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Machine Learning Approach to Burned Area Mapping for Southern California.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning Approach to Burned Area Mapping for Southern California./
Author:	Ross, Chandler.
Description:	1 online resource (51 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Remote sensing. -
Online resource:	click for full text (PQDT)
ISBN:	9798379426668

Machine Learning Approach to Burned Area Mapping for Southern California.
Ross, Chandler.

Machine Learning Approach to Burned Area Mapping for Southern California. - 1 online resource (51 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--San Diego State University, 2023.

Includes bibliographical references

Accurate representation of the location and amount of burned areas is vital to the understanding of spatial and temporal patterns of fires, and to assessing their environmental impacts. Extant burned area maps currently have high commission errors, which lead to an overrepresentation of burned area. The primary research objective of this thesis was to assess whether localized training data used for image-based machine learning routines improve accuracy of burned area products for western San Diego County. I used localized training data derived from fine scale aerial imagery to create and compare three training sets, with a Landsat scale pixel burn threshold of 20%, 50%, or 80%. These training data were input into a gradient-boosted regression model with the same 64 spectral vegetation indices (SVI) inputs as the US Geological Survey's Landsat Burned Area (LBA) product to classify burned and not burned lands. I compared the burned area product from the localized gradient boosted model (L-GBRM) to the three other products: Monitoring Trends in Burn Severity (MTBS), Fire and Resource Assessment Program (FRAP), and LBA. I found 20% to be the burn pixel threshold for the training data that yielded the most accurate classification. I used a 50% pixel burn threshold for the reference data since the burned area associated with it is closely aligned with the fine-scale burn area. I conducted an accuracy assessment for each of the products by randomly sampling 300 points from the validation dataset. The L-GBRM was the most accurate product while also mapping the smallest area burned, suggesting that the extant products have high commission errors, often through omitting interior unburned patches. Using local training data achieved a higher accuracy than nationwide training data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379426668Subjects--Topical Terms:

557272
Remote sensing.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Approach to Burned Area Mapping for Southern California.
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502 $a Thesis (M.S.)--San Diego State University, 2023.
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520 $a Accurate representation of the location and amount of burned areas is vital to the understanding of spatial and temporal patterns of fires, and to assessing their environmental impacts. Extant burned area maps currently have high commission errors, which lead to an overrepresentation of burned area. The primary research objective of this thesis was to assess whether localized training data used for image-based machine learning routines improve accuracy of burned area products for western San Diego County. I used localized training data derived from fine scale aerial imagery to create and compare three training sets, with a Landsat scale pixel burn threshold of 20%, 50%, or 80%. These training data were input into a gradient-boosted regression model with the same 64 spectral vegetation indices (SVI) inputs as the US Geological Survey's Landsat Burned Area (LBA) product to classify burned and not burned lands. I compared the burned area product from the localized gradient boosted model (L-GBRM) to the three other products: Monitoring Trends in Burn Severity (MTBS), Fire and Resource Assessment Program (FRAP), and LBA. I found 20% to be the burn pixel threshold for the training data that yielded the most accurate classification. I used a 50% pixel burn threshold for the reference data since the burned area associated with it is closely aligned with the fine-scale burn area. I conducted an accuracy assessment for each of the products by randomly sampling 300 points from the validation dataset. The L-GBRM was the most accurate product while also mapping the smallest area burned, suggesting that the extant products have high commission errors, often through omitting interior unburned patches. Using local training data achieved a higher accuracy than nationwide training data.
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653 $a Area mapping
653 $a Environmental impacts
653 $a Burned lands
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