Advances in Forest Inventory Using Airborne Laser Scanning

Abstract

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m 2 . With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were OPEN ACCESSRemote Sens. 2012, 4 1191 fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.

Outlook for the Next Generation’s Precision Forestry in Finland

“…Forest attribute maps with spatial resolution of 10-20 m can be produced by current ABA inventory techniques [1][2][3][4][5][6]14,17]. In thinning stands, mapping and timing of forest management operations are highly important from a silvicultural point of view [24].…”

“…Thus, there is growing interest for more detailed forest measurements. Single trees can be detected from the ALS data [13,14]. Nevertheless, single-tree techniques have failed to challenge ABA to date, mainly because of problems with reliable tree detection in various forest conditions [15].…”

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

“…Active sensors, including mainly LiDAR, have been proven the most effective sources of forest structure information (Lindberg and Hollaus 2012;Hyyppä et al 2012;Koch 2010). Besides the usually employed first pulse and statistical point height metrics, last pulse and individual tree-based features have shown increased accuracy in approximating tree height, density at breast height, and stem volume in a boreal managed forest (Hyyppä et al 2012). However, although the omission errors in tree detection are reduced, the commission errors are increased; therefore, a synergy of first and last pulse data might combine the benefits of the former in detecting non-overlapping trees and of the latter in overlapping ones.…”

An integrated airborne laser scanning approach to forest management and cultural heritage issues: a case study at Porolissum, Romania

“…They can be classified into first returns, reflected from the tree tops, intermediate returns, from the leaves or branches, and last returns, reflected from the ground. Aerial laser scanning has many uses: measuring agricultural productivity (Saeys et al 2009), distinguishing faint archaeological evidences (Bennett 2012), forestry practices (Hyyppä et al 2012), advancing the science of geomorphology (Sofia et al 2014), measuring volcano uplift (Whelley et al 2014), glacier decline and snowpack (Abermann et al 2010), and providing data for topographic mapping, to name just a few. Using the LiDAR point cloud data, one can extract specific features, such as dimensions of underground ancient structures or aboveground parameters of individual trees (Popescu et al 2003, Popescu 2007, Edson & Wing 2011, Dalponte et al 2014, and obtain ecosystem level information such as forests biomass or carbon sequestration capacity (Lefsky et al 2005, Popescu 2007, García et al 2010, Lee et al 2013.…”