scholarly journals Using floristic gradient mapping to assess seasonal thaw depth in interior Alaska

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Veronika Döpper ◽  
Santosh Panda ◽  
Christine Waigl ◽  
Matthias Braun ◽  
Hannes Feilhauer
Keyword(s):  
Interior Alaska ◽  
Gradient Mapping ◽  
Thaw Depth

Identifying vegetation-geomorphology relationships in permafrost with airborne LiDAR, electrical resistivity tomography, seasonal thaw depth measurements, and machine learning

2020 ◽  
Author(s):  
Thomas Douglas ◽  
Christopher Hiemstra ◽  
John Anderson ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Snow Depth ◽  
Electrical Resistivity Tomography ◽  
Airborne Lidar ◽  
Spruce Forest ◽  
Snow Pack ◽  
Resistivity Tomography ◽  
Interior Alaska ◽  
Thaw Depth ◽  
Ice Content

<p>Mean annual temperatures in interior Alaska, currently -1°C, are projected to increase as much as 5°C by 2100. An increase in mean annual temperatures is expected to degrade permafrost and alter hydrogeology, soils, vegetation, and microbial communities. Ice and carbon rich “yedoma type” permafrost in the area is ecosystem protected against thaw by a cover of thick organic soils and mosses. As such, interactions between vegetation, permafrost ice content, the snow pack, and the soil thermal regime are critical in maintaining permafrost. We studied how and where vegetation and soil surface characteristics can be used to identify subsurface permafrost composition. Of particular interest were potential relationships between permafrost ice content, the soil thermal regime, and vegetation cover. We worked along 400-500 m transects at sites that represent the variety of ecotypes common in interior Alaska. Airborne LiDAR imagery was collected from May 9-11, 2014 with a spatial resolution of 0.25 m. During the winters from 2013-2019 snow pack depths have been made at roughly 1 m intervals along site transects using a snow depth datalogger coupled with a GPS. In late summer from 2013-2019 maximum seasonal thaw depths have been measured at 4 m intervals along each transect. Electrical resistivity tomography measurements were collected across the site transects. A variety of machine learning geospatial analysis approaches were also used to identify relationships between ecosystem characteristics, seasonal thaw, and permafrost soil and ice composition. Wintertime measurements show a clear relationship between vegetation cover and snow depth. Interception (and shallow snow) was evident in the birch and white spruce forests and where dense shrubs are present while the open tussock and intermittent shrub regions yield the greatest snow depths. Results from repeat seasonal thaw depth measurements also show a strong relationship with vegetation where mixed birch and spruce forest is associated with the deepest seasonal thaw. The tussock/shrub and spruce forest zones consistently exhibited the shallowest seasonal thaw. Roughly 60% of the seasonal thaw along the transects occurred by mid-July and downward movement of the thaw front had mostly ceased by late August with little additional thaw between August 20 and early October. Summer precipitation shows a relationship with seasonal thaw depth with the wettest summers associated with the deepest thaw. Results from this study identify clear relationships between ecotype, permafrost composition, and seasonal thaw dynamics that can help identify how and where permafrost degradation can be expected in a warmer future arctic.</p>

Potential for economical recovery of fuel from land clearing residue in interior Alaska.

1983 ◽  
Author(s):  
George R. Sampson ◽  
Forrest A. Ruppert
Keyword(s):  
Land Clearing ◽  
Interior Alaska

Combustion efficiency and emissions analysis for a school wood energy system in interior Alaska

2020 ◽  
Author(s):  
David L. Nicholls ◽  
Daisy Huang
Keyword(s):  
Energy System ◽  
Combustion Efficiency ◽  
Interior Alaska ◽  
Wood Energy

Geologic Map of Central (Interior) Alaska

1998 ◽  
Author(s):  
Frederic H. Wilson ◽  
James H. Dover ◽  
Dwight C. Bradley ◽  
Florence R. Weber ◽  
Thomas K. Bundtzen ◽  
...  
Keyword(s):  
Interior Alaska ◽  
Geologic Map

scholarly journals Effect of fire on dissolved organic carbon and inorganic solutes in spruce forest in the permafrost region of interior Alaska

2003 ◽  
Vol 49 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Hideaki Shibata ◽  
Kevin C. Petrone ◽  
Larry D. Hinzman ◽  
Richard D. Boone
Keyword(s):  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Permafrost Region ◽  
Spruce Forest ◽  
Interior Alaska ◽  
Inorganic Solutes

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Spatial variations in the growth rate of Hylocomium splendens and the thickness of the organic layer on a north-facing slope in Interior Alaska

Polar Science ◽  
2021 ◽  
pp. 100654
Author(s):  
Jumpei Toriyama ◽  
Tomoaki Morishita ◽  
Yojiro Matsuura ◽  
Kyotaro Noguchi
Keyword(s):  
Growth Rate ◽  
Spatial Variations ◽  
Organic Layer ◽  
Hylocomium Splendens ◽  
Interior Alaska

Comparative drought sensitivity of co‐occurring white spruce and paper birch in interior Alaska

2021 ◽  
Author(s):  
Patrick F. Sullivan ◽  
Annalis H. Brownlee ◽  
Sarah B.Z. Ellison ◽  
Sean M.P. Cahoon
Keyword(s):  
White Spruce ◽  
Drought Sensitivity ◽  
Interior Alaska ◽  
Paper Birch
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