Mapping fractional cover of major fuel type components across Alaskan tundra

2019 ◽  
Vol 232 ◽  
pp. 111324 ◽  
Author(s):  
Jiaying He ◽  
Tatiana V. Loboda ◽  
Liza Jenkins ◽  
Dong Chen
Keyword(s):  
Fuel Type ◽  
Fractional Cover ◽  
Alaskan Tundra

Exploring environmental factors driving wildfire occurrences in Alaskan tundra

2021 ◽  
Author(s):  
Jiaying He ◽  
Tatiana Loboda ◽  
Nancy French ◽  
Dong Chen
Keyword(s):  
Environmental Factors ◽  
Weather Conditions ◽  
Empirical Modeling ◽  
The Arctic ◽  
Fire Weather ◽  
Fuel Type ◽  
Ignition Probability ◽  
Alaskan Tundra ◽  
Fire Ignition ◽  
Cg Lightning

<p>Tundra fires are common across the pan-Arctic region, particularly in Alaska. Fires lead to significant impacts on terrestrial carbon balance and ecosystem functioning in the tundra. They can even affect the forage availability of herbivorous wildlife and living resources of local human communities. Also, interactions between fire and climate change can enhance the fire impacts on the Arctic ecosystems. However, the drivers and mechanisms of wildland fire occurrences in Alaskan tundra are still poorly understood. Research on modeling contemporary fire probability in the tundra is also lacking. This study focuses on exploring the critical environmental factors controlling wildfire occurrences in Alaskan tundra and modeled the fire ignition probability, accounting for ignition source, fuel types, fire weather conditions, and topography. The fractional cover maps of fuel type components developed Chapter 2 serve as input data for fuel type distribution. The probability of cloud-to-ground (CG) lightning and fire weather conditions are simulated using WRF. Topographic features are also calculated from the Digital Elevation Model (DEM) data. Additionally, fire ignition locations are extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product for Alaskan tundra from 2001 to 2019. Empirical modeling methods, including RF and logistic regression, are then utilized to model the relationships between environmental factors and wildfire occurrences in the tundra and to evaluate the roles of these factors. Our results suggested that CG lightning is the primary driver controlling fire ignitions in the tundra, while warmer and drier weather conditions also support fires. We also projected future potential of wildland fires in this tundra region with Coupled Model Intercomparison Projects Phase 6 (CMIP6) data. The results of this study highlight the important role of CG lightning in driving tundra fires and that incorporating CG lightning modeling is necessary and essential for fire monitoring and management efforts in the High Northern Latitudes (HNL).</p>

The morphology of experimentally produced charcoal distinguishes fuel types in the Arctic tundra

The Holocene ◽  
2020 ◽  
Vol 30 (7) ◽  
pp. 1091-1096 ◽  
Author(s):  
Eleanor MB Pereboom ◽  
Richard S Vachula ◽  
Yongsong Huang ◽  
James Russell
Keyword(s):  
Arctic Tundra ◽  
Global Carbon Cycle ◽  
Fire Frequency ◽  
The Arctic ◽  
Fuel Type ◽  
The Past ◽  
Primary Means ◽  
Forested Ecosystems ◽  
Global Carbon ◽  
Tundra Ecosystems

Wildfires in the Arctic tundra have become increasingly frequent in recent years and have important implications for tundra ecosystems and for the global carbon cycle. Lake sediment–based records are the primary means of understanding the climatic influences on tundra fires. Sedimentary charcoal has been used to infer climate-driven changes in tundra fire frequency but thus far cannot differentiate characteristics of the vegetation burnt during fire events. In forested ecosystems, charcoal morphologies have been used to distinguish changes in fuel type consumed by wildfires of the past; however, no such approach has been developed for tundra ecosystems. We show experimentally that charcoal morphologies can be used to differentiate graminoid (mean = 6.77; standard deviation (SD) = 0.23) and shrub (mean = 2.42; SD = 1.86) biomass burnt in tundra fire records. This study is a first step needed to construct more nuanced tundra wildfire histories and to understand how wildfire will impact the region as vegetation and fire change in the future.

scholarly journals Towards nationwide mapping of bamboo resources in the Philippines: testing the pixel-based and fractional cover approaches

2021 ◽  
Vol 42 (9) ◽  
pp. 3380-3404
Author(s):  
Jan Joseph V. Dida ◽  
Arnan B. Araza ◽  
Gerald T. Eduarte ◽  
Arthur Glenn A. Umali ◽  
Pastor L. Malabrigo ◽  
...  
Keyword(s):  
The Philippines ◽  
Fractional Cover

scholarly journals Non-Destructive Fuel Volume Measurements Can Estimate Fine-Scale Biomass across Surface Fuel Types in a Frequently Burned Ecosystem

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Quinn A. Hiers ◽  
E. Louise Loudermilk ◽  
Christie M. Hawley ◽  
J. Kevin Hiers ◽  
Scott Pokswinski ◽  
...  
Keyword(s):  
Bulk Density ◽  
Pinus Palustris ◽  
Field Measurements ◽  
Complex Surface ◽  
Fuel Type ◽  
Field Methods ◽  
Volume Measurements ◽  
Woody Litter ◽  
Non Destructive ◽  
Surface Vegetation

Measuring wildland fuels is at the core of fire science, but many established field methods are not useful for ecosystems characterized by complex surface vegetation. A recently developed sub-meter 3D method applied to southeastern U.S. longleaf pine (Pinus palustris) communities captures critical heterogeneity, but similar to any destructive sampling measurement, it relies on separate plots for calculating loading and consumption. In this study, we investigated how bulk density differed by 10-cm height increments among three dominant fuel types, tested predictions of consumption based on fuel type, height, and volume, and compared this with other field measurements. The bulk density changed with height for the herbaceous and woody litter fuels (p < 0.001), but live woody litter was consistent across heights (p > 0.05). Our models predicted mass well based on volume and height for herbaceous (RSE = 0.00911) and woody litter (RSE = 0.0123), while only volume was used for live woody (R2 = 0.44). These were used to estimate consumption based on our volume-mass predictions, linked pre- and post-fire plots by fuel type, and showed similar results for herbaceous and woody litter when compared to paired plots. This study illustrates an important non-destructive alternative to calculating mass and estimating fuel consumption across vertical volume distributions at fine scales.

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