, Gyanesh ;
Markham, Brian L. ;
Helder, Dennis L. .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (05) :893-903
[9]   Assessment of urban growth in Guangzhou using multi-temporal, multi-sensor Landsat data to quantify and map impervious surfaces [J].
Chen, Youjun ;
Yu, Shixiao .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2016, 37 (24) :5936-5952
[10]   Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information [J].
Cheng Yang ;
Zhao Limin ;
Wan Wei ;
Li Lingling ;
Yu Tao ;
Gu Xingfa .
JOURNAL OF GEOGRAPHICAL SCIENCES, 2016, 26 (03) :325-338