scholarly journals Extending the DMSP Nighttime Lights Time Series beyond 2013

2021 ◽  
Vol 13 (24) ◽  
pp. 5004
Author(s):  
Tilottama Ghosh ◽  
Kimberly E. Baugh ◽  
Christopher D. Elvidge ◽  
Mikhail Zhizhin ◽  
Alexey Poyda ◽  
...  
Keyword(s):  
Time Series ◽  
Infrared Imaging ◽  
Earth Observation ◽  
Observation Group ◽  
Diurnal Pattern ◽  
High Brightness ◽  
The Earth ◽  
Early Evening ◽  
Nighttime Lights ◽  
Meteorological Satellite

Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights.

scholarly journals Assessing the Ability of Luojia 1-01 Imagery to Detect Feeble Nighttime Lights

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3708 ◽  
Author(s):  
Li ◽  
Liu ◽  
Chen ◽  
Sun
Keyword(s):  
Infrared Imaging ◽  
Detection Accuracy ◽  
Application Research ◽  
Threshold Method ◽  
Minimum Area ◽  
Nighttime Lights ◽  
Nighttime Light ◽  
Data Source ◽  
Meteorological Satellite ◽  
Cutoff Threshold

The Luojia1-01 (LJ1-01) satellite launched on June 2, 2018 provides a new option for nighttime light (NTL) application research. In this paper, four types of human settlements, such as cities, counties, towns and villages, are sampled to evaluate the potential of LJ1-01 to detect feeble NTL by comparing with the NTL images from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-Orbiting Partnership Satellite. First, the landscape indices and cutoff threshold method are applied to enhance signal-noise ratio (SNR). Then, the detection accuracy of samples is evaluated to determine the optimal cutoff threshold for each NTL data source. After that, the spatial correspondence of different NTL images and the area consistency between the samples and NTL footprints are compared. Finally, after the discussion of feeble NTL detection and the influence of clouds, moonlight and image composites, it can be concluded that LJ1-01 is more suitable for detection feeble NTL objects, while great importance should be attached to the measures to eliminate the noise in LJ1-01 image and make LJ1-01 more widely used: (1) In the study area, a suitable cutoff threshold of LJ1-01 image can be set to 0.1 nano-Wcm−2sr−1, which is lower than that of VIIRS image (0.3 nano-Wcm−2sr−1), and this enables LJ1-01 to reserve more information of NTL, especially the feeble NTL. Moreover, the minimum area that can be identified by NTL footprints from LJ1-01 is 0.02 km2, while that of VIIRS and DMSP are 0.3 km2 and 4.5 km2, respectively. (2) The cutoff threshold method can identify the range of NTL with more noise, but cannot eliminate the noise separately. The filtering method and the image composition method may play more important role in the applications of LJ1-01 data.

scholarly journals Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

2021 ◽  
Author(s):  
John Gibson ◽  
Geua Boe-Gibson
Keyword(s):  
Time Series ◽  
Economic Activity ◽  
Validation Studies ◽  
National Level ◽  
Predictive Performance ◽  
The United States ◽  
Cross Sectional ◽  
Nation States ◽  
Nighttime Lights ◽  
Meteorological Satellite

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional Gross Domestic Product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defence Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. Yet applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001-19 time-series of GDP for over 3100 US counties as a benchmark to examine the usefulness of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts are made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries are used to examine what types of economic activity are best proxied by NTL data and comparisons are also made with the predictive performance of earlier NTL data products.

scholarly journals Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019

2021 ◽  
Vol 13 (5) ◽  
pp. 922
Author(s):  
Christopher D. Elvidge ◽  
Mikhail Zhizhin ◽  
Tilottama Ghosh ◽  
Feng-Chi Hsu ◽  
Jay Taneja
Keyword(s):  
Time Series ◽  
Infrared Imaging ◽  
Noise Filtering ◽  
Grid Cells ◽  
Low Light ◽  
Nighttime Lights ◽  
Additional Noise ◽  
Annual Time Series ◽  
Annual Time ◽  
Processing Steps

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

The cooperation between ESA and EU regarding the Earth observation

2006 ◽  
Author(s):  
Madel Carmen Muñoz Rodríguez ◽  
Juan Manuel de Faramiñán Gilbert
Keyword(s):  
Earth Observation ◽  
The Earth

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

scholarly journals Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform

2021 ◽  
Vol 13 (11) ◽  
pp. 2201
Author(s):  
Hanlin Ye ◽  
Huadong Guo ◽  
Guang Liu ◽  
Jinsong Ping ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Single Point ◽  
Longwave Radiation ◽  
Earth Observation ◽  
Artificial Satellites ◽  
Earth Observations ◽  
The Earth ◽  
Observation Geometry ◽  
Global Mean

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

scholarly journals A Sequential Autoencoder for Teleconnection Analysis

2020 ◽  
Vol 12 (5) ◽  
pp. 851 ◽  
Author(s):  
Jiena He ◽  
J. Ronald Eastman
Keyword(s):  
Time Series ◽  
Neural Representation ◽  
Strongly Correlated ◽  
Atmospheric Sciences ◽  
Teleconnection Patterns ◽  
The Earth ◽  
Unsupervised Neural Network ◽  
Residual Series ◽  
Components Analysis ◽  
Computationally Intensive

Many aspects of the earth system are known to have preferred patterns of variability, variously known in the atmospheric sciences as modes or teleconnections. Approaches to discovering these patterns have included principal components analysis and empirical orthogonal teleconnection (EOT) analysis. The latter is very effective but is computationally intensive. Here, we present a sequential autoencoder for teleconnection analysis (SATA). Like EOT, it discovers teleconnections sequentially, with subsequent analyses being based on residual series. However, unlike EOT, SATA uses a basic linear autoencoder as the primary tool for analysis. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. With SATA, the input is an image time series and the neural representation is a unidimensional time series. SATA then locates the 0.5% of locations with the strongest correlation with the neural representation and averages their temporal vectors to characterize the teleconnection. Evaluation of the procedure showed that it is several orders of magnitude faster than other approaches to EOT, produces teleconnection patterns that are more strongly correlated to well-known teleconnections, and is particularly effective in finding teleconnections with multiple centers of action (such as dipoles).

scholarly journals Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

2021 ◽  
Vol 13 (14) ◽  
pp. 2741
Author(s):  
John Gibson ◽  
Geua Boe-Gibson
Keyword(s):  
Time Series ◽  
Economic Activity ◽  
Validation Studies ◽  
National Level ◽  
Predictive Performance ◽  
The United States ◽  
Cross Sectional ◽  
Nation States ◽  
Nighttime Lights ◽  
Defense Meteorological Satellite Program

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

scholarly journals Learning from Nighttime Observations of Gas Flaring in North Dakota for Better Decision and Policy Making

2021 ◽  
Vol 13 (5) ◽  
pp. 941
Author(s):  
Rong Lu ◽  
Jennifer L. Miskimins ◽  
Mikhail Zhizhin
Keyword(s):  
Time Series ◽  
North Dakota ◽  
Policy Making ◽  
Nearest Neighbor ◽  
Negative Binomial ◽  
Infrared Imaging ◽  
Gaussian Mixture Models ◽  
Data Gathering ◽  
Gaussian Mixture ◽  
Estimation Errors

In today’s oil industry, companies frequently flare the produced natural gas from oil wells. The flaring activities are extensive in some regions including North Dakota. Besides company-reported data, which are compiled by the North Dakota Industrial Commission, flaring statistics such as count and volume can be estimated via Visible Infrared Imaging Radiometer Suite nighttime observations. Following data gathering and preprocessing, Bayesian machine learning implemented with Markov chain Monte Carlo methods is performed to tackle two tasks: flaring time series analysis and distribution approximation. They help further understanding of the flaring profiles and reporting qualities, which are important for decision/policy making. First, although fraught with measurement and estimation errors, the time series provide insights into flaring approaches and characteristics. Gaussian processes are successful in inferring the latent flaring trends. Second, distribution approximation is achieved by unsupervised learning. The negative binomial and Gaussian mixture models are utilized to describe the distributions of field flare count and volume, respectively. Finally, a nearest-neighbor-based approach for company level flared volume allocation is developed. Potential discrepancies are spotted between the company reported and the remotely sensed flaring profiles.

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