Regional dynamics of DMSP-like nighttime lights 1992-2019

Title Slide

• A Harmonized Global Nighttime Light Dataset (1992–2018)
• Authors: Xuecao Li, Yuyu Zhou, Min Zhao, & Xia Zhao
• Published in: Scientific Data (2020)
• DOI: https://doi.org/10.1038/s41597-020-0510-y

🌍 Introduction

• Nighttime light (NTL) data provide insights into human activity, urbanization, and economic development.
• Two primary sources: DMSP/OLS (1992–2013) & VIIRS (2012–2018).
• Challenge: Significant inconsistency between DMSP and VIIRS data.
• Objective: Develop a harmonized global NTL dataset for long-term analysis.

👩‍💻 Data Collection

• DMSP/OLS NTL Data (1992–2013):
  • Downloaded from the Payne Institute for Public Policy.
  • Digital number (DN) values range from 0 to 63.
  • Spatial resolution: 30 arc-seconds.
• VIIRS/DNB Data (2012–2018):
  • Higher spatial & radiometric resolution.
  • Monthly composites were processed into annual data.
  • Spatial resolution: 15 arc-seconds.

🔄 Methodology

• Three-step harmonization process:
  1. Annual Composition of VIIRS Data:
     • Used cloud-free coverage data as a weighting factor.
     • Removed noise from aurora, fires, and temporary sources using thresholding techniques.
     • Applied a weighted averaging approach to generate annual composite images from monthly VIIRS data.
  2. Conversion of VIIRS to DMSP-like Data:
     • Kernel Density (KD) Approach:
       • Aggregated VIIRS radiance data (15 arc-seconds) to match DMSP resolution (30 arc-seconds).
       • Used a Gaussian point-spread function to reduce differences in radiance distribution.
     • Logarithmic Transformation:
       • Applied logarithmic transformation to adjust radiance variations in urban, suburban, and rural areas.
       • Reduced differences in brightness levels between high and low radiance pixels.
     • Sigmoid Function Conversion:
       • Developed a sigmoid function based on 2013 data to map transformed VIIRS data to DMSP-like DN values.
       • Parameters of the function were optimized at a global scale and validated at continental and national levels.
  3. Integration of DMSP & VIIRS Data:
     • Inter-calibrated DMSP data (1992–2013) using a stepwise calibration approach.
     • Applied derived sigmoid function to convert VIIRS data (2014–2018) into DMSP-like DN values.
     • Merged both datasets to create a consistent 27-year global NTL dataset.

🌍 Technical Validation

• Histogram Comparison:
  • Compared DN distributions of inter-calibrated DMSP and VIIRS-derived DMSP-like data.
  • Verified similarity in data distributions for overlapping years (2012–2013).
  • Identified a slight increase in high DN values (>60) due to DMSP saturation effects.
• Temporal Consistency (1992–2018):
  • Assessed trends in total nighttime light (NTL) intensity and number of lit pixels.
  • Conducted analysis using different DN thresholds (7, 20, 30) to minimize low-luminance noise.
  • Observed a stable and continuous trend in high-luminance areas (DN > 20).
• Spatial Validation:
  • Evaluated spatial accuracy using major metropolitan areas (e.g., Beijing, New York).
  • Compared observed DMSP, raw VIIRS radiance, and DMSP-like VIIRS data.
  • Verified agreement in urban spatial patterns, indicating robustness of the integration approach.
• Independent Socioeconomic Correlations:
  • Compared trends with external socioeconomic indicators (e.g., GDP, electricity consumption).
  • Strong correlations between harmonized NTL dataset and economic development patterns.
  • Ensures reliability of dataset for studies on urbanization and economic growth.

🏰 Applications of the Dataset

• Urban expansion analysis (e.g., Beijing-Tianjin region).
• Socioeconomic studies (e.g., GDP estimation, electricity consumption).
• Environmental monitoring (e.g., light pollution, carbon emissions).
• Disaster impact assessments (e.g., conflict zones, power outages).

📊 Key Findings & Conclusion

• The harmonized NTL dataset enables long-term analysis (1992–2018).
• Overcomes DMSP-VIIRS inconsistencies using a systematic integration approach.
• Provides a valuable resource for urbanization, economics, and environmental studies.
• Dataset Access: Original data repository
• GEE dataset Access: Awesomme GEE community catalog
• Exploratory Tool: GEE web app by Carlos Mendez

See web app in full screen HERE