This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.

• Data link
• Metadata link
• undefined
• Metadata link
• Browse image link
• undefined

ORNL_CLOUD Short Name: MODIS_CCaN_NDVI_Trends_Alaska_1666 Version ID: 1 Unique ID: C2170972734-ORNL_CLOUD

This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz.

• Data link
• Metadata link
• undefined
• Metadata link
• Metadata link
• Browse image link
• undefined
• undefined

ORNL_CLOUD Short Name: Rain-on-Snow_Data_1611 Version ID: 1 Unique ID: C2162145449-ORNL_CLOUD

This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.

• Data link
• Metadata link
• undefined
• Metadata link
• Browse image link
• undefined

ORNL_CLOUD Short Name: AGB_Pantropics_Amazon_Mexico_1824 Version ID: 1 Unique ID: C2345897759-ORNL_CLOUD

Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included.

• Data link
• Metadata link
• aster

AU_AADC Short Name: aster Version ID: 1 Unique ID: C1214313130-AU_AADC

Snow, glaciers and permafrost in cold mountain areas such as the Swiss Alps are especially sensitive to changes in environmental conditions due to their proximity to melting conditions. In addition, mass wasting is most intensive in those mountain areas with high relief energy. Environmental changes in high mountain regions substantially influence the potential for glacial and periglacial hazards. Ice- and moraine-dammed lakes represent a widespread hazard potential closely related to glacier fluctuations. Magnitude and frequency of ice avalanches from steep glaciers - in principle a normal expression of mass exchange under such topographic conditions - are coupled with stability conditions affected by glacier advance/retreat and, hence, with long-term atmospheric impacts. Steep and unstable reservoirs of loose debris, a potential source of debris flows, are often the result of glacier shrinkage. In a similar way, changes in the stress regime due to vanishing glaciers lead to potential destabilization of adjacent valley flanks. Since the Alps are among the most densely populated high mountain areas in the world, Switzerland is particularly impacted by glacial and periglacial hazards but, on the other hand, also has an extensive and well-recognized tradition in investigating such processes. A number of specific monitoring and modeling studies related to single hazardous situations have been performed, mainly based on recent catastrophes or imminent hazard situations. An urgent need exists for area-wide modeling of glacier hazard potentials with a view to establishing an integrated and adequate information base for planning and detailed monitoring, but a corresponding systematic approach is, for the present, still lacking. The proposed project aims at closing this gap in several ways: Work Package (WP) (1): By developing techniques for detection of glacier hazard potentials based on optical spaceborne remote sensing data which rarely has been used to date in Swiss glacier monitoring; multispectral analyses and multitemporal and multiscale fusion will play a major role in this, with a special focus on recent or upcoming high resolution sensors. WP (2): By integrating empirical models for glacier hazard assessment into geographical information systems (GIS) which have proven to be successful for hazard simulation but have not been used yet for determining glacier hazard potentials; GIS modeling especially allows for the fusion of remote sensing and elevation data for spatial (3D) analyses. To ensure high synergy, WPs (1) and (2) will be closely related to the ongoing SNF project "The Swiss Glacier Inventory 2000" (SWI 2000) (no. 21-54073.98) and the international project "Global Land Ice Monitoring from Space" (GLIMS). WP (3): By applying the methods from WPs (1) and (2), an initial attempt will be undertaken to implement an area-wide model for integrating glacier hazard potentials of extensive regions in the Swiss Alps following a downscaling strategy with varying resolution and accuracy levels, both with respect to data and to models. As hazard management in Switzerland is the domain of local and regional authorities, the proposed project does not aim at preparing detailed local hazard maps (Gefahrenkarten), but rather will provide new remote sensing and modeling techniques for decision support. It should demonstrate the usefulness of these techniques for overview mapping (Gefahrenhinweiskarten) as a basis for decision-making and for scenario simulations in connection with climate change effects. The efforts made in this project will contribute to handle economically complex mathematical and physical models and represent a decision basis for the specific need of further detailed case studies. A further outcome will be a documentation of historical glacier catastrophes in the Swiss Alps, which will - among others - be used for model calibration and verification. [Summary provided by Christian Huggel, University of Zurich.]

• Metadata link
• Metadata link
• Metadata link
• Metadata link
• UNIZH_AGREG

SCIOPS Short Name: UNIZH_AGREG Version ID: Not provided Unique ID: C1214614963-SCIOPS