Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

Abstract

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

“…The assessment of the status of forest regeneration using aerial photogrammetry and UAV was carried out by Goodbody et al (2018). Moreover, young and advanced regenerating forests were studied using UAV-based hyperspectral imagery and photogrammetric point clouds (Imangholiloo et al 2019) and multispectral ALS data (Imangholiloo et al 2020), both under leaf-off and leaf-on conditions in boreal forests. Similarly, UAV data have been applied for regeneration assessments in seedling stands (Puliti et al 2019;Castilla et al 2020;Green and Burkhart, 2020).…”

Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning

“…The use of remote sensing (RS) techniques, especially airborne laser scanning (ALS), for forest characterization has been intensively investigated during the past two decades, and its capacity to support forest management is well known (e.g., Hyyppä et al, 2012;White et al, 2016). However, a majority of prior studies have primarily investigated the use and development of RS techniques in mature forest stands, while less attention has been paid to forests at the early development stages (Ørka et al, 2016;Imangholiloo et al, 2020). Information from these early development stages would be important for silvicultural decisionmaking.…”

“…Hence, there is a crucial need to supply the required spatially explicit and timely information using RS technologies capable of covering large areas. Thus, in fact, there is an obvious knowledge gap regarding the feasibility of ALS in providing reliable information on the characteristics of seedling stands (Imangholiloo et al, 2019;Imangholiloo et al, 2020).…”

“…Next, watershed segmentation in ArcGIS (ESRI, 2011) was applied to delineate the boundaries of the segments (Fig. 3) above a canopy threshold of 0.4 m (as concluded in Imangholiloo et al (2020). Then, the ALS point clouds were delineated according to the crown segments to extract point clouds for each tree in the seedling stands.…”

“…Regarding the use of remote sensing techniques and individual tree detection for seedling stand characterization, the obtained results were somewhat in line with those of prior studies. Imangholiloo et al (2020) used multispectral ALS data (57 points/m 2 ) to detect and classify trees in seedling stands, using randomForest classifier and spectral values from three different laser wavelengths. They reported an rRMSE of 37.9% for the density of seedling stands.…”