ASTER Digital Elevation Models
This document is based on information and ideas developed by members of the ASTER Digital Elevation Model Working Group (ADEMWG).
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This document is based on information and ideas developed by members of the ASTER Digital Elevation Model Working Group (ADEMWG).
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Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.
Mangroves supply many essential environmental amenities, such as preventing soil erosion, filtering water pollution, and protecting shorelines from harmful waves, floods, storms and winds. The Mangroves in Myanmar not only provide citizens with a food source, but they also offer firewood, charcoal, and construction materials. The depletion of mangroves is threatening more than the biodiversity however; Myanmar's fiscal livelihood is also in harm's way. Mangroves are valued at $100,000 to $277,000 per square kilometer and if managed in a sustainable fashion, can infuse constant income to the emerging Myanmarese economy. This study analyzed three coastline regions, the Ayeyarwady Delta, Rakhine and Tanintharyi, and mapped the spatial extent of mangrove forest during the dry season in 2000 and 2013. The classifications were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operation Land Imager (OLI) imagery, as well as the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model information. This data was atmospherically corrected, mosaicked, masked and classified in ENVI, followed by ArcGIS to perform raster calculations and create final products. Forest degradation collected from 2000 to 2013 was later used to forecast the density and health of Mangroves in the year 2030. These results were subsequently presented to project partners Dr. Peter Leimgruber and Ellen Aiken at the Smithsonian Conservation Biology Institute in Front Royal, VA. After the presentation of the project to the partners, these organizations formally passed on to the Myanmar Ministry of Environment, Conservation and Forestry for policy makers and forest managers to utilize in order to protect the Myanmar mangrove ecosystem while sustaining a healthy economy.
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NASADEM (NASA Digital Elevation Model) is a near-global elevation model that is being produced primarily by completely reprocessing the Shuttle Radar Topography Mission (SRTM) radar data and then merging it with refined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) GDEM (Global Digital Elevation Model) elevations. The new and improved SRTM elevations in NASADEM result from better vertical control of each SRTM data swath via reference to ICESat (Ice, Cloud, and land Elevation Satellite) elevations and from SRTM void reductions using advanced interferometric unwrapping algorithms. Remnant voids will be filled primarily by GDEM3, but with reduction of GDEM glitches (mostly related to clouds) and therefore with only minor need for secondary sources of fill.
Colombia is a country with highly variable terrain, from the Andes Mountains to plains and coastal areas, many of these areas are prone to flooding disasters. To identify these risk areas NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study region. The preliminary risk assessment was applied to a pilot study area, the La Mosca River basin. Precipitation data from the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM)'s near-real-time rainfall products as well as precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM) and stations in the La Mosca River Basin were used to create rainfall distribution maps for the region. Using the precipitation data and the ASTER DEM, the web application, Mi Pronóstico, run by IDEAM, was updated to include an interactive map which currently allows users to search for a location and view the vulnerability and current weather and flooding conditions. The geospatial information was linked to an early warning system in Mi Pronóstico that can alert the public of flood warnings and identify locations of nearby shelters.
Developing countries often struggle with providing water security and sanitation services to their populations. An important aspect of improving security and sanitation is developing a comprehensive understanding of the country's water budget. Water For People, a non-profit organization dedicated to providing clean drinking water, is working with the Peruvian government to develop a water budget for the La Libertad region of Peru which includes the creation of an extensive watershed management plan. Currently, the data archive of the necessary variables to create the water management plan is extremely limited. Implementing NASA Earth observations has bolstered the dataset being used by Water For People, and the METRIC (Mapping EvapoTranspiration at High Resolution and Internalized Calibration) model has allowed for the estimation of the evapotranspiration values for the region. Landsat 8 imagery and the DEM (Digital Elevation Model) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor onboard Terra were used to derive the land cover information, and were used in conjunction with local weather data of Cascas from Peru's National Meteorological and Hydrological Service (SENAMHI). Python was used to combine input variables and METRIC model calculations to approximate the evapotranspiration values for the Ochape sub-basin of the Chicama River watershed. Once calculated, the evapotranspiration values and methodology were shared Water For People to help supplement their decision support tools in the La Libertad region of Peru and potentially apply the methodology in other areas of need.
The amount of water stored and moving through the surface water bodies of large river basins (river, floodplains, wetlands) plays a major role in the global water and biochemical cycles and is a critical parameter for water resources management. However, the spatio-temporal variations of these freshwater reservoirs are still widely unknown at the global scale. Here, we propose a hypsographic curve approach to estimate surface freshwater storage variations over the Amazon basin combining surface water extent from a multi-satellite-technique with topographic data from the Global Digital Elevation Model (GDEM) from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Monthly surface water storage variations for 1993-2007 are presented, showing a strong seasonal and interannual variability, and are evaluated against in situ river discharge and precipitation. The basin-scale mean annual amplitude of approx. 1200 cu km is in the range of previous estimates and contributes to about half of the Gravity Recovery And Climate Experiment (GRACE) total water storage variations. For the first time, we map the surface water volume anomaly during the extreme droughts of 1997 (October-November) and 2005 (September-October) and found that during these dry events the water stored in the river and flood-plains of the Amazon basin was, respectively, approx. 230 (approx. 40%) and 210 (approx. 50%) cu km below the 1993-2007 average. This new 15year data set of surface water volume represents an unprecedented source of information for future hydrological or climate modeling of the Amazon. It is also a first step toward the development of such database at the global scale.
The U.S. Government has initiated the Global Change Research program, a systematic study of the Earth as a complete system. NASA's contribution of the Global Change Research Program is the Earth Observing System (EOS), a series of orbital sensor platforms and an associated data processing and distribution system. The EOS Data and Information System (EOSDIS) is the archiving, production, and distribution system for data collected by the EOS space segment and uses a multilayer architecture for processing, archiving, and distributing EOS data. The first layer consists of the spacecraft ground stations and processing facilities that receive the raw data from the orbiting platforms and then separate the data by individual sensors. The second layer consists of Distributed Active Archive Centers (DAAC) that process, distribute, and archive the sensor data. The third layer consists of a user science processing network. The EOSDIS is being developed in a phased implementation. The initial phase, Version 0, is a prototype of the operational system. Version 0 activities are based upon existing systems and are designed to provide an EOSDIS-like capability for information management and distribution. An important science support task is the creation of simulated data sets for EOS instruments from precursor aircraft or satellite data. The Land Processes DAAC, at the EROS Data Center (EDC), is responsible for archiving and processing EOS precursor data from airborne instruments such as the Thermal Infrared Multispectral Scanner (TIMS), the Thematic Mapper Simulator (TMS), and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). AVIRIS, TIMS, and TMS are flown by the NASA-Ames Research Center ARC) on an ER-2. The ER-2 flies at 65000 feet and can carry up to three sensors simultaneously. Most jointly collected data sets are somewhat boresighted and roughly registered. The instrument data are being used to construct data sets that simulate the spectral and spatial characteristics of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument scheduled to be flown on the first EOS-AM spacecraft. The ASTER is designed to acquire 14 channels of land science data in the visible and near-IR (VNIR), shortwave-IR (SWIR), and thermal-IR (TIR) regions from 0.52 micron to 11.65 micron at high spatial resolutions of 15 m to 90 m. Stereo data will also be acquired in the VNIR region in a single band. The AVIRIS and TMS cover the ASTER VNIR and SWIR bands, and the TIMS covers the TIR bands. Simulated ASTER data sets have been generated over Death Valley, California, Cuprite, Nevada, and the Drum Mountains, Utah using a combination of AVIRIS, TIMS, amd TMS data, and existing digital elevation models (DEM) for the topographic information.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer is a high-spatial-resolution multispectral imager scheduled to fly in Earth orbit in mid-1998.