digitalearth-gm_s2_annual en utf8 dataset Digital Earth Africa Digital Earth Africa GPO Box 378 Canberra ACT 2609 Australia digitalearthafrica.org// distributor 2024-06-24 ISO 19115:2003 - Geographic information - Metadata ISO 19115:2003 urn:ogc:def:crs:EPSG:4326 gm_s2_annual 2024-06-24 publication Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period. One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year. In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics. Three of these metrics are adopted to highlight different types of changes in the landscape: - Euclidean distance (EMAD), which is more sensitive to changes in target brightness. - Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response. - Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time. More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html) This product has a spatial resolution of 10 m and is available annually for 2017 to 2021. It is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021. Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year. For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312 onGoing Digital Earth Africa Digital Earth Africa GPO Box 378 Canberra ACT 2609 Australia digitalearthafrica.org// distributor continual time-series africa landsat fractional-cover WOfS land LSC categories Earth observation LSC tags otherRestrictions unknown eng utf8 geoscientificInformation 1 -27.85888307654 65.6673672518444 -48.3104217167035 38.9988828990037 Digital Earth Africa Digital Earth Africa GPO Box 378 Canberra ACT 2609 Australia digitalearthafrica.org// distributor https://ows.digitalearth.africa/ OGC:WMS gm_s2_annual Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period. One approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year. In addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics. Three of these metrics are adopted to highlight different types of changes in the landscape: - Euclidean distance (EMAD), which is more sensitive to changes in target brightness. - Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response. - Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time. More techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html) This product has a spatial resolution of 10 m and is available annually for 2017 to 2021. It is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021. Annual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year. For more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312 https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/wms/digitalearth-gm_s2_annual.yml canonical Source of the record canonical dataset continual