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- Publisher Website: 10.1007/s11069-020-04474-y
- Scopus: eid_2-s2.0-85098737335
- WOS: WOS:000604222500003
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Article: Quantifying the impact of ecosystem services for landscape management under wildfire hazard
Title | Quantifying the impact of ecosystem services for landscape management under wildfire hazard |
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Authors | |
Keywords | Decision-making under uncertainty Disaster management Fire risk Fire-resilient landscapes Wildfire risk analysis Natural hazard |
Issue Date | 2021 |
Citation | Natural Hazards, 2021, v. 106 n. 1, p. 531-560 How to Cite? |
Abstract | In recent years, the frequency, intensity, and severity of wildfires have been on the rise due to various environmental factors. Several studies show that the strategic application of fuel treatments is effective at altering fire behavior and its spread patterns. Effective planning for mitigating future expected losses under wildfire risk is a complex challenge that requires the integration of fire spread, simulation, and optimization models as well as the inclusion of multiple objectives into a unified framework. Previous works simplify the analysis by valuing the landscape regions using a unique objective (e.g., minimize the average expected area burned) or a predefined objective function. However, such an assumption is a simplification of the real system as multiple parts of the landscape have different values based on factors such as the presence of human settlements and infrastructure, availability of environmental services, and forest health. In this work, we expand these previous attempts by providing an integrated framework to naturally include and weight multiple objectives into the optimization model and analyze the trade-off between present objectives and future protection against wildfire risk. We study three key regions based on their recent fire history, landscape diversity, and demographic variety to quantify the impact of multiple objectives in landscape management. We obtain treatment plans using various combinations of these layers reflecting how different priorities of the decision-makers could affect treatment policies. |
Persistent Identifier | http://hdl.handle.net/10722/296012 |
ISSN | 2023 Impact Factor: 3.3 2023 SCImago Journal Rankings: 0.797 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Elimbi Moudio, Pelagie | - |
dc.contributor.author | Pais, Cristobal | - |
dc.contributor.author | Shen, Zuo Jun Max | - |
dc.date.accessioned | 2021-02-11T04:52:39Z | - |
dc.date.available | 2021-02-11T04:52:39Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Natural Hazards, 2021, v. 106 n. 1, p. 531-560 | - |
dc.identifier.issn | 0921-030X | - |
dc.identifier.uri | http://hdl.handle.net/10722/296012 | - |
dc.description.abstract | In recent years, the frequency, intensity, and severity of wildfires have been on the rise due to various environmental factors. Several studies show that the strategic application of fuel treatments is effective at altering fire behavior and its spread patterns. Effective planning for mitigating future expected losses under wildfire risk is a complex challenge that requires the integration of fire spread, simulation, and optimization models as well as the inclusion of multiple objectives into a unified framework. Previous works simplify the analysis by valuing the landscape regions using a unique objective (e.g., minimize the average expected area burned) or a predefined objective function. However, such an assumption is a simplification of the real system as multiple parts of the landscape have different values based on factors such as the presence of human settlements and infrastructure, availability of environmental services, and forest health. In this work, we expand these previous attempts by providing an integrated framework to naturally include and weight multiple objectives into the optimization model and analyze the trade-off between present objectives and future protection against wildfire risk. We study three key regions based on their recent fire history, landscape diversity, and demographic variety to quantify the impact of multiple objectives in landscape management. We obtain treatment plans using various combinations of these layers reflecting how different priorities of the decision-makers could affect treatment policies. | - |
dc.language | eng | - |
dc.relation.ispartof | Natural Hazards | - |
dc.subject | Decision-making under uncertainty | - |
dc.subject | Disaster management | - |
dc.subject | Fire risk | - |
dc.subject | Fire-resilient landscapes | - |
dc.subject | Wildfire risk analysis | - |
dc.subject | Natural hazard | - |
dc.title | Quantifying the impact of ecosystem services for landscape management under wildfire hazard | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11069-020-04474-y | - |
dc.identifier.scopus | eid_2-s2.0-85098737335 | - |
dc.identifier.volume | 106 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 531 | - |
dc.identifier.epage | 560 | - |
dc.identifier.eissn | 1573-0840 | - |
dc.identifier.isi | WOS:000604222500003 | - |
dc.identifier.issnl | 0921-030X | - |