Native diversity buffers against severity of non-native tree invasions.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

New forest biomass carbon stock estimates in Northeast Asia based on multisource data.

Forests play an important role in both regional and global C cycles. However, the spatial patterns of biomass C density and underlying factors in Northeast Asia remain unclear. Here, we characterized spatial patterns and important drivers of biomass C density for Northeast Asia, based on multisource data from in situ forest inventories, as well as remote sensing, bioclimatic, topographic, and human footprint data. We derived, for the first time, high-resolution (1 km × 1 km) maps of the current and future forest biomass C density for this region. Based on these maps, we estimated that current biomass C stock in northeastern China, the Democratic People's Republic of Korea, and Republic of Korea to be 2.53, 0.40, and 0.35 Pg C, respectively. Biomass C stock in Northeast Asia has increased by 20%-46% over the past 20 years, of which 40%-76% was contributed by planted forests. We estimated the biomass C stock in 2080 to be 6.13 and 6.50 Pg C under RCP4.5 and RCP8.5 scenarios, respectively, which exceeded the present region-wide C stock value by 2.85-3.22 Pg C, and were 8%-14% higher than the baseline C stock value (5.70 Pg C). The spatial patterns of biomass C densities were found to vary greatly across the Northeast Asia, and largely decided by mean diameter at breast height, dominant height, elevation, and human footprint. Our results suggest that reforestation and forest conservation in Northeast Asia have effectively expanded the size of the carbon sink in the region, and sustainable forest management practices such as precision forestry and close forest monitoring for fire and insect outbreaks would be important to maintain and improve this critical carbon sink for Northeast Asia.

Forest cover classification by optimal segmentation of high resolution satellite imagery.

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens(®) Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the "salt-and-pepper effect" and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Estimating stem volume and biomass of Pinus koraiensis using LiDAR data.

The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R (2) = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R (2) = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R (2) = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm(3)), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI).

Estimation of effective plant area index for South Korean forests using LiDAR system.

Light Detection and Ranging (LiDAR) systems can be used to estimate both vertical and horizontal forest structure. Woody components, the leaves of trees and the understory can be described with high precision, using geo-registered 3D-points. Based on this concept, the Effective Plant Area Indices (PAI(e)) for areas of Korean Pine (Pinus koraiensis), Japanese Larch (Larix leptolepis) and Oak (Quercus spp.) were estimated by calculating the ratio of intercepted and incident LIDAR laser rays for the canopies of the three forest types. Initially, the canopy gap fraction (G ( LiDAR )) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAI(e) was then estimated by using G ( LIDAR ) with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAI(e) revealed the coefficients of determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences between field-based and LIDAR-based PAI(e) for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, in the case of both Larch and Oak, meant that the LiDAR pulses were only reflected from branches. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density. This is because the size of the branch is smaller than the resolution across and along the 1 meter LIDAR laser track. Therefore, a better predictive accuracy would be expected for the model if the study would be repeated in late spring when the shoots and leaves of the deciduous trees begin to appear.