On the public citation graph

Estimating Fine Fuel Loads in Vertical Forest Layers with Airborne Lidar and Forest Inventory Data

https://doi.org/10.2139/ssrn.5049769
0 signed · 52 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ecs2.1964openalex-snapshot
Overstory‐derived surface fuels mediate plant species diversity in frequently burned longleaf pine forests
inferredcites →https://doi.org/10.1016/j.envsoft.2017.07.007openalex-snapshot
Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data
inferredcites →https://doi.org/10.1016/j.foreco.2012.02.003openalex-snapshot
Changes in behaviour of fire in dry eucalypt forest as fuel increases with age
inferredcites →https://doi.org/10.1016/j.foreco.2012.03.036openalex-snapshot
Diversity and equitability ordering profiles applied to study forest structure
inferredcites →https://doi.org/10.1016/j.foreco.2012.06.056openalex-snapshot
Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements
inferredcites →https://doi.org/10.1016/j.foreco.2019.02.002openalex-snapshot
The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration
inferredcites →https://doi.org/10.1016/j.foreco.2024.121851openalex-snapshot
Estimating fine fuel loads in Eucalypt forests using forest inventory data and a modelling approach
inferredcites →https://doi.org/10.1016/j.isprsjprs.2017.07.001openalex-snapshot
Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds
inferredcites →https://doi.org/10.1016/j.jag.2020.102261openalex-snapshot
A comparison of terrestrial and UAS sensors for measuring fuel hazard in a dry sclerophyll forest
inferredcites →https://doi.org/10.1016/j.jag.2022.103040openalex-snapshot
Dynamic stratification for vertical forest structure using aerial laser scanning over multiple spatial scales
inferredcites →https://doi.org/10.1016/j.jag.2022.103101openalex-snapshot
Forest foliage fuel load estimation from multi-sensor spatiotemporal features
inferredcites →https://doi.org/10.1016/j.rse.2010.10.012openalex-snapshot
Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems
inferredcites →https://doi.org/10.1016/j.rse.2012.01.020openalex-snapshot
3-D mapping of a multi-layered Mediterranean forest using ALS data
inferredcites →https://doi.org/10.1016/j.rse.2014.10.004openalex-snapshot
Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data
inferredcites →https://doi.org/10.1016/j.rse.2016.10.024openalex-snapshot
Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data
inferredcites →https://doi.org/10.1016/j.rse.2018.06.023openalex-snapshot
Quantifying understory vegetation density using small-footprint airborne lidar
inferredcites →https://doi.org/10.1016/s0034-4257(99)00052-8openalex-snapshot
Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests
inferredcites →https://doi.org/10.1016/s0378-1127(00)00403-5openalex-snapshot
Composition, structure, light attenuation and nutrient content of the understorey vegetation in a Eucalyptus sieberi regrowth stand 6 years after thinning and fertilisation
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1037/h0026143openalex-snapshot
A general canonical correlation index.
inferredcites →https://doi.org/10.1071/bt9760249openalex-snapshot
Geographical Elements in the Flora of Victoria
inferredcites →https://doi.org/10.1071/wf10116openalex-snapshot
Linking complex forest fuel structure and fire behaviour at fine scales
inferredcites →https://doi.org/10.1071/wf13054openalex-snapshot
Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data
inferredcites →https://doi.org/10.1071/wf13086openalex-snapshot
Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data
inferredcites →https://doi.org/10.1080/00049158.1983.10674414openalex-snapshot
Dynamics of fine fuels in recurrently burnt eucalypt forests
inferredcites →https://doi.org/10.1093/biomet/28.3-4.321openalex-snapshot
RELATIONS BETWEEN TWO SETS OF VARIATES
inferredcites →https://doi.org/10.1093/forestry/cpad033openalex-snapshot
Automated characterization of forest canopy vertical layering for predicting forest inventory attributes by layer using airborne LiDAR data
inferredcites →https://doi.org/10.1093/forestry/cpl007openalex-snapshot
Estimation of stem volume using laser scanning-based canopy height metrics
inferredcites →https://doi.org/10.1111/2041-210x.12510openalex-snapshot
Using discrete‐return airborne laser scanning to quantify number of canopy strata across diverse forest types
inferredcites →https://doi.org/10.1111/2041-210x.12759openalex-snapshot
Non‐destructive estimation of above‐ground surface and near‐surface biomass using 3D terrestrial remote sensing techniques
inferredcites →https://doi.org/10.1111/ecog.04714openalex-snapshot
Climatic and edaphic gradients predict variation in wildland fuel hazard in south‐eastern Australia
inferredcites →https://doi.org/10.1111/gcb.14987openalex-snapshot
Causes and consequences of eastern Australia's 2019–20 season of mega‐fires
inferredcites →https://doi.org/10.1117/12.2239715openalex-snapshot
Estimation of forest surface fuel load using airborne lidar data
inferredcites →https://doi.org/10.1139/x10-024openalex-snapshot
Comparisons between field- and LiDAR-based measures of stand structural complexity
inferredcites →https://doi.org/10.1177/030913339802200402openalex-snapshot
Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters
inferredcites →https://doi.org/10.1186/s40663-017-0093-zopenalex-snapshot
The influence of canopy-layer composition on understory plant diversity in southern temperate forests
inferredcites →https://doi.org/10.1214/07-aoas148openalex-snapshot
Predictive learning via rule ensembles
inferredcites →https://doi.org/10.1371/journal.pone.0176114openalex-snapshot
Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data
inferredcites →https://doi.org/10.14358/pers.77.3.261openalex-snapshot
Characterizing Forest Succession in Central Ontario using Lidar-derived Indices
inferredcites →https://doi.org/10.14358/pers.79.1.37openalex-snapshot
Predicting Surface Fuel Models and Fuel Metrics Using Lidar and CIR Imagery in a Dense, Mountainous Forest
inferredcites →https://doi.org/10.18637/jss.v021.i07openalex-snapshot
ks : Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
inferredcites →https://doi.org/10.1890/120332openalex-snapshot
Temperate and boreal forest mega‐fires: characteristics and challenges
inferredcites →https://doi.org/10.1890/140231openalex-snapshot
Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes
inferredcites →https://doi.org/10.2307/1932254openalex-snapshot
On Bird Species Diversity
inferredcites →https://doi.org/10.32614/cran.package.leafropenalex-snapshot
leafR: Calculates the Leaf Area Index (LAD) and Other Related Functions
inferredcites →https://doi.org/10.32614/rj-2015-018openalex-snapshot
VSURF: An R Package for Variable Selection Using Random Forests
inferredcites →https://doi.org/10.3390/f6082608openalex-snapshot
Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling
inferredcites →https://doi.org/10.3390/rs11222638openalex-snapshot
A Review of the Applications of Remote Sensing in Fire Ecology
inferredcites →https://doi.org/10.3390/s8063938openalex-snapshot
A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest
inferredcites →https://doi.org/10.48550/arxiv.1612.08468openalex-snapshot
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1801.01489openalex-snapshot
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
inferredcites →https://doi.org/10.5558/tfc2013-132openalex-snapshot
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.