Mohamed Abdelkader

Stevens Institute of Technology

Subject Areas: hydrology

 Recent Activity

ABSTRACT:

This resource allows users to obtain the location and metadata of USGS cameras from the Hydrologic Imagery Visualization and Information System (HIVIS). It provides a Python notebook for accessing and processing data, including the retrieval of camera locations and related information directly from the USGS API. Users can filter data based on specific attributes, generate URLs for individual camera stations, and save the filtered data locally. Additionally, the resource includes functionality to clip camera data using a shapefile of a selected area, allowing for targeted analysis. The Python notebook uses common libraries such as pandas and geopandas, making it accessible to those familiar with basic data manipulation and geographical data handling.

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ABSTRACT:

This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

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ABSTRACT:

This HydroShare resource provides a detailed tutorial on the Stevens River Ice Mapping System, a novel platform that combines multi-satellite imagery and citizen science data through Google Earth Engine to monitor river ice dynamics in the United States and Canada. Addressing the challenge of river ice monitoring, particularly in remote areas like Alaska, this document provides users with instructions on navigating the system's interface, visualizing ice conditions, and downloading relevant data.
The system is accessible at: https://web.stevens.edu/ismart/land_products/rivericemapping.html

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ABSTRACT:

This resource provides users with valuable access to the NOAA National Water Model (NWM) CONUS Retrospective Dataset version 2.1. The data, offered in Zarr format, can be downloaded and converted into user-specific CSV files, corresponding to individual forecast points identified by the user. Accompanying the code, users will discover a comprehensive list of USGS stations, each corresponding to a forecast point from the NWM, aiding in the forecast precision and data extraction process. The resource is further enhanced by a station description file, providing in-depth information about various streams and drainage areas retrieved from the NHDPlus Version 2dataset. The combination of these tools and datasets offers an effective means to analyze and visualize hydrological conditions across the CONUS region, benefitting researchers, planners, and policy-makers in their water management decisions.

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Resource Resource

ABSTRACT:

This resource provides users with valuable access to the NOAA National Water Model (NWM) CONUS Retrospective Dataset version 2.1. The data, offered in Zarr format, can be downloaded and converted into user-specific CSV files, corresponding to individual forecast points identified by the user. Accompanying the code, users will discover a comprehensive list of USGS stations, each corresponding to a forecast point from the NWM, aiding in the forecast precision and data extraction process. The resource is further enhanced by a station description file, providing in-depth information about various streams and drainage areas retrieved from the NHDPlus Version 2dataset. The combination of these tools and datasets offers an effective means to analyze and visualize hydrological conditions across the CONUS region, benefitting researchers, planners, and policy-makers in their water management decisions.

Show More
Resource Resource

ABSTRACT:

This HydroShare resource provides a detailed tutorial on the Stevens River Ice Mapping System, a novel platform that combines multi-satellite imagery and citizen science data through Google Earth Engine to monitor river ice dynamics in the United States and Canada. Addressing the challenge of river ice monitoring, particularly in remote areas like Alaska, this document provides users with instructions on navigating the system's interface, visualizing ice conditions, and downloading relevant data.
The system is accessible at: https://web.stevens.edu/ismart/land_products/rivericemapping.html

Show More
Resource Resource

ABSTRACT:

This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

Show More
Resource Resource

ABSTRACT:

This resource allows users to obtain the location and metadata of USGS cameras from the Hydrologic Imagery Visualization and Information System (HIVIS). It provides a Python notebook for accessing and processing data, including the retrieval of camera locations and related information directly from the USGS API. Users can filter data based on specific attributes, generate URLs for individual camera stations, and save the filtered data locally. Additionally, the resource includes functionality to clip camera data using a shapefile of a selected area, allowing for targeted analysis. The Python notebook uses common libraries such as pandas and geopandas, making it accessible to those familiar with basic data manipulation and geographical data handling.

Show More