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

Yantao Xi,Nguyen Thinh,Cheng Li

doi:10.3390/su10114287

Yantao Xi, Nguyen Thinh + Show 1 more

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https://doi.org/10.3390/su10114287

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Abstract

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 × 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.

Highlights

In China, significant economic development has resulted in the rapid expansion of urban areas in cities since the 1980s, placing tremendous stress on the environment, natural resources, and public health [1,2,3,4]
Remote sensing provides a useful tool for monitoring and quantifying land use/land cover (LULC) changes at high spatial resolution and annual temporal scales, to systematically track the magnitude and spatial trends of urbanization [2,5,13]
Urban land has significantly increased, while the agricultural land was drastically reduced, which is supported in the Percentage of Landscape (PLAND) of different land uses in different years (Figure 8)

Summary

Introduction

In China, significant economic development has resulted in the rapid expansion of urban areas in cities since the 1980s, placing tremendous stress on the environment, natural resources, and public health [1,2,3,4]. Several LULC datasets have been developed using remote sensing, such as the 500-m Moderate-Resolution Imaging Spectroradiometer Global Yearly Land Cover Type (MCD12Q1, from 2001 to 2013) [14], 30-m National Land Cover Database (NLCD, 1992, 2001, 2006, and 2011) [15], and the 30-m Finer Resolution Observation and Monitoring-Global Land Cover (GLOBELAND30, 2000 and 2010) [16] These datasets are either not accurate enough for city-level research or lack sufficient temporal resolution, and are hard to use to characterize the urban long-term spatial-temporal dynamics [17,18,19].

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