Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan

Determining forest structural complexity,i.e.,a measure of the number of different attributes of a forest and the relative abundance of each attribute,is important for forest management and conservation.In this study,we examined the structural complexity of mixed conifer–broadleaf forests by integrating multiple forest structural attributes derived from airborne Li DAR data and aerial photography.We sampled 76 plots from an unmanaged mixed conifer–broadleaf forest reserve in northern Japan.Plot-level metrics were computed for all plots using both field and remote sensing data to assess their ability to capture the vertical and horizontal variations of forest structure.A multivariate set of forest structural attributes that included three Li DAR metrics(95 th percentile canopy height,canopy density and surface area ratio) and one image metric(proportion of broadleaf cover),was used to classify forest structure into structural complexity classes.Our results revealed significant correlation between field and remote sensing metrics,indicating that these two sets of measurements captured similar patterns of structure in mixed conifer–broadleaf forests.Further,cluster analysis identified six forest structural complexity classes includingtwo low-complexity classes and four high-complexity classes that were distributed in different elevation ranges.In this study,we could reliably analyze the structural complexity of mixed conifer–broadleaf forests using a simple and easy to calculate set of forest structural attributes derived from airborne Li DAR data and high-resolution aerial photography.This study provides a good example of the use of airborne Li DAR data sets for wider purposes in forest ecology as well as in forest management.

Effects of silviculture treatments in a hurricane-damaged forest on carbon storage and emissions in central Hokkaido, Japan

Hurricanes cause abrupt carbon reduction in forests, but silviculture treatment can be an effective means of quickly regenerating and restoring hurricane-damaged sites. This study assessed how silviculture treatments affect carbon balance after hurricane damage in central Hokkaido, Japan. We examined carbon storage in trees and underground vegetation as well as carbon emissions from silviculture operations in 25-year-old stands, where scarification and plantation occurred just after hurricane damage. The amount of carbon stored varied according to silviculture treatment. Among three scarification treatments, a scarified depth of 0 cm （understory vegetation removal） led to the largest amount of carbon stored （64.7 t·ha^-1 C）. Among four plantation treatments, the largest amount of carbon was stored in a Larix hybrid （L. gmelinii var. japonica × L. kaempferi） plantation （80.3 t·ha^-1 C）. The plantation of Abies sachalinensis was not successful at accumulating carbon （40.5·ha^-1 C）. The amount of carbon emitted from silviculture operations was 0.05-0.14 t·ha^-1 C, and it marginally affected the net carbon balance of the silviculture project. Results indicate that silviculture treatments should beperformed in an appropriate way to effectively recover the ability of carbon sequestration in hurricane-damaged forests.