Degradation of White Birch Shelterbelts by the Attack of White-Spotted Longicorn Beetles in Central Hokkaido, Northern Japan

A widespread decline of white birch (Betula platyphylla var. japonica) shelterbelts was observed in central Hokkaido, Japan. Many exit holes bored by white-spotted longicorn beetles (Anoplophora malasiaca) were found at the base of the trunks of trees in these stands. The present study aims to evaluate the effects of infestation on the degradation, and demonstrates whether the number of exit holes (Nholes) can be used as an index of the decline of trees. We selected 35 healthy appearing stands and 16 degraded stands in the study area. A generalized linear mixed model with zero inflation revealed that Nholes of standing dead trees tended to be greater than that of living trees, and the tree vigor decreased with increasing Nholes. These results implied that the degradation of the shelterbelts was caused by the beetle. We also found size-dependent mortality, i.e., only a few larvae can cause the death of smaller trees, but not larger trees. Furthermore, evaluation of the degradation at the stand level (Nholes) using a logistic regression analysis revealed that the degradation at the stand level could be predicted by Nholes. Our findings can be used as a useful index marker for diagnosing white birch shelterbelts.

Stock of standing dead trees in boreal forests of Central Siberia

A significant part of carbon assimilated by forest is deposited in tree trunks. Growth and development of tree stands is accompanied by accumulation of standing dead trees (snags) due to natural tree mortality and as a result of the impact of exogenous factors. Carbon accumulated in these dead trunks is excluded from the fast turnover due to low rate of wood decomposition, so that snags can be considered as a pool of organic carbon with a slow rate of its return to the atmosphere. We estimated stock of snags on 54 sample plots, which represent the main types of forest ecosystems in the northern and middle taiga of Central Siberia. In the middle taiga, stock of snags varied from up to 7 m3 ha-1 in Siberian spruce forests to 20-42 m3 ha-1 in Scots pine forests. Larch forests in the northern taiga had the similar stock of snags as larch forests in the middle taiga despite significantly higher growing stock in the later. Snags contributed from 4 to 19% to the total stock of woody biomass in studied forests. This study indicated the significance of snags and can be used to estimate carbon budget of forest ecosystems of the region.

Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot

A mechanistic understanding of how tropical tree mortality responds to climate variation is urgently needed to predict how tropical forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used five years of approximately monthly drone-acquired RGB imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure, temporal variation, and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or collapse of standing dead trees. Treefalls accounted for 77 % of the total area and 60 % of the total number of canopy disturbances in treefalls and branchfalls combined. The size distribution of canopy disturbances was close to a power function for sizes above 25 m2, and best fit by a Weibull function overall. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though windspeeds were lower in the wet season. The strongest correlate of temporal variation in canopy disturbance rates was the frequency of 1-hour rainfall events above the 99.4th percentile (here 35.7 mm hour−1, r = 0.67). We hypothesize that extreme high rainfall is associated with both saturated soils, increasing risk of uprooting, and with gusts having high horizontal and vertical windspeeds that increase stresses on tree crowns. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates over large spatial scales at fine temporal and spatial resolution, thereby enabling strong tests of linkages to drivers. Future studies should include high frequency measurements of vertical and horizontal windspeeds and soil moisture to better capture proximate drivers, and incorporate additional image analyses to quantify standing dead trees in addition to treefalls.

Drivers of Greenhouse Gas Emissions from Standing Dead Trees in Ghost Forests

Coastal freshwater forested wetlands are rapidly transitioning from forest to marsh, leaving behind many standing dead trees (snags) in areas often called ‘ghost forests’. Snags can act as conduits for soil produced greenhouse gases (GHG) and can also be sources as they decompose. Thus, snags have the potential to contribute GHGs to the atmosphere, but emissions are not well understood. We assessed GHG emissions (carbon dioxide - CO 2 , methane - CH 4 , and nitrous oxide - N 2 O) from snags and soils in five ghost forests along a salinity gradient on the coast of North Carolina, USA. Mean (± SE) soil GHG fluxes (416 ± 44 mg CO 2 m -2 hr -1 , 5.9 ± 1.9 mg CH 4 m -2 hr -1 , and 0.1 ± 0.06 mg N 2 O m -2 hr -1 ) were ~4 times greater than mean snag GHGs (116 ± 15 mg CO 2 m -2 hr -1 , 0.3 ± 0.09 mg CH 4 m -2 hr -1 , and 0.04 ± 0.009 mg N 2 O m -2 hr -1 ). Hydrological conditions and salinity influenced soil GHG fluxes between the two field campaigns, but snags were less predictable and more variable. Snag and soil CO 2 /N 2 O fluxes were influenced by similar environmental parameters. The drivers for soil and snag CH 4 however, were often not the same and at times oppositely correlated. Our results illustrate the need to include tree stem GHGs in regional and global budgets.

CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.