Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density

Abstract

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas.

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

“…For forest ecology applications, estimates of structural parameters obtained from lidar data have been used to model the probability of vegetation types in boreal forests (Vehmas et al, 2009), to map woodland understories (Hill & Broughton, 2009) and to quantify forest floristics (Tickle et al, 2006). Hill and Thomson (2005) reported strong relationships between tree and shrub species composition and structure as forest attributes and structural metrics obtained from lidar data.…”

“…Wulder et al (2008a) assert that lidar now has moved from research and development to operational implementation over a broad range of forest inventory attributes. Tests are also being conducted to incorporate ALS as an operational tool to facilitate forest management and inventories in other parts of the world: Australia (Rombouts et al, 2010), Austria (Hollaus et al, 2007), Canada (Thomas et al, 2006), Germany (Breidenbach et al, 2008) and the United States of America (Jensen et al, 2006).…”

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.…”

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

“…Canopy quantile metrics, representing the height below X% of the LiDAR point cloud, are one of most frequently used LiDAR products for estimating the forest parameters that cannot be obtained directly from a LiDAR point cloud, e.g., DBH and biomass (Lim and Treitz 2004;Thomas et al 2006). In this study, 11 quantile metrics, including 0%, 1%, 5%, 10%, 25%, 50%, 75%, 90%, 95%, 99%, and 100%, were calculated in 20-m resolution directly from the LiDAR point cloud.…”