On the public citation graph

A Novel Method for Estimating Fine Fuel Loads in Vertical Forest Layers with Airborne Lidar

https://doi.org/10.2139/ssrn.4851451
0 signed · 70 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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-extracted
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-extracted
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.2012.03.036openalex-extracted
Diversity and equitability ordering profiles applied to study forest structure
inferredcites →https://doi.org/10.1016/j.foreco.2012.06.012openalex-extracted
Predicting fire behaviour in dry eucalypt forest in southern Australia
inferredcites →https://doi.org/10.1016/j.foreco.2012.06.056openalex-extracted
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-extracted
The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration
inferredcites →https://doi.org/10.1016/j.foreco.2021.119894openalex-extracted
Fuel consumption rates in resprouting eucalypt forest during hazard reduction burns, cultural burns and wildfires
inferredcites →https://doi.org/10.1016/j.foreco.2024.121851openalex-extracted
Estimating fine fuel loads in Eucalypt forests using forest inventory data and a modelling approach
inferredcites →https://doi.org/10.1016/j.isprsjprs.2015.01.016openalex-extracted
Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
inferredcites →https://doi.org/10.1016/j.isprsjprs.2017.07.001openalex-extracted
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-extracted
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-extracted
Dynamic stratification for vertical forest structure using aerial laser scanning over multiple spatial scales
inferredcites →https://doi.org/10.1016/j.jag.2022.103101openalex-extracted
Forest foliage fuel load estimation from multi-sensor spatiotemporal features
inferredcites →https://doi.org/10.1016/j.rse.2010.10.012openalex-extracted
Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems
inferredcites →https://doi.org/10.1016/j.rse.2012.01.020openalex-extracted
3-D mapping of a multi-layered Mediterranean forest using ALS data
inferredcites →https://doi.org/10.1016/j.rse.2012.11.024openalex-extracted
Tradeoffs between lidar pulse density and forest measurement accuracy
inferredcites →https://doi.org/10.1016/j.rse.2014.10.004openalex-extracted
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-extracted
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-extracted
Quantifying understory vegetation density using small-footprint airborne lidar
inferredcites →https://doi.org/10.1016/j.rse.2020.111770openalex-extracted
Detection of sub-canopy forest structure using airborne LiDAR
inferredcites →https://doi.org/10.1016/j.rse.2020.112061openalex-extracted
lidR: An R package for analysis of Airborne Laser Scanning (ALS) data
inferredcites →https://doi.org/10.1016/j.rse.2020.112282openalex-extracted
Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behaviour
inferredcites →https://doi.org/10.1016/j.rse.2021.112464openalex-extracted
Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification
inferredcites →https://doi.org/10.1016/j.rse.2021.112857openalex-extracted
Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)
inferredcites →https://doi.org/10.1016/j.rse.2023.113711openalex-extracted
Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations
inferredcites →https://doi.org/10.1016/s0034-4257(01)00290-5openalex-extracted
Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
inferredcites →https://doi.org/10.1016/s0034-4257(99)00052-8openalex-extracted
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-extracted
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-extracted
Random Forests
inferredcites →https://doi.org/10.1071/bt9760249openalex-extracted
Geographical Elements in the Flora of Victoria
inferredcites →https://doi.org/10.1071/wf10116openalex-extracted
Linking complex forest fuel structure and fire behaviour at fine scales
inferredcites →https://doi.org/10.1071/wf12211openalex-extracted
Changes in the spectral features of fuel layers of an Australian dry sclerophyll forest in response to prescribed burning
inferredcites →https://doi.org/10.1071/wf13054openalex-extracted
Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data
inferredcites →https://doi.org/10.1071/wf13086openalex-extracted
Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data
inferredcites →https://doi.org/10.1080/00049158.1983.10674414openalex-extracted
Dynamics of fine fuels in recurrently burnt eucalypt forests
inferredcites →https://doi.org/10.1093/forestry/cpl007openalex-extracted
Estimation of stem volume using laser scanning-based canopy height metrics
inferredcites →https://doi.org/10.1109/tgrs.2014.2315649openalex-extracted
Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data
inferredcites →https://doi.org/10.1111/2041-210x.12510openalex-extracted
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-extracted
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-extracted
Climatic and edaphic gradients predict variation in wildland fuel hazard in south‐eastern Australia
inferredcites →https://doi.org/10.1111/gcb.14987openalex-extracted
Causes and consequences of eastern Australia's 2019–20 season of mega‐fires
inferredcites →https://doi.org/10.1111/gcb.16283openalex-extracted
The fuel–climate–fire conundrum: How will fire regimes change in temperate eucalypt forests under climate change?
inferredcites →https://doi.org/10.1111/rssb.12377openalex-extracted
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
inferredcites →https://doi.org/10.1117/12.2239715openalex-extracted
Estimation of forest surface fuel load using airborne lidar data
inferredcites →https://doi.org/10.1139/x10-024openalex-extracted
Comparisons between field- and LiDAR-based measures of stand structural complexity
inferredcites →https://doi.org/10.1177/030913339802200402openalex-extracted
Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters
inferredcites →https://doi.org/10.1186/s40663-017-0093-zopenalex-extracted
The influence of canopy-layer composition on understory plant diversity in southern temperate forests
inferredcites →https://doi.org/10.1214/07-aoas148openalex-extracted
Predictive learning via rule ensembles
inferredcites →https://doi.org/10.1371/journal.pone.0176114openalex-extracted
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-extracted
Characterizing Forest Succession in Central Ontario using Lidar-derived Indices
inferredcites →https://doi.org/10.14358/pers.79.1.37openalex-extracted
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-extracted
ks : Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
inferredcites →https://doi.org/10.1890/120332openalex-extracted
Temperate and boreal forest mega‐fires: characteristics and challenges
inferredcites →https://doi.org/10.1890/140231openalex-extracted
Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes
inferredcites →https://doi.org/10.2307/1932254openalex-extracted
On Bird Species Diversity
inferredcites →https://doi.org/10.32614/cran.package.leafropenalex-extracted
leafR: Calculates the Leaf Area Index (LAD) and Other Related Functions
inferredcites →https://doi.org/10.32614/rj-2015-018openalex-extracted
VSURF: An R Package for Variable Selection Using Random Forests
inferredcites →https://doi.org/10.3390/f6082608openalex-extracted
Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling
inferredcites →https://doi.org/10.3390/rs11030292openalex-extracted
Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds
inferredcites →https://doi.org/10.3390/rs11070743openalex-extracted
Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness
inferredcites →https://doi.org/10.3390/rs11222638openalex-extracted
A Review of the Applications of Remote Sensing in Fire Ecology
inferredcites →https://doi.org/10.3390/rs12203333openalex-extracted
Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest
inferredcites →https://doi.org/10.3390/rs13061189openalex-extracted
Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data
inferredcites →https://doi.org/10.3390/rs13245170openalex-extracted
Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data
inferredcites →https://doi.org/10.3390/rs9040394openalex-extracted
Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data
inferredcites →https://doi.org/10.3390/s8063938openalex-extracted
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-extracted
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1801.01489openalex-extracted
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-extracted
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach
inferredcites →https://doi.org/10.5558/tfc84827-6openalex-extracted
Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence forest of Ontario
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.