Vegetation Adjusted Nighttime Light Urban Index Research Articles

Refining Long-Time Series of Urban Built-Up-Area Extraction Based on Night-Time Light—A Case Study of the Dongting Lake Area in China

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Impact of nighttime light data saturation correction on the application of carbon emissions spatialization: A comparative study of the correction effect and application effect based on five methods in China

Nighttime light (NTL) remote sensing data have attracted wide attention in monitoring carbon dioxide (CO2) emissions of fossil fuel consumption in recent years due to its unique perspective of observing human activity and low cost. However, saturation errors in NTL data are believed to limit the accuracy of the application. Therefore, this study compared the desaturation ability and the effect of spatializing CO2 emissions in China based on five saturation correction methods of Letu + Tong, SARMRC (the saturation correction method based on a regression model and radiance-calibrated NTL data), VANUI (index-based vegetation adjusted NTL urban index), EANTLI (enhanced vegetation index (EVI)-adjusted NTL index) and LERNCI (land surface temperature (LST)-and-EVI-regulated-NTL-city index). Based on this, we explored the influence of saturation correction on the spatialization of CO2 emissions. The main findings of this study include: (1) The saturation correction effect is limited by the bias of the NTL image itself in some regions, the auxiliary data, and the regional differences of the fitting models, which fail to reach the best effect in spatializing CO2 emissions at the provincial level. However, as downscaling to the prefectural level, the Letu + Tong image with the advantage of desaturation has a better effect on spatializing CO2 emissions. (2) The saturation correction effect of Letu + Tong and EANTLI images is relatively better with advantages in three evaluation criteria than other images. In most provinces, the original NTL and SARMRC images are better at spatializing CO2 emissions. (3) Implementing inter-calibration was found to be generally beneficial in enhancing the effects of saturation correction. This study provides an important basis for improving the quality of NTL data and helps to reflect the spatial pattern of CO2 emissions more accurately and efficiently.

Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.

An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

Anthropogenic Heat Flux Estimation Based on Luojia 1-01 New Nighttime Light Data: A Case Study of Jiangsu Province, China

With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTLnor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R2 of 0.8444 along with the smallest RMSE of 4.8277 W·m−2 and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m−2. Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m−2, followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m−2, while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m−2. This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively.

A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction.

Nighttime light (NTL) images have been broadly applied to extract urban built-up areas in recent years. However, the typical NTL images provided by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) have the drawbacks of low resolution and blooming effect, which bring difficulty for the application of them in urban built-up area extraction. Therefore, this paper proposes the POI (point of interest) and LST (land surface temperature) adjusted NTL urban index (PLANUI) to extract the urban built-up areas with high accuracy. PLANUI is the first urban index to integrate POI and NTL for urban built-up area extraction. In this paper, NPP/VIIRS and Luojia 1-01 images were introduced as the original NTL data and the vegetation adjusted NTL urban index (VANUI) was selected as the comparison item. The threshold method was utilized to extract urban built-up areas from these data. The results show that: (1) Based on the comparison with the reference data, the PLANUI can make up the shortcoming of low resolution and the blooming effect of NTL effectively. (2) Compared with the VANUI, the PLANUI can significantly improve the accuracy of the urban built-up areas extracted and characterize urban features. (3) According to the results based on NPP/VIIRS and Luojia 1-01 images, the PLANUI has extensive applicability, both for regions with different degrees of economic development and NTL data with different resolutions. PLANUI can enhance the features of urban built-up areas with social sensing data and natural remote sensing data, which helps to weaken the NTL blooming effect and improve the extraction accuracy. PLANUI can provide an effective approach for urban built-up area extraction, which plays a certain guiding role for the study of urban structure, urban expansion, and urban planning and governance.

Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data

Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.

Understanding the spatiotemporal development of human settlement in hurricane-prone areas on the US Atlantic and Gulf coasts using nighttime remote sensing

Abstract. Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience.

Evacuation Priority Method in Tsunami Hazard Based on DMSP/OLS Population Mapping in the Pearl River Estuary, China

Evacuation plans are critical in case of natural disaster to save people’s lives. The priority of population evacuation on coastal areas could be useful to reduce the death toll in case of tsunami hazard. In this study, the population density remote sensing mapping approach was developed using population records in 2013 and Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) night-time light (NTL) image of the same year for defining the coastal densest resident areas in Pearl River Estuary (PRE), China. Two pixel-based saturation correction methods were evaluated for application of population density mapping to enhance DMSP/OLS NTL image. The Vegetation Adjusted NTL Urban Index (VANUI) correction method (R2 (original/corrected): 0.504, Std. error: 0.0069) was found to be the better-fit correction method of NTL image saturation for the study area compared to Human Settlement Index (HSI) correction method (R2 (original/corrected): 0.219, Std. error: 0.1676). The study also gained a better dynamic range of HSI correction (0~25 vs. 0.1~5.07) compared to the previous one [27]. The town-level’s population NTL simulation model is built (R2 = 0.43, N = 47) for the first time in PRE with mean relative error (MSE) of 32% (N = 24, town level), On the other side, the tsunami hazard map was produced based on numerical modeling of potential tsunami wave height and velocity, combining with the river net system, elevation, slope, and vegetation cover factors. Both results were combined to produce an evacuation map in PRE. The simulation of tsunami exposure on density of population showed that the highest evacuation priority was found to be in most of Zhuhai city area and the coastal area of Shenzhen City under wave height of nine meters, while lowest evacuation priority was defined in Panyu and Nansha Districts of Guangzhou City, eastern and western parts of Zhongshan City, and northeast and northwest parts of Dongguan City. The method of tsunami risk simulation and the result of mapped tsunami exposure are of significance for direction to tsunami disaster-risk reduction or evacuation traffic arrangement in PRE or other coastal areas in the world.

Improving estimates of built-up area from night time light across globally distributed cities through hierarchical modeling

Built-up area has become an important indicator for studying urban environments, but mapping built-up area at the regional/global scale remains challenging due to the complexity of impervious surface features. Nighttime light data (NTL) is one of the major remote sensing data sources for regional/global built-up or impervious surface mapping. A single regression relationship between fractional built-up/impervious area and NTL or various indices derived based on NTL and vegetation index (e.g., NDVI) data had been established in many previous studies. However, due to the varying geographical, climatic, and socio-economic characteristics of cities, the same regression relationship may vary significantly across cities. In this study, we examined the regression relationship between percentage of built-up area (pBUA) and vegetation adjusted nighttime light urban index (VANUI) for 120 randomly selected cities around the world with a hierarchical hockey-stick regression model. We found that there is a substantial variability in the slope (0.658 ± 0.318), the threshold VANUI (−1.92 ± 0.769, log scale) after which the linear relationship holds, and the coefficient of determination R2 (0.71 ± 0.14) among globally distributed cities. A small proportion of this substantial variability can be attributed to socio-economic status (e.g., total population, GDP per capita) and landscape structures (e.g., compactness and fragmentation). Due to these variations, our hierarchical model or no-pooling model (i.e., fit each city individually) can significantly improve model prediction accuracy (17% in terms of root mean squared error) over a complete-pooling model. We, however, recommend hierarchical models as they can provide meaningful priors for future modeling under a Bayesian framework, and achieve higher prediction accuracy than no-pooling models when sample size is small.

A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis

Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data.

Improving Satellite-Driven PM2.5 Models with VIIRS Nighttime Light Data in the Beijing–Tianjin–Hebei Region, China

Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5) using satellite-derived aerosol optical depth (AOD) in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime light (NTL) data, acquired with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, could be useful for predictions because they have been shown to be good indicators of the urbanization and human activity that can affect PM2.5 concentrations. This study investigated the potential of incorporating VIIRS NTL data in statistical models for PM2.5 concentration predictions. We developed a mixed-effects model to derive daily estimations of surface PM2.5 levels in the Beijing–Tianjin–Hebei region using 3 km resolution satellite AOD and VIIRS NTL data. The results showed the addition of NTL information could improve the performance of the PM2.5 prediction model. The NTL data revealed additional details for predication results in areas with low PM2.5 concentrations and greater apparent seasonal variation due to the seasonal variability of human activity. Comparison showed prediction accuracy was improved more substantially for the model using NTL directly than for the model using the vegetation-adjusted NTL urban index that included NTL. Our findings indicate that VIIRS NTL data have potential for predicting PM2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM2.5 distributions.

An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China

Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO2 emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO2 emission, but also provided implications for low-carbon and sustainable development plans.

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction.

Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data

Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data have been used for ISA mapping, but high uncertainty existed due to mixed-pixel and data-saturation problems. This paper presents a new index called normalized impervious surface index (NISI), which is an integration of DMSP-OLS and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, in order to reduce these problems. Meanwhile, this newly developed index is compared with previously used indices—Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI)—in ISA mapping performance. We selected China as an example to map fractional ISA distribution through a support vector regression approach based on the relationship between the index and Landsat-derived ISA data. The results indicate that the proposed NISI provided better ISA estimation accuracy than HSI and VANUI, especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e., <0.2). This approach can be used to rapidly update ISA datasets at regional and global scales.

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales.

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.

A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area

Mapping Impervious Surface Area (ISA) at regional and global scales has attracted increasing interest. DMSP-OLS nighttime light (NTL) data have proven to be successful for mapping urban land in large areas. However, the well-documented issues of pixel blooming and saturation limit the ability of DMSP-OLS data to provide accurate urban information. In this paper, a multi-source composition index is proposed to overcome the limitations of extracting urban land using only the NTL data. We combined three data sources (i.e., DMSP-OLS, MODSI EVI and NDWI) to generate a new index called the Normalized Urban Areas Composite Index (NUACI). This index aims to quickly map impervious surface area at regional and global scales. Experimental results indicate that NUACI has the ability to reduce the pixel saturation of NTL and eliminate the blooming effect. With the reference data derived from Landsat TM/ETM+, regression models based on normalized DMSP-OLS, Human Settlement Index (HSI), vegetation adjusted NTL urban index (VANUI), and NUACI are then established to estimate ISA. Our assessments reveal that the NUACI-based regression model yields the highest performance. The NUACI-based regression models were then used to map ISA for China for the years 2000, 2005 and 2010 (Free download link for the ISA products can be found at the end of this paper).

An improved method of night-time light saturation reduction based on EVI

Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) night-time light (NTL) data have been widely applied to studies on anthropogenic activities and their interactions with the environment. Due to limitations of the OLS sensor, DMSP NTL data suffer from a saturation problem in central urban areas, which further affects studies based on nocturnal lights. Recently, the vegetation-adjusted NTL urban index (VANUI) has been developed based on the inverse correlation of vegetation and urban surfaces. Despite its simple implementation and ability to effectively increase variations in NTL data, VANUI does not perform well in certain rapidly growing cities. In this study, we propose a new index, denoted enhanced vegetation index (EVI)-adjusted NTL index (EANTLI), that was developed by reforming the VANUI algorithm and utilizing the EVI. Comparisons with radiance-calibrated NTL (RCNTL) and the new Visible Infrared Imager Radiometer Suite (VIIRS) data for 15 cities worldwide show that EANTLI reduces saturation in urban cores and mitigates the blooming effect in suburban areas. EANTLI’s similarity to RCNTL and VIIRS is consistently higher than VANUI’s similarity to RCNTL and VIIRS in both spatial distribution and latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption of 166 Chinese prefecture-level cities. In conclusion, EANTLI can effectively reduce NTL saturation in urban centres, thus presenting great potential for wide-range applications.

A dynamic model for population mapping: a methodology integrating a Monte Carlo simulation with vegetation-adjusted night-time light images

Population is attracting increasing attention as a driver of resource overexploitation, environmental degradation, loss of biodiversity, and other environmental challenges. Timely and accurately updating maps of population distribution are thus urgently needed. Images of night-time lights from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have been used for years in population mapping as an alternative to human settlement distribution. The capacity of night-time light images for gridding populations, however, is compromised by the dual effects of saturation and overglow. Static models of the human settlement index (HSI), elevation-adjusted human settlement index (EAHSI), and vegetation-adjusted night-time light urban index (VANUI) have been developed to counteract these negative effects by using constant coefficients. The static models, however, retain disadvantages due to the negative effects of the high variation of socio-economic backgrounds in different study areas. In this study, we integrate Monte Carlo simulation with the above three static indices and propose the dynamic model VANUI Supported by Monte Carlo Simulation (VANUIMCS) for mapping the population of Liaoning Province, China. We assess the accuracy of the simulation using data for 60 counties and 1251 townships. The VANUIMCS improve the accuracy of population mapping, with the mean percentage errors of 19.43% at the county level and 43.19% at the township level.