Towards an Optimization of Sample Plot Size and Scanner Position Layout for Terrestrial Laser Scanning in Multi-Scan Mode

Tim Ritter,Christoph Gollob,Arne Nothdurft

doi:10.3390/f11101099

Tim Ritter, Christoph Gollob + Show 1 more

Open Access

https://doi.org/10.3390/f11101099

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Abstract

A novel approach is presented to model the tree detection probability of terrestrial laser scanning (TLS) in forest inventory applications using a multi-scan mode. The traditional distance sampling framework is further extended to account for multiple scan positions at a single sample plot and to allow for an imperfect detection probability at distance r = 0. The novel methodology is tested with real world data, as well as in simulations. It is shown that the underlying detection model can be parameterized using only data from single scans. Hereby, it is possible to predict the detection probability also for different sample plot sizes and scanner position layouts in a multi-scan setting. Simulations showed that a minor discretization bias can occur if the sample size is small. The methodology enables a generalized optimization of the scanning layout in a multi-scan setting with respect to the detection probability and the sample plot area. This will increase the efficiency of multi-scan TLS-based forest inventories in the future.

Highlights

Forest inventories provide relevant information on the status and changes of forest landscapes.Traditionally, forest inventories were designed to provide precise information on the timber growing stock
We show that the detection rate with different scanner position layouts could be generally predicted for different sample plot sizes in order to find an optimal design for a terrestrial laser scanning (TLS)-based forest inventory
Any uncritical adoption of classic distance sampling methods would have resulted in a severe overestimation of Pa (Table 2) and the classic distance sampling methods would have resulted in a severe overestimation of P

Summary

Introduction

Forest inventories provide relevant information on the status and changes of forest landscapes. Forest inventories were designed to provide precise information on the timber growing stock. Tree attributes and positions are manually measured in forest inventories, using simple mechanical or optical instruments, such as calipers, hypsometers, compasses, and measuring tapes [1,5,6]. Measurements of tree attributes with the traditional instruments are time-consuming, cost-intensive, and prone to manifold measurement errors; they are still regarded as the gold standard to which new measuring techniques are compared [7,8,9]. Sampling schemes of forest inventories have been optimized for efficiency and precision [10,11] and can further be adopted in case new sensor techniques replace the traditional measuring equipment in the future

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