Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data

Wang, Congxiao; Chen, Zuoqi; Yang, Chui‐Ping; Li, Qiaoxuan; Wu, Qiusheng; Wu, Jianping; Zhang, Guo; Yu, Bo

doi:10.1016/j.jag.2019.101989

Cited by 51 publications

(34 citation statements)

References 43 publications

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“…Nighttime light data provide an effective analysis perspective for measuring the intensity of urban social and economic activities. The Luojia-1 satellite launched on 2 June, 2018 and can provide nighttime light data with a resolution of 130 m [37,38]. Nighttime light data, acquired in 31 August, 2018, were downloaded from the website http://59.175.109.173:8888/.…”

Section: Poi Datasetmentioning

confidence: 99%

Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

et al. 2020

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Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.

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Section: Poi Datasetmentioning

confidence: 99%

Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

et al. 2020

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“…Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL) ( Bennett and Smith, 2017 , Elvidge et al, 2017 , Li et al, 2019 ). NTL reflects the dynamics and intensity of socioeconomic activities, which has been widely used in mapping urban areas, estimating population and GDP, and monitoring disasters and conflicts ( Chen and Nordhaus, 2011 , Chen et al, 2017 , Levin et al, 2020 , Li and Li, 2014 , Li et al, 2015 , Ou et al, 2019 , Wang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities

Xiu

et al. 2021

International Journal of Applied Earth Observation and Geoinfor

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The lockdown of cities to against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA’s Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there were no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.

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“…The newly launched Luojia1-01 satellite provides a new data source of NTL at 130 m resolution [51,52]. The latest study [53] demonstrated that compared with the NPP-VIIRS, Luojia 1-01 has fewer "blooming" phenomena and appears to be more suitable for providing local variations of socioeconomic and human activities. The main objective of this study was to propose a novel framework for estimating building-level populations by incorporating Luojia1-01 NTL, LiDAR, and POI data.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data

Chen

et al. 2021

J Remote Sens

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Building-level population data are of vital importance in disaster management, homeland security, and public health. Remotely sensed data, especially LiDAR data, which allow measures of three-dimensional morphological information, have been shown to be useful for fine-scale population estimations. However, studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution. In this article, we proposed a framework to estimate population at the building level by integrating POI data, nighttime light (NTL) data, and LiDAR data. Building objects were first derived using LiDAR data and aerial photographs. Then, three categories of building-level features, including geometric features, nighttime light intensity features, and POI features, were, respectively, extracted from LiDAR data, Luojia1-01 NTL data, and POI data. Finally, a well-trained random forest model was built to estimate the population of each individual building. Huangpu District in Shanghai, China, was chosen to validate the proposed method. A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R2=0.65 at the building level and R2=0.79 at the community level. The NTL radiance intensity was found to have a positive relationship with population in residential areas, while a negative relationship was found in office and commercial areas. Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data, the accuracy of building-level population estimation can be improved.

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Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data

Cited by 51 publications

References 43 publications

Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities

A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data

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