Geology topics

Suzanne M. Noble

Publications and source records attributed to Suzanne M. Noble.

4 recordsLinked to original sources

USGS2015

Use of satellite images to determine surface-water cover during the flood event of September 13, 2013, in Lyons and western Longmont, Colorado

The flooding that occurred in north-central Colorado in 2013 was some of the most destructive in the state’s history. Following a summer of drought and wildfires, a wet weather system stalled over the Front Range area from Fort Collins in the north to Colorado Springs in the south, including the cities of Lyons and Longmont. This weather system produced rainfall amounts that greatly exceeded historical highs. The Colorado Office of Emergency Management reported at least eight deaths. More than 11,000 people were evacuated from their homes, and the flooding caused an estimated $2 billion dollars in damages. On September 14, 2013, President Barack Obama issued a major disaster declaration for 15 Colorado counties affected by the severe weather and related damage. To support local, State, and Federal disaster response coordination efforts in Colorado, the U.S. Geological Survey (USGS) developed a geospatial product to identify surface-water cover from Lyons to western Longmont. This information was derived from September 13, 2013 WorldView-2 multispectral imagery at a spatial resolution of 3 meters (m). These data were orthocorrected and pan-sharpened. Three spectral indices (NDVI, NDWI, and NDTI) that were computed from this processed imagery were used to help determine the extent of the surface-water cover. The result was converted to a vector format. This surface-water cover dataset was created as a timely representation of post-flood ground conditions to support response efforts. This dataset and all processed imagery and derived products were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hddsexplorer.usgs.gov/uplift/hdds/ ) for distribution to those responding to the flood event.

Colorado↗

USGS2015

Documenting 35 years of land cover change: Lago Cachet Dos drainage, Chile

The U.S. Geological Survey (USGS) Special Applications Science Center is monitoring temporal changes at the Colonia Glacier and Lago Cachet Dos, Northern Patagonia Icefield of southern Chile. This location is one of the newest international sites in the USGS Global Fiducial Program (GFP)—a program which provides systematic monitoring of dynamic and environmentally critical areas with high-resolution imagery (http://gfp.usgs.gov/). In 2008, Lago Cachet Dos began experiencing glacial lake outburst floods (GLOFs) during which the entire pool of water (about 200 million cubic meters) rapidly drains from the lake and flows south-southeast through the Colonia Glacier. These catastrophic events cause massive erosion of valley-fill deposits and consequent upstream expansion of Lago Cachet Dos towards Lago Cachet Uno. Panchromatic and multispectral images for 1979, 2007, and 2014 highlight the dramatic changes that have occurred at this site over a 35-year period. The lake was smallest in 1979, when the Colonia Glacier was at its maximum extent during the study period. Between 1979 and 2007, the glacier shrank causing an increase in the surface area of the lake. The size of the lake increased substantially, from 2.98 square kilometers (km 2 ) in 1979 to 4.41 km 2 in 2014, primarily due to erosion of valley-fill deposits upstream of its northern edge by the 15 GLOFs that occurred between April 2008 and February 2014. Ongoing studies of the Colonia Glacier and Lago Cachet Dos are focused on providing real-time monitoring of Lago Cachet Dos lake levels, understanding the history of advances and retreats of the Colonia Glacier, and determining the physical mechanisms and hazards associated with the GLOFs that come from Lago Cachet Dos.

Lago Cachet Dos↗

USGS2010

Landsat Thematic Mapper Image Mosaic of Colorado

The U.S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) produced a seamless, cloud-minimized remotely-sensed image spanning the State of Colorado. Multiple orthorectified Landsat 5 Thematic Mapper (TM) scenes collected during 2006-2008 were spectrally normalized via reflectance transformation and linear regression based upon pseudo-invariant features (PIFS) following the removal of clouds. Individual Landsat scenes were then mosaicked to form a six-band image composite spanning the visible to shortwave infrared spectrum. This image mosaic, presented here, will also be used to create a conifer health classification for Colorado in Scientific Investigations Map 3103. An archive of past and current Landsat imagery exists and is available to the scientific community (http://glovis.usgs.gov/), but significant pre-processing was required to produce a statewide mosaic from this information. Much of the data contained perennial cloud cover that complicated analysis and classification efforts. Existing Landsat mosaic products, typically three band image composites, did not include the full suite of multispectral information necessary to produce this assessment, and were derived using data collected in 2001 or earlier. A six-band image mosaic covering Colorado was produced. This mosaic includes blue (band 1), green (band 2), red (band 3), near infrared (band 4), and shortwave infrared information (bands 5 and 7). The image composite shown here displays three of the Landsat bands (7, 4, and 2), which are sensitive to the shortwave infrared, near infrared, and green ranges of the electromagnetic spectrum. Vegetation appears green in this image, while water looks black, and unforested areas appear pink. The lines that may be visible in the on-screen version of the PDF are an artifact of the export methods used to create this file. The file should be viewed at 150 percent zoom or greater for optimum viewing.

Scientific Investigations Map↗

USGS2010

Conifer health classification for Colorado, 2008

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Scientific Investigations Map↗