scholarly journals RELATION BETWEEN LAND COVERAGE AND THERMAL ENVIRONMENT IN RESIDENTIAL AREAS : A digital analysis of the urban themal environment using remote sensing data Part 3

1983 ◽  
Vol 331 (0) ◽  
pp. 38-44 ◽  
Author(s):  
AKIRA HOYANO ◽  
YOSHIMI URANO ◽  
KENICHI ODA ◽  
NORISHIGE HAMAGUCHI
Keyword(s):  
Remote Sensing ◽  
Thermal Environment ◽  
Remote Sensing Data ◽  
Digital Analysis ◽  
Residential Areas ◽  
Sensing Data

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

2019 ◽  
Vol 11 (16) ◽  
pp. 4488 ◽  
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.

scholarly journals Street-Scale Analysis of Population Exposure to Light Pollution Based on Remote Sensing and Mobile Big Data—Shenzhen City as a Case

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2728 ◽  
Author(s):  
Bo Sun ◽  
Yang Zhang ◽  
Qiming Zhou ◽  
Duo Gao
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Population Exposure ◽  
Light Pollution ◽  
Scale Analysis ◽  
Residential Areas ◽  
Sensing Data ◽  
Shenzhen City ◽  
Mobile Big Data

Most studies on light pollution are based on light intensity retrieved from nighttime light (NTL) remote sensing with less consideration of the population factors. Furthermore, the coarse spatial resolution of traditional NTL remote sensing data limits the refined applications in current smart city studies. In order to analyze the influence of light pollution on populated areas, this study proposes an index named population exposure to light pollution (PELP) and conducts a street-scale analysis to illustrate spatial variation of PELP among residential areas in cites. By taking Shenzhen city as a case, multi-source data were combined including high resolution NTL remote sensing data from the Luojia 1-01 satellite sensor, high-precision mobile big data for visualizing human activities and population distribution as well as point of interest (POI) data. Results show that the main influenced areas of light pollution are concentrated in the downtown and core areas of newly expanded areas with obvious deviation corrected like traditional serious light polluted regions (e.g., ports). In comparison, commercial–residential mixed areas and village-in-city show a high level of PELP. The proposed method better presents the extent of population exposure to light pollution at a fine-grid scale and the regional difference between different types of residential areas in a city.

scholarly journals Identification of genetic soil types based on remote sensing data

2021 ◽  
Vol 333 ◽  
pp. 02018
Author(s):  
Ekaterina Kulik ◽  
Arslanbek Bajshuakov
Keyword(s):  
Remote Sensing ◽  
Social Development ◽  
Remote Sensing Data ◽  
Soil Types ◽  
Digital Analysis ◽  
Soil Maps ◽  
Present Stage ◽  
Sensing Data ◽  
Information Interpretation ◽  
Field Works

At the present stage of social development traditional contact methods of studying the territory features are inferior to other, less costly and more profitable technologies. Such technology is automation of space imagery information interpretation. It allows to minimize scope of field works and it is economically and managerially profitable method of territory analysis. So, in the work the methods of digital analysis and automatic Earth remote data sensing interpretation are used for identification of genetic soil types. During the research conditions of the land has been determined by calculation and index images analysis. The application of interpretational signs had allowed to identify genetic soil types. The methodology and the results of the analysis can serve as a basis for solving the problems of updating soil maps.

Sign in / Sign up

Export Citation Format

Share Document