Assessing the potential of mobile laser scanning for stand-level forest inventories in near-natural forests

Abstract

Recent advances in LiDAR sensors and robotic technologies have raised the question of whether handheld mobile laser scanning (HMLS) systems can allow for the performing of forest inventories (FIs) without the use of conventional ground measurement (CGM) techniques. However, the reliability of such an approach for forest planning applications, particularly in non-uniform forests under mountainous conditions, remains underexplored. This study aims to address these issues by assessing the accuracy of HMLS-derived data based on the calculation of basic forest attributes such as the number of trees, dominant height and basal area. To this end, near-natural forests of a national park (NE Türkiye) were surveyed using the HMLS and CGM techniques for a management plan renewal project. Taking CGM results as reference, we compared each forest attribute pair based on two datasets collected from 39 sample plots at the forest (landscape) scale. Diameter distributions and the influence of stand characteristics on HMLS data accuracy were also analyzed at the plot scale. The statistical results showed no significant difference between the two datasets for any investigated forest attributes (P > 0.05). The most and the least accurately calculated attributes were quadratic mean diameter (root mean square error (RMSE) = 1.3 cm, 4.5 per cent) and stand volume (RMSE = 93.7 m3 ha−1, 16.4 per cent), respectively. The stand volume bias was minimal at the forest scale (15.65 m3 ha−1, 3.11 per cent), but the relative bias increased to 72.1 per cent in a mixed forest plot with many small and multiple-stemmed trees. On the other hand, a strong negative relationship was detected between stand maturation and estimation errors. The accuracy of HMLS data considerably improved with increased mean diameter, basal area and stand volume values. Eventually, we conclude that many forest attributes can be quantified using HMLS at an accuracy level required by forest planning and management-related decision making. However, there is still a need for CGM in FIs to capture qualitative attributes, such as species mix and stem quality.

Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area

“…As a result, broad-leaved trees exhibited higher accuracy values compared to coniferous trees. This observation aligns with findings from previous studies conducted in challenging forest environments [4,34].…”

“…Similarly, ref. [4] analyzed the influence of stand characteristics (such as the number of trees, stand basal area, dominant height, understorey cover, slope, etc.) on HLS data accuracy using datasets from 39 sample plots.…”

“…These services include promoting health and social well-being, enhancing children's cognitive development and educational success rate, fostering a strong economy and providing numerous resources, mitigating the urban heat island effect, storing and sequestering carbon, offering habitat and food for animals, and managing stormwater through green infrastructure [1,2]. Precise inventories of individual trees or sample-based parameters are fundamental for achieving a comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests [3,4]. In traditional plot-based forest inventories, the most frequently measured individual attributes are tree species, diameter at breast height (DBH), and tree height [5,6].…”

“…Its platform can be a vehicle [6,25] or a person, the latter also known as personal laser scanning (PLS) [26,27] or wearable laser scanning [28]. Depending on the platform they are mounted on, PLS can be further categorized into backpack laser scanning (BPLS) [29,30] or handheld laser scanning (HLS) [4,27,[31][32][33]. A typical PLS system comprises a LiDAR sensor, a global navigation satellite system (GNSS) receiver, and an inertial measurement unit (IMU) [18].…”

“…The introduction of simultaneous localization and mapping (SLAM) has empowered the latest PLS systems, particularly HLS devices, with the capability to capture detailed 3D scenarios while in motion without relying on GNSS signals. By providing spatial data in a local coordinate system, SLAM software enables raw data to be quickly pre-processed and exported to various point cloud formats even on a modest laptop [4]. Thus, PLS exhibits superior mobility and higher efficiency compared to TLS.…”