Evan A. Lyons

The Impact of Boreal Forest Fire on Climate Warming

We report measurements and analysis of a boreal forest fire, integrating the effects of greenhouse gases, aerosols, black carbon deposition on snow and sea ice, and postfire changes in surface albedo. The net effect of all agents was to increase radiative forcing during the first year (34 ± 31 Watts per square meter of burned area), but to decrease radiative forcing when averaged over an 80-year fire cycle (–2.3 ± 2.2 Watts per square meter) because multidecadal increases in surface albedo had a larger impact than fire-emitted greenhouse gases. This result implies that future increases in boreal fire may not accelerate climate warming.

Changes in surface albedo after fire in boreal forest ecosystems of interior Alaska assessed using MODIS satellite observations

We assessed the multidecadal effects of boreal forest fire on surface albedo using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations within the perimeters of burn scars in interior Alaska. Fire caused albedo to increase during periods with and without snow cover. Albedo during early spring had a mean of 0.50 ± 0.03 for the first three decades after fire, substantially higher than that observed in evergreen conifer forests (0.34 ± 0.04). In older stands between 30 and 55 years, albedo showed a decreasing trend during early spring, probably from a growing spruce understory that masked surface snow and caused increases in both simple ratio (SR) and enhanced vegetation index (EVI). During summer, albedo decreased by 0.012 ± 0.005 in the year immediately after fire (from 0.112 ± 0.005 to 0.100 ± 0.010). In subsequent years, summer albedo increased rapidly at first and then more gradually, reaching a broad maximum in 20–35 year stands (0.135 ± 0.006). These measurements provide evidence for a well-developed deciduous shrub and tree phase during intermediate stages of succession. Averaged over the first 5 decades, shortwave surface forcing from fires was −6.2 W m−2 relative to an evergreen conifer control and −3.0 W m−2 relative to a control constructed from 2000 to 2003 preburn observations. These forcing estimates had a magnitude substantially smaller than previous estimates and suggest that, at a regional scale, evergreen conifer stand density may be lower than that inferred from chronosequence studies.

Quantifying sources of error in multitemporal multisensor lake mapping

Regional- to global-scale lake maps can now be produced using existing technology and freely available data and serve as powerful tools for a variety of lake- and water-related studies. The accuracy of these studies depends in part on the accuracy of the lake map that they use. Mapping lakes using remote sensing requires a careful study of error and uncertainty. Errors in lake maps are caused by sensor-specific, lake-specific, and processing-specific factors. These can be further broken down to spatial, spectral/radiometric, and temporal factors. In this study, we analyse and compare these factors using modern and historical Landsat images along with intensive ground surveys of lakes in northern Alaska. Percentage error in lake area (relative to lake size) decreases for larger and more circular lakes, making a minimum size threshold an effective error mitigation practice. Image resampling involved in image transformation significantly increased error in lake area and is easily avoided by performing co-registration in the vector domain. Spectral properties varied for individual lakes due to depth, suspended sediment, vegetation, and other in situ factors, necessitating a normalized water index and independently derived threshold values for each lake. For lake change detection studies, spatially degrading a finer resolution image to the resolution of the coarser image (a common practice) does not significantly affect the difference in observed lake area. Due to the large numbers of lakes, particularly in the climatologically sensitive Arctic region, small errors in individual lake areas can compound to significantly impact results on regional to global scales. This study is intended to inform future static and multitemporal lake remote-sensing studies by evaluating errors and uncertainties in lake area, as measured by remote sensing.

Regional lake ice meltout patterns near Barrow, Alaska

Abstract The coastal plain of Arctic Alaska contains many thousands of lakes developed in continuous permafrost, which are ice-free for only 3–4 months each year. The spatial pattern of lake ice meltout for 1870 lakes (>10 ha) in a ~9200 km2 study area near Barrow, Alaska, is analyzed using five Landsat scenes spanning a 35-year record. For each available year, a spectra-based clustering algorithm is used to differentiate water from ice during springtime meltout and is overlain on a common lake shoreline template to determine the percentage of ice covering each lake. Analysis of these ‘ice-cover ratios’ for each scene reveals that there is pronounced interannual variation in the timing of lake ice meltout. The spatial pattern consistently demonstrates an increase in the percentage of ice coverage on lakes further north, reflecting the regional climatic gradient. However, the ice cover on many lakes near the northeastern coast persists for a substantially longer period into summer due to cooler temperatures associated with onshore winds from the Beaufort Sea. A similar maritime effect is not observed along the Chukchi Sea, and lake ice meltout is not delayed along this littoral zone.

LakeTime: Automated Seasonal Scene Selection for Global Lake Mapping Using Landsat ETM+ and OLI

The Landsat series of satellites provide a nearly continuous, high resolution data record of the Earth surface from the early 1970s through to the present. The public release of the entire Landsat archive, free of charge, along with modern computing capacity, has enabled Earth monitoring at the global scale with high spatial resolution. With the large data volume and seasonality varying across the globe, image selection is a particularly important challenge for regional and global multitemporal studies to remove the interference of seasonality from long term trends. This paper presents an automated method for selecting images for global scale lake mapping to minimize the influence of seasonality, while maintaining long term trends in lake surface area dynamics. Using historical meteorological data and a simple water balance model, we define the most stable period after the rainy season, when inflows equal outflows, independently for each Landsat tile and select images acquired during that ideal period for lake surface area mapping. The images selected using this method provide nearly complete global area coverage at decadal episodes for circa 2000 and circa 2014 from Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors, respectively. This method is being used in regional and global lake dynamics mapping projects, and is potentially applicable to any regional/global scale remote sensing application.