Date of Image: 10/30/2020Date of Next Image: None ExpectedSummary:The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology, both in Pasadena, California, created this displacement map with measurements of the surface displacements, in the radar line-of-sight, caused by the recent Mw 7.0 (October 30, 2020) earthquake beneath the Aegean Sea between Samos, Greece and Izmir, Turkey. The map was derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1 satellites, operated by the European Space Agency (ESA). The team computed the interferometric difference (interferogram or interferometric SAR) between the post-event image acquired on October 30, 2020 with a pre-event image acquired on October 24, 2020, on the Sentinel-1 ascending (satellite moving north) track 131.The map covers an area of about 100 by 80 kilometers (60 by 50 miles). The color variation from blue to red shows displacements in the direction between the ground and the satellite (up and west). The western part of Samos island moved as much as 10 cm upward (red) and a small area of the north coast of Samos moved downward (blue) by up to 10 cm. The pattern of displacements is consistent with a fault sloping from the north coast of Samos to the north to the location where the earthquake rupture started. This data has not yet been validated. This displacement map should be used as guidance to identify areas of significant ground displacement, and may be less reliable over heavily vegetated areas and steeper slopes.Suggested Use:Areas in red are surface displacement up and to the west. Areas in blue are downward and to the east.This displacement map should be used as guidance to identify areas of significant ground displacement, and may be less reliable over heavily vegetated areas and steeper slopes.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:90 metersCredits:Contains modified Copernicus Sentinel data, processed by ESA. Analyzed by the NASA-JPL/Caltech ARIA team. This task was carried out at JPL funded by NASA. Esri REST Endpoint:See URL Section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/aegean_sea_earthquake_202010/aria_displacement_sentinel1_20201030/ImageServer/WMSServerData Download:https://aria-share.jpl.nasa.gov/20201030-Samos-Izmir-EQ/Displacements/

This research proposes a gradient boosted decision tree (GBDT)-based machine-learning framework for the enhancement of global DEMs.

Benchmark dataset containing slope, elevation, Landsat temporal composites and night light raster layers, and the training samples (LUCAS and CORINE samples compilation) to map the land-cover in different areas of the European Union-EU.

The slope and elevation refers to Digital Terrain Model for Continental Europe, and the night light images are from VNP46A1 product (VIIRS/NPP Daily Gridded Day Night Band 500m). The temporal composites were based on GLAD Landsat ARD, considering the 4 seasons and 3 percentiles per season (25, 50 and 75), for 6 spectral (blue, green, red, NIR, SWIR1, SWIR2) and 1 thermal band, resulting at end in 88 Landsat composites per year. The images for each season were selected using the same calendar dates for all period:

• Winter: December 2 of previous year until March 20 of current year
• Spring: March 21 until June 24 of current year
• Summer: June 25 until September 12 of current year
• Fall: September 13 until December 1 of current year

The temporal composites were generated to Sentinel-2 L2A for 2018, 2019 and 2020, using the same approach (4 seasons x 3 percentiles x 6 spectral bands).

The benchmark areas were selected according to the EU tiling system, which consists of 7,042 regular tiles with 30 x 30 km. The dataset uses the ETRS89-extended / LAEA Europe as spatial reference system (EPSG:3035), and all the raster layers have 1,000 x 1,000 pixels and 30m of spatial resolution.

For all the EU the training samples will have 32 land-cover classes, varying according to the benchmark area:

• 111: Urban fabric
• 122: Road and rail networks and associated land
• 123: Port areas
• 124: Airports
• 131: Mineral extraction sites
• 132: Dump sites
• 133: Construction sites
• 141: Green urban areas
• 211: Non-irrigated arable land
• 212: Permanently irrigated arable land
• 213: Rice fields
• 221: Vineyards
• 222: Fruit trees and berry plantations
• 223: Olive groves
• 231: Pastures
• 311: Broad-leaved forest
• 312: Coniferous forest
• 321: Natural grasslands
• 322: Moors and heathland
• 323: Sclerophyllous vegetation
• 324: Transitional woodland-shrub
• 331: Beaches, dunes, sands
• 332: Bare rocks
• 333: Sparsely vegetated areas
• 334: Burnt areas
• 335: Glaciers and perpetual snow
• 411: Inland wetlands
• 421: Maritime wetlands
• 511: Water courses
• 512: Water bodies
• 521: Coastal lagoons
• 522: Estuaries
• 523: Sea and ocean

See the eumap library for more information about the gapfiling approach and land-cover mapping using this dataset.

This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and "msw_flag" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.

This data set and collected from the usga official website of Nepal and the domestic geological hazard point as sample library, collecting samples and corresponding to the landslide factor layer, using the known sample data training random forest model;At the same time in the open source web site to collect the qinghai-tibet plateau elevation (https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm), land use (https://land.copernicus.eu/global/products/lc), the lithology (doi: 10.12029 / gc2017Z103) (https://gpm.nasa.gov/), and other factors, the average annual rainfall, slope direction, slope, relative elevation difference, curvature factor by using gis software, through the calculation of elevation of each factor resolution of 250 meters, the various factors of the qinghai-tibet plateau band combination as forecast data layer, with has trained model to forecast, finally finally concluded that the qinghai-tibet plateau geologic disaster danger.As a result, the raster data, the data value range of 0-1;The risk level in accordance with the natural breakpoint method is divided into five types: 0-0.36 for 0.36 0.53 for low risk, low risk;0.53 to 0.69 for the dangerous;0.69 to 0.90 for high risk;0.9 1 for high risk.