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Geology topics

Carl H. Key

Publications and source records attributed to Carl H. Key.

10 recordsLinked to original sources
USGS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO 2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Global Change Biology↗
USGS2012

Mapped versus actual burned area within wildfire perimeters: Characterizing the unburned

For decades, wildfire studies have utilized fire occurrence as the primary data source for investigating the causes and effects of wildfire on the landscape. Fire occurrence data fall primarily into two categories: ignition points and perimeter polygons which are used to calculate a ‘burned area’ for a fire. However, understanding the relationships between climate and fire or between fire and its ecological effects requires an understanding of the burn heterogeneity across the landscape and the area within fire perimeters that remains unburned. This research characterizes unburned areas within fire perimeters, which provide ecological refugia and seed source for post-fire regeneration. We utilized differenced Normalized Burn Ratio (dNBR) data to examine the frequency, extent, and spatial patterns of unburned area in three national parks across the western US (Glacier, Yosemite, and Yukon-Charley Rivers). We characterized unburned area within fire perimeters by fire size and severity, characterized distance to an unburned area across the burned portion of the fire, and investigated patch dynamics of unburned patches within the fire perimeter. From 1984 through 2009, the total area within the fire perimeters that was classified as unburned from dNBR was 37% for Yosemite, 17% for Glacier, and 14% for Yukon-Charley. Variation in unburned area between fires was highest in Yosemite and lowest in Yukon-Charley. The unburned proportion significantly decreased with increasing fire size and severity across all three parks. Unburned patch size increased with size of fire perimeter, but patches decreased in density. There were no temporal trends in unburned area found. These results raise questions about the validity of relationships found between external forcing agents, such as climate, and ‘burned area’ values derived solely from polygon fire perimeters.

Alaska, Montana, Wyoming↗
USGS2011

Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can providemultitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.

International Journal of Remote Sensing↗
USGS2011

Fire frequency, area burned, and severity: A quantitative approach to defining a normal fire year

Fire frequency, area burned, and fire severity are important attributes of a fire regime, but few studies have quantified the interrelationships among them in evaluating a fire year. Although area burned is often used to summarize a fire season, burned area may not be well correlated with either the number or ecological effect of fires. Using the Landsat data archive, we examined all 148 wildland fires (prescribed fires and wildfires) >40 ha from 1984 through 2009 for the portion of the Sierra Nevada centered on Yosemite National Park, California, USA. We calculated mean fire frequency and mean annual area burned from a combination of field- and satellite-derived data. We used the continuous probability distribution of the differenced Normalized Burn Ratio (dNBR) values to describe fire severity. For fires >40 ha, fire frequency, annual area burned, and cumulative severity were consistent in only 13 of 26 years (50 %), but all pair-wise comparisons among these fire regime attributes were significant. Borrowing from long-established practice in climate science, we defined “fire normals” to be the 26 year means of fire frequency, annual area burned, and the area under the cumulative probability distribution of dNBR. Fire severity normals were significantly lower when they were aggregated by year compared to aggregation by area. Cumulative severity distributions for each year were best modeled with Weibull functions (all 26 years, r 2 ≥ 0.99; P < 0.001). Explicit modeling of the cumulative severity distributions may allow more comprehensive modeling of climate-severity and area-severity relationships. Together, the three metrics of number of fires, size of fires, and severity of fires provide land managers with a more comprehensive summary of a given fire year than any single metric.

Wyoming↗
USGS2004

Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity

Our study compares data on burn severity collected from multi-temporal Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) with similar data from the Enhanced Thematic Mapper Plus (ETM+) using the differenced Normalized Burn Ratio (dNBR). Two AVIRIS and ETM+ data acquisitions recorded surface conditions immediately before the Hoover Fire began to spread rapidly and again the following year. Data were validated with 63 field plots using the Composite Burn Index (CBI). The relationship between spectral channels and burn severity was examined by comparing pre- and post-fire datasets. Based on the high burn severity comparison, AVIRIS channels 47 and 60 at wavelengths of 788 and 913 nm showed the greatest negative response to fire. Post-fire reflectance values decreased the most on average at those wavelengths, while channel 210 at 2370 nm showed the greatest positive response on average. Fire increased reflectance the most at that wavelength over the entire measured spectral range. Furthermore, channel 210 at 2370 nm exhibited the greatest variation in spectral response, suggesting potentially high information content for fire severity. Based on general remote sensing principles and the logic of variable spectral responses to fire, dNBR from both sensors should produce useful results in quantifying burn severity. The results verify the band–response relationships to burn severity as seen with ETM+ data and confirm the relationships by way of a distinctly different sensor system.

Remote Sensing of Environment↗
USGS2003

Lessons from the fires of 2000: Post-fire heterogeneity in ponderosa pine forests

We evaluate burn-severity patterns for six burns that occurred in the southern Rocky Mountains and the Colorado Plateau in 2000. We compare the results of two data sources: Burned Area Rehabilitations Teams (BAER) and a spatial burnseverity model derived from satellite imagery (the Normalized Burn Ratio; NBR). BAER maps tended to overestimate area of severe burns and underestimate area of moderate-severity burns relative to NBR maps. Low elevation and more southern ponderosa pine burns were predominantly understory burns, whereas burns at higher elevations and farther north had a greater component of high-severity burns. Thus, much, if not most, of the area covered by these burns appears to be consistent with historic burns and contributes to healthy functioning ecosystems.

Arizona, Colorado, New Mexico↗
USGS2002

Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.

Montana↗
USGS1998

Assessing simulated ecosystem processes for climate variability research at Glacier National Park, USA

Glacier National Park served as a test site for ecosystem analyses that involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter–cooler, drier–warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur.

Montana↗
USGS1991

Satellite telemetry: performance of animal-tracking systems

t: We used 10 Telonics ST-3 platform transmitter terminals (PTT's) configured for wolves and ungulates to examine the performance of the Argos satellite telemetry system. Under near-optimal conditions, 68 percentile errors for location qualities (NQ) 1, 2, and 3 were 1,188, 903, and 361 m, respectively. Errors (rE) exceeded expected values for NQ = 2 and 3, varied greatly among PTT's, increased as the difference (HE) between the estimated and actual PTT elevations increased, and were correlated nonlinearly with maximum satellite pass height (P,). We present a model of the relationships among rE, HE, and PH. Errors were bimodally distributed along the east-west axis and tended to occur away from the satellite when HE was positive. A southeasterly bias increased with HE, probably due to the particular distribution of satellite passes and effects of HE on rE. Under near-optimal conditions, 21 sensor message was received for up to 64% of available (PH, 50) satellite passes, and a location (NQ 2 1) was calculated for up to 63% of such passes. Sampling frequencies of sensor and location data declined 13 and 70%, respectively, for PTT's in a valley bottom and 65 and 86%, respectively, for PTT's on animals that were in valley bottoms. Sampling frequencies were greater for ungulate than for wolf collars.

Journal of Wildlife Management↗