Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

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Mapping Changing Population Distribution on the Qinghai–Tibet Plateau since 2000 with Multi-Temporal Remote Sensing and Point-of-Interest Data

Remote Sensing ◽

10.3390/rs12244059 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4059

Author(s):

Lanhui Li ◽

Yili Zhang ◽

Linshan Liu ◽

Zhaofeng Wang ◽

Huamin Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Population Density ◽

Rural Areas ◽

Population Distribution ◽

Local Population ◽

Tibet Plateau ◽

Point Of Interest ◽

Tibet Autonomous Region ◽

Multi Temporal ◽

Qinghai Tibet Plateau

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.

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A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Remote Sensing ◽

10.3390/rs12061032 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1032

Author(s):

Shengyu Xu ◽

Linbo Qing ◽

Longmei Han ◽

Mei Liu ◽

Yonghong Peng ◽

...

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Image Data ◽

Urban Region ◽

Remote Sensing Images ◽

Social Sensing ◽

Sensing Data ◽

Functional Regions ◽

Point Of Interest ◽

Wide Range

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

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Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques

Open Geosciences ◽

10.2478/v10085-009-0008-5 ◽

2009 ◽

Vol 1 (1) ◽

Cited By ~ 50

Author(s):

Biswajeet Pradhan

Keyword(s):

Remote Sensing ◽

Process Model ◽

Remote Sensing Data ◽

Groundwater Potential ◽

Remotely Sensed Data ◽

Drainage Pattern ◽

Ancillary Data ◽

Sensing Data ◽

Gis Techniques ◽

Gis Tools

AbstractThis paper summarizes the findings of groundwater potential zonation mapping at the Bharangi River basin, Thane district, Maharastra, India, using Satty’s Analytical Hierarchal Process model with the aid of GIS tools and remote sensing data. To meet the objectives, remotely sensed data were used in extracting lineaments, faults and drainage pattern which influence the groundwater sources to the aquifer. The digitally processed satellite images were subsequently combined in a GIS with ancillary data such as topographical (slope, drainage), geological (litho types and lineaments), hydrogeomorphology and constructed into a spatial database using GIS and image processing tools. In this study, six thematic layers were used for groundwater potential analysis. Each thematic layer’s weight was determined, and groundwater potential indices were calculated using groundwater conditions. The present study has demonstrated the capabilities of remote sensing and GIS techniques in the demarcation of different groundwater potential zones for hard rock basaltic basin.

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Surge potential and drainage-basin characteristics in East Greenland

Annals of Glaciology ◽

10.3189/172756403781816220 ◽

2003 ◽

Vol 36 ◽

pp. 142-148 ◽

Cited By ~ 39

Author(s):

Hester Jiskoot ◽

Tavi Murray ◽

Adrian Luckman

Keyword(s):

Remote Sensing ◽

Drainage Basin ◽

Remote Sensing Data ◽

Environmental Data ◽

Glacier Inventory ◽

East Greenland ◽

Sensing Data ◽

Logistic Analysis ◽

Geological Conditions ◽

Multivariate Logistic Analysis

AbstractWe introduce a new glacier inventory of central East Greenland and use the collected data to test proposed theories on surging. The glacier inventory contains 259 glaciers, of which 10 have observed surges and a further 61 are inferred surge-type. The total glaciated area is 5.5×103 km2. The inventory was created from a combination of remote-sensing data and maps, and some 24 glacial and geological inventory parameters were collected for each glacier. A multivariate logistic analysis is used to test which combination of glacial and environmental data is conducive to surging behaviour in East Greenland. Three different models suggest that glaciers with a large complexity, low slope and oriented in a broad arc from northeast to south are most likely to be of surge type. Geological conditions, and hence substrate character, appeared not to be related to surge potential. On the basis of these results and the surge dynamics in this region, we suggest a hydrologically controlled surge mechanism operates in central East Greenland.

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Urban Functional Zone Mapping with a Bi-branch Neural Network via Fusing Remote Sensing and Social Sensing Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3127246 ◽

2021 ◽

pp. 1-1

Author(s):

Runyu Fan ◽

Ruyi Feng ◽

Wei Han ◽

Lizhe Wang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Social Sensing ◽

Sensing Data ◽

Functional Zone

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Using remote sensing data in population density estimation

10.25743/sdm.2021.34.56.066 ◽

2021 ◽

Author(s):

E.G. Shvetsov ◽

N.M. Tchebakova ◽

E.I. Parfenova

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Population Density ◽

Vegetation Index ◽

Census Data ◽

Remote Sensing Data ◽

Satellite System ◽

Population Census ◽

Krasnoyarsk Krai ◽

Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

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Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

KnE Social Sciences ◽

10.18502/kss.v3i21.4981 ◽

2019 ◽

Author(s):

Iswari Nur Hidayati ◽

R Suharyadi ◽

Projo Danoedoro

Keyword(s):

Remote Sensing ◽

Land Use ◽

Spatial Heterogeneity ◽

Urban Ecology ◽

Vegetation Cover ◽

Land Use Changes ◽

Remote Sensing Data ◽

Environmental Data ◽

Ecological Index ◽

Sensing Data

The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

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Can More Accurate Night-Time Remote Sensing Data Simulate a More Detailed Population Distribution?

Sustainability ◽

10.3390/su11164488 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4488 ◽

Cited By ~ 5

Author(s):

Nannan Gao ◽

Fen Li ◽

Hui Zeng ◽

Daniël van Bilsen ◽

Martin De Jong

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Large Scale ◽

Population Distribution ◽

Remote Sensing Data ◽

Urban Land Use ◽

Residential Areas ◽

Night Time ◽

Sensing Data ◽

Population Distributions

Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.

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Urban Residential Land Suitability Analysis Combining Remote Sensing and Social Sensing Data: A Case Study in Beijing, China

Sustainability ◽

10.3390/su11082255 ◽

2019 ◽

Vol 11 (8) ◽

pp. 2255 ◽

Cited By ~ 7

Author(s):

Huiping Huang ◽

Qiangzi Li ◽

Yuan Zhang

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Urban Land ◽

Urban Land Use ◽

Land Suitability ◽

Residential Land ◽

Social Sensing ◽

Sensing Data ◽

Beijing China

With the degradation of the environment and the acceleration of urbanization, urban residential land has been undergoing rapid changes and has attracted great attention worldwide. Meanwhile, the quantitative evaluation of the suitability of urban residential land is essential for a better and more powerful understanding of urban residential land planning and improvement. Most urban land suitability studies rely solely on remote sensing data and GIS data to evaluate natural suitability, and few studies have focused on urban land suitability from a socioeconomic perspective. Consequently, this paper integrates remote sensing data (GaoFen-2 satellite image) and social sensing data (Tencent User Density data, Point-of-interest data and OpenStreetMap data) to establish an evaluation framework for analyzing the suitability of urban residential land in the Haidian District, Beijing, China, in which, ecological comfortability, locational livability and overall suitability were evaluated according to five attributes extracted from urban residential land via the factor analysis method. The evaluation results of this case study show that, compared with the suburban area in the northwest, the urban area tends to have lower ecological comfortability and higher locational livability. The overall suitability increases from southeast to northwest, consistent with the spatial distribution of ecological comfortability. This framework can potentially assist with the sustainable development of residential lands and urban land use planning.

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