Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

Abstract:As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with day/night band (DNB), which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP), may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based) and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs), show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore, the analysis results across 99 cities in 10 urban agglomerations in China reveal inter-regional variations in partition thresholds and human settlement patterns related to the urban size and form. Our partition method and relative results can provide insight into the further application of VIIRS DNB nighttime light data in spatially delineated urbanization processes and socioeconomic activities in human settlements.

Section: Discussionmentioning

confidence: 99%

Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach

Yin

Zhou

2018

“…Different nighttime light indexes [20,23,25,27,28] (such as light area, mean light, and the total amount of lights) and function forms (such as linear regression, and log-log equation) have been proposed in previous research to model CO 2 emissions, and a good correlation between nighttime light index and CO 2 emissions has been indicated. DN values of nighttime light data represent the intensity of human activities, energy consumption and CO 2 emissions associated with human production and living activities.…”

Section: Methodsmentioning

confidence: 99%

“…As shown in Table 1, compared to DMSP-OLS, NPP-VIIRS are superior in spatial and radiometric resolution, radiometric detection range, and onboard calibration [16,17]. The potential advantages of NPP-VIIRS for mapping socio-economic activities have been established, and especially good correlations have been obtained with socioeconomic parameters, such as Gross Domestic Product (DP), electric power consumption, and population [18][19][20][21][22]. Table 1.…”

Section: Introductionmentioning

confidence: 99%

The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

Zhang

Peng

et al. 2017

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO 2 ) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO 2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO 2 emissions, and provide guidance and a reference for modeling CO 2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO 2 emissions dataset (PKU-CO 2 ) were used to perform simple regressions at provincial, prefectural and 0.1 • × 0.1 • grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO 2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO 2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO 2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO 2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO 2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights -CO 2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.

“…Because of the improved performance, VIIRS DNB data have been used in various applications, such as extracting urban areas [23][24][25][26][27], reflecting demographic and socioeconomic conditions [25,[28][29][30][31][32][33], monitoring nocturnal surface air quality [34][35][36], and so on [26,[37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

An Object Similarity-Based Thresholding Method for Urban Area Mapping from Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Data

2018

Nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) provides a unique data source for mapping and monitoring urban areas at regional and global scales. This study proposes an object similarity-based thresholding method using VIIRS DNB data to map urban areas. The threshold for a target potential urban object was determined by comparing its similarity with all reference urban objects with known optimal thresholds derived from Landsat data. The proposed method includes four major steps: potential urban object generation, threshold optimization for reference urban objects, object similarity comparison, and urban area mapping. The proposed method was evaluated using VIIRS DNB data of China and compared with existing mapping methods in terms of threshold estimation and urban area mapping. The results indicated that the proposed method estimated thresholds and mapped urban areas accurately and generally performed better than the cluster-based logistic regression method. The correlation coefficients between the estimated thresholds and the reference thresholds were 0.9201-0.9409 (using Euclidean distance as similarity measure) and 0.9461-0.9523 (using Mahalanobis distance as similarity measure) for the proposed method and 0.9435-0.9503 for the logistic regression method. The average Kappa Coefficients of the urban area maps were 0.58 (Euclidean distance) and 0.57 (Mahalanobis distance) for the proposed method and 0.51 for the logistic regression method. The proposed method shows potential to map urban areas at a regional scale effectively in an economic and convenient way.