Table 5 Other studies predicting surface fuel variables from airborne lidar
From: Multitemporal lidar captures heterogeneity in fuel loads and consumption on the Kaibab Plateau
Authors | Year | Forest type | Location | Surface fuel response variable(s) | Variance explained (%) |
|---|---|---|---|---|---|
Pesonen et al. | 2008 | Spruce and hardwood | Finland | Downed dead wood volume | 61 |
Jakubowski et al. | 2013 | Mixed conifer | California, USA | 1000-h fuel load, fuel bed depth | 31, 35 |
Hudak et al. | 2015 | Longleaf pine savanna | Florida, USA | ln(surface fuel load) | 44 |
Price and Gordon | 2016 | Dry Sclerophyll Forest | Australia | Surface fuel load | 24 |
Bright et al. | 2017 | Pine and spruce-fir | Colorado, USA | Litter and duff, 1- to 100-h, 1000-h, and total surface fuel loads | 24–32 |
Stefanidou et al. | 2020 | Fir | Greece | Transformed litter, grass/forbs, 1-h, 10-h, and total surface fuel loads | 60–71 |
McCarley et al. | 2020 | Conifer | New Mexico and Oregon, USA | Understory fuel load | 16–63 |
Mauro et al. | 2020 | Conifer | Oregon, USA | Downed woody biomass | 14 |
Alonso-Rego et al. | 2021 | Pine | Spain | Understory fuel, litter and duff, and downed woody debris loads | 35–42 |
Bright et al. (this study) | 2022 | Pine, mixed-conifer, spruce-fir | Arizona, USA | Litter and duff, 1- to 1000-h and total surface fuel loads | 39–59 |