Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Abstract

). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Received Nov. 11, 2003. Accepted Aug. 10, 2004. Scand. J. For. Res. 19: 558 Á/570, 2004 A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in rootmean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha(1 , respectively.

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

“…Nelson et al [70] found that average height, quadratic mean height, and canopy cover accounted for the most of the variation in AGB. Lim and Treitz [77] concluded that laser-based quantiles of canopy heights are useful in predicting forest biomass.…”

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

“…Numerous studies have demonstrated that forest inventory variables can be measured and modeled accurately (and precisely) from LiDAR height and density metrics [1][2][3]. These include critical parameters, such as species identification [4], mean diameter at breast height (DBH) [5,6], stand and canopy structural complexity [7,8], forest succession [8], fractional cover [9], leaf area index (LAI) [9,10], crown closure [11], timber volume [6,12,13] and biomass [14][15][16][17]. Estimation of many forest inventory variables using LiDAR data is now moving beyond the research realm and into the operational forum [18][19][20][21][22].…”

Operational implementation of a LiDAR inventory in Boreal Ontario

“…Examples include predictive hydrology modeling (Murphy et al 2008, Mandlburger et al 2009), road location optimization and construction (Akay et al 2004, Aruga et al 2005, White et al 2010, harvest block engineering (Chung et al 2004), habitat definition (Clawges et al 2008, Hinsley et al 2008, and timber quantification (Holmgren and Jonsson 2004, Naesset 2004, Parker and Evans 2007. Research conducted specifically in Ontario has focused on estimating forest inventory and biophysical variables for tolerant northern hardwoods (Lim et al 2001(Lim et al , 2002(Lim et al , 2003Todd et al 2003;Lim and Treitz 2004;Woods et al 2008), boreal mixedwoods (Thomas et al 2006(Thomas et al , 2008 and conifer plantations (Chasmer et al 2006). Current acquisition costs, including classification of LiDAR points, derivation of digital elevation models (DEMs) and digital surface models (DSMs), have positioned LiDAR as a potentially operationally affordable alternative when the development of a precision forest inventory is considered a requirement.…”