Predicting post-fire vegetation recovery patterns in three different forest types Article Swipe
Spatola Maria Floriana
,
Borghetti Marco
,
Rita Angelo
,
Angelo Nolè
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu21-12800
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu21-12800
<p>Wildfire disturbances severely modifies the ecosystem structure and natural regeneration processes. Predicting mid- to long-term post-fire vegetation recovery patterns, is pivotal to improve post-fire management and restoration of burned areas forest ecosystems management. Currently, many research efforts have been conducted, in order to monitor and predict wildfires, using Machine Learning and Remote sensing techniques. Instead, the method proposed in this study combines Satellite images and Data Mining algorithm to process data collected by time series and regional forest dataset to predict post-fire vegetation recovery patterns. For this reason, we analysed Normalized Burn Ratio (NBR) patterns from Landsat Time series (LTS), to assess post-fire vegetation recovery for several wildfires that occurred in three different forest Corine Land Cover classes (311, 312, 313) in the Basilicata region during the period 2005-2012. Random Forest model, was used to classify the observed recovery patterns and investigate the influence of burn severity, topographic variables, climate and spectral vegetation indices on post-fire recovery. Image acquisition and Random Forest classifier was undertaken in Google Earth Engine (GEE). Results of bootstrapping classification, across forest type, show high percentage for high recovered (HR) classes and moderate recovered (MR) classes and moderate-low percentage for low (LR) and unrecovered (UR) classes. Specifically, in the holm- and cork-oak and oak forests show medium to high recovery rates, while Mediterranean pine and conifer-oak forests show the slowest recovery rates. Different post-fire recovery patterns are related to fire severity, vegetation type and post-fire environmental conditions. Our methodology shows that post-fire recovery classification, using RF classifier provides a robust method for both local and broad scale monitoring for mid- to long-term recovery response.</p><p>Keywords: Wildfires, Post-fire recovery, Landsat Time Series (LTS), Google Earth Engine, Wildfires, Machine Learning, Random Forest.</p>
Related Topics
Concepts
Environmental science
Vegetation (pathology)
Random forest
Mediterranean climate
Understory
Fire regime
Forestry
Satellite imagery
Ecosystem
Physical geography
Aleppo Pine
Remote sensing
Hydrology (agriculture)
Geography
Ecology
Canopy
Computer science
Machine learning
Medicine
Geotechnical engineering
Pathology
Engineering
Archaeology
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu21-12800
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3174485842
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3174485842Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu21-12800Digital Object Identifier
- Title
-
Predicting post-fire vegetation recovery patterns in three different forest typesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-04Full publication date if available
- Authors
-
Spatola Maria Floriana, Borghetti Marco, Rita Angelo, Angelo NolèList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-egu21-12800Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-egu21-12800Direct OA link when available
- Concepts
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Environmental science, Vegetation (pathology), Random forest, Mediterranean climate, Understory, Fire regime, Forestry, Satellite imagery, Ecosystem, Physical geography, Aleppo Pine, Remote sensing, Hydrology (agriculture), Geography, Ecology, Canopy, Computer science, Machine learning, Medicine, Geotechnical engineering, Pathology, Engineering, Archaeology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.response.&lt;/p&gt;&lt;p&gt;Keywords: | 267 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
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| citation_normalized_percentile.value | 0.06758209 |
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| citation_normalized_percentile.is_in_top_10_percent | False |