VIIRS Images Research Articles

Comparative Analysis Of Aqua MODIS and VIIRS Sensors In Mapping Chlorophyll-A Concentration and Sea Surface Temperature In The South Coast Of East Java

The South Coast of East Java is one of the areas that has marine and fisheries potential. This is utilized by local residents as a source of livelihood. To maximize this potential, it is necessary to know how the water quality in an area. The water quality of a water body can be known through the measurement of chlorophyll-a concentration and sea surface temperature with the help of remote sensing technology. This study aims to analyze the comparison of Aqua MODIS and VIIRS images on mapping chlorophyll-a concentration and sea surface temperature with the help of the SeaDAS application whose data is downloaded through the NASA oceancolor website. To map chlorophyll-a concentration and sea surface temperature, an analysis method using the most optimal mathematical model is needed. The results of the Aqua MODIS satellite image chlorophyll-a concentration research in the year (2020-2024) in the South Coast of East Java has an average of 0.24282 mg/m3, 0.19144 mg/m3, 0.51777 mg/m3, 0.37524 mg/m3, and 4.0542 mg/m3. The highest average chlorophyll-a concentration value of Aqua MODIS satellite imagery occurred in May 2024. This condition indicates that the water quality in the South Coast of East Java is low. While the chlorophyll-a concentration of VIIRS satellite images in the year (2020-2024) in the South Coast of East Java has an average of 0.23197 mg/m3, 0.31974 mg/m3, 0.19304 mg/m3, 0.38974 mg/m3, and 1.58583 mg/m3. The highest average chlorophyll-a concentration value of VIIRS satellite imagery occurred in May 2024. This condition indicates that the water quality in the South Coast of East Java is low.

Characterisation of night-time outdoor lighting in urban centres using cluster analysis of remotely sensed light emissions

Evidence of the negative impact of light pollution on ecosystems is increasing every year. Its monitoring and study requires the identification, characterisation and control of the emitting sources. This is the case of urban centres with outdoor lighting that spills light outside the place it is intended to illuminate. The quantity and nature of the pollutant (artificial light at night) depends on the lamps used and how they are positioned, which is important because a greater proportion of blue light means a greater scattering effect. In this study, we analysed the emissions of 100 urban centres in the north of Granada province (Spain), using images from International Space Station (ISS) in 2012 and 2021, and compared the results with public lighting inventories and with data from the VIIRS instrument. Using inference and cluster analysis techniques, we confirmed an overall increase in emissions and a shift in their colour towards blue, consistent with the results of the lighting inventory analysis. However, there is a discrepancy with the results obtained from VIIRS images, which is explained by the lack of sensitivity of this instrument to blue light. We concluded that the analysis of ISS images is a powerful tool for the study and characterisation of street lighting and its evolution, especially when the changes occur in the blue band.

Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation

Forest fires are one of the most devastating natural disasters, and technologies based on remote sensing satellite data for fire prevention and control have developed rapidly in recent years. Early forest fire smoke in remote sensing images, on the other hand, is thin and tiny in area, making it difficult to detect. Satellites with high spatial resolution sensors can collect high-resolution photographs of smoke, however the impact of the satellite’s repeat access time to the same area means that forest fire smoke cannot be detected in time. Because of their low spatial resolution, photos taken by satellites with shorter return durations cannot capture small regions of smoke. This paper presents an early smoke detection method for forest fires that combines a super-resolution reconstruction network and a smoke segmentation network to address these issues. First, a high-resolution remote sensing multispectral picture dataset of forest fire smoke was created, which included diverse years, seasons, areas, and land coverings. The rebuilt high-resolution images were then obtained using a super-resolution reconstruction network. To eliminate data redundancy and enhance recognition accuracy, it was determined experimentally that the M11 band (2225–2275 nm) is more sensitive to perform smoke segmentation in VIIRS images. Furthermore, it has been demonstrated experimentally that improving the accuracy of reconstructed images is more effective than improving perceptual quality for smoke recognition. The final results of the super-resolution image segmentation experiment conducted in this paper show that the smoke segmentation results have a similarity coefficient of 0.742 to the segmentation results obtained using high-resolution satellite images, indicating that our method can effectively segment smoke pixels in low-resolution remote sensing images and provide early warning of forest fires.

Monitoring the light pollution changes of China’s mangrove forests from 1992-2020 using nighttime light data

Mangrove forests are one of the most biologically diverse and productive ecosystems on Earth. They are important breeding and nursing grounds for amphibians, invertebrates, birds, fish, etc. Light pollution may cause serious degradation of biodiversity in the ecosystem. A report of the long-term holistic views of light pollution changes is essential for sustainable management of mangrove ecosystems. However, to date, such studies have rarely been carried out. This study aimed to monitor the long-term light pollution changes of China’s mangrove forests. To achieve this goal, we used time-series nighttime light (NTL) data to build continuous light pollution data. NTL maps made with DMSP-OLS (DNL) or NPP-VIIRS (VNL) are widely utilized in research on human activity. However, DMSP and VIIRS images are different in spatial resolution, radiation resolution, and data saturation. Thus, this study innovatively set an optimal threshold for generating consistent light pollution data in mangrove areas from 1992-2020. The results showed that: (1) the proportion of light-polluted mangrove forests in China increased from 12% in 1992 to 52% in 2020; (2) the largest net increase occurred in Guangxi with an area of 4,086 ha, followed by Guangdong (3,365 ha) and Hainan (2,944 ha); (3) Zhejiang had the largest proportion of net growth (from 0% in 1992 to 99% in 2020), followed by Hainan (66%) and Fujian (59%). Mangrove forests have been protected and restored for decades in China; this study indicates that the establishment of nature reserves is effective in preventing the light pollution of mangroves and provides the first long-term multi-temporal dataset of light pollution in China’s mangrove forests. This comprehensive information could support related studies and facilitate the development of applicable coastal management strategies in China.

Hotspot Spatial Patterns Using SNNP-VIIRS for Fire Potential Monitoring

The province of East Kalimantan is officially designated as the State Capital because the area has the least risk of disaster, even though it cannot be separated from disasters such as forest and land fires. This study aims to determine the spatial pattern of hotspots using SNPP-VIIRS for monitoring potential fires. The research used the descriptive-analytic method to identify the research area and collect secondary data. Secondary data is spatial and nonspatial data consisting of hotspot data from the recording of the SNPP -VIIRS image, including frequency and distribution of hotspots. The data usage from 2012–2021 using SNPP-VIIRS morning and evening recordings. The study results show that the spatial pattern of potential hotspots in the capital city of a new country is quite varied. The spatial pattern of hotspots shows that Kutai Kartanegara Regency as one of the locations for the new State Capital, has the highest number of hotspots, namely 38,970 with the highest accuracy in East Kalimantan Province, namely, 1,616 (low), 36,253 (nominal), and 1,101 (high). The potential for fire disasters in Kutai Kartanegara Regency as an IKN location is high, so planning is urgently needed for future fire prevention, mitigation, and prevention strategies. The spatial pattern of hotspots is known, so it can be used to monitor potential fires and minimize fire occurrences.

Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak

COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country’s urban area.

S-NPP VIIRS Lunar Calibrations over 10 Years in Reflective Solar Bands (RSB)

Since 28 October 2011, the VIIRS Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (S-NPP) has operated over 10 years and successfully generated scientific global images for the Earth’s environment and climate studies. Besides thermal and day night bands, VIIRS has 14 reflective solar bands (RSBs) that cover a spectral range of 0.41 µm to 2.25 µm. The primary and daily source of calibration for the RSBs is the Solar Diffuser (SD) as an onboard calibrator, and its degradations are tracked by the Solar Diffuser Stability Monitor (SDSM). Alternatively, monthly scheduled lunar calibration has provided long-term on-orbit trends that validate the corresponding SD-based calibration results. In this paper, on-orbit lunar calibration and comparison results are focused on, in conjunction with the SD calibrations that are performed by the National Oceanic and Atmospheric Administration (NOAA) VIIRS team. In addition, a recent study showed that there is increasing striping in the VIIRS images in the RSBs caused by the non-uniform SD degradation. The estimation of the SD non-uniformity and a mitigation method is proposed along with the striping reductions.

Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery

Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong–Hong Kong–Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson’s coefficient of 0.9613, and R2 (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources.

Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS nighttime light imagery

In the last two decades, the advance in nighttime light (NTL) remote sensing has fueled a surge in extensive research towards mapping human footprints. Nevertheless, the full potential of NTL data is largely constrained by the blooming effect. In this study, we propose a new concept, the Pixel Blooming Effect (PiBE), to delineate the mutual influence of lights from a pixel and its neighbors, and an integrated framework to eliminate the PiBE in radiance calibrated DMSP-OLS datasets (DMSPgrc). First, lights from isolated gas flaring sources and a Gaussian model were used to model how the PiBE functions on each pixel through point spread function (PSF). Second, a two-stage deblurring approach (TSDA) was developed to deconvolve DMSPgrc images with Tikhonov regularization to correct the PiBE and reconstruct PiBE-free images. Third, the proposed framework was assessed by synthetic data and VIIRS imagery and by testing the resulting image with two applications. We found that high impervious surface fraction pixels (ISF > 0.6) were impacted by the highest absolute magnitude of PiBE, whereas NTL pattern of low ISF pixels (ISF < 0.2) was more sensitive to the PiBE. By using TSDA the PiBE in DMSPgrc images was effectively corrected which enhanced data variation and suppressed pseudo lights from non-built-up pixels in urban areas. The reconstructed image had the highest similarity to reference data from synthetic image (SSIM = 0.759) and VIIRS image (r = 0.79). TSDA showed an acceptable performance for linear objects (width > 1.5 km) and circular objects (radius > 0.5 km), and for NTL data with different noise levels (<0.6σ). In summary, the proposed framework offers a new opportunity to improve the quality of DMSP-OLS images and subsequently will be conducive to NTL-based applications, such as mapping urban extent, estimating socioeconomic variables, and exploring eco-impact of artificial lights.

NIGHT-SKY BRIGHTNESS IN BURKINA FASO: OUAGADOUGOU LIGHT POLLUTION ASSESSMENT AND IMPACT ON THE STARRY SKY OBSERVATION .

The quest for well-being and improvement of the populations living conditions in the borders of the big cities have changed habits in terms of lighting. The use of LED lamps and the reduction in the cost of photovoltaic panels, have allowed easy access to electricity nowadays. The lack of education on the nature of outdoor lighting has unfortunately led to an increase of light pollution. This study uses VIIRS DNB Free Cloud Composites imagery to perform light pollution in Burkina Faso mainly in Ouagadougou, the main cities and surrounding areas. VIIRS imagery data obtained have been processed to get information of light pollution by classifying the information into several classes presented in a map. Using VIIRS images, we show that light pollution has increased by 20% in Burkina Faso in major cities such Ouagadougou. Despite the fact that the illuminated surface per thousand inhabitants, ~0.09km2, remains almost stable over the years. Areas of the city experience a decrease (&lt;1%/year) in nighttime light while some areas have an explosion up to 50nW/cm2sr a year due to the presence of new infrastructure. Light pollution increases inside the city and the observation of the night sky becomes quite difficult in the inner city and still feasible on the outskirts of these cities.

Pemanfaatan Citra VIIRS untuk Deteksi Asap Kebakaran Hutan dan Lahan di Indonesia

The observation of smoke because of land and forest fires in some regions in Indonesia mostly use the composite image visually. This study aims to develop the detection model of forest and land fire smoke using a digital analysis, which will be faster in supporting spatial information on emergency response in monitoring forest and land fire smoke. The method used is multi-threshold method and compare it with the existing model that is by modification of method Li et al. (2015). The data used is Suomi NPP-VIIRS satellite imagery. The results concluded that the VIIRS image can be used to detect the smoke and smoke distribution of forest fire and digital smoke. The multi-threshold model uses reflectance data obtained from the M4 visible channel, and the brightness temperature data obtained from the LWIR VIIRS M14 channel, with an average accuracy of 82.2% with a Commision error of 9.8% and an Ommision error of 10%. While the model of modification Li is based only on reflectance of visible-channel data i.e. channel M1, M2, M3, and SWIR VIIRS M11 channel, which has an average accuracy of 72.3% with a Commision error of 0.3% and an Ommision error of 27.4%. The multi-threshold model is a model that has the potential to be applied to detect forest and land fire smoke.

Aligning Pixel Values of DMSP and VIIRS Nighttime Light Images to Evaluate Urban Dynamics

The brightness of pixels in nighttime light images (NTL) has been regarded as the proxy of the urban dynamics. However, the great difference between the pixel values of NTL from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS) poses obstacles to analyze economic and social development with NTL in a continuous temporal sequence. This research proposes a methodology to align the pixel values of both NTL by calibrating annual DMSP images between the years 1992–2013 with a robust regression algorithm with a quadratic polynomial regression model and simulating annual DMSP images with VIIRS images between years 2012 and 2018 with a model consisting of a power function and a Gaussian low pass filter. As a result, DMSP annual images between years 1992–2018 can be produced. Case study of Beijing and Yiwu are conducted and evaluated with local gross domestic product (GDP). Compared with the values of DMSP and VIIRS annual composites, the Pearson correlation coefficients of DMSP and simulated DMSP annual composites in 2012 and in 2013 increase significantly, while the root mean square error (RMSE) decrease evidently. In addition, the correlation of the sum of light of NTL and local GDP is enhanced with a simulation process. These results demonstrate the feasibility of the proposed method in narrowing the gap between DMSP and VIIRS NTL in pixel values.

Capturing the Urban Divide in Nighttime Light Images From the International Space Station

Earlier studies utilizing coarse resolution DMSP-OLS nighttime light (NTL) imagery suggest a negative correlation between the amount of NTL and urban deprivation. The International Space Station (ISS) NTL images offer higher resolution images compared to DMSP-OLS or VIIRS images, allowing an analysis of intraurban NTL variations. The aim of this study is to examine the capacity of ISS images for analyzing the intraurban divide. NTL images of four cities (one African, two Asian, and one South American) have been processed and analyzed. The results show that deprived areas are generally the darker spots of built-up areas within cities, illustrating the urban divide in terms of access to street lighting. However, differences exist between cities: Deprived areas in the African city (Dar es Salaam) generally feature lower NTL emissions compared to the examined cities in South America (Belo Horizonte) and Asia (Mumbai and Ahmedabad). Beyond, variations exist in NTL emissions across deprived areas within cities. Deprived areas at the periphery show less NTL compared to central areas. Edges of deprived areas have higher NTL emissions compared to internal areas. NTL emission differences between types of deprived areas were detected. The correlation between ISS NTL images and population densities is weak; this can be explained by densely built-up deprived areas having less NTL compared to lower density formal areas. Our findings show ISS data complement other data to capture the urban divide between deprived and better-off areas and the need to consider socioeconomic conditions in estimating populations.