Geology topics

Megan K. Caldwell

Publications and source records attributed to Megan K. Caldwell.

5 recordsLinked to original sources

USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗

USGS2014

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Remote Sensing↗

USGS2013

Mountain pine beetle impacts on vegetation and carbon stocks

In the Southern Rocky Mountains, an epidemic outbreak of mountain pine beetle (Dendroctonus ponderosae; MPB) has caused levels of tree mortality unprecedented in recorded history. The impacts of this mortality on vegetation composition, forest structure, and carbon stocks have only recently received attention, although the impacts of other disturbances such as fires and land-use/land-cover change are much better known. This study, initiated in 2010, aims to increase our understanding of MPB outbreaks and their impacts. We have integrated field-collected data with vegetation simulation models to assess and quantify how long-term patterns of vegetation and carbon stocks have and may change in response to MPB outbreaks and other disturbances.

Colorado↗

USGS2013

Simulated impacts of mountain pine beetle and wildfire disturbances on forest vegetation composition and carbon stocks in the Southern Rocky Mountains

Forests play an important role in sequestering carbon and offsetting anthropogenic greenhouse gas emissions, but changing disturbance regimes may compromise the capability of forests to store carbon. In the Southern Rocky Mountains, a recent outbreak of mountain pine beetle (Dendroctonus ponderosae; MPB) has caused levels of tree mortality that are unprecedented in recorded history. To evaluate the long-term impacts of both this insect outbreak and another characteristic disturbance in these forests, high-severity wildfire, we simulated potential changes in species composition and carbon stocks using the Forest Vegetation Simulator (FVS). Simulations were completed for 3 scenarios (no disturbance, actual MPB infestation, and modeled wildfire) using field data collected in 2010 at 97 plots in the lodgepole pine-dominated forests of eastern Grand County, Colorado, which were heavily impacted by MPB after 2002. Results of the simulations showed that (1) lodgepole pine remained dominant over time in all scenarios, with basal area recovering to pre-disturbance levels 70–80 yr after disturbance; (2) wildfire caused a greater magnitude of change than did MPB in both patterns of succession and distribution of carbon among biomass pools; (3) levels of standing-live carbon returned to pre-disturbance conditions after 40 vs. 50 yr following MPB vs. wildfire disturbance, respectively, but took 120 vs. 150 yr to converge with conditions in the undisturbed scenario. Lodgepole pine forests appear to be relatively resilient to both of the disturbances we modeled, although changes in climate, future disturbance regimes, and other factors may significantly affect future rates of regeneration and ecosystem response.

Biogeosciences Discussions↗

USGS2011

Responses of soil and water chemistry to mountain pine beetle induced tree mortality in Grand County, Colorado, USA

Pine forest in northern Colorado and southern Wyoming, USA, are experiencing the most severe mountain pine beetle epidemic in recorded history, and possible degradation of drinking-water quality is a major concern. The objective of this study was to investigate possible changes in soil and water chemistry in Grand County, Colorado in response to the epidemic, and to identify major controlling influences on stream-water nutrients and C in areas affected by the mountain pine beetle. Soil moisture and soil N increased in soils beneath trees killed by the mountain pine beetle, reflecting reduced evapotranspiration and litter accumulation and decay. No significant changes in stream-water NO 3 - "> NO 3 - or dissolved organic C were observed; however, total N and total P increased, possibly due to litter breakdown or increased productivity related to warming air temperatures. Multiple-regression analyses indicated that % of basin affected by mountain pine beetles had minimal influence on stream-water NO 3 - "> NO 3 - and dissolved organic C; instead, other basin characteristics, such as percent of the basin classified as forest, were much more important.

Colorado↗