Downloads provided by UsageCounts
handle: 10451/56667 , 10400.5/30281
We characterize fire regimes in central Portugal and investigate the degree to which the differences between regimes are influenced by a set of biophysical drivers. Using civil parishes as units of analysis, we employ three complementary parameters to describe the fire regime over a reference period of 44 years (1975–2018), namely cumulative percentage of parish area burned, Gini concentration index of burned area over time, and area-weighted total number of wildfires. Cluster analysis is used to aggregate parishes into groups with similar fire regimes based on these parameters. A classification tree model is then used to assess the capacity of a set of potential biophysical drivers to discriminate between the different parish groups. The results allowed us to distinguish four types of fire regime and show that these can be significantly differentiated using the biophysical drivers, of which land use/land cover (LULC), slope, and spring rainfall are the most important. Among LULC classes, shrubland and herbaceous vegetation play the foremost role, followed by agriculture. Our results highlight the importance of vegetation type, availability, and rate of regeneration, as well as that of topography, in influencing fire regimes in the study area, while suggesting that these regimes should be subject to specific wildfire prevention and mitigation policies.
Physics, QC1-999, Biophysical drivers, central Portugal, Fire regime, classification and regression trees, Central Portugal, machine learning, Machine learning, biophysical drivers, Classification and regression trees, fire regime
Physics, QC1-999, Biophysical drivers, central Portugal, Fire regime, classification and regression trees, Central Portugal, machine learning, Machine learning, biophysical drivers, Classification and regression trees, fire regime
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
| views | 13 | |
| downloads | 6 |

Views provided by UsageCounts
Downloads provided by UsageCounts