Introducing NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset
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Keywords: Nighttime lights, normalized difference urban index, DMSP-OLS, data continuity, calibration, urbanization, remote sensing
Abstract Type: Paper Abstract
Authors:
Subhasis Ghosh, Department of Geosciences, Auburn University, Auburn, Alabama, United States
Manmeet Singh, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, United States
Harsh Kamath, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, United States
Shivam Saxena, National Institute of Technology, Rourkela, India
Vaisakh SB, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
Chandana Mitra, Department of Geosciences, Auburn University, Auburn, Alabama, United States
Naveen Sudharsan, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, United States
Suryachandra Rao, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
Hassan Dashtian, Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, United States
Lori Magruder, Centre for Space Research, The University of Texas at Austin, Austin, TX, United States
Abstract
Urbanization is accelerating globally. Despite covering less than 2% of the Earth’s surface, urban areas continue to experience many extreme weather impacts. The increase in population and economic activity necessitates access to different urban data for effective management and planning. Because of complexity of urban problems and diversity of models being employed urban datasets of different temporal and spatial resolutions are used for global urban climate analyses such as the upcoming IPCC City Climate Special Assessments. Therefore, in this study we introduce NDUI+, a novel global urban dataset developed using advanced remote sensing and deep learning techniques. NDUI+ utilizes the Normalized Difference Urban Index (NDUI) matrix but overcome the discontinuity challenges caused by the DMSP-OLS System ending in 2012. NDUI+ is developed in this study as a continuous, global NDUI time series data combining DMSP-OLS, VIIRS Nighttime Light data, and Landsat 7 NDVI products using deep-learning approach. This integration addresses issues related to sensor discontinuity and varying data quality, ensuring credible, long-term urban monitoring by normalizing nighttime light data across different satellite products. The NDUI+ global dataset provides 30 meters spatial and annual temporal resolution from 1999 to the present, suitable for a comprehensive view of urban growth and dynamics. The dataset’s capabilities are demonstrated by comparing it with existing urban datasets, such as the Dynamic World data. NDUI+ shows excellent ability to capture urban dynamics with high precision and granularity. This dataset is expected to be of value for the IPCC city climate assessments, urbanization time-series studies, microclimatic variability studies.
Introducing NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset
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Paper Abstract
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Submitted by:
Subhasis Ghosh Auburn University
subhasis@auburn.edu
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