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Many tropical urban cities faced significant impacts due to hydrometeorological hazards, particularly during the extreme rainfall and rainfall deficits. Having an efficient geodatabase consisting of significant high temporal and large hydrometeorological dataset is one of the keys to successful hazard adaptation, mitigation, and prevention. However, acquiring and managing such large raster data for that database is a huge challenging task because many of them were categorized as developing countries and facing the data conflict situations (DCS). The DCS is either the ground data is (i) unavailable, (ii) scarce, (iii) may involve unavailable ground data, scarce data, data interoperability and sharing conflicts, or (iv) unknown data quality. With the availability of publicly accessed remote sensing raster data, there is an opportunity to utilize them as alternative solutions. However, those data required specific downscaling and localization as they come in various spatial, temporal, and radiometric characteristics. It is theorized that storing all the variables in a single consistent vector-based grid GIS layers upon the completion of downscaling could be a useful alternative in anticipating the above-mentioned conflicts. Therefore, this paper initiates the development of the framework to create a spatial vector grid database consisting of multiple hydrometeorological variables derived from the open-source remote sensing data. The framework place significant emphasis on the (1) hardware and software requirements, (2) methodology on the deriving an appropriate environmental variables, indicators, and their validation, and (3) methodology for creating the spatial database based on a vector grid approach. The indicators and variables that will be incorporated are (1) rainfall, (2) solar radiation, (3) Standardized Precipitation Index, (4) topography, (5) impervious surfaces, (6) vegetation index, and (7) land surface temperature. The successful realization of the framework would enabled spatial-based hazards adaptation initiatives at the city scale, including near-real-time forecasting or nowcasting, early warning, post-disaster assessment, and future risk assessment.

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A Framework of the Remote Sensing Database for Hydrometeorological Hazards Adaptation in Urban City of Malaysia

  • 会议论文

  • 发表日期：

    2025年

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  • Mohd.Rizaludin Mahmud,
  • Nor Wahida Awang,
  • Siti Solehah Azmi,
  • Visvamalar Vijayakumaran

摘要

Many tropical urban cities faced significant impacts due to hydrometeorological hazards, particularly during the extreme rainfall and rainfall deficits. Having an efficient geodatabase consisting of significant high temporal and large hydrometeorological dataset is one of the keys to successful hazard adaptation, mitigation, and prevention. However, acquiring and managing such large raster data for that database is a huge challenging task because many of them were categorized as developing countries and facing the data conflict situations (DCS). The DCS is either the ground data is (i) unavailable, (ii) scarce, (iii) may involve unavailable ground data, scarce data, data interoperability and sharing conflicts, or (iv) unknown data quality. With the availability of publicly accessed remote sensing raster data, there is an opportunity to utilize them as alternative solutions. However, those data required specific downscaling and localization as they come in various spatial, temporal, and radiometric characteristics. It is theorized that storing all the variables in a single consistent vector-based grid GIS layers upon the completion of downscaling could be a useful alternative in anticipating the above-mentioned conflicts. Therefore, this paper initiates the development of the framework to create a spatial vector grid database consisting of multiple hydrometeorological variables derived from the open-source remote sensing data. The framework place significant emphasis on the (1) hardware and software requirements, (2) methodology on the deriving an appropriate environmental variables, indicators, and their validation, and (3) methodology for creating the spatial database based on a vector grid approach. The indicators and variables that will be incorporated are (1) rainfall, (2) solar radiation, (3) Standardized Precipitation Index, (4) topography, (5) impervious surfaces, (6) vegetation index, and (7) land surface temperature. The successful realization of the framework would enabled spatial-based hazards adaptation initiatives at the city scale, including near-real-time forecasting or nowcasting, early warning, post-disaster assessment, and future risk assessment.