Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario

Abstract

Models were developed to predict forest stand variables for common species of the Great Lakes -St. Lawrence forest of central Ontario, Canada from light detection and ranging (LiDAR) data. Stands that had undergone various ranges of partial harvesting or initial spacing treatments from multiple geographic sites were considered. A broad forest stratification was adopted and consisted of: (i) natural hardwoods; (ii) natural conifers; and (iii) plantation conifers. Stand top height (R 2 = 0.96, 0.98, and 0.98); average height (R 2 = 0.86, 0.76, and 0.98); basal area (R 2 = 0.80, 0.80, and 0.85); volume (R 2 = 0.89, 0.81, and 0.91); quadratic mean diameter (R 2 = 0.80, 0.68, and 0.83); and density (R 2 = 0.74, 0.71, and 0.73) were predicted from low density (i.e., 0.5 point m -2 ) LiDAR data for these 3 strata, respectively. Mots clés : détection de la lumière et calcul de la distance, LiDAR, numérisation par laser aéroporté, modélisation forestière, télédétection, variables de peuplement forestier, forêt des Grands Lacs et du Saint-Laurent.

Operational implementation of a LiDAR inventory in Boreal Ontario

“…Since tree density, BA, and QMDBH are mathematically related and predictions of tree density (stems per ha) from LiDAR data have been reported as weak relative to predictions of basal area and QMDBH (Woods et al 2008), we decided to mathematically recover tree density from basal area and QMDBH:…”

“…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.…”

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].…”

Extraction of Urban Trees From Integrated Airborne Based Digital Image and Lidar Point Cloud Datasets - Initial Results

“…The capability of LiDAR to pass through vegetation has attracted remarkable concern from the field of natural resource management (Gaulton, et al, 2010, Hudak, et al, 2009, Liang, et al, 2007. From a forest management stand-point, LiDAR has been used to define information about trees (Coops et al, 2007, Brolly, et al, 2013, Lang, et al, 2006, measure carbon stocks (Patenaude et al, 2004), compute fuel quantity (Seielstad and Queen, 2003) and create habitat models (Vierling, et al, 2008), develop forest inventories (Zhang, C., 2010, Woods, et al, 2008. Even though considerable research has been carried out regarding LiDAR applications in forestry, its usage in the study of urban trees has been limited.…”