Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

ABSTRACT Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km2 with a root mean squared error (RMSE) of 71.56 persons/km2, and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km2 cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.

[1]  Xiaohang Liu.  Estimation of the spatial distribution of urban population using high spatial resolution satellite imagery , 2003 .

[2]  Jean-Michel Guldmann,et al.  Landscape ecology, land-use structure, and population density: Case study of the Columbus Metropolitan Area , 2012 .

[3]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[4]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[5]  Ramakrishna R. Nemani,et al.  International Journal of Remote Sensing the Nightsat Mission Concept the Nightsat Mission Concept , 2022 .

[6]  A. Schneider,et al.  Understanding the demographic implications of climate change: estimates of localized population predictions under future scenarios of sea-level rise , 2011 .

[7]  D. Hogan,et al.  Bringing a population-environment perspective to hazards research , 2012 .

[8]  Le Wang,et al.  Improving the housing-unit method for small-area population estimation using remote-sensing and GIS information , 2010 .

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

[10]  F. Pozzi,et al.  MODELING THE DISTRIBUTION OF HUMAN POPULATION WITH NIGHT-TIME SATELLITE IMAGERY AND GRIDDED POPULATION OF THE WORLD , 2002 .

[11]  E. Moran.  Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery. , 2010, Photogrammetric engineering and remote sensing.

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

[13]  Gilberto Câmara,et al.  Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data , 2005, Comput. Environ. Urban Syst..

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

[15]  C. P. Lo,et al.  Applied Remote Sensing , 1988 .

[16]  Xu Han-qiu,et al.  A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index （MNDWI） , 2005, National Remote Sensing Bulletin.

[17]  David J. Martin,et al.  Developing a Flexible Framework for Spatiotemporal Population Modeling , 2015 .

[18]  John S. Gulliver,et al.  Dasymetric modelling of small-area population distribution using land cover and light emissions data , 2007 .

[19]  Luca Congedo,et al.  The demographic dimension of climate change vulnerability: exploring the relation between population growth and urban sprawl in Dar es Salaam , 2015 .

[20]  Qihao Weng,et al.  Using Landsat ETM+ Imagery to Measure Population Density in Indianapolis, Indiana, USA , 2005 .

[21]  J. E. Dobson,et al.  LandScan: Locating People is What Matters , 2002 .

[22]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[23]  D. Lu,et al.  Extraction of urban impervious surfaces from an IKONOS image , 2009 .

[24]  Dengsheng Lu,et al.  Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis , 2016, Remote. Sens..

[25]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[26]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[27]  D. Lu,et al.  A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces , 2014 .

[28]  Kytt MacManus,et al.  Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4 , 2015 .

[29]  S I Hay,et al.  Determining global population distribution: methods, applications and data. , 2006, Advances in parasitology.

[30]  Peter A. Rogerson,et al.  Assessing fine-spatial-resolution remote sensing for small-area population estimation , 2010 .

[31]  Limin Yang,et al.  An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery , 2003 .

[32]  Paolo Gamba,et al.  Combining SAR-Based and Multispectral-Based Extractions to Map Urban Areas at Multiple Spatial Resolutions , 2015, IEEE Geoscience and Remote Sensing Magazine.

[33]  Wei Guo,et al.  Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data , 2015, Remote. Sens..

[34]  A. Monteiro,et al.  Using remote sensing and census tract data to improve representation of population spatial distribution: case studies in the Brazilian Amazon , 2012 .

[35]  D. Carr,et al.  Proximate Population Factors and Deforestation in Tropical Agricultural Frontiers , 2003, Population and environment.

[36]  P. Dong,et al.  Evaluation of small-area population estimation using LiDAR, Landsat TM and parcel data , 2010 .

[37]  Xiaomin Qiu,et al.  Population Estimation Methods in GIS and Remote Sensing: A Review , 2005 .

[38]  Jungho Im,et al.  Population estimation based on multi-sensor data fusion , 2010 .

[39]  D. Hogan,et al.  Vulnerabilities and risks in population and environment studies , 2006 .

[40]  Jiyuan Liu,et al.  The rapid and massive urban and industrial land expansions in China between 1990 and 2010: A CLUD-based analysis of their trajectories, patterns, and drivers , 2016 .

[41]  Dengsheng Lu,et al.  Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. , 2011, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[42]  Guiying Li,et al.  Methods to extract impervious surface areas from satellite images , 2014, Int. J. Digit. Earth.

[43]  J. A. Quintanilha,et al.  DMSP/OLS night‐time light imagery for urban population estimates in the Brazilian Amazon , 2006 .

[44]  Qihao Weng,et al.  Fine-scale population estimation: how Landsat ETM+ imagery can improve population distribution mapping , 2010 .

[45]  G. Foody.  Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority , 2009 .

[46]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[47]  Christopher D. Elvidge,et al.  Using DMSP OLS Imagery to Characterize Urban Populations in Developed and Developing Countries , 2010 .

[48]  D. Lu,et al.  Residential population estimation using a remote sensing derived impervious surface approach , 2006 .

[49]  C. P. Lo.  Modeling the population of China using DMSP operational linescan system nighttime data , 2001 .

[50]  U. Deichmann,et al.  World population in a grid of spherical quadrilaterals. , 1997, International journal of population geography : IJPG.

[51]  D. Lu,et al.  Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery , 2004 .

[52]  J. Harvey.  POPULATION ESTIMATION MODELS BASED ON INDIVIDUAL TM PIXELS , 2002 .

[53]  S. Goetz,et al.  IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region , 2003 .

[54]  A. Barbieri,et al.  Population Dynamics and Tropical Deforestation: State of the Debate and Conceptual Challenges , 2005, Population and environment.

[55]  Dar A. Roberts,et al.  A Comparison of Nighttime Satellite Imagery and Population Density for the Continental United States , 1997 .

[56]  Peter M. Atkinson,et al.  Fine spatial resolution residential land-use data for small-area population mapping: a case study in Riyadh, Saudi Arabia , 2015 .

[57]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[58]  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 .

[59]  R. Engstrom,et al.  Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti , 2010 .

[60]  C. Homer,et al.  Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods , 2010 .

[61]  S. Cassels,et al.  Population and Environment. , 2007, Annual review of environment and resources.

[62]  Alan T. Murray,et al.  A cokriging method for estimating population density in urban areas , 2005, Comput. Environ. Urban Syst..

[63]  William A. Gould,et al.  Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data , 2007 .

[64]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[65]  Fahui Wang,et al.  Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti , 2012 .

[66]  Richard E Bilsborrow,et al.  The use of survey data to study migration–environment relationships in developing countries: alternative approaches to data collection , 2012, Population and environment.

[67]  B. Bhaduri,et al.  LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics , 2007 .