Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery

Huang, Xin; Yang, Junjing; Li, Jiayi; Wen, Dawei

doi:10.1016/j.isprsjprs.2021.03.019

Cited by 62 publications

(33 citation statements)

References 57 publications

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“…However, these multi-view observations are still limited by their spatial resolution less than or equivalent to 30 m, which makes it challenging to quantify urban structures at fine scales, especially for those dense and high-rise cities. The Chinese launched Ziyuan3-01 (ZY3-01) satellite can simultaneously acquire multi-view images in nadir, forward, and backward modes, but its spatial resolution is much higher at 2.1 m. Recently, Huang et al (2021) integrated the multispectral and multi-view ZY3-01 images to capture urban vertical information, which can be helpful and cost-effective in urban land use classification.…”

Section: Multi-angular Remote Sensingmentioning

confidence: 99%

“…Given the advantage of nighttime light observations in characterizing human activities and socioeconomics, some recent satellite initiatives and missions have delved into the exploration of fine-scale nighttime light sensors. For example, the Jilin1-07 satellite can provide high-resolution nighttime light images at 0.92 m (Huang et al, 2021). All these efforts will potentially contribute to better measures and approaches for depicting fine-scale urban structures and functions.…”

Section: Nighttime Light Remote Sensingmentioning

confidence: 99%

“…For example, Zong et al (2020) integrated road network datasets of Gaode and OpenStreetMap (OSM) to divide urban parcels and integrated Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data, and other data to derive final land use classifications. Huang, Yang, Li, and Wen (2021) fused the multi-view optical imagery and high-resolution nighttime light imagery to derive urban land use function for two megacities of Wuhan and Beijing in China.…”

Section: Introductionmentioning

confidence: 99%

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Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Чэн

Gong

2021

Big Earth Data

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Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research.

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Section: Multi-angular Remote Sensingmentioning

confidence: 99%

Section: Nighttime Light Remote Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Чэн

Gong

2021

Big Earth Data

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“…Recently, night light (NTL) data have been incorporated with other data sources to improve population estimation [9,30,31]. The existing NTL data include the DMSP/OLS NTL, NPP/VIIRS NTL, and Luojia-1 NTL [32][33][34]. The first two types of NTL data have a low resolution and sensitivity, particularly for low radiation brightness areas [34,35].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Zang

Zhang

Chen

et al. 2021

Land

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Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

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“…For instance, the shortwavelength light stronger suppresses melatonin production and distort circadian rhythms in mammals (Haim and Portnov, 2013;Hatori et al, 2017;Lockley et al, 2003), while the long-wavelength light stronger disrupts the magnetic orientation of migratory birds (Wiltschko et al, 1993). As far as adverse effects of NTL become more recognized to depend on light spectrum, multispectral satellite imagery analysis becomes more numerous (Guk and Levin, 2020;Huang et al, 2021;Levin and Duke, 2012;Rybnikova and Portnov, 2017;Zheng et al, 2018). Such studies usually aim at revealing associations between light of certain spectra and land-use types or economic activities.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Identify on-Ground Lamp Types From Night-Time Iss Images

Rybnikova¹,

Miguel²,

Rybnikov³

et al. 2021

Preprint

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Artificial night-time light (NTL), emitted by various on-ground human activities, becomes further intensive in many regions worldwide. Its adverse effects on humans’ and ecosystems’ health crucially depend on the light spectrum, making the remote discrimination between different lamps a highly important task. However, such studies remain extremely limited, and none of them exploits freely available satellite imagery. In the analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, the radiometrically calibrated ISS RGB image is used. Spatial resolution of the image is ~20 meters, implying that each pixel may represent a mixture of different lamp types. Unmixing analysis to the detailed spectral signatures of the corresponding in situ measurements is performed, with ‘pure’ lamps’ signatures as the endmembers. Afterward, statistical models to reproduce the results of unmixing based on the broad-band RGB image from the ISS are run. The built models predict well (with R2 reaching ~0.87) the contribution of high-pressure sodium (HPS) and metal-halide (MH) lamps, the most spread in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport.

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Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery

Cited by 62 publications

References 57 publications

Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

A New Approach to Identify on-Ground Lamp Types From Night-Time Iss Images

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