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.

Predicting post-fire canopy mortality in the boreal forest from dNBR derived from time series of Landsat data

2016 ◽  
Vol 25 (7) ◽  
pp. 762 ◽  
Author(s):  
Ignacio San-Miguel ◽  
David W. Andison ◽  
Nicholas C. Coops ◽  
Gregory J. M. Rickbeil
Keyword(s):  
Boreal Forest ◽  
Natural Disturbance ◽  
Ground Truth ◽  
Cost Effective ◽  
Burn Severity ◽  
Ancillary Data ◽  
Validation Data ◽  
Ground Truth Data ◽  
Forest Fire Management ◽  
Canadian Boreal Forest

Regulatory and certification agencies need historical fire pattern information across the Canadian boreal forest to support natural disturbance-based management. Landsat-derived spectral indices have been used extensively to map burn severity in North America. However, satellite-derived burn severity is difficult to define and quantify, and relies heavily on ground truth data for validation, which hinders fire pattern analysis over broad scales. It is therefore critical to translate burn severity estimates into more quantifiable measurements of post-fire conditions and to provide more cost-effective methods to derive validation data. We assessed the degree to which Landsat-derived indices and ancillary data can be used to classify canopy mortality for 10 fires in the boreal forest of Alberta and Saskatchewan, Canada. Models based on two and three mortality classes had overall accuracies of 91 and 72% respectively. The three-level classification has more utility for resource management, with improved accuracy at predicting unburned and complete canopy mortality classes (93 and 66%), but is relatively inaccurate for the partial mortality class (56%). The results presented here can be used to assess the suitability of different canopy mortality models for forest fire management goals, to help provide objective, consistent and cost-effective results to analyse historical fire patterns across the Canadian boreal forest.

Evaluating the ability of the differenced Normalized Burn Ratio (dNBR) to predict ecologically significant burn severity in Alaskan boreal forests

2008 ◽  
Vol 17 (4) ◽  
pp. 490 ◽  
Author(s):  
Karen A. Murphy ◽  
Joel H. Reynolds ◽  
John M. Koltun
Keyword(s):  
Boreal Forest ◽  
Boreal Forests ◽  
Burn Severity ◽  
Fire Season ◽  
Vegetation Composition ◽  
Management Objectives ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio ◽  
Processing Errors ◽  
The Relationship

During the 2004 fire season ~6.6 million acres (~2.7 million ha) burned across Alaska. Nearly 2 million of these were on National Wildlife Refuge System lands inaccessible from the state’s limited road system. Many fires burned through September, driven by unusually warm and dry temperatures throughout the summer. Using several fires from this season, we assessed the national burn severity methodology’s performance on refuge lands. Six fires, spanning 814 489 acres (329 613 ha), were sampled on five boreal forest refuges. In total, 347 sites were sampled for vegetation composition and ground-based burn severity estimates following the national protocols. The relationship between the differenced Normalized Burn Ratio (dNBR) and composite burn index (CBI) was unexpectedly weak (R2adjusted, 0.11–0.64). The weak relationship was not a result of data or image processing errors, nor of any biotic or abiotic confounding variable. The inconsistent results, and dNBR’s limited ability to discern the ecologically significant differences within moderate and high severity burn sites, indicate that the current methodology does not satisfy key Alaskan boreal forest management objectives.

Object-Based Method for Estimating Tsunami-Induced Damage Using TerraSAR-X Data

2016 ◽  
Vol 11 (2) ◽  
pp. 225-235 ◽  
Author(s):  
Hideomi Gokon ◽  
◽  
Shunichi Koshimura ◽  
Masashi Matsuoka ◽  
◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Tohoku Earthquake ◽  
Ground Truth ◽  
Test Site ◽  
Damage Function ◽  
Building Damage ◽  
The 2011 Tohoku Earthquake ◽  
Ground Truth Data ◽  
Object Based ◽  
The Relationship

The object-based method we developed to estimate building damage uses high-resolution synthetic aperture radar (TerraSAR-X) data from the 2011 Tohoku earthquake and tsunami. The damage function we developed involves the relationship between changes in the sigma nought values of pre- and postevent TerraSAR-X data and the damage ratio of washed-away buildings. We confirmed that the function performed as expected by estimating the number of washed-away buildings in homogeneous areas, agreeing well with ground truth data verified by a Pearson fs correlation coefficient of 0.99. The same damage function applied at another test site yielded a Pearson's correlation coefficient of 0.98. These results are sufficient to ensure transferability. We then simplified and semiautomated these processes in an ArcGIS environment, estimating building damage in the city of Sendai within 26 minutes.

scholarly journals Extraction of Spread Surface Water Body using Supervised and Unsupervised Classification Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 2345-2350
Keyword(s):  
Surface Water ◽  
Ground Truth ◽  
Unsupervised Classification ◽  
Water Area ◽  
Landsat 8 ◽  
Classification Methods ◽  
Statistical Parameters ◽  
Ground Truth Data ◽  
Classification Techniques ◽  
Water Board

In this paper different classification techniques are applied to extract spread surface water area in the Nagarjuna sagar reservoir, Andhra Pradesh from Landsat-8 (OLI) image. In addition, the separability of reservoir features are tested to evaluate the thematic correctness of the classified data. This is to evaluate the application of a supervised and unsupervised classification techniques using the ERDAS software to extract the changes of surface water features for the period of 2014 to 2019. Furthermore, the statistical parameters are evaluated for the classification techniques. In supervised and unsupervised classification methods the minimum distance classifier gives better result (overall accuracy is 98.01%) than other classification methods. These obtained results are validated with ground truth data which is provided by Central Water-board Commission(CWC).

scholarly journals Monitoring, classification, and characterization of interior Alaska forests using AIRSAR and ERS-1 SAR

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 227-234 ◽  
Author(s):  
C.L. Williams ◽  
K. McDonald ◽  
E. Rignot ◽  
L.A. Viereck ◽  
J.B. Way ◽  
...  
Keyword(s):  
Ground Truth ◽  
Synthetic Aperture ◽  
Ecological Research ◽  
Ground Truth Data ◽  
Radar Remote Sensing ◽  
Interior Alaska ◽  
Modelling Techniques ◽  
Successional Stages ◽  
Biomass Monitoring

AbstractAt the Bonanza Creek Experimental Forest (BCEF), past ecological research has been directed at forest successional processes on the floodplain of the Tanana River and adjacent uplands. Research at the Bonanza Creek site continues on the mosaic of forests, shrublands, and wetlands in a wide variety of successional stages on the Tanana floodplain. This paper reviews research since 1988 into the capabilities of Synthetic Aperture Radar (SAR) for monitoring, classification, and characterization of these forests using radar remote sensing and modelling techniques. Classifications of successional stages, obtained by use of different classifiers on multi-frequency and multi-polarimetric AIRSAR data, are contrasted; these classifications have been used to predict classification accuracies obtained with ERS-1 data, and to estimate the utility of an ERS-1 and RADARSAT combination for classification. Forest classifications, used in combination with ground-truth data for more than 50 forest stands, are used to summarize the distribution of biomass on the landscape. This will allow projections of future biomass. Monitoring of forest phenology, seasonality of flooding, and freeze–thaw transitions is ongoing. Also, direct monitoring of dominant tree species is demonstrating diurnal variation and interrelationships among environmental, physiological, and backscatter measurements.

Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation

2003 ◽  
Vol 33 (5) ◽  
pp. 770-782 ◽  
Author(s):  
Dorte Dissing ◽  
David L Verbyla
Keyword(s):  
Boreal Forest ◽  
Spatial Scales ◽  
Fire Service ◽  
Lightning Strike ◽  
Convective Activity ◽  
Mountainous Areas ◽  
Air Mass ◽  
Lightning Strikes ◽  
Interior Alaska ◽  
The Relationship

The relationship between lightning strike density, vegetation, and elevation was investigated at three different spatial scales: (i) interior Alaska (~630 000 km2), (ii) six longitudinal transects (~100 000 km2), and (iii) 17 individual physiographic subregions (~50 000 km2) within Alaska. The data consisted of 14 years (1986–1999) of observations by the Alaska Fire Service lightning strike detection network. The best explanation for the variation in lightning strike density was provided by a combination of the areal coverage of boreal forest and elevation. Each of these factors has the potential to influence the convective activity. Our study suggests that in a region that is climatically favorable for air-mass thunderstorms, surface properties may enhance local lightning storm development in the boreal forest. Lightning strikes were found to occur frequently both in mountainous areas and at river flats, which is contrary to results from previous Alaskan studies.

Analysis of Alaskan burn severity patterns using remotely sensed data

2007 ◽  
Vol 16 (3) ◽  
pp. 277 ◽  
Author(s):  
Paul A. Duffy ◽  
Justin Epting ◽  
Jonathan M. Graham ◽  
T. Scott Rupp ◽  
A. David McGuire
Keyword(s):  
Boreal Forest ◽  
Spatial Correlation ◽  
Large Scale ◽  
Linear Models ◽  
Vegetation Type ◽  
Burn Severity ◽  
Remotely Sensed Data ◽  
Large Scale Disturbance ◽  
The Relationship ◽  
Complex Landscapes

Wildland fire is the dominant large-scale disturbance mechanism in the Alaskan boreal forest, and it strongly influences forest structure and function. In this research, patterns of burn severity in the Alaskan boreal forest are characterised using 24 fires. First, the relationship between burn severity and area burned is quantified using a linear regression. Second, the spatial correlation of burn severity as a function of topography is modelled using a variogram analysis. Finally, the relationship between vegetation type and spatial patterns of burn severity is quantified using linear models where variograms account for spatial correlation. These results show that: 1) average burn severity increases with the natural logarithm of the area of the wildfire, 2) burn severity is more variable in topographically complex landscapes than in flat landscapes, and 3) there is a significant relationship between burn severity and vegetation type in flat landscapes but not in topographically complex landscapes. These results strengthen the argument that differential flammability of vegetation exists in some boreal landscapes of Alaska. Additionally, these results suggest that through feedbacks between vegetation and burn severity, the distribution of forest vegetation through time is likely more stable in flat terrain than it is in areas with more complex topography.

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