Geology topics

Jill J. Cress

Publications and source records attributed to Jill J. Cress.

7 recordsLinked to original sources

USGS2015

U.S. Geological Survey Unmanned Aircraft Systems (UAS) Roadmap 2014

The U.S. Department of the Interior (DOI) is responsible for protecting the natural resources and heritage contained on almost 20 percent of the land in the United States. This responsibility requires acquisition of remotely sensed data throughout vast lands, including areas that are remote and potentially dangerous to access. One promising new technology for data collection is unmanned aircraft systems (UAS), which may be better suited (achieving superior science, safety, and savings) than traditional methods. UAS, regardless of their size, have the same operational components: aircraft, payloads, communications unit, and operator control unit. The aircraft is the platform that flies and carries any required payloads. For Department of the Interior missions these payloads will be either a sensor or set of sensors that can acquire the specific type of remotely sensed data that is needed. The aircraft will also carry the payload that is responsible for transmitting live airborne video images, compass headings, and location information to the operator control unit. The communications unit, which transfers information between the aircraft and the operator control unit, consists of the hardware and software required to establish both uplink and downlink communications. Finally, the operator control unit both controls and monitors the aircraft and can be operated either by a pilot on the ground or autonomously. This Roadmap provides operational procedures and lessons learned from completed proof-of-concept UAS missions in areas such as wildlife management, resource monitoring, and public land inspections. This information provides not only an implementation framework but can also help increase the awareness by resource managers, scientists, and others of the ability of UAS technology to advance data quality, improve personnel safety, and reduce data acquisition costs.

Open-File Report↗

USGS2013

A new map of standardized terrestrial ecosystems of Africa

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

African Geographical Review↗

USGS2009

Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated land surface form classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe . A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Since land surface forms strongly influence the differentiation and distribution of terrestrial ecosystems, they are one of the key input layers in this biophysical stratification. After extensive investigation into various land surface form mapping methodologies, the decision was made to use the methodology developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's land surface form classification, which allowed the use of 30-meter source data and a 1-km2 window for analyzing the data cell and its surrounding cells (neighborhood analysis). While Hammond's methodology was based on three topographic variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief. Using the MoRAP method, slope is classified as gently sloping when more than 50 percent of the area in a 1-km2 neighborhood has slope less than 8 percent, otherwise the area is considered moderately sloping. Local relief, which is the difference between the maximum and minimum elevation in a neighborhood, is classified into five groups: 0-15 m, 16-30 m, 31-90 m, 91-150 m, and >150 m. The land surface form classes are derived by combining slope and local relief to create eight landform classes: flat plains (gently sloping and local relief =< 15 m), smooth plains (gently sloping and 15 m < local relief =< 30 m), irregular plains (gently sloping and 30 m < local relief =< 90 m), escarpments (gently sloping and local relief > 90 m), low hills (not gently sloping and local relief =< 30 m), hills (not gently sloping and 30 m < local relief =< 90 m), breaks/foothills (not gently sloping and 90 m < local relief =< 150 m), and low mountains (not gently sloping and local relief > 150 m). However, in the USGS application of the MoRAP methodology, an additional local relief group was used (> 400 m) to capture additional local topographic variation. As a result, low mountains were redefined as not gently sloping and 151 m < local relief < 400 m, and a new land surface form class, high mountains/deep canyons, was identified as not gently sloping and local relief > 400 m. The final application of the MoRAP methodology was implemented using the USGS 30-meter National Elevation Dataset and an existing USGS slope dataset that had been derived by calculating the slope from the NED in Universal Transverse Mercator (UTM) coordinates in each UTM zone, and then combining all of the zones into a national dataset. This map shows a smoothed image of the nine land surface form classes based on MoRAP's methodology. Additional information about this map and any data developed for the ecosystems modeling of the conterminous United States is available online at http://rmgsc.cr.usgs.gov/ecosystems/.

Scientific Investigations Map↗

USGS2009

Terrestrial Ecosystems - Topographic Moisture Potential of the Conterminous United States

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated topographic moisture potential classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe. A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Substrate moisture regimes strongly influence the differentiation and distribution of terrestrial ecosystems, and therefore topographic moisture potential is one of the key input layers in this biophysical stratification. The method used to produce these topographic moisture potential classes was based on the derivation of ground moisture potential using a combination of computed topographic characteristics (CTI, slope, and aspect) and mapped National Wetland Inventory (NWI) boundaries. This method does not use climate or soil attributes to calculate relative topographic moisture potential since these characteristics are incorporated into the ecosystem model though other input layers. All of the topographic data used for this assessment were derived from the USGS 30-meter National Elevation Dataset (NED ) including the National Compound Topographic Index (CTI). The CTI index is a topographically derived measure of slope for a raster cell and the contributing area from upstream raster cells, and thus expresses potential for water flow to a point. In other words CTI data are 'a quantification of the position of a site in the local landscape', where the lowest values indicate ridges and the highest values indicate stream channels, lakes and ponds. These CTI values were compared to independent estimates of water accumulation by obtaining geospatial data from a number of sample locations representing two types of NWI boundaries: freshwater emergent wetlands and freshwater forested/shrub wetlands. Where these shorelines (the interface between the NWI wetlands and adjacent land) occurred, the CTI values were extracted and a histogram of their statistical distributions was calculated. Based on an evaluation of these histograms, CTI thresholds were developed to separate periodically saturated or flooded land, mesic uplands (moderately moist), and uplands. After the range of CTI values for these three different substrate moisture regimes was determined, the CTI values were grouped into three initial topographic moisture potential classes. As a final step in the generation of this national data layer, the uplands classification was subdivided into either very dry uplands or dry uplands. Very dry uplands were defined as uplands with relatively steep, south-facing slopes, and identification of this class was based on the slope and aspect datasets derived from the NED. The remaining uplands that did not meet these additional criteria were simply re-classified as dry uplands. The final National Topographic Moisture Potential dataset for the conterminous United States contains four classes: periodically saturated or flooded land (CTI = 18.5), mesic uplands (12 =< CTI < 18.5), dry uplands (CTI < 12), and very dry uplands (CTI < 12, Slope > 24 degrees and 91 degrees =< Aspect =< 314 degrees). This map shows a smoothed and generalized image of the four topographic moisture potential classes. Additional information about this map and any of the data developed for the ecosystems modeling of the conterminous United States is available online at http://rmgsc.cr.usgs.gov/ecosystems/.

Scientific Investigations Map↗

USGS2007

USGS Rocky Mountain Geographic Science Center’s 2005 hurricane response and recovery activities

Sophisticated monitoring systems and analysis products were key components to emergency response and Federal recovery activities during the 2005 hurricane season. The U.S. Geological Survey's (USGS) Rocky Mountain Geographic Science Center worked with a number of Federal agencies to provide these types of near real-time geospatial monitoring systems and analysis products in support of crucial preassessment activities and posthurricane response.

Louisiana;Mississippi↗

USGS2007

Global Data Toolset (GDT)

According to the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) approximately 60 percent of the data contained in the World Database on Protected Areas (WDPA) has missing or incomplete boundary information. As a result, global analyses based on the WDPA can be inaccurate, and professionals responsible for natural resource planning and priority setting must rely on incomplete geospatial data sets. To begin to address this problem the World Data Center for Biodiversity and Ecology, in cooperation with the U. S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC), the National Biological Information Infrastructure (NBII), the Global Earth Observation System, and the Inter-American Biodiversity Information Network (IABIN) sponsored a Protected Area (PA) workshop in Asuncion, Paraguay, in November 2007. The primary goal of this workshop was to train representatives from eight South American countries on the use of the Global Data Toolset (GDT) for reviewing and editing PA data. Use of the GDT will allow PA experts to compare their national data to other data sets, including non-governmental organization (NGO) and WCMC data, in order to highlight inaccuracies or gaps in the data, and then to apply any needed edits, especially in the delineation of the PA boundaries. In addition, familiarizing the participants with the web-enabled GDT will allow them to maintain and improve their data after the workshop. Once data edits have been completed the GDT will also allow the country authorities to perform any required review and validation processing. Once validated, the data can be used to update the global WDPA and IABIN databases, which will enhance analysis on global and regional levels.

Fact Sheet↗

USGS2007

Rapid Data Delivery System (RDDS)

Since the start of the active 2000 summer fire season, the U. S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) has been actively engaged in providing crucial and timely support to Federal, State, and local natural hazards monitoring, analysis, response, and recovery activities. As part of this support, RMGSC has developed the Rapid Data Delivery System (RDDS) to provide emergency and incident response teams with timely access to geospatial data. The RDDS meets these needs by combining a simple web-enabled data viewer for the selection and preview of vector and raster geospatial data with an easy to use data ordering form. The RDDS viewer also incorporates geospatial locations for current natural hazard incidents, including wildfires, earthquakes, hurricanes, and volcanoes, allowing incident responders to quickly focus on their area of interest for data selection.

Fact Sheet↗