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  • Product description: DN_Winter_DSM_Corr1.tif Drone-derived DSM Date of survey: 20170220 Personnel: BRE, EKC, IM, MS Coverage: Area surrounding wave logging transport and survey quadrats at Donna Nook, UK Platform: DJI Matrice 100 Camera: Zenmuse X5 GCP survey equipment: Leica GS08 RTK GPS GCP locational accuracy: X:0.02m, Y:0.02m, Z:0.02m Average GSD: 1.66cm Processing report: dn_20170220_zenmuse_report.pdf

  • Global vegetation change estimates are provided globally as GeoTIFF files with three bands and UINT16 pixel depth. Folder names indicate the region of the Earth to which the contents relate: Estimates are based on difference between vegetation presence/absence maps derived for the periods 16/071984-16/07/1989 and 01/01/2013-15/12/2016 using Google Earth Engine scripe Global_Veg_Change20.js Mean NDVI and seasonal NDVI fluctuation amplitude thresholds used for determination of presence/absence = 0.20. Bands: Band 1: Change estimate - 0= NoData, 1= Vegetation present in both times, 2= Vegetation loss, 3=Vegetation gain Band 2: Number of images used to estimate presence/abence of vegetation in 1984-1989 time period Band 3: Number of images used to estimate presence/absence of vegetation in 2013-2016 time period

  • Estimates of the change in coherence of marsh surfaces are provided as a polygon vector layer on a 30m grid. Coherence is defined as the percentage of a 30m cell that is covered by vegetation. The product therefore describes the change in this value over time. The product is based on a modified trend analysis of NDVI values derived from the LandSat archive. The time period of observations varies on a per-pixel basis depending on cloud presence and overpass schedules, but is approximately mid-1980s to 2016. The satellite-derived trends are calibrated against changes observed in aerial photography from 1992 (panchromatic, 25cm pixels) and 2014 (RGBI, 20cm pixels), and validated using the same photography at different locations (R-square = 0.81). Changes in percentage vegetation cover are then discretised into 8 classes and provided as a text field (‘PVC_Text’). String representations of change range from ‘Less than -25%’ (meaning that the percentage of the cell that is covered by vegetation has decreased by 25% or more over the observation period) to ‘More than +50%’ (meaning that the percentage of the cell that is vegetated has increased by more than 50% over the observation period). An additional field is provided entitled ‘SMExts_Class’. This field represents the proportion of the 30m grid cell that was covered by vegetation (ca. 2006-2009) according to the saltmarsh extents layer of Phelan et al. (2011). The data are provided in ten classes as numeric values signifying the upper bound of deciles. Further details of the method and non-discretised data will be provided in forthcoming publications. Phelan, N., Shaw, A., and Baylis, A. (2011). The extent of saltmarsh in England and Wales:2006 – 2009.

  • A Sentinel-2 image was atmospherically corrected using Sen2Cor in SNAP 4, and then used to extract Leaf Area Index (LAI), with the proviso that NDVI is larger than 0.3 to include marsh only. Dike line was used to mask any area outside of the intertidal and subtidal zone. Coordinate system: WGS_84_UTM. Attribution: This product is developed by NIOZ for EU FAST project (Foreshore Assessment Using Space Technology). Contains modified Copernicus Sentinel data (2015/2016). See also https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice

  • NDVI Paulinapolder Map based on L3A RapidEye data, image 05 June 2015, top of atmosphere radiance converted to surface reflectance and atmospheric correction using 6S This is an unvalidated product for demo purposes only! For more information, please see EU FAST Internal Deliverable 3.8 (Example Geospatial products)

  • Product description: TF_Winter_dsm.zip Type: Drone-derived DSM Date of survey: 20170217 Personnel: BRE, EKC, IM, MS, HB Coverage: Area surrounding wave logging trasect and survey quadrats at Tillingham, UK Platform: DJI Matrice 100 Camera: Zenmuse X5 GCP survey equipment: Leica GS08 RTK GPS GCP locational accuracy: X:0.02m, Y:0.02m, Z:0.02m Average GSD: 1.66cm Processing report: tf_20170217_zenmuse_report.pdf

  • A Sentinel-2 image was atmospherically corrected using Sen2Cor in SNAP 4, and then used to extract Leaf Area Index (LAI), with the proviso that NDVI is larger than 0.3 to include marsh only. Dike line was used to mask any area outside of the intertidal and subtidal zone. Coordinate system: WGS_84_UTM. Attribution: This product is developed by NIOZ for EU FAST project (Foreshore Assessment Using Space Technology). Contains modified Copernicus Sentinel data (2015/2016).See also https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice

  • A Sentinel-2 image was atmospherically corrected using Sen2Cor in SNAP 4, and then used to extract Leaf Area Index (LAI), with the proviso that NDVI is larger than 0.3 to include marsh only. Dike line was used to mask any area outside of the intertidal and subtidal zone. Coordinate system: WGS_84_UTM. Attribution: This product is developed by NIOZ for EU FAST project (Foreshore Assessment Using Space Technology). Contains modified Copernicus Sentinel data (2015/2016). See also https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice

  • This data consists of 33 files of global topography (SRTM v4) completed with downscaled bathymetry for the coastal zone of the world. SRTM tiles have been downloaded from http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp, GEBCO originates from http://www.bodc.ac.uk/data/online_delivery/gebco/

  • A Sentinel-2 image was atmospherically corrected using Sen2Cor in SNAP 4, and then used to extract Leaf Area Index (LAI), with the proviso that NDVI is larger than 0.3 to include marsh only. Dike line was used to mask any area outside of the intertidal and subtidal zone. Coordinate system: WGS_84_UTM. Attribution: This product is developed by NIOZ for EU FAST project (Foreshore Assessment Using Space Technology). Contains modified Copernicus Sentinel data (2015/2016). See also https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice