Individual tree measurements in a planted woodland with terrestrial laser scanner

Abstract

Introduction Forest structural diversity is considered to be one of the most important components of biological diversity because it affects the ecological factor beneath the canopy and creates suitable niches for fauna. Therefore, more complex structures indicate greater biodiversity (Szmyt, 2014). Forest structure is also accepted as one of the main naturalness traits, which may include tree density, vertical heterogeneity, canopy cover, and forest layering (Winter, 2012). Hence, quantitative tree and stand structural parameters must be accurately measured for ecosystem service assessment and state of stand development (Moskal and Zheng, 2012). Traditionally, some structural attributes have been measured in the context of forest inventories using field measurements. With the development of technology, the combination of forest inventory methods and new remote sensing technologies have become powerful integrated tools (Brack, 1997). The airborne laser scanning (ALS) technology, which offers high resolution data to derive structural forest parameters, has become notably popular (Means et al., 2000; Drake et al., 2002; Naesset and Økland, 2002; Morsdorf et al., 2004). However, the measurement of ground-truth data still remains in demand in addition to measurements for forest inventory. In the last decade, terrestrial laser scanner (TLS) based data have also become an alternative tool in forest management studies (Bienert et al., 2006). TLS systems can produce 3D point clouds that allow quantitative analysis of tree parameters (Raumonen et al., 2013). TLS has been used in many forestry studies, such as biomass estimation (

Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey

“…Using TLS, Pazhouhan et al (2017) saw a strong relationship between the two methods with an R 2 value of 0.98 for DBH in the Hyrcanian forest. Similarly, Yurtseven et al (2019) found an R 2 value of 0.99 for DBH in Istanbul, Turkey. In another study, Akgül et al (2016) reached an R 2 value of 0.97 for the same measure in Istanbul too.…”

“…First, scanning from a fixed position limits usage due to obstructed areas behind large trunks and branches (Leeuwen and Nieuwenhuis, 2010;Bauwens et al, 2016). This so-called occlusion effect may be overcome by scanning from multiple points as Yurtseven et al (2019) did on a forest plot -but it is almost impossible in an FI survey (Oveland et al, 2018), as hundreds of plots are sampled for only one forest enterprise. Other limitations embedded within TLS are its weight as well as equipment acquisition cost.…”

“…However, no forestry studies have been conducted using this technology in Turkey until the last few years, mainly due to the limitations of traditional LiDAR instruments described above. Finally, such studies have progressively emerged in Turkey (see Yurtseven et al, 2019;Arslan et al, 2016;Büyüksalih et al, 2017, among others), like in many other developed countries. Akgül et al (2016), for instance, estimated DBH, tree height, and crown base height using TLS on the campus of Istanbul University.…”

Remote Sensing of Savannas and Woodlands

“…Of the 87, only 15 studies worked in the savanna biome [39,145,146]. The most frequently employed methods to derive vegetation attributes are the RANdom SAmple Consensus (RANSAC) algorithm for extracting DBH, stem curve profiles and the detection of stems [97,147], the use of the highest z coordinate of the point cloud for estimating heights [61,148], voxel-based and radiative transfer models for assessing LAI [149,150], Canopy Height Models (CHMs) for delineating crown attributes [91,92], and Quantitative Structure Models (QSMs) for computing tree volume and branch parameters [151,152]. Popular software packages to implement and work with these methods are Lastools [153], Matlab [154], R [155] and Python Programming [156], Cyclone [157], FARO Scene [158], RiscanPro [159], CompuTree [160] and Cloud Compare [161].…”

“…Similarly, Calders et al [40] and Chen et al [167] obtained R 2 values of 0.97 when using least squares circle fitting for DBH estimation with a multi-scan TLS scan with 5 positions in a eucalypt open forest and boreal forest respectively. Yurtseven et al [148] obtained an R 2 value of 0.99 with a multi-scan with 8 positions in a Mediterranean forest employing randomised hough transformation with least square regression. In general, multiple TLS scans and a lower stem density yielded the best results [114,168,169].…”