Kirsten Barrett

Potential shifts in dominant forest cover in interior Alaska driven by variations in fire severity

Large fire years in which >1% of the landscape burns are becoming more frequent in the Alaskan (USA) interior, with four large fire years in the past 10 years, and 79 000 km2 (17% of the region) burned since 2000. We modeled fire severity conditions for the entire area burned in large fires during a large fire year (2004) to determine the factors that are most important in estimating severity and to identify areas affected by deep-burning fires. In addition to standard methods of assessing severity using spectral information, we incorporated information regarding topography, spatial pattern of burning, and instantaneous characteristics such as fire weather and fire radiative power. Ensemble techniques using regression trees as a base learner were able to determine fire severity successfully using spectral data in concert with other relevant geospatial data. This method was successful in estimating average conditions, but it underestimated the range of severity. This new approach was used to identify black spruce stands that experienced intermediate- to high-severity fires in 2004 and are therefore susceptible to a shift in regrowth toward deciduous dominance or mixed dominance. Based on the output of the severity model, we estimate that 39% (approximately 4000 km2) of all burned black spruce stands in 2004 had <10 cm of residual organic layer and may be susceptible a postfire shift in plant functional type dominance, as well as permafrost loss. If the fraction of area susceptible to deciduous regeneration is constant for large fire years, the effect of such years in the most recent decade has been to reduce black spruce stands by 4.2% and to increase areas dominated or co-dominated by deciduous forest stands by 20%. Such disturbance-driven modifications have the potential to affect the carbon cycle and climate system at regional to global scales.

Modeling the effects of fire severity and climate warming on active layer thickness and soil carbon storage of black spruce forests across the landscape in interior Alaska

There is a substantial amount of carbon stored in the permafrost soils of boreal forest ecosystems, where it is currently protected from decomposition. The surface organic horizons insulate the deeper soil from variations in atmospheric temperature. The removal of these insulating horizons through consumption by fire increases the vulnerability of permafrost to thaw, and the carbon stored in permafrost to decomposition. In this study we ask how warming and fire regime may influence spatial and temporal changes in active layer and carbon dynamics across a boreal forest landscape in interior Alaska. To address this question, we (1) developed and tested a predictive model of the effect of fire severity on soil organic horizons that depends on landscape-level conditions and (2) used this model to evaluate the long-term consequences of warming and changes in fire regime on active layer and soil carbon dynamics of black spruce forests across interior Alaska. The predictive model of fire severity, designed from the analysis of field observations, reproduces the effect of local topography (landform category, the slope angle and aspect and flow accumulation), weather conditions (drought index, soil moisture) and fire characteristics (day of year and size of the fire) on the reduction of the organic layer

Vegetation shifts observed in arctic tundra 17 years after fire

With anticipated climate change, tundra fires are expected to occur more frequently in the future, but data on the long-term effects of fire on tundra vegetation composition are scarce. This study addresses changes in vegetation structure that have persisted for 17 years after a tundra fire on the North Slope of Alaska. Fire-related shifts in vegetation composition were assessed from remote-sensing imagery and ground observations of the burn scar and an adjacent control site. Early-season remotely sensed imagery from the burn scar exhibits a low vegetation index compared with the control site, whereas the late-season signal is slightly higher. The range and maximum vegetation index are greater in the burn scar, although the mean annual values do not differ among the sites. Ground observations revealed a greater abundance of moss in the unburned site, which may account for the high early growing season normalized difference vegetation index (NDVI) anomaly relative to the burn. The abundance of graminoid species and an absence of Betula nana in the post-fire tundra sites may also be responsible for the spectral differences observed in the remotely sensed imagery. The partial replacement of tundra by graminoid-dominated ecosystems has been predicted by the ALFRESCO model of disturbance, climate and vegetation succession.

Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands

Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications to the segmentation of ITCs. Research has yet to address this issue in order to suggest appropriate techniques to apply under conditions of forest stands that are infected by phytopathogens. This study aimed to find the best method of ITC delineation for larch canopies affected by defoliation as a result of a Phytophthora ramorum infection. Sample plots from two study sites in Wales, United Kingdom, were selected for ITC segmentation assessment across a range of infection levels and stand characteristics. The performance of two segmentation algorithms (marker-controlled watershed and region growing) were tested for a series of CHMs generated by a standard normalised digital surface model and a pit-free algorithm, across a range of spatial resolutions (0.15 m, 0.25 m and 0.5 m). The results show that the application of a pit-free CHM generation method produced improved segmentation accuracies in moderately and heavily infected larch forest, compared to the standard CHM. The success of ITC delineations was also influenced by CHM resolution. Across all plots the CHMs with a 0.25 m pixel size performed consistently well. However, lower and higher CHM resolutions also provided improved delineation accuracies in plots dominated by larger and smaller canopies respectively. The selected segmentation method also influenced the success of ITC delineations, with the marker-controlled watershed algorithm generating significantly more accurate results than the region growing algorithm (p < 0.10). The results demonstrate that ITCs in forest stands infected with Phytophthora ramorum can be successfully delineated from ALS when a pit-free algorithm is applied to CHM generation.

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches.