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25 results for “Nighttime Light”
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
A Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.</p> <p>We used a series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).</p> <p>The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number).</p> <p>Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename:</p> <p>CCNL_DMSP_1992_V1</p> <p>Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)</p> <p>Field 2: Platform "DMSP"</p> <p>Field 3: Year “1992”</p> <p>Field 4: version “V1”</p>
Nighttime Lights PC1-4 based on the Version 4 DMSP-OLS Nighttime Lights Time Series 1997–2014
<p>Nighttime Lights PC1-4 based on the Version 4 <a href="https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html">DMSP-OLS Nighttime Lights Time Series</a> 1997–2014. Derived using SAGA GIS Principal Component analysis. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul>
Code for producing a consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>The DMSP-OLS NTL product suffers from three main problems, i.e.inter-annual inconsistency, saturation, and blooming effect which will affect the accuracy of urban extraction and the estimation of the social-economic indexes. To address these problems, we adopted three correction methods to rectify inter-annual inconsistency, saturation, and blooming effects.<br> The code is written based on the Javascript API provided by the Google Earth Engine platform(https://earthengine.google.com/)</p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></p>
Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe
<p>Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.</p>
A Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2024) Combining DMSP-OLS and NPP-VIIRS
<h1><strong>Updated to 2024! Welcome to download!</strong></h1> <p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21/V22 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p> <p> </p> <p><strong>Please refer to the paper for detailed information.</strong></p> <div> <div>Li, S., Cao, X*., Zhao, C., Jie, N., Liu, L., Chen, X., Cui, X., (2023). Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS. <em>Remote Sensing,</em> 15, 3925. <a href="https://doi.org/10.3390/rs15163925" target="_blank" rel="noopener">https://doi.org/10.3390/rs15163925</a></div> </div> <p> </p> <p><strong>Other recent publications using PCNL:</strong></p> <p>Li, S., Cao, X.*, (2024). Monitoring the modes and phases of global human activity development over 30 years: Evidence from county-level nighttime light. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 126, 103627. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jag.2023.103627" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jag.2023.103627</a></p> <div> <div> <div>Li, S., Cao, X.*, Liu, L., Li, A., (2025). Inequality of divided and shared socio-economic resources in 15-minute cities of China. <em>Geography and Sustainability, </em>100337. <a href="https://doi.org/10.1016/j.geosus.2025.100337" target="_blank" rel="noopener">https://doi.org/10.1016/j.geosus.2025.100337</a></div> </div> </div>
Data from: Artificial nighttime lighting and herbivory interactively reduce the biomass production of invasive plants while enhancing that of native plants
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Reconstructed Three-decade Global Fine-Grained Nighttime Light Dataset
<p>Nighttime light (NTL) is a foundational data source for studying human activities from a remote sensing perspective. This dataset from 1992 to 2021 is the first global long-term and fine-grained NTL observations. It represents a milestone in facilitating the study of human activities. It is created by a new super-resolution model DeepNTL which converts DMSP-OLS images into NPP-VIIRS images. Compared with baseline models, including RCAN, SwinIR and AutoEncoder, DeepNTL has the hightest accuracy and best generalization ability for untrained years. It provides a good extension of NPP-VIIRS to the early years, and the future annual NPP-VIIRS data can be directly appended to this dataset by users own. More information about the dataset can be found in the "read_me.txt" file. Technical details and evaluations are presented in our paper. Any questions are welcome to be sent to jinyuguo23@m.fudan.edu.cn.</p>
Nighttime Atmospheric Scattering Phase Function Derived from the Scattered Light of a Laser Beam - supplementary material
<p>The ZIP-package contains:</p> <ul> <li>Canon RAW (CR2) and DCRAW pre-processed (PGM) pictures of the sky with the green laser switched on/off, elevations 10 and 20 degrees</li> <li>Calibration data for Canon EOS 6D Mark II + Fish-Eye lens EF8-16mm at 8mm (wignetting, geometrical distortion)</li> <li>C/C++ software 'green_laser.cpp' for extracting the data from the pictures (incl. Windows-executable). Version 2 has improved median filtering and built-in correction if the laser beam does not go exactly overhead. </li> <li>Detailed description of the software and the method of taking and processing the RAW pictures in PDF format</li> <li>Extracted data in EXCEL workbooks</li> </ul>
Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)
<p>We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.</p> <p>TRACE-NTL:</p> <p>├───ancillary<br>├───data<br>└───metrics<br> ├───disruption<br> │ ├───change_segment<br> │ ├───city_uncertainty<br> │ ├───daily_change<br> │ └───daily_qa_flags<br> └───recovery</p> <p><a title="Dataset description" href="https://github.com/srijac/covid-19_Nightlights">https://github.com/srijac/covid-19_Nightlights</a></p> <p>Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. <em>Sci Data</em> <strong>12</strong>, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x </p>
QGIS Data for Canadian Population, PM2.5, Nighttime lights.
<p>This is the dataset for the QGIS analysis for Canadian Population, PM2.5, Nighttime lights. </p>
Code for Producing a Pixel-scale Corrected Nighttime Light Dataset (PCNL, 1992-2021) Combining DMSP-OLS and NPP-VIIRS
<p>We proposed a set of DMSP-OLS and NPP-VIIRS inter-correction methods and produced a pixel-scale corrected nighttime light dataset (PCNL, 1992-2021).</p> <p>Two sets of reliable global nighttime light datasets were chosen as the basis. CCNL-DMSP (1992-2013) is a consistent and corrected nighttime light dataset produced from DMSP-OLS. CCNL-DMSP mainly solved three problems of DMSP-OLS, namely, interannual inconsistency, saturation and blooming. The annual VNL-VIIRS dataset available on the Earth Observation Group website was also used, and the monthly median masked data of V21 was selected. Using filtering and employing outlier removal, VNL-VIIRS has removed sunlit, moonlit and cloudy pixels, and has discarded biomass burning pixels.</p> <p>PCNL shows a great temporal and spatial consistency at both the pixel scale and the regional scale.</p>
Severe and widespread reductions in nighttime activity of nocturnal moths under modern artificial lighting spectra
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Data from: Artificial light increases nighttime prevalence of predatory fishes, altering community composition on coral reefs
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The power of nighttime lights: Exploring their suitability as a proxy for GDP, population, and other socioeconomic activity
<p>Final datasets that were used to arrive at my results in my bachelor's thesis.</p>
Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
<p>Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood e<span>xtremum</span> method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.</p>
Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
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Associations of Nighttime Light Exposure During Pregnancy With Neonatal Jaundice：a Multi-centre Prospective Study in China
ClinicalTrials.gov study NCT03805165. IPD Sharing: Not stated. Countries: 1. Publications: 0.
VIIRS/JPSS1 Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid
The NOAA-20 VIIRS Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid product, short-name VJ146A2 is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product. This product is available at 15 arc-second resolution and contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. The VJ146A2 product files are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format.The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at:https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf
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