1. MMC
  2. All Volumes and Issues
  3. Volume 8, Number 2, 2021
  4. Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data

Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data

MMC.
2021;
Volume 8, Number 2
: pp. 304–316
https://doi.org/10.23939/mmc2021.02.304
Received: March 05, 2021
Accepted: May 10, 2021

Mathematical Modeling and Computing, Vol. 8, No. 2, pp. 304–316 (2021)

Authors:
  1. V. Kinakh
  2. T. Oda
  3. R. Bun
  4. O. Novitska
1
Lviv Polytechnic National University
2
Universities Space Research Association, Columbia, MD, USA; University of Maryland, College Park, MD, USA; Osaka University, Suita, Osaka, Japan
3
Lviv Polytechnic National University; Academy of Business in Dąbrowa Górnicza
4
Lviv Polytechnic National University

Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems.  Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission product.  It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC.  Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites field of view, is especially critical for the spatial analysis of emissions from cities.  In this paper, there is proposed a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC.  To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities.  Assumption is that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data.  Therefore, it is necessary to find an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields.  In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields.  Hence, an original approach for restoring data in such pixels is considered using elimination of the factor that distorted the ODIAC emission fields.  It is also proposed a bias correction method for shifted high-resolution emission fields in ODIAC.  The bias correction was applied to multiple cities from the different continents.  It is shown that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.

remote sensing
nighttime lights data
greenhouse gas emission
satellite data bias
bias analysis algorithm
  1. Yeh C., Perez A., Driscoll A., Azzari G., Tang Z., Lobell D., Ermon S., Burke M.  Using publicly available satellite imagery and deep learning to understand economic well-being in Africa.  Nat. Commun. 11 (1), 1–11 (2020).
  2. Lespinas F., Wang Y., Broquet G., Breon F.-M., Buchwitz M., Reuter M., Meijer Y., Loescher A., Janssens-Maenhout G., Zheng B., Ciais P.  The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources.  Carbon Balance and Management. 15 (1), 18 (2020).
  3. Sutton P., Dar R., Elvidge C., Kimberly B.  An estimate of the global human population using night-time satellite imagery.  Int. J. Remote Sens. 22 (16), 3061–3076 (2001).
  4. Bennett M. M., Smith L. C.  Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics.  Remote Sens. Environ. 192, 176–197 (2017).
  5. Elvidge C. D., Baugh K. E., Kihn E. A., Kroehl H. W., Davis E. R.  Mapping city lights with nighttime data from the DMSP operational linescan system.  Photogramm. Eng. Rem. S. 63, 727–734 (1997).
  6. Baugh K., Elvidge C., Ghosh T., Ziskin D.  Development of a 2009 stable lights product using DMSP-OLS data.  Proc. of the Asia-Pacific Advanced Network. 30, 114–130 (2010).
  7. DMSP OLS.  Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System. https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html
  8. Small C., Pozzi F., Elvidge C. D.  Spatial analysis of global urban extent from DMSP-OLS night lights.  Remote Sens. Environ. 96 (3–4), 277–291 (2005).
  9. Ghosh T., Anderson S. J., Elvidge C. D., Sutton P. C.  Using nighttime satellite imagery as a proxy measure of human well-being.  Sustainability. 5, 4988–5019 (2013).
  10. Bruederle A., Hodler R.  Nighttime lights as a proxy for human development at the local level.  PLoS ONE. 13 (9), e0202231 (2018).
  11. Li L., Yu T., Zhao L., Zhan Y., Zheng F., Zhang Y., Mumtaz F., Wang C.  Characteristics and trend analysis of the relationship between land surface temperature and nighttime light intensity levels over China.  Infrared Phys. Techn. 97, 381–390 (2019).
  12. Oda T., Maksyutov S.  A very high-resolution (1 km ${\times}$ 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos. Chem. Phys. 11, 543–556 (2011).
  13. Oda T., Maksyutov S., Andres R. J.  The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO${}_{2}$ gridded emissions data product for tracer transport simulations and surface flux inversions.  Earth Syst. Sci. Data. 10, 87–107 (2018).
  14. ODIAC fossil fuel emission dataset.  http://db.cger.nies.go.jp/dataset/ODIAC/
  15. Chen J., Zhao F., Zeng N., Oda T.  Comparing a global high-resolution downscaled fossil fuel CO${}_{2}$ emission dataset to local inventory-based estimates over 14 global cities.  Carbon Balance and Management. 15 (9), 1–15 (2020).
  16. Gaughan A.E., Oda T., Sorichetta A., Stevens F.R., Krauser L., Yetman G., Bun R., Bondarenko M., Nghiem S. V.  Evaluation of gridded CO2 emissions from night-time lights compared with geospatially-derived population distributions for Vietnam, Cambodia and Laos.  IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 1625–1628 (2019).
  17. Han P., Zeng N., Oda T., Lin X., Crippa M., Guan D., Janssens-Maenhout G., Ma X., Liu Z., Shan Y., Tao  S., Wang H., Wang R., Wu L., Yun X., Zhang Q., Zhao F., Zheng B.  Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories.  Atmos. Chem. Phys. 20, 11371–11385 (2020).
  18. Oda T., Bun R., Kinakh V., Topylko P., Halushchak M., Marland G., Lauvaux T., Jonas M., Maksyutov S., Nahorski Z., Lesiv M., Danylo O., Horabik-Pyzel J.  Errors and uncertainties in a gridded carbon dioxide emissions inventory.  Mitig. Adapt. Strat. Gl. 24 (6), 1007–1050 (2019).
  19. Jokar Arsanjani J., Zipf A., Mooney P., Helbich M.  OpenStreetMap in GIScience - Experiences, Research, and Applications.  Springer (2015).
  20. Bun R., Nahorski Z., Horabik-Pyzel J., Danylo O., See L., Charkovska N., Topylko P., Halushchak M., Lesiv M., Valakh M., Kinakh V.  Development of a high resolution spatial inventory of GHG emissions for Poland from stationary and mobile sources.  Mitig. Adapt. Strat. Gl.  24 (6), 853–881 (2019).
  21. Charkovska N., Halushchak M., Bun R., Nahorski Z., Oda T., Jonas M., Topylko P.  A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: Reducing the errors and uncertainties in global emission modelling.  Mitig. Adapt. Strat. Gl. 24 (6), 941–968 (2019).
  22. Danylo O., Bun R., See L., Charkovska N.  High resolution spatial distribution of greenhouse gas emissions in the residential sector.  Mitig. Adapt. Strat. Gl.  24 (6), 907–939 (2019).
  23. Kinakh V., Bun R., Danylo O.  Geoinformation technology for analysis and visualisation of high spatial resolution greenhouse gas emissions data using a cloud platform.  Advances in Intelligent Systems and Computing II.  689, 217–229 (2018).
  24. Crisp D., Pollock H. R., Rosenberg R., Chapsky L., Lee R. A. M., Oyafuso F. A., Frankenberg C., O'Dell C. W., Bruegge C. J., Doran G. B., Eldering A., Fisher B. M., Fu D., Gunson M. R., Mandrake L., Osterman G. B., Schwandner F. M., Sun K., Taylor T. E., Wennberg P. O., Wunch D.  The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products.  Atmos. Meas. Tech. 10, 59–81 (2017).
  25. Eldering A., Taylor T. E., O’Dell C. W., Pavlick R.  The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data.  Atmos. Meas. Tech. 12, 2341–2370 (2019).
  26. Zheng Z., Chen Y., Wu Z., Ye X., Guo G., Qian Q.  The desaturation method of DMSP/OLS nighttime light data based on vector data: taking the rapidly urbanized China as an example.  Int. J. Geogr. Inf. Sci. 33 (3), 431–453 (2018).
  27. de Miguel A. S., Kyba C. C., Zamorano J., Gallego J.  The nature of the diffuse light near cities detected in nighttime satellite imagery.  Sci. Rep. 10, 7829 (2020).
  28. Li X., Zhou Y., Zhao M., Zhao X.  A harmonized global nighttime light dataset 1992-2018.  Scientific Data. 7, 168 (2020).
  29. Letu H., Hara M., Tana G., Nishio F.  A saturated light correction method for DMSP/OLS nighttime satellite imagery.  IEEE T. Geosci. Remote. 50 (2), 389–396 (2012).
  30. Zhenga Q., Wenga Q., Wang K.  Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS nighttime light imagery.  Remote Sens. Environ. 240, 111707 (2020).
  31. Ash K., Mazur K.  Identifying and correcting signal shift in DMSP-OLS data.  Remote Sens. 12 (14), 2219 (2020).
  32. Ren C., Yu Z., Deng K., Pan Y.  Deblurring study of DMSP/OLS nighttime light data by RTSVD.  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (2020).
  33. Zheng Z., Yang Z., Chen Y., Wu Z., Marinello F.  The interannual calibration and global nighttime light fluctuation assessment based on pixel-level linear regression analysis.  Remote Sens. 11 (18), 2185 (2019).
  34. Kinakh V., Oda T., Bun R.  Formulating a geolocation bias correction for DMSP nighttime lights of global cities.  Advances in Intelligent Systems and Computing V.  1293, 383–398 (2021).