The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland

Abstract

Key message The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

“…6) Other: 9.1% of the papers [148][149][150][151][152][153][154][155][156][157][158][159][160] include a variety of applications, such as mapping the pigment distribution and quantifying taxonomic, functional, and phylogenetic diversity, tree age estimation etc. 7) Fuel load: 6.3% of the papers [161][162][163][164][165][166][167][168][169] include applications that deal with fuel load and forest fire modeling.…”

“…The 'other' applications included LiDAR data fusion studies focused on wetland/marsh areas, boreal forests and a natural disaster impact assessment [155,156,158]. For example, [158] fused airborne LiDAR with MS imagery to assess forest loss in a wetland zone.…”

“…[155] demonstrated that their automatic ALS and TLS point cloud co-registration resulted in a denser point cloud, in which the stems and canopy of individual trees were better represented than in the single LiDAR datasets, but provided no quantitative improvement on retrieval of canopy/forest/tree information in a boreal forest. [156] developed a method to assess wind damage by fusing ALS and MS imagery. They conclude that adding the structural metrics from ALS to the spectral information provides estimates of structural damages that cannot be retrieved with spectral data alone.…”

Terrestrial laser scanning: a new standard of forest measuring and modelling?

“…For example, the species diversity and structural diversity of forest vegetation has obvious links with processes maintaining the biodiversity of other organisms as they predict the availability of food and microclimatic conditions suitable for different organisms ( O’Sullivan et al , 2021 ). Sample studies have linked forest structure with diverse features such as carbon fluxes and storage ( Hardiman et al , 2013 ), spatial patterns of nutrient cycling ( de Godoy Fernandes et al , 2021 ), variation of photosynthetically active radiation ( Ma et al , 2021 ), forest productivity ( Pretzsch and Schütze, 2021 ), composition of bird communities ( Hollie et al , 2020 ), foraging paths of mammals ( Moorcroft, 2012 ), rainfall interception ( Yu et al , 2020 ), risks of fire and wind ( Gopalakrishnan et al , 2020 ; Gale et al , 2021 ), canopy interactions ( van der Zee et al , 2021 ), and insect abundance ( Knuff et al , 2020 ).…”