Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Natural regeneration in forest management, which relies on artificial planting, is considered a desirable alternative to reforestation. However, there are large uncertainties regarding the natural regeneration processes, such as seed production, seed dispersal, and seedling establishment. Among these processes, seed dispersal by wind must be modeled accurately to minimize the risks of natural regeneration. This study aimed to (1) review the main mechanisms of seed dispersal models, their characteristics, and their applications and (2) suggest prospects for seed dispersal models to increase the predictability of natural regeneration. With improving computing and observation systems, the modeling technique for seed dispersal by wind has continued to progress steadily from a simple empirical model to the Eulerian-Lagrangian model. Mechanistic modeling approaches with a dispersal kernel have been widely used and have attempted to be directly incorporated into spatial models. Despite the rapid development of various wind-dispersal models, only a few studies have considered their application in natural regeneration. We identified the potential attributes of seed dispersal modeling that cause high uncertainties and poor simulation results in natural regeneration scenarios: topography, pre-processing of wind data, and various inherent complexities in seed dispersal processes. We suggest that seed dispersal models can be further improved by incorporating (1) seed abscission mechanisms by wind, (2) spatiotemporally complex wind environments, (3) collisions with the canopy or ground during seed flight, and (4) secondary dispersal, long-distance dispersal, and seed predation. Interdisciplinary research linking climatology, biophysics, and forestry would help improve the prediction of seed dispersal and its impact on natural regeneration.

The velocity and impact of climate change on forest appear to be site, environment, and tree speciesspecific. The primary objective of this research is to assess the changes in productivity of five major temperate tree species (Pinus densiflora, PD; Larix kaempferi, LK; Pinus koraiensis, PK; Quercus variabilis, QV; and Quercus mongolica, QM) in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODerate resolution Imaging Spectroradiometer (MODIS) net primary productivity as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation in primary productivity by tree species over the past four decades. Based on our result from NFI plots and increment core data sets, the productivity of PD, QV, and QM in LLF was relatively higher than those in HMF, while LK and PK in HMF were more productive than lowland ones. Our analysis of the increment core data revealed a contrasting pattern of long-term productivity changes between coniferous and oak tree species. While the productivity of oak tree species tended to increase after the 1990s, the productivity in coniferous forests tended to decrease. These differences across forest types and their altitudinal classes are also noticeable from the MODIS product. The results of our study can be used to develop climate-smart forest management strategies to ensure that the forests continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region.