Innovative cloud computing resources for remote sensing science have enabled advanced capabilities and analysis for solving complex large-scale data gap challenges within the USGS Water Availability and Use Science Program. With a vision for water budget estimation for the entire Nation, this research program integrates big data research and development into model applications, evaluation, and results.
The U.S. Geological Survey (USGS) is both a user and a provider of remotely sensed data. The USGS manages the Landsat satellite series and a Web-enabled archive of global Landsat imagery dating back to 1972. Landsat represents the world’s longest continuously acquired collection of space-based moderate-resolution land remote sensing data and the entire archive became available for download at no charge in December 2008. The USGS also distributes aerial photography through The National Map, and archives and distributes historical aerial photography, light detection and ranging (lidar) data, declassified imagery, hyperspectral imagery, data collected by Unmanned Aircraft Systems, and imagery from a variety of government, foreign, and commercial satellites. These data are used for a wide variety of applications such as mineral resource development, monitoring the health of U.S. and global ecosystems, land use change, emergency response, and assessments of natural hazards such as fires, hurricanes, earthquakes, droughts, and floods.
Meeting demand for agricultural water use and ecosystems has become a challenge for the Upper Klamath Basin, which stretches across southern Oregon and northern California. This basin is home to several threatened and endangered species and to more than 200,000 acres of irrigation land on the Bureau of Reclamation’s (BOR) Klamath Project.
Water resources are one of the Nation’s most important natural resources, especially for America’s farmland. However, changes in water management, land use, population, and climate are placing unprecedented demands on water supplies in the United States.
The flow of water in a river channel redistributes various materials, including organisms and pollutants, through a process called dispersion. Understanding this mechanism is critical for applications ranging from species conservation to hazardous waste management. Tracer tests with a visible dye are often used to study dispersion, typically by measuring dye concentration directly in the field at a few fixed locations.
Cyanobacterial blooms in eutrophic inland waters are a worldwide concern and are exacerbated by high nutrient inputs and warmer waters. Blooms are appearing with increasing frequency in water bodies used for drinking water supply or recreation, a problem which will likely worsen as the climate warms.
Cyanobacterial blooms are a global concern because they pose a threat to human and aquatic ecosystem health and cause economic damage. Cyanobacteria can produce toxins potent enough to adversely affect the health of humans, pets, livestock, and wildlife. The USGS is collaborating with the U.S.
The USGS presently operates 102 streamgaging stations distributed throughout Alaska. As many of these stations are quite remote, considerable effort is needed to collect periodic measurements and maintain gages. Thus, developing remote sensing methods for measuring streamflow in this vast, largely inaccessible State is valuable for many reasons.
High-resolution hydrographic mapping, which provides essential data for flood mitigation and planning, has been completed on thirteen, 12-digit hydrologic units near Sioux Falls in southeastern South Dakota. A lidar-derived digital elevation model was processed to include culvert locations into the modeled drainage network.
Archival aerial stereo-photographs are a source of information that can be used to create historical topography models for rapidly changing landscapes. Researchers completed a pilot study in Malakoff Diggins State Historic Park (MDSHP), which was once the largest hydraulic mine (1.6 square kilometers) in the Sierra Nevada of California. Legacy impacts in these mined landscapes include remnant steep exposures of highly erodible Eocene-aged auriferous sediments.
Vegetation growth is important to monitor in areas undergoing restoration. Color imagery collected using an unmanned aircraft system (UAS) at a bottomland hardwood restoration site in northeast Indiana was used to derive a vegetation height model using Structure from Motion (SfM) image processing. Data from that model were then compared to vegetation height data collected in field plots.