Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China

被引:33
作者
Zhang, Dong-Dong [1 ]
Zhang, Lei [2 ]
机构
[1] East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 201100, Peoples R China
[2] East China Normal Univ, MOE Int Joint Lab Trustworthy Software, Shanghai 200062, Peoples R China
基金
国家重点研发计划;
关键词
land-use/cover change; Google Earth Engine; spatiotemporal analysis; driving mechanism; Nanjing; DENSE TIME STACKS; SERIES DATA; URBAN; AREAS; WATER; CLASSIFICATION; DELTA; MODEL; MAP; TM;
D O I
10.3390/s20072091
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km(2) to 1500.5 km(2), and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km(2). The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km(2). Barren areas accounted for 6.6% of the total area in the period 2015-2017, which was an increase of 94.8% relative to the period 1987-1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km(2)) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.
引用
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页数:20
相关论文
共 46 条
[1]   Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015 [J].
Abatzoglou, John T. ;
Dobrowski, Solomon Z. ;
Parks, Sean A. ;
Hegewisch, Katherine C. .
SCIENTIFIC DATA, 2018, 5
[2]   DMSP/OLS night-time light imagery for urban population estimates in the Brazilian Amazon [J].
Amaral, S ;
Monteiro, AMV ;
Camara, G ;
Quintanilha, JA .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2006, 27 (5-6) :855-870
[3]   The dimensions of global urban expansion: Estimates and projections for all countries, 2000-2050 [J].
Angel, Shlomo ;
Parent, Jason ;
Civco, Daniel L. ;
Blei, Alexander ;
Potere, David .
PROGRESS IN PLANNING, 2011, 75 :53-107
[4]   Obtaining rubber plantation age information from very dense Landsat TM & ETM plus time series data and pixel-based image compositing [J].
Beckschaefer, Philip .
REMOTE SENSING OF ENVIRONMENT, 2017, 196 :89-100
[5]  
Breiman L., 2001, IEEE Trans. Broadcast., V45, P5
[6]   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
[7]   A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform [J].
Chen, Bangqian ;
Xiao, Xiangming ;
Li, Xiangping ;
Pan, Lianghao ;
Doughty, Russell ;
Ma, Jun ;
Dong, Jinwei ;
Qin, Yuanwei ;
Zhao, Bin ;
Wu, Zhixiang ;
Sun, Rui ;
Lan, Guoyu ;
Xie, Guishui ;
Clinton, Nicholas ;
Giri, Chandra .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2017, 131 :104-120
[8]   The Importance of Natural Variability in Lake Areas on the Detection of Permafrost Degradation: A Case Study in the Yukon Flats, Alaska [J].
Chen, Min ;
Rowland, Joel C. ;
Wilson, Cathy J. ;
Altmann, Garrett L. ;
Brumby, Steven P. .
PERMAFROST AND PERIGLACIAL PROCESSES, 2013, 24 (03) :224-240
[9]   Built-up land efficiency in urban China: Insights from the General Land Use Plan (2006-2020) [J].
Chen, Yi ;
Chen, Zhigang ;
Xu, Guoliang ;
Tian, Zhiqiang .
HABITAT INTERNATIONAL, 2016, 51 :31-38
[10]  
Cohen WB, 2004, BIOSCIENCE, V54, P535, DOI 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO