Digital Earth Pacific Fractional Land Cover (Landsat, 30 m)

Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the Joint Remote Sensing Research Program. Digital Earth Africa's FC service has two components. Fractional Cover is estimated from each Landsat scene, providing measurements from individual days. Fractional Cover Annual Summary (Percentiles) provides 10th, 50th, and 90th percentiles estimated independently for the green vegetation, non-green vegetation, and bare soil fractions observed in each calendar year (1st of January - 31st December). While the scene based Fractional Cover can be used to study dynamic processes, the annual summaries make it easier to analyse year to yea[...]

This dataset provides annual coastline positions across the Pacific region derived from Landsat satellite imagery. It captures long-term shoreline change by mapping coastal boundaries over time, providing insights into processes including erosion (where landmass area decreases) and accretion or deposition (where landmass area increases). The dataset supports coastal change analysis, erosion monitoring, and climate adaptation planning. Annual shoreline vectors are extracted and composited from Landsat imagery, and are published as a vector time series in both GeoPackage and PMTiles formats. A WMTS service is also available at https://tileser ver.prod.digitalearthpacific.io/styles/coastlines/wmts.xml.

This dataset provides annual cloud-free composite imagery derived from Sentinel-2 observations across the Pacific region using geometric median and absolute deviation (GeoMAD) methods. The geometric median uses a more robust method of determining the median observation than a simple median, and represents stable surface conditions. Along with the median observation, the GeoMAD produces three measures of variance, or absolute deviations (emad, smad and bcmad), which help to understand how the data change over the time period. For example, some areas, such as bare rock, will change very little, whereas crop land will change more. The dataset is available annually from 2017 and supports monitoring of vegetation, land cover, and coastal environments.

This dataset provides annual radar-based composite imagery derived from Sentinel-1 observations across the Pacific region. Sentinel-1 carries a C-band Synthetic Aperture Radar (SAR), which acquires data day and night and in all weather conditions, so the composite is independent of cloud cover. Multi-temporal Sentinel-1 backscatter observations (VV and VH polarisations) are aggregated into annual geometric median composites, along with median absolute deviation measures (emad, smad and bcmad) and a count of observations. The dataset is available annually from 2017 and supports monitoring of land, water, and environmental change.