Airborne scanning LiDAR in a double sampling forest carbon inventory

Stephens, P. R.; Kimberley, Mark O.; Beets, Peter N.; Paul, Thomas; Searles, Nigel; Bell, Alan; Brack, Cristopher; Broadley, James

doi:10.1016/j.rse.2011.10.009

Cited by 74 publications

(47 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to previous studies [22,26,[58][59][60], one source of error that may influence the relationship between the lidar-and the field-estimated AGB at different spatial resolutions is the disagreement between the lidar and field plot measurements over which trees or parts of trees are inside the calibration plots. In the lidar measurements, tree crowns are bisected exactly at the plot edge, while in the field plot measurements, an individual is included in the plot if its basal area contributes to the basal area of the plot; in other words, if the DBH trunk point of measure is inside the plot.…”

Section: Lidar Processingmentioning

confidence: 99%

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

View full text Add to dashboard Cite

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km 2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R 2 (calibration) = 0.89, R 2 (validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha −1 , and RMSE(validation) = 14.80 t·ha −1 ). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

show abstract

Section: Lidar Processingmentioning

confidence: 99%

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The use of airborne LiDAR for estimating forest inventory parameters and structural characteristics is reviewed by van Leeuwen and Nieuwenhuis (2010), and a meta-analysis of 70 articles has been conducted by Zolkos et al (2013). Airborne LiDAR is already being used to support the national carbon stock estimates, such as in New Zealand (Stephens et al 2012) or in Panama .…”

Section: Light Detection and Ranging Systemsmentioning

confidence: 99%

An overview of existing and promising technologies for national forest monitoring

Henry

Réjou‐Méchain

Cifuentes-Jara

et al. 2015

Annals of Forest Science

View full text Add to dashboard Cite

show abstract

“…Il trasferimento di tali procedure alla pratica operativa è avvenuto rapidamente in Nord America e Nord Europa (Naesset et al 2004, Hyyppä et al 2008, Wulder et al 2008, Nord-Larsen & Schumacher 2012, Stephens et al 2012, Wulder et al 2012, mentre procede più lentamente nel nostro Paese (Montaghi et al 2013), soprattutto a causa della relativa scarsità di riprese LiDAR su estensioni sufficientemente ampie, legata al costo di acquisizione, in particolare per rilievi ad alta densità di punti (Pirotti 2011). Laddove esistenti, tali riprese non sono inoltre quasi mai specificatamente orientate al monitoraggio del patrimonio forestale (Corona et al 2012).…”

Section: Introduzioneunclassified

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Scrinzi¹,

Floris²,

Clementel³

et al. 2017

Forest@

View full text Add to dashboard Cite

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy). The AlForLab project is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region. Estimating the main dendrometric variables of Calabrian forests using models based on publicly available remote sensed data is one of the main purposes of the project. This paper describes the procedures used to develop several thematic maps (raster and vector) of timber volume and phytomass to be used in planning and management activities at both regional and forest property scale, as well as for felling plans, logging projects etc. We used public LiDAR data at medium-low resolution (1.6 pts m -2 ), acquired on about 90% of Calabrian territory in the frame of a national remote sensing programme of the Italian Ministry of the Environment. Field data from the second National Forest Inventory (INFC 2005) on 311 sample points were used for model calibration, as well as new field data acquired specifically for AlForLab project on 143 angle count samples. A series of regression models to predict volume and its corresponding aboveground biomass (dry and fresh weight) were developed and digital maps at different spatial resolutions were produced, as well as their estimate uncertainties. These models and their mapping products are also an important part of the new-establishing forest Decision Support System CFOR. The adopted models, though based on the same mathematical equation, have specific coefficients for different species and groups of species, according to a forest type classification system compatible with the fourth level of Corine Land Cover. In this way it is possible to apply the models without accessing more detailed forest type maps. All estimation methods and procedures are consistent with national forest inventory models, and with the other new tools proposed by AlForLab project to estimate timber volume, such as the regional tariffs and the field sampling inventory procedures. R 2 adjusted values (for models at the highest typological detail) are between 60% and 85%, whereas uncertainties of timber volume estimate (ESS%) range from 25% (for main forest species) up to 50% (for less spread forest types). All processing steps to produce digital maps were performed on open-source environment (R and QGIS).Keywords: ALS, LiDAR, Timber Volume, Forest Biomass, Estimation Models, CHM, AlForLab Received: Feb 09, 2017; Accepted: Apr 19, 2017; Published online: May 15, 2017 Citation: Scrinzi G, Floris A, Clementel F, Bernardini V, Chianucci F, Greco S, Michelini T, Penasa A, Puletti N, Rizzo M, Turco R, Corona P, 2017. Modelli di stima del volume e delle fitomasse del soprassuolo arboreo delle principali formazioni forestali della Calabria mediante dati LiDAR. Forest@ 14: 175-187 [online 2017-05-15]

show abstract

Airborne scanning LiDAR in a double sampling forest carbon inventory

Cited by 74 publications

References 25 publications

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

An overview of existing and promising technologies for national forest monitoring

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Contact Info

Product

Resources

About