Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas

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.

Download Full-text

Linking thermal UAS-based imagery and Cosmic-Ray Neutron Sensing data

10.5194/egusphere-egu2020-19609 ◽

2020 ◽

Author(s):

Veronika Döpper ◽

Tobias Gränzig ◽

Michael Förster ◽

Birgit Kleinschmit

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Hydrological Modeling ◽

Regional Scale ◽

Remote Sensing Data ◽

Cosmic Ray ◽

Added Value ◽

Innovative Technology ◽

Sensing Data ◽

Local Point

Soil moisture content (SMC) is of fundamental importance to many hydrological, biological, biochemical and atmospheric processes. Common soil moisture measurements range from local point measurements to global remote sensing-based SMC datasets. Nevertheless, they always compromise between temporal and spatial resolution. Thus, it is still challenging to quantify spatially and temporally distributed SMC at a regional scale which is extremely relevant for hydrological modeling or agricultural management. The innovative technology Cosmic-Ray Neutron Sensing (CRNS) shows significant potential to fill this gap by quantifying the present hydrogen pools within footprints larger than 0.1 ha.Owing to the difference in scale between the ground resolution of satellites used to retrieve soil moisture and the common point scale of ground-based soil moisture instruments, the large footprint of the CRNS poses a high potential for the validation of SMC remote sensing products. When linking the CRNS measurements with remote sensing data, the vertical and horizontal characteristics of its footprint need to be considered.To examine the influence of the CRNS footprint characteristics on the linkage of CRNS and remote sensing data, we couple CRNS measurements with high-resolution UAS-based thermal imagery acquired at two sites in Bavaria and Brandenburg (Germany) using a radiometrically calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) with a focal length of 9 mm. Within this context, we evaluate the added value of applying a horizontal weighting function to the spatially distributed thermal data in comparison to an unweighted mean when statistically representing the corrected neutron counting rates.The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.

Download Full-text

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-6917-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 6917-6969 ◽

Cited By ~ 7

Author(s):

M. Schneider ◽

Y. González ◽

C. Dyroff ◽

E. Christner ◽

A. Wiegele ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapour ◽

Lower Troposphere ◽

Remote Sensing Data ◽

Added Value ◽

Empirical Validation ◽

Transport Pathways ◽

Sensing Data ◽

Interferometer Observation

Abstract. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isototopologue remote sensing and in-situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products (generated from ground-based FTIR, Fourier Transform InfraRed, spectrometer and space-based IASI, Infrared Atmospheric Sounding Interferometer, observation). As reference we use well calibrated in-situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues and the scatter with respect to the in-situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In addition, we find indications for a positive δD bias in the remote sensing products. The δD data are scientifically interesting only if they add information to the H2O observations. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data by comparing δD-vs.-H2O curves. First, we show that the added value of δD as seen in the Picarro data is similarly seen in FTIR data measured in coincidence. Second, we document that the δD-vs.-H2O curves obtained from the different in-situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

Download Full-text

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

Sensors ◽

10.3390/s20092728 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2728 ◽

Cited By ~ 1

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.

Download Full-text

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

Atmospheric Measurement Techniques ◽

10.5194/amt-8-483-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 483-503 ◽

Cited By ~ 16

Author(s):

M. Schneider ◽

Y. González ◽

C. Dyroff ◽

E. Christner ◽

A. Wiegele ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapour ◽

Moisture Transport ◽

Lower Troposphere ◽

Remote Sensing Data ◽

Added Value ◽

Data Sets ◽

Empirical Validation ◽

Sensing Data

Abstract. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isotopologue remote sensing and in situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products, generated from ground-based FTIR (Fourier transform infrared), spectrometer and space-based IASI (infrared atmospheric sounding interferometer) observation. The study is made in the area of the Canary Islands in the subtropical northern Atlantic. As reference we use well calibrated in situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues, and the scatter with respect to the in situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In both remote sensing data sets we find a positive δD bias of 30–70‰. Complementing H2O observations with δD data allows moisture transport studies that are not possible with H2O observations alone. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data. We document that the δD–H2O curves obtained from the different in situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

Download Full-text

INFLUENCE OF MIXELS ON LAND COVER CLASSIFICATION IN RESIDENTIAL AREAS USING REMOTE SENSING DATA

Journal of Architecture Planning and Environmental Engineering (Transactions of AIJ) ◽

10.3130/aijax.394.0_1 ◽

1988 ◽

Vol 394 (0) ◽

pp. 1-9

Author(s):

AKIRA HOYANO ◽

YOSHINORI KOMATSU

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Remote Sensing Data ◽

Land Cover Classification ◽

Residential Areas ◽

Sensing Data

Download Full-text

Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data

Remote Sensing ◽

10.3390/rs13245153 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5153

Author(s):

Liangliang Zhou ◽

Yishao Shi ◽

Jianwen Zheng

Keyword(s):

Remote Sensing ◽

Urban Area ◽

Remote Sensing Data ◽

Residential Areas ◽

Economic Activities ◽

Night Time ◽

Sensing Data ◽

Dbscan Clustering ◽

The Difference ◽

The Impact

The activity of the urban night-time economy is one of the most important indicators reflecting the prosperity of an urban economy. The business circle is an important carrier of urban commercial activities and the core area of urban nightlife. This paper takes the main urban area of Yiwu city as the research object. Based on POI data and night-time light remote sensing data, two-factor mapping, kernel density analysis, DBSCAN clustering, and local contour tree methods are adopted to identify the business circle structure of the main urban area of Yiwu city and analyse the relationship between business circle characteristics and the night-time economy. The following conclusions can be drawn. (1) The spatial superimposition relationship between the night-time remote sensing data and points of interest (POI) data in the main urban area of Yiwu city is good, and the overall coupling results show obvious circle structure characteristics. (2) The spatial distribution of different business combinations has obvious regularity: comprehensive shopping business shows a multicentre distribution pattern and has a hierarchical feature. In contrast, professional food and beverage and leisure and entertainment businesses are close to urban residential areas, and different groups of people live in different places with their own characteristics. (3) From 2015 to 2019, the brightness value of each business circle showed a continuously increasing trend. In 2020, due to the impact of COVID-19, most of them declined. (4) Overall, the difference in business circle tiers reflects the difference in the level of night-time economic activities.

Download Full-text

The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region

Weather and Forecasting ◽

10.1175/2010waf2222451.1 ◽

2011 ◽

Vol 26 (2) ◽

pp. 166-183 ◽

Cited By ~ 137

Author(s):

T. Haiden ◽

A. Kann ◽

C. Wittmann ◽

G. Pistotnik ◽

B. Bica ◽

...

Keyword(s):

Remote Sensing ◽

Global Radiation ◽

Precipitation Amount ◽

Remote Sensing Data ◽

Comprehensive Analysis ◽

Added Value ◽

Limited Area ◽

Mountainous Terrain ◽

Sensing Data ◽

Station Data

Abstract This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2–6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output.

Download Full-text

Urban Biophysical Quality Modelling Based On Remote Sensing Data In Semarang, Indonesia

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2020-173 ◽

2021 ◽

Vol 14 (3) ◽

pp. 14-23

Author(s):

Iswari Nur Hidayati ◽

Karunia Pasya Kusumawardani ◽

A. G. Ayudyanti ◽

R. R. Prabaswara

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Industrial Revolution ◽

Environmental Changes ◽

Rapid Assessment ◽

Remote Sensing Data ◽

Urban Ecosystems ◽

Residential Areas ◽

Sensing Data ◽

Housing Complex

Cities are centres of economic growth with fascinating dynamics, including persistent urbanisation that encroaches adjacent arable lands to build urban physical features and sustain services offered by urban ecosystems. Even though industrial revolution, economic dynamics, and environmental changes affect spatial feasibility for housing, complex urban growth is always followed by the development of environmentally friendly cities. However, with such quality having multiple facets, it is necessary to assess and map liveable areas from a more comprehensive and objective perspective. This study aimed to assess, map and identify the biophysical quality of an urban environment using a straightforward technique that allows rapid assessment for early detection of changes in the quality. It proposed a multi-index approach termed the urban biophysical environmental quality (UBEQ) based on spectral characteristic of remote sensing data for residential areas calculated using various data derived from remote sensing. Statistical analyses were performed to test data reliability and normality. Further, many indices were analysed, then employed as indicators in UBEQ modelling and tested with sensitivity and factor analysis to obtain the best remote sensing index in the study area. Based on PCA Results, it was found that the built-up land index and vegetation index mainly contributed to the UBEQ index. The generated model had 86.5% accuracy. Also, the study area, Semarang City, had varying UBEQ index values, from high to low levels.

Download Full-text