Description: A global latitude-longitude grid with 1-degree intervals for world-level reference and mapping. && This layer provides a global latitude and longitude grid at 1-degree intervals, suitable for detailed mapping and analysis. It is part of the World Latitude and Longitude Grids dataset, which offers multiple grid resolutions for use as overlays in GIS applications. The 1-degree grid is ideal for applications requiring a fine-grained reference system for global data visualization and analysis (https://hub.arcgis.com/maps/ece08608f53949a4a4ee827fd5c30da1/about).
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Description: A global latitude-longitude grid with 5-degree intervals for mid-scale mapping and analysis. && This layer provides a global latitude and longitude grid at 5-degree intervals, balancing detail and readability for medium-scale world maps. It belongs to the World Latitude and Longitude Grids dataset, designed for use as a base reference in GIS applications. The 5-degree grid is well-suited for regional studies, thematic maps, and general global reference overlays (https://hub.arcgis.com/maps/ece08608f53949a4a4ee827fd5c30da1/about).
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Description: A global latitude-longitude grid with 25-degree intervals for small-scale global reference. && This layer provides a global latitude and longitude grid at 25-degree intervals, designed for small-scale, global overviews. It is part of the World Latitude and Longitude Grids dataset, which provides grids at multiple resolutions to support diverse mapping needs. The 25-degree grid is most effective for broad-scale visualization, navigation reference, and educational use in global mapping projects (https://hub.arcgis.com/maps/ece08608f53949a4a4ee827fd5c30da1/about).
Description: Mecca Region Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022.https://www.arcgis.com/home/item.html?id=5aa5fd4f9664472db9f4f48587cda7bf
Description: Riyadh Region Boundary provides a 2022 boundary with a total population count, designed for mapping and analysis in ArcGIS. && Riyadh Region Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022.https://www.arcgis.com/home/item.html?id=5aa5fd4f9664472db9f4f48587cda7bf
Description: Land use map derived from the ESA 2021 Land Cover dataset, showing built areas, cropland, and roads. Useful for environmental monitoring, climate studies, and land management. && The land-cover product we have generated from the ESA 2021 Land Cover dataset offers a high-quality, satellite-based map that provides detailed information on the Earth's surface cover. The map is highly regarded for its accuracy, resolution, and the use of advanced remote sensing technology to classify different types of land cover, such as vegetation, water bodies, urban areas, bare surfaces, etc. It's considered a good quality product because of its comprehensive coverage, frequent updates, and the rigorous validation processes it undergoes. These features make it particularly useful for environmental monitoring, climate change studies, and land management. The detailed land cover information helps distinguish dunes from other land cover types, facilitating targeted analysis and interventions in those areas. The source data can be downloaded at https://worldcover2021.esa.int/.
Description: This hillshade was generated from the 30-meter resolution Copernicus Digital Elevation Model (DEM). A vertical exaggeration factor of 10 was applied to enhance terrain relief and improve the visualization of topographic features.
Description: 100 m Sentinel-1 12-day median coherence mosaic from summer 2019, highlighting dune activity, vegetation dynamics, and seasonal surface changes; a 15 m version from 2021 is also available. && We have included a 100 m resolution mosaic of Sentinel-1 12-daily median coherence image. This mosaic was generated from 12 daily pairs of all the Sentinel-1 images acquired over the study area in the summer (June-August) of 2019. The source data is accessible at: https://asf.alaska.edu/datasets/derived/global-seasonal-sentinel-1-interferometric-coherence-and-backscatter-dataset/. Radar coherence is a measure that is sensitive to surface changes (dunes activity, vegetation, construction). The dunes have low coherence values and appear in dark shades in the provided coherence image. This dataset offers a unique perspective on the slower, cumulative changes affecting dune landscapes by providing median coherence measurements at 12-day repeat intervals, specifically during the summer months. It is invaluable for studying the broader patterns of dune migration, seasonal variations in sand deposition, and the long-term impacts of climate change on desertification processes.
Description: This product is a 15 m resolution median coherence mosaic generated from Sentinel-1 SLC scenes (June–August 2021). It highlights dune activity, vegetation, and other surface changes, and is used to support the dune distribution map. && To improve the resolution of the previous Sentinel-1 coherence mosaic product, we have generated our own a 15 m resolution mosaic of Sentinel-1 12-daily median coherence mosaic from original the Sentinel-1 SLC scenes collected in the summer of 2021. The product was generated by calculating per pixel median of all 12-daily Sentinel-1 coherence image pairs acquired over the study area in the summer (June-August) of 2021. Radar coherence is a measure that is sensitive to surface changes (dunes activity, vegetation, construction). The dunes have low coherence values and appear in dark shades in the provided coherence image mosaic. This product is used to drive the dune distribution map.
Description: Distribution of dune fields in the Mecca and Riyadh provinces, extracted from 1:250,000-scale Saudi geological maps. && From the 1:250,000 Saudi geologic maps, we extracted the distribution of dune fields in Mecca and Riyadh provinces
Description: We used autoRIFT (Lei et al., 2021; Gardner et al., 2018), an open-source software package, to track dune migration between 2000 and 2023 using the Landsat panchromatic image catalog over the Riyadh and Mecca provinces. AutoRIFT is a feature-tracking algorithm that measures and geocodes inter-image displacements in optical or radar remote sensing imagery. It detects pixel offsets between two images captured at different times using the normalized cross-correlation (NCC) method (Gardner et al., 2018)—a statistical similarity measure that identifies optimal pixel shifts by comparing reference and template image patches. The NCC process evaluates the correlation between pixel intensity patterns and identifies the displacement corresponding to the peak correlation value using a Gaussian pyramid-based oversampling technique. The method employs a nested grid approach with progressively larger chip sizes to optimize resolution and signal-to-noise ratio. This sparse-to-dense search strategy enables the exclusion of low-coherence regions (Lei et al., 2021).To implement this methodology in tracking dune migration over the Mecca and Riyadh provinces, we selected cloud-free Landsat image pairs (2000–2023) from similar dates and seasons to minimize illumination differences due to shadows, topography, and sun angle elevation. Landsat scenes from the same date, path, and UTM zone were mosaicked and processed to avoid reprojection-related offsets and ensure a seamless mosaic. The generated offset images in the X and Y directions for each image pair were merged to produce a consistent displacement mosaic for the entire study area.Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.
Description: We used autoRIFT (Lei et al., 2021; Gardner et al., 2018), an open-source software package, to track dune migration between 2000 and 2023 using the Landsat panchromatic image catalog over the Riyadh and Mecca provinces. AutoRIFT is a feature-tracking algorithm that measures and geocodes inter-image displacements in optical or radar remote sensing imagery. It detects pixel offsets between two images captured at different times using the normalized cross-correlation (NCC) method (Gardner et al., 2018)—a statistical similarity measure that identifies optimal pixel shifts by comparing reference and template image patches. The NCC process evaluates the correlation between pixel intensity patterns and identifies the displacement corresponding to the peak correlation value using a Gaussian pyramid-based oversampling technique. The method employs a nested grid approach with progressively larger chip sizes to optimize resolution and signal-to-noise ratio. This sparse-to-dense search strategy enables the exclusion of low-coherence regions (Lei et al., 2021).To implement this methodology in tracking dune migration over the Mecca and Riyadh provinces, we selected cloud-free Landsat image pairs (2000–2023) from similar dates and seasons to minimize illumination differences due to shadows, topography, and sun angle elevation. Landsat scenes from the same date, path, and UTM zone were mosaicked and processed to avoid reprojection-related offsets and ensure a seamless mosaic. The generated offset images in the X and Y directions for each image pair were merged to produce a consistent displacement mosaic for the entire study area.Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.
Description: We used autoRIFT (Lei et al., 2021; Gardner et al., 2018), an open-source software package, to track dune migration between 2000 and 2023 using the Landsat panchromatic image catalog over the Riyadh and Mecca provinces. AutoRIFT is a feature-tracking algorithm that measures and geocodes inter-image displacements in optical or radar remote sensing imagery. It detects pixel offsets between two images captured at different times using the normalized cross-correlation (NCC) method (Gardner et al., 2018)—a statistical similarity measure that identifies optimal pixel shifts by comparing reference and template image patches. The NCC process evaluates the correlation between pixel intensity patterns and identifies the displacement corresponding to the peak correlation value using a Gaussian pyramid-based oversampling technique. The method employs a nested grid approach with progressively larger chip sizes to optimize resolution and signal-to-noise ratio. This sparse-to-dense search strategy enables the exclusion of low-coherence regions (Lei et al., 2021).To implement this methodology in tracking dune migration over the Mecca and Riyadh provinces, we selected cloud-free Landsat image pairs (2000–2023) from similar dates and seasons to minimize illumination differences due to shadows, topography, and sun angle elevation. Landsat scenes from the same date, path, and UTM zone were mosaicked and processed to avoid reprojection-related offsets and ensure a seamless mosaic. The generated offset images in the X and Y directions for each image pair were merged to produce a consistent displacement mosaic for the entire study area.Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.
Description: This layer represents the magnitude of dune displacement in meters, measured between 2000 and 2023 using AutoRIFT (Lei et al., 2021; Gardner et al., 2018). The product was derived from Landsat panchromatic imagery using normalized cross-correlation feature tracking. Unlike the vector field product, which includes both X and Y offsets, this map shows only the total displacement magnitude as a combined offset product.Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.
Description: This dataset contains dune sample locations provided by the Saudi Geological Survey (SGS) in 2024. The sampling sites are distributed across the Mecca and Riyadh provinces and represent surface dune collections for XRD Mineralogical analysis.
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Name: Mean Wind Patterns (Kling and Ackerly, 2020)
Display Field: Wind_Sector
Type: Feature Layer
Geometry Type: esriGeometryPoint
Description: This dataset represents wind direction and speed across the Arabian Peninsula. It is derived from the global wind dataset presented in Global wind patterns and the vulnerability of wind-dispersed species to climate change (Nature Climate Change, 2020).