Authors: Min Zhao*, Nanjing University, Yuyu Zhou, Iowa State University, Xuecao Li, Iowa State University, Chenghu Zhou, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Weiming Cheng, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Manchun Li, Nanjing University
Topics: Remote Sensing, Geographic Information Science and Systems
Keywords: nighttime light, integration, time series, fitting model, Southeast Asia
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Senate Room, Omni, West
Presentation File: No File Uploaded
Satellite-derived nighttime light data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) have been extensively used as observationally based metrics for monitoring human activities and urbanization processes. Differences of these two datasets in spatial and radiometric properties, as well as their temporal discontinuity, make it difficult to realize a longer, more continuous and consistent monitoring of human activities related to urbanization worldwide. In this study, we developed a new approach for integrating DMSP and VIIRS data to realize the pixel-level matching of their nighttime light brightness. Based on this approach, we generated the time series DMSP nighttime light data for Southeast Asia during 1992-2017, and analyzed its nighttime light dynamics at different scales. The evaluation results from different aspects indicate that our approach can achieve a good agreement between DMSP and simulated DMSP data in the same year. Generally, nighttime lights of Southeast Asia exhibit a continuous and fluctuate growth since the 1990s, and this increase is prominent in recent years. Moreover, the total nighttime light brightness and pixel sums of lit areas at both country and local scales show diverse trends and levels. This new approach offers the great potential for generating a longer time series of DMSP nighttime light data from 1992 to date, which can contribute to a more continuous and consistent monitoring of human activities and a better understanding of their corresponding environmental consequences.