Abstract
Timber assortments are some of the most important goods provided by forests worldwide. To quantify the amount and type of timber assortment is strongly important for socio-economic purposes, but also for accurate assessment of the carbon stored in the forest ecosystems, regardless of their main function. Terrestrial laser scanning (TLS) became a promising tool for timber assortment assessment compared to the traditional surveys, allowing reconstructing the tree architecture directly and rapidly. This study aims to introduce an approach for timber assortment assessment using TLS data in a mixed and multi-layered Mediterranean forest. It consists of five steps: (1) pre-processing, (2) timber-leaf discrimination, (3) stem detection, (4) stem reconstruction, and (5) timber assortment assessment. We assume that stem form drives the stem reconstruction, and therefore, it influences the timber assortment assessment. Results reveal that the timber-leaf discrimination accuracy is 0.98 through the Random Forests algorithm. The overall detection rate for all trees is 84.4%, and all trees with a diameter at breast height larger than 0.30 m are correctly identified. Results highlight that the main factors hindering stem reconstruction are the presence of defects outside the trunk, trees poorly covered by points, and the stem form. We expect that the proposed approach is a starting point for valorising the timber resources from unmanaged/managed forests, e.g., abandoned forests. Further studies to calibrate its performance under different forest stand conditions are furtherly required.
Highlights
Roundwood represents one of the most important goods provided by forest ecosystems worldwide, feeding the forest products supply chain
This study aims to introduce an innovative procedure using the promising approaches for assessing timber assortments from standing trees using Terrestrial laser scanning (TLS) data in a natural mixed and multi-layered Mediterranean forest
The results reveal that the cylinder-fitting algorithm is less suitable for detecting small trees, this challenge has even been found for 18 automatic and semi-automatic algorithms [16], and in such a study a similar challenge is related to incomplete TLS point cloud coverage, while in our case it can be caused by the nearest trees growing from coppice shoots
Summary
Roundwood represents one of the most important goods provided by forest ecosystems worldwide, feeding the forest products supply chain. Despite the fact that roundwood production of Europe’s forests has been growing, reaching about 550 million m3 annually [1], recognizing the European countries as some of the main producers of industrial roundwood [2], uncertainties about timber assortment and fuelwood estimates are still unsolved. These uncertainties might be associated with the low performance of traditional surveys lacking for accurately depicting the trees’ architecture, since bark irregularities, such as bulges, holes, cavities, and other defects [3] are often ignored. Measuring the tree characteristics on standing trees through traditional surveys became challenging, especially in deciduous or mixed forest stands [8]
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