Abstract
Accurate information on both the current stock and future growth and yield of forest resources is critical for sustainable forest management. We demonstrate a novel approach to utilizing airborne laser scanning (ALS)-derived forest stand attributes to determine future growth and yield of six attributes at a sub-stand (25 m grid cell) level of detail: dominant height (HMAX), Lorey’s height (HL), quadratic mean diameter (QMD), basal area (BA), whole stem volume (V), and trees per hectare (TPH). The approach is designed to find the most appropriate matching yield curve and project the attributes to the age of 80 years. Comparisons to conventional plot-level projections resulted in relative mean differences of 13.4% (HMAX), −27.1% (HL), 18.8% (QMD), 12.0% (BA), 18.6% (V), and −17.5% (TPH). The respective relative root mean squared difference values were: 31.1%, 38.4%, 19.8%, 19.8%, 21.8%, and 38.4%. Differences were driven mostly by stand-level age and site index. The uncertainty of cell-level yield curve assignment was used to refine stand-level summaries. The novel contribution of this study is in the application of growth and yield models at the cell level, combined with the use of ALS-derived attributes to optimize yield curve selection via template matching.
Highlights
Sustainable management of forest ecosystems requires accurate information on forest composition and structure
From the available stand attributes predicted with the yield model we demonstrate six attributes (HMAX, HL, quadratic mean diameter (QMD), basal area (BA), V, trees per hectare (TPH)) that together provide a comprehensive set of information on forest characteristics within each cell
In this study we used an innovative template matching approach, combined with the enriched spatial detail and accuracy enabled by airborne laser scanning (ALS)-predicted forest inventory attributes, to optimize the selection of growth and yield curves for subsequent cell-level growth and yield modelling
Summary
Sustainable management of forest ecosystems requires accurate information on forest composition and structure. This data, collected during forest inventories, provides information on forest stand attributes and their extent, driving many management decisions, such as date of harvest and silvicultural practices. Growth models can generally be divided into three groups, according to the level of abstraction: (1) whole stand models; Forests 2016, 7, 255; doi:10.3390/f7110255 www.mdpi.com/journal/forests (2) size-class models; and (3) single-tree models. Whole-stand models are the simplest, they are insufficient when information on size class or individual trees is required [3]. Size-class models are a compromise between whole-stand models and single-tree models and provide some information on both stand structure and individual tree attributes (e.g., histogram of DBH) [3]. All three types of models are based on multiple stand characteristics, with site quality (i.e., site index or SI) often being among the most important characteristics for modelling growth
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