The dataset contains all ESA acquisitions over the ADEN zone (Europe, Africa and the Middle East) plus some products received from JAXA over areas of interest around the world. Further information on ADEN zones can be found in this technical note. ALOS PALSAR products are available in following modes: Fine Beam Single polarisation (FBS), single polarisation (HH or VV), swath 40-70 km, resolution 10 m, temporal coverage from 02/05/2006 to 30/03/2011 Fine Beam Double polarisation (FBD), double polarisation (HH/HV or VV/VH), swath 40-70 km, resolution 10 m, temporal coverage from 02/05/2006 to 30/03/2011 Polarimetry mode (PLR), with four polarisations simultaneously: swath 30 km, resolution 30 m, temporal coverage from 26/08/2006 to 14/04/2011 ScanSAR Burst mode 1 (WB1), single polarisation: swath 250-350 km, resolution 100 m, temporal coverage from 12/06/2006 to 21/04/2011. Following processing levels are available: RAW (Level 1.0): Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene SLC (Level 1.1): Slant range single look complex product. Not available for WB1 GDH (Level 1.5): Ground range Detected, Normal resolution product GEC (Level 1.5): Geocoded product. The table summarises the ALOS PALSAR offer. Instrument mode Product type Processing level description JAXA processing level equivalent Fine Beam Single polarisation (HH or VV) FBS_RAW_0P Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene 1.0 FBS_GDH_1P Ground range Detected, Normal resolution product 1.5 FBS_GEC_1P Geocoded product 1.5 FBS_SLC_1P Slant range single look complex product 1.1 Fine Beam Double polarisation (HH/HV or VV/VH) FBD_RAW_0P Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene 1.0 FBD_GDH_1P Ground range Detected, Normal resolution product 1.5 FBD_GEC_1P Geocoded product 1.5 FBD_SLC_1P Slant range single look complex product 1.1 Polarimetry mode (4 polarisation) PLR_RAW_0P Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene 1.0 PLR_GDH_1P Ground range Detected, Normal resolution product 1.5 PLR_GEC_1P Geocoded product 1.5 PLR_SLC_1P Slant range single look complex product 1.1 ScanSAR Burst mode 1 (single polarisation) WB1_RAW_0P Raw data generated by every downlink segment and every band. Divided into an equivalent size to one scene 1.0 WB1_GDH_1P Ground range Detected, Normal resolution product 1.5 WB1_GEC_1P Geocoded product 1.5

A newer version of this dataset with data for 2015-2021 can be found in JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH The global 25m PALSAR/PALSAR-2 mosaic is a seamless global SAR image created by mosaicking strips of SAR imagery from PALSAR/PALSAR-2. For each year and location, the strip data were selected through visual inspection of the browse mosaics available over the period, with those showing minimum response to surface moisture preferentially used. In cases where the availability was limited (e.g., because of the requirement for observations during specific emergencies), data were necessarily selected from the year before or after, including from 2006. Shimada et al. 2014 There is no data for 2011-2014 due to the gap between ALOS and ALOS-2 temporal coverage. The SAR imagery was ortho-rectificatied and slope corrected using the 90m SRTM Digital Elevation Model. A destriping process (Shimada & Isoguchi, 2002, 2010) was applied to equalize the intensity differences between neighboring strips, occurring largely due to seasonal and daily differences in surface moisture conditions. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ₀ = 10log₁₀(DN²) - 83.0 dB Attention: Backscatter values may vary significantly from path to path over high latitude forest areas. This is due to the change of backscattering intensity caused by freezing trees in winter. More information is available in the provider's Dataset Description.

ALOS PALSAR Level 2.2

This dataset provides a paired low- and high-resolution DEM dataset designed for super-resolution reconstruction tasks in digital elevation modeling. Unlike traditional SR datasets that rely on simple interpolation-based downscaling, our dataset is constructed from real-world DEM sources to ensure realistic low-to-high resolution mappings.The high-resolution DEM data is sourced from the Institut Cartogràfic i Geològic de Catalunya (ICGC) with a spatial resolution of 2 meters, covering the Catalonia region in Spain. The low-resolution counterpart is derived from ALOS PALSAR DEM at 12.5 meters resolution, which offers extensive land coverage but lower spatial detail. Additionally, paired RGB optical remote sensing images from ICGC's aerial orthophotos (0.5m resolution) are included to facilitate multi-modal learning approaches.This dataset contains approximately 7,000 training pairs and around 600 test pairs, providing a robust benchmark for DEM super-resolution, deep learning-based terrain modeling, and geospatial data fusion research.

JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found here. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS PALSAR Level 1.1 and 2.1 data. Level 1.1 is range and single look azimuth compressed data represented by complex I and Q channels to preserve the magnitude and phase information. Range coordinate is in slant range. In the case of ScanSAR mode, an image file is generated per each scan. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.

ALOS PALSAR Level 1.1

Este - Modelo Digital de Terreno - (MDT ou DEM) representa o relevo da área do Geoparque Caminhos dos Cânions do Sul e entorno, abrangendo parte dos estados de Santa Catarina e Rio Grande do Sul, Brasil. O modelo foi gerado a partir da integração de dois conjuntos de dados:ALOS PALSAR L1 (SAR - Radar de Abertura Sintética): Utilizado para fornecer base altimétrica de média resolução com elevada coerência espacial e qualidade radiométrica. Dados pré-processados para correção de distorções geométricas e remoção de ruído.Modelo de Elevação do SIGSC (Sistema de Informações Geográficas de Santa Catarina): Utilizado como base complementar de alta acurácia regional, particularmente para melhorar a coerência topográfica em áreas críticas do relevo na fronteira entre os estados do Rio Grande do Sul e Santa Catarina.Metodologia de Produção:A fusão dos dados foi realizada por meio de processos de interpolação e mosaico raster no QGIS, priorizando as áreas de maior qualidade em cada fonte e realizando correções de borda e preenchimento de falhas. O produto final foi reprojetado para o sistema UTM, zona 22S, Datum SIRGAS 2000.Características do Produto Final:Resolução espacial: 12,5 metrosSistema de Coordenadas: UTM - Zona 22 SulDatum: SIRGAS 2000Tipo de dado: Raster GeoTIFF (float32) com valores altimétricos em metrosUnidade de medida: Metros acima do nível do marAplicações recomendadas:Análise geomorfológicaGeração de curvas de nível e modelos derivados (sombra, declividade, orientação)Planejamento territorial e ambientalEstudos hidrológicos e geotécnicosVisualizações 3D e simulações de terrenoObservações:Este modelo não deve ser confundido com produtos oficiais do INPE, IBGE ou JAXA. Trata-se de uma integração original elaborada com fins científicos e educacionais, respeitando as licenças de uso dos dados originais.

Landslide4Sense

  Dataset Description

This dataset is originally introduced in GitHub repo Landslide4Sense-2022. The Landslide4Sense dataset has three splits, training/validation/test, consisting of 3799, 245, and 800 image patches, respectively. Each image patch is a composite of 14 bands that include:

Multispectral data from Sentinel-2: B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12.

Slope data from ALOS PALSAR: B13.

Digital elevation model (DEM) from ALOS… See the full description on the dataset page: https://huggingface.co/datasets/harshinde/LandSlide4Sense.

Layers in this composition were delineated based on the ArcHydro extension. The hydrologically correct ALOS PALSAR digital terrain model was used (cell size = 12,5m). Process of watershed delineation can be burdened with a certain degree of uncertainty, which is determined by the accuracy of the DEM. Due to the great dynamics of erosion processes, the DEM is rapidly becoming obsolete. The size of the grid cell can also play a significant negative role. However, it is still the most accurate digital elevation model that covers the entire area of interest.

Puedes encontrar una versión más reciente de este conjunto de datos con datos del período 2015-2021 en JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH. El mosaico global de 25 m de PALSAR/PALSAR-2 es una imagen SAR global sin interrupciones creada a partir de mosaicos de bandas de imágenes SAR de PALSAR/PALSAR-2. Para cada año y ubicación, los datos de las tiras se seleccionaron a través de la inspección visual de los mosaicos de exploración disponibles durante el período, y se utilizaron preferentemente aquellos que mostraban una respuesta mínima a la humedad de la superficie. En los casos en que la disponibilidad era limitada (p.ej., debido al requisito de observaciones durante emergencias específicas), los datos se seleccionaron necesariamente del año anterior o posterior, incluido el 2006. Shimada et al., 2014 No hay datos disponibles para el período 2011-2014 debido a la brecha entre la cobertura temporal de ALOS y ALOS-2. Las imágenes de SAR se ortorrectificaron y se corrigieron según la pendiente con el Modelo Digital de Elevación SRTM de 90 m. Se aplicó un proceso de eliminación de bandas (Shimada & Isoguchi, 2002, 2010) para igualar las diferencias de intensidad entre las bandas vecinas, que se producen principalmente debido a las diferencias estacionales y diarias en las condiciones de humedad de la superficie. Los datos de polarización se almacenan como números digitales (DN) de 16 bits. Los valores de DN se pueden convertir en valores de gamma naught en unidades de decibelios (dB) con la siguiente ecuación: γ₀ = 10log₁₀(DN²) – 83.0 dB Atención: Los valores de dispersión pueden variar significativamente de una ruta a otra en las áreas forestales de latitudes altas. Esto se debe al cambio en la intensidad de la retrodispersión causada por los árboles congelados en invierno. Puedes encontrar más información en la Descripción del conjunto de datos del proveedor.

Radar satellite (SAR) images for Hornsund: ERS-1, ERS-2, ENVISAT, ALOS Palsar, TerraSAR-X, TandemX-1, acquired between 1992 and 2014. 210 archival SAR data were provided at the SLC level, so that both radiometric and geometric corrections were applied using the same methods, and with the same digital elevation model (2008 DEM SPOT developed by the IPY-SPIRIT Project; Korona et al., 2009). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam).

The dataset contains depth information (in meters) for pixels of subglacial overdeepenings with an area of 10000 m2 or larger, across five regions: Alaska, European Alps, New Zealand Southern Alps, the Central Himalayas, and the Peruvian Andes. These overdeepenings were derived by subtracting ice thickness data from Digital Elevation Models (DEMs) that describe the surface topography. Five ice thickness models were used for all regions: Farinotti''s Ensemble, GlabTop2, Huss and Farinotti were downloaded from Farinotti et al., (2019). OGGM''s ice thickness was downloaded from Farinotti et al., 2019 for all regions except for the Himalayas where it was generated by keeping the default same. Millan''s ice thickness was downloaded from Millan et al., (2022). The primary DEM used was the ALOS PALSAR DEM. For Central Himalayas, in addition to ALOS PALSAR, three other DEMs were used: ASTER, SRTM, and Copernicus. NERC standard grant NE/S013318/1.

Land Capability Classification 250m resolution Senegal. Considering all potential soil limitations. Built with 250m SoilGrids data 0-100cm and ALOS PALSAR DEM data 30m resolution.

The dataset consists of archived global seismicity data (from two different sources), RTK DGPS point cloud data of river terraces collected in the field, and ALOS PALSAR DEM data used for the scarp profile.

The positions of the glacier termini in Hornsund are derived with very high frequency in the period 1991–2018. Over 230 multispectral and Synthetic Aperture Radar (SAR) data were used: LANDSAT 5, LANDSAT 7, LANDSAT 8, Terra ASTER, Alos AVNIR, SPOT 5, ERS-1, ERS-2, ENVISAT, Alos PALSAR, TerraSAR-X, TanDEM-X, and Sentinel-1. SAR data were used to detect any variability in the glacier front during the polar night. The satellite data were digitized manually to obtain the ice cliff position. Multispectral images were orthorectified and geocoded in PCI Geomatica and ArcGIS software. SAR data were usually provided at the SLC level, so that both radiometric and geometric corrections could be applied using the same methods, and with the same digital elevation model (2008 DEM SPOT developed by the IPY-SPIRIT Project; Korona et al., 2009). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam). Sentinel data downloaded from the Sentinel’s Data Hub were already processed. Data not published.

The layer was delineated based on the ArcHydro extension. The hydrologically correct ALOS PALSAR digital terrain model was used (cell size = 12,5m). Process of watershed delineation can be burdened with a certain degree of uncertainty, which is determined by the accuracy of the DEM. Due to the great dynamics of erosion processes, the DEM is rapidly becoming obsolete. The size of the grid cell can also play a significant negative role. However, it is still the most accurate digital elevation model that covers the entire area of interest.

The position of the terminus of Hansbreen is derived with very high frequency in the period 1991–2015. Over 160 multispectral and Synthetic Aperture Radar (SAR) data were used: LANDSAT 5, LANDSAT 7, LANDSAT 8, Terra ASTER, Alos AVNIR, SPOT 5, ERS-1, ERS-2, ENVISAT, Alos PALSAR, TerraSAR-X, TanDEM-X, and Sentinel-1. Terra ASTER images were orthorectified with use of 2008 DEM SPOT and geocoded in PCI Geomatica and ArcGIS software. Multispectral, already terrain-corrected images were rectified in ArcGIS software. SAR data were provided at the Single Look Complex level and that both radiometric and geometric corrections were applied using the same methods, and with the same digital elevation model (2008 DEM SPOT). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam). Sentinel data downloaded from the Sentinel’s Data Hub were already processed. The satellite data were digitized manually to obtain the front position. The database is the supplement to the paper: M. Błaszczyk, J.A. Jania, M. Ciepły, M. Grabiec, D. Ignatiuk, L. Kolondra, A.Kruss, B. Luks, M. Moskalik, T. Pastusiak, A. Strzelewicz, W. Walczowski, T. Wawrzyniak, Factors controlling terminus position of Hansbreen, a tidewater glacier in Svalbard, Journal of Geophysical Research: Earth Surface, DOI: 10.1029/2020JF005763

This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides

The dataset comprises 4 different shapefiles:

"LandslideInventory_FarWesternNepal_Pol.shp": Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide

"LandslideInventory_FarWesternNepal_Points.shp": Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM.

"LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp": Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides.

"LandslideInventory_FarWesternNepal_AOI.shp": Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.

All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).

This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.

The dataset is a rock glacier inventory of the West Kunlun Mountains of China. It contains the boundaries of the landforms, their geometric information, and kinematic data. The inventory has been compiled followed the IPA guidelines on mapping rock glaciers. Remote sensing data adopted to produce this dataset includes optical images (Google Earth and Sentinel-2 images) and Interferometric SAR (InSAR) images (ALOS-1 PALSAR). Both manual inspection and deep learning-powered automatic detection have been used to identify and delineate the landforms. The geometric information of the inventoried landforms has been derived from SRTM DEM and Tandem-X DEM. The kinematic data has been quantified from ALOS-1 PALSAR data during 2006–2010. Specific temporal coverage for each displacement rate is included in the attribute table of the ESRI Shapefile. It is the first rock glacier inventory of the arid West Kunlun region, aiming to provide the baseline knowledge of these ice-rich landforms for long-term monitoring in the warming climate.