Refining Urban Built-Up Area via Multi-Source Data Fusion for the Analysis of Dongting Lake Eco-Economic Zone Spatiotemporal Expansion

Rapid urbanization has given rise to serious urban problems. It is crucial to understand the urbanization process to accurately and quickly identify boundary changes in urban built-up areas and implement planning schemes and adjustments in scientific and effective ways. This study proposes a new method to automate and refine the extraction of urban built-up areas by using Landsat and nighttime light (NTL) imagery. The urban agglomeration of Dongting Lake Ecological Economic Zone (UADLEEZ) Landsat data are mapped to NTL data using resampling, superpixel segmentation, and assigning the blank part with the Euclidean distance method. We then compared our findings with those produced via traditional threshold extraction methods. In total, 33 built-up areas of UADLEEZ boundary maps were produced between 1992 and 2018. Thus, we reached the following conclusions: (1) the urban built-up areas obtained via our proposed method are finer than those obtained via other threshold extraction methods; (2) we applied the extraction method to UADLEEZ, and analyzed the expansion of the urban agglomeration based on expansion scale, gravity center offset, and landscape pattern index, the analysis of expansion process is consistent with the actual situation; (3) the proposed method can be used to draw long-term dynamic maps of urban extents in units of years, and the results can be used to update the existing products. This study can serve as a reference for future urban planning, and provide both adjustment programs for relevant departments, and an objective basis for governmental decision-making.

[1]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[2]  Forrest R. Stevens,et al.  Multitemporal settlement and population mapping from Landsat using Google Earth Engine , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Robert C. Balling,et al.  Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover , 2018 .

[4]  A. Thomson,et al.  A cluster-based method to map urban area from DMSP/OLS nightlights , 2014 .

[5]  Paolo Gamba,et al.  Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor , 2015 .

[6]  Jianping Wu,et al.  Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas , 2014 .

[7]  Jon Atli Benediktsson,et al.  Spatial Density Peak Clustering for Hyperspectral Image Classification With Noisy Labels , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Xiaohua Tong,et al.  Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas , 2017, Remote. Sens..

[9]  M. Bennett,et al.  Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics , 2017 .

[10]  Huanguo Zhang,et al.  URBAN BOUNDARY EXTRACTION AND URBAN SPRAWL MEASUREMENT USING HIGH-RESOLUTION REMOTE SENSING IMAGES: A CASE STUDY OF CHINA’S PROVINCIAL , 2018 .

[11]  Gui-zhou Wang,et al.  WE-NDBI-A new index for mapping urban built-up areas from GF-1 WFV images , 2020 .

[12]  Jianjun Zhang,et al.  Built-up land expansion and its impacts on optimizing green infrastructure networks in a resource-dependent city , 2020 .

[13]  Yu Chen,et al.  Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest , 2018, Remote. Sens..

[14]  Chao Wang,et al.  Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin , 2020 .

[15]  Sérgio Freire,et al.  Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer , 2018, Remote. Sens..

[16]  Y. Yamagata,et al.  Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data , 2015 .

[17]  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 .

[18]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[19]  Tao Zhou,et al.  Spatial-Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013 , 2019, ISPRS Int. J. Geo Inf..

[20]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[21]  Michael Jendryke,et al.  Mapping urban expansion using night-time light images from Luojia1-01 and International Space Station , 2020 .

[22]  Chengle Zhou,et al.  Hyperspectral anomaly detection via density peak clustering , 2020, Pattern Recognit. Lett..

[23]  Noam Levin,et al.  High spatial resolution night-time light images for demographic and socio-economic studies , 2012 .

[24]  R. Nemani,et al.  Global Distribution and Density of Constructed Impervious Surfaces , 2007, Sensors.

[25]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[26]  Paul C. Sutton,et al.  A scale-adjusted measure of Urban sprawl using nighttime satellite imagery , 2003 .

[27]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[28]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[29]  M. Kwan,et al.  How does urban expansion impact people’s exposure to green environments? A comparative study of 290 Chinese cities , 2020 .

[30]  P. Gong,et al.  Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa , 2018, Remote Sensing of Environment.

[31]  K. Seto,et al.  A Meta-Analysis of Global Urban Land Expansion , 2011, PloS one.

[32]  Weiqi Zhou,et al.  Urban mapping needs up-to-date approaches to provide diverse perspectives of current urbanization: A novel attempt to map urban areas with nighttime light data , 2020, Landscape and Urban Planning.

[33]  M. Friedl,et al.  Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights , 2016 .

[34]  Wei Tu,et al.  Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data , 2018, Remote. Sens..

[35]  Yaping Yang,et al.  Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques , 2015, Remote. Sens..

[36]  Weibo Liu,et al.  Quantifying Spatiotemporal Patterns and Major Explanatory Factors of Urban Expansion in Miami Metropolitan Area During 1992-2016 , 2019, Remote. Sens..

[37]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[38]  Taiyang Zhong,et al.  The Spatial Pattern of Urban Settlement in China from the 1980s to 2010 , 2019 .

[39]  Jianping Wu,et al.  Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China , 2011, Environmental monitoring and assessment.

[40]  Chengle Zhou,et al.  Hyperspectral Classification With Noisy Label Detection via Superpixel-to-Pixel Weighting Distance , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Ola Hall,et al.  Monitoring economic development from space : using nighttime light and land cover data to measure economic growth , 2015 .

[42]  Qian Zhang,et al.  Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures , 2013, Remote. Sens..