Day/Night Band Data Research Articles

Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea

Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

Continuous monitoring of nighttime light changes based on daily NASA's Black Marble product suite

Monitoring nighttime light (NTL) change enables us to quantitatively analyze the dynamic patterns of human activity and socioeconomic features. NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) atmospheric- and Lunar-BRDF-corrected Black Marble product (VNP46A2) provides daily global nighttime radiances with high temporal consistency. However, timely and continuous monitoring of NTL changes based on the dense daily DNB time series is still lacking. In this study, we proposed a novel Viewing Zenith Angle (VZA) stratified COntinuous monitoring of Land Disturbance (COLD) algorithm (VZA-COLD) to detect NTL change at 15 arc-second spatial resolution with daily updating capability based on NASA's Black Marble products. Specifically, we divided the clear observations into four VZA intervals (0–20°, 20°–40°, 40°–60°, and 0–60°) to mitigate the temporal variation of the NTL data caused by the combined angular effects of viewing geometry and the complex surface conditions (e.g., building heights, vegetation canopy covers, etc.). Single-term harmonic models were continuously estimated for new observations from each VZA interval, and by comparing the model predictions with the actual DNB observations, a unified set of NTL changes can be captured continuously among the different VZA intervals. The final NTL change maps were generated after excluding the consistent dark pixels. Results showed that the VZA-COLD algorithm reduced the DNB data temporal variations caused by disparities among different viewing angles and surface conditions, and successfully detected NTL changes for six globally distributed test sites with an overall accuracy of 99.71%, a user's accuracy of 87.18%, and a producer's accuracy of 68.88% for the NTL change category.

Automatic boat detection based on diffusion and radiation characterization of boat lights during night for VIIRS DNB imaging data.

Visible infrared imaging radiometer suite (VIIRS) day/night band (DNB) data has been used to detect lit boats during night as it is very sensitive to low radiances. The existing methods for boat detection from VIIRS DNB data are mainly based on thresholds that are estimated by the statistical characteristics of pixels or artificial experience. This may generate detection errors and poor adaptability due to the lack of characterization of boat lights. In this paper, a two-step threshold detection algorithm based on the point spread and the radiative characteristics of nightlight point sources is proposed, so that the interference from adjacent pixels could be reduced as much as possible and a reasonable threshold could be determined. Meanwhile, this algorithm is applied to three study areas, namely the sea area around Tianjin Port in Bohai Sea, the sea area around Shanghai Port in East China Sea, and the sea area around Port Sulphur in Gulf of Mexico. It is demonstrated that the detection precision of the proposed algorithm reaches up to 90% and the recall rate reaches up to 85% in three areas when validated by visual interpretation, and the precision is 85.71% when validated by automatic identification system (AIS) data in the study area of the sea area around Port Sulphur in Gulf of Mexico, which approximately increases by 5% compared with the previous algorithm.

Characterization of Urban Heat Islands Using City Lights: Insights from MODIS and VIIRS DNB Observations

An urban heat island (UHI) is a phenomenon whereby the temperature in an urban area is significantly warmer than it a rural area. To further advance the characterization and understanding of UHIs within urban areas, nighttime light measured by the Day/Night Band (DNB) onboard the Visible Infrared Imaging Radiometer Suite (VIIRS) and the land surface temperature (LST) data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with principal component analysis (PCA) are used here. Beijing (highly developed) and Pyongyang (less developed) are selected as the two case studies. Linear correlation analysis is first used, with higher correlations being found between DNB and LST data at nighttime than between population and LST data for both cities, although none of the correlation coefficients are particularly high because of noise. Principal component analysis (PCA), a method that can remove random noise, is used to extract more useful information. Two types of PCA are conducted, focusing on spatial (S) and temporal (T) patterns. The results of the S-mode PCA reveal that the typical temporal variation is a seasonal cycle for both LST and DNB data in Beijing and Pyongyang. Furthermore, there are monthly cycles for DNB data related to the moon phase in two cities. The T-mode PCA results show important spatial information, while the spatial pattern of the first mode explains over 50% of the variation. This study is among the first to demonstrate the advantages of using urban light to study the spatial variation of urban heat, especially for nighttime urban temperatures measured from space, at the street and neighborhood scales.

Assessment of BRDF Impact on VIIRS DNB from Observed Top-of-Atmosphere Reflectance over Dome C in Nighttime

The Day–Night Band (DNB) imaging sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) adds nighttime monitoring capability to the Suomi National Polar-Orbiting Partnership and National Oceanic and Atmospheric Administration 20 weather satellite launched in 2011 and 2017, respectively. Nighttime visible imagery has already found diverse applications, but image quality is often unsatisfactory. In this study, variations in observed top-of-atmosphere (TOA) reflectance were examined in terms of nighttime bidirectional effects. The Antarctica Dome C ground site was selected due to high uniformity. First, variation of reflectance was characterized in terms of viewing zenith angle, lunar zenith angle, and relative lunar azimuth angle, using DNB data from 2012 to 2020 and Miller–Turner 2009 simulations. Variations in reflectance were observed to be strongly anisotropic, suggesting the presence of bidirectional effects. Then, based on this finding, three popular bidirectional reflectance distribution function (BRDF) models were evaluated for effectiveness in correcting for these effects on the nighttime images. The observed radiance of VIIRS DNB was compared with the simulated radiance respectively based on the three BRDF models under the same geometry. Compared with the RossThick-LiSparseReciprocal (RossLi) BRDF model and Hudson model, the Warren model has a higher correlation coefficient (0.9899–0.9945) and a lower root-mean-square-error (0.0383–0.0487). Moreover, the RossLi BRDF model and Hudson model may have similar effects in the description of the nighttime TOA over Dome C. These findings are potentially useful to evaluate the radiometric calibration stability and consistency of nighttime satellite sensors.

Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas).

Rapid assessment of cyclone damage using NPP-VIIRS DNB and ancillary data.

Rapid damage assessment of natural disasters is essential for the fast recovery and strategic post-disaster reconstructions. In the present study, National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB)-extracted night-time lights (NTL) data were explored for damage assessment caused by extremely severe cyclonic storm ‘AMPHAN’ that struck one of the most populous regions in India. The disaster impact was measured on two parameters: population and crop land area, where NTL density and population density were found to be strongly correlated (r2 > 0.8). From power outage intensity, three ‘crisis zones’ indicating the severity of cyclone damage were identified. Finally, the assessment found that the total affected population and crop land area were nearly 70% and 66%, respectively, of the study area. Therefore, NPP-VIIRS DNB image-based rapid damage assessment is potentially a useful tool for generating first hand information about the physical damages caused by extreme events. Supplementary InformationThe online version of this article (10.1007/s11069-020-04477-9) contains supplementary material, which is available to authorized users.

Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

Evaluation of Luojia 1–01 Nighttime Light Imagery for Built-Up Urban Area Extraction: A Case Study of 16 Cities in China

On June 2, 2018, the Luojia 1-01 (LJ1-01) nighttime light satellite was launched from China with a spatial resolution of 130 m at nadir, which is a significant improvement over the resolutions of previous nighttime light satellites. However, few studies have focused on the applications of LJ1-01 imagery. This letter aimed to evaluate the potential of utilizing LJ1-01 data to extract built-up urban areas in comparison with Visible Infrared Imaging Radiometer Suite (VIIRS) day-night band (DNB) data by conducting a case study of 16 cities involved in the Belt and Road Initiative in China. The built-up urban areas of the 16 cities were extracted by thresholding segmentation in reference to administrative statistical data, and 30-m-resolution artificial impervious data were adopted as the benchmark (ground truth). Qualitative and quantitative assessments were implemented for an evaluation, revealing that the built-up urban areas extracted by LJ1-01 data outperformed the areas extracted by VIIRS DNB data. We argue that these improvements originate from the superior spatial resolution and image quality of LJ1-01 over its predecessors. The LJ1-01 data presented excellent suitability for the extraction of built-up urban areas at the city scale, and hence, these data may be further applied to other related investigations.

Detecting nighttime fire combustion phase by hybrid application of visible and infrared radiation from Suomi NPP VIIRS

An accurate estimation of biomass burning emissions is partially limited by the lack of knowledge of fire burning phase (smoldering vs. flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in nighttime scenes. In contrast, VIIRS 4 μm moderate resolution band #13 (M13), though capable of detecting fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered by their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to the footprint of M-band pixels, accounting for onboard detector aggregation schemes and bowtie effect removals. The visible energy fraction (VEF) is subsequently introduced as an indicator of fire burning phase. VEF is calculated as the ratio of visible light power (VLP) to FRP for each fire pixel retrieved from the VIIRS 750 m active fire product. A global distribution of VEF values is quantitatively obtained, showing smaller VEF values in regions with mostly smoldering wildfires, such as peatland fires in Indonesia, larger VEF values in regions with flaming wildfires over grasslands and savannas in the sub-Sahelian region, and the largest VEF values associated with gas flaring in the Middle East. Mean VEF for different land cover types or regions is highly correlated with modified combustion efficiency (MCE). These results, together with a case study of the 2018 California Camp Fire, show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.

Calibration Improvements in S-NPP VIIRS DNB Sensor Data Record Using Version 2 Reprocessing

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi-NPP is equipped with a Day/Night Band (DNB), a major advancement in nighttime imaging capability. DNB data quality has been largely improved through several calibration updates since early launch. The reprocessed VIIRS DNB sensor data record (SDR) data at the NOAA Center for Satellite Applications and Research (STAR) accommodate all updates, such as DNB modulated relative spectral responses (RSRs), monthly DNB offset and gain ratio, straylight correction, and geolocation terrain correction since launch. In addition, one of the major improvements is in the DNB offset computing using the pitch maneuver-based deep space view that is free of airglow. The reprocessed DNB data results in radiometrically consistent calibrated SDR that allows the user community to use high-quality DNB data in long-term applications. Until the end of 2016, DNB high gain stage (HGS) operational calibration involved estimation of dark offsets using the new moon-based nighttime measurements over the Pacific Ocean. However, the faint emission from airglow during the new moon nights led to an overestimation of the dark offset by an amount equivalent to that of the airglow. This directly impacts the calibrated data by underestimating the calibrated radiance values. The impact is prominent at low light radiance, which can also lead to negative radiances. This paper quantifies the reprocessing led improvements in the calibrated VIIRS DNB data, analyzes the impact of airglow on operational calibrated radiance product, and explains how the absolute accuracy for low light radiance has been largely improved for the entire reprocessed DNB archive.

Variation of Individual Location Radiance in VIIRS DNB Monthly Composite Images

With the growing size and use of night light time series from the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB), it is important to understand the stability of the dataset. All satellites observe differences in pixel values during repeat observations. In the case of night light data, these changes can be due to both environmental effects and changes in light emission. Here we examine the stability of individual locations of particular large scale light sources (e.g., airports and prisons) in the monthly composites of DNB data from April 2012 to September 2017. The radiances for individual pixels of most large light emitters are approximately normally distributed, with a standard deviation of typically 15–20% of the mean. Greenhouses and flares, however, are not stable sources. We observe geospatial autocorrelation in the monthly variations for nearby sites, while the correlation for sites separated by large distances is small. This suggests that local factors contribute most to the variation in the pixel radiances and furthermore that averaging radiances over large areas will reduce the total variation. A better understanding of the causes of temporal variation would improve the sensitivity of DNB to lighting changes.

NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies

Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.

An Estimate of the Pixel-Level Connection between Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Nighttime Lights and Land Features across China

Satellite-derived nighttime light images are increasingly used for various studies in relation to demographic, socioeconomic and urbanization dynamics because of the salient relationships between anthropogenic lighting signals at night and statistical variables at multiple scales. Owing to a higher spatial resolution and fewer over-glow and saturation effects, the new generation of nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), which is located on board the Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite, is expected to facilitate the performance of nocturnal luminosity-based investigations of human activity in a spatially explicit manner. In spite of the importance of the spatial connection between the VIIRS DNB nighttime light radiance (NTL) and the land surface type at a fine scale, the crucial role of NTL-based investigations of human settlements is not well understood. In this study, we investigated the pixel-level relationship between the VIIRS DNB-derived NTL, a Landsat-derived land-use/land-cover dataset, and the map of point of interest (POI) density over China, especially with respect to the identification of artificial surfaces in urban land. Our estimates suggest that notable differences in the NTL between urban (man-made) surfaces and other types of land surfaces likely allow us to spatially identify most of the urban pixels with relatively high radiance values in VIIRS DNB images. Our results also suggest that current nighttime light data have a limited capability for detecting rural residential areas and explaining pixel-level variations in the POI density at a large scale. Moreover, the impact of non-man-made surfaces on the partitioned results appears inevitable because of the spatial heterogeneity of human settlements and the nature of remotely sensed nighttime light data. Using receiver operating characteristic (ROC) curve-based analysis, we obtained optimal thresholds of the nighttime light radiance, by equally weighting the sensitivity and specificity of the identification results, for extracting the nationwide distribution of lighted urban man-made pixels from the 2015 annual composite of VIIRS DNB data. Our findings can provide the basic knowledge needed for the further application of current nighttime light data to investigate spatiotemporal patterns in human settlements.

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

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.

Stability Monitoring of the VIIRS Day/Night Band over Dome C with a Lunar Irradiance Model and BRDF Correction

The unique feature of the Visible Infrared Imager Radiometer Suite (VIIRS) day/night band (DNB) is its ability to take quantitative measurements of low-light scenes at night. In order to monitor the stability of the high gain stage (HGS) of the DNB, nighttime observations over the Dome C site under moonlight are analyzed in this study. The Miller and Turner 2009 (MT2009) lunar irradiance model has been used to simulate lunar illumination over Dome C. However, the MT2009 model does not differentiate the waxing and waning lunar phases. In this paper, the MT-SWC (SeaWiFS Corrected) lunar irradiance model differentiating the waxing and waning lunar phases is derived by correcting the MT2009 model using lunar observations made by the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). In addition, a top of atmosphere (TOA) bi-directional reflectance distribution function (BRDF) model during nighttime over Dome C is developed to remove the angular dependence from the nighttime TOA reflectance. The long-term stability monitoring of the DNB high-gain stage (HGS) reveals a lower reflectance factor in 2012 in comparison to the following years, which can be traced back to the change in relative spectral response (RSR) of National Oceanic & Atmospheric Administration’s (NOAA’s) Interface Data Processing Segment (IDPS) VIIRS DNB in April 2013. It also shows the radiometric stability of DNB data, with long-term stability of less than 1.58% over the periods from 2013 to 2016. This method can be used to monitor the radiometric stability of other low-light observing sensors using vicarious calibration sites under moonlight illumination.

Reliable estimation of IUU fishing catch amounts in the northwestern Pacific adjacent to the Japanese EEZ: Potential for usage of satellite remote sensing images

To establish an estimation procedure for reliable catch amount of illegal, unreported and unregulated (IUU) fishing, light-gathering fishing operations in the northwestern Pacific were analyzed based on the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) data provided by the Suomi National Polar Partnership (SNPP) satellite. The estimated fishing activities were compared with the navigation tracks of vessels obtained from the automatic identification system (AIS). As a model case, the fishing activities of Chinese fishing boats using fish aggregation lights outside the Japanese EEZ in the northwestern Pacific were analyzed from mid-June to early-September 2016. Integration analyses of VIIRS DNB data and AIS information provided reliable data for estimating the fishing activities of Chinese fishing boats and suggested the importance of estimating fish carrier ship movements. The total amount of the chub mackerel (Scomber japonicus) catch during this period was independently estimated from three angles: 1) the fishing capacity of the fishing boats, 2) the freezing capacity of refrigeration factory ships and 3) the fish hold capacity of the fish carrier ships, based on information obtained from interviews with Chinese fisheries companies. These estimates indicated that the total amount of mackerel catch by Chinese fisheries was more than 80% of the allowable biological catch (ABC) of Japan in this area in 2016. This suggests that Pacific high seas fishing has a significant impact on the future of fish abundance. Our proposed procedure raises the possibility of evaluating the fishing impact of some forms of IUU fisheries independently from conventional statistical reports.

Tropical Cyclone Characterization via Nocturnal Low-Light Visible Illumination

AbstractGlobal monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

VIIRS night-time lights

ABSTRACTThe Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) collects global low-light imaging data that have significant improvements over comparable data collected for 40 years by the DMSP Operational Linescan System. One of the prominent features of DNB data is the detection of electric lighting present on the Earth’s surface. Most of these lights are from human settlements. VIIRS collects source data that could be used to generate monthly and annual science grade global radiance maps of human settlements with electric lighting. There are a substantial number of steps involved in producing a product that has been cleaned to exclude background noise, solar and lunar contamination, data degraded by cloud cover, and features unrelated to electric lighting (e.g. fires, flares, volcanoes). This article describes the algorithms developed for the production of high-quality global VIIRS night-time lights. There is a broad base of science users for VIIRS night-time lights products, ranging from land-use scientists, urban geographers, ecologists, carbon modellers, astronomers, demographers, economists, and social scientists.