Soil carbon stocks are the second largest natural reservoir of carbon globally, after oceans. Soil carbon sequestration is a key process that supports both climate change mitigation and adaptation strategies. Thus, accurate mapping of soil organic carbon (SOC) stocks is essential for estimating their regional distribution and assessing soil functions. Regular ground sampling for estimating SOC is often expensive, prompting exploration of alternative, cost-effective techniques such as remote sensing. Many studies have applied remote sensing and machine learning to identify factors influencing SOC and predict its levels, but they often rely on limited data, such as single-year soil samples and small sample sizes, restricting their ability to capture SOC's variability and dynamics. This study aims to combine high sampling density (1.15 samples/km(-2) in agricultural areas and 0.38/km(-2) for forest areas) and long-term SOC data (2010-2021) with remote sensing, topographic, and climatic data using random forest (RF) modeling to estimate SOC stocks in surface soils (0-30 cm) and identify key influencing factors across Taiwan's agricultural and forest lands. Initially, 22 variables were considered, and the least important ones were excluded. Two models were developed: one for agricultural land and one for forest land, with 14 variables selected, in both models. The agricultural model emphasized elevation, climate, and green soil-adjusted vegetation index (GSAVI), while the forest model focused on vegetation indices and elevation. The models successfully predicted SOC stocks (in kg m(-2)), showing a strong correlation (R-2 > 0.75). Results revealed higher SOC in mountainous regions and lower SOC in suburban and low-lying agricultural areas, with SOC stocks ranging from 0.64 to 8.83 kg m(-2) in agriculture land and 2.41 to 21.34 kg m(-2) in forest land. This research significantly contributes to SOC monitoring and informs carbon sequestration policy development in Taiwan. The findings from this study offer important insights for the long-term tracking of soil organic carbon stocks in Taiwan and provide a useful foundation for shaping future carbon sequestration policies.

Deforestation assessments often treat tropical moist forest loss as a uniform process, obscuring the frontier dynamics that shape where and how forests are lost and the spatial correlates associated with them. Here, we show that Central America’s tropical moist forest frontiers form a heterogeneous regional mosaic by combining annual 30-m forest-change data from 1990 to 2023 with a frontier-metrics framework aggregated to 1.5-km cells. We derive three frontier typologies and five archetypes from six metrics and use a Bayesian multinomial logit model to quantify the covariate associations for each archetype. Region-wide, the Other accounts for the largest share of mapped area (34.7%), followed by Consolidated (16.1%), Fragmented (15.1%), Critical (13.3%), Developing (11.5%), and Dormant (9.3%). Critical, Developing and Dormant frontiers occur in more remote, undisturbed forest contexts than Consolidated and Fragmented frontiers. Protected-area associations differ by archetype, with Critical frontiers less associated with protected areas and Dormant frontiers more associated with them. These findings support regional archetype-specific conservation targeting strategies. Active deforestation frontiers remain widespread across Central America’s tropical moist forests, with frontier archetypes differing in remoteness, disturbance context, and protected-area association, based on 30-m forest-change data and a frontier-metrics framework from 1990 to 2023.

Community forests (CFs) are widely adopted in tropical regions as a strategy to conserve forests and improve local livelihoods. However, their effectiveness is vulnerable to political instability, which disrupts governance structures. These disruptions may accelerate deforestation and forest degradation, yet the shifting influence of underlying factors remains poorly understood. This study investigated how geographical and community characteristics shaped deforestation and forest degradation in 24 CFs in Shan State, Myanmar, across two periods: a stable period in 2017-2019 (Period 1) and a politically unstable period in 2021-2023 (Period 2). Using forest cover/disturbance maps derived from the Dynamic World dataset, we applied generalized linear mixed models via the INLA-SPDE framework to identify the factors influencing deforestation and forest degradation across each period. Deforestation was marginally higher in Period 2 (0.75%) than in Period 1 (0.71%), and forest degradation was higher in Period 1 (1.46%) than in Period 2 (0.99%). Forest cover ratio and distance to the CF boundary exhibited no significant temporal shifts in their effects on forest outcomes. Slope indicated a significant temporal change in its association with deforestation. Leadership demonstrated a significant temporal shift in its influence on forest degradation. Notably, community attributes were measured once (pre-instability) and treated as time-invariant, though their levels may have changed over time. Examining how the influence of geographical and community characteristics varies across stable and unstable governance conditions clarifies how political instability alters the drivers of these ecological outcomes, informing strategies for resilience in community-based conservation efforts.

Numerous studies have explored the effectiveness of community forests (CFs) in addressing deforestation and forest degradation and promoting regrowth. However, systematic evaluations of CFs' effectiveness in preventing deforestation and degradation of undisturbed tropical moist forests (TMF) and fostering regrowth remain limited. Here, we conducted a comprehensive analysis of long-term (40-year contract) community forest enterprises (CFEs) in Honduras's TMF. We compared CFEs with both protected and unprotected areas, distinguishing between degraded and undisturbed forests. Using Google Earth Engine and R, we processed Vancutsem's TMF dataset and employed the Mahalanobis distance matching method to account for confounding factors. Our findings indicate the odds of forest degradation and deforestation of undisturbed TMF are 17.8 % and 10.9 % higher, respectively, in CFEs compared with unprotected areas. The odds of degraded forest being deforested are 11.6 % lower, and the odds of regrowth are 177.3 % higher in CFEs compared with unprotected areas. For CFEs and protected areas, there is no significant difference in the three outcomes except for degraded forest being deforested, where the odds are 14.7 % lower in CFEs. However, because CFEs allow selective logging which can cause temporary degradation and the dataset cannot detect recovery within degraded pixels, degradation values within CFEs should not be interpreted as entirely negative outcomes. Instead, they should be seen as conservative indicators of deforestation risk, especially considering that 31.4 % of degraded pixels in CFEs later transitioned to deforested land. The results indicate that the effectiveness of CFEs in forest conservation is not solely dependent on granting long-term management contracts. It is also crucial to consider the status of the forest (undisturbed or degraded) when planning conservation efforts, as this influences the outcomes of CFEs in achieving forest conservation objectives. These findings provide a more accurate and meaningful assessment of CFEs' effectiveness that can inform better policy decisions and management practices, leading to more effective conservation strategies.

A group selection system may be a suitable forest management option that considers both ecological and economic factors. A concern with the group selection system is that the growth of trees planted in group selection openings is affected by their position in the opening. However, the long-term effects of tree position on tree growth beyond the juvenile stage are still unknown. We assessed the long-term effects of group selection on tree growth near the edge of openings using long-term measurement data for a group selection stand of Cryptomeria japonica in Japan. The size of individuals planted within group selection openings in 1966 and 1985 was measured three times at approximate 10-year intervals between 2002 and 2022. The growth of the measured individuals for two consecutive measurement periods was calculated and the relationship with distance from the edge of the openings was analyzed. Structural equation modeling revealed that both diameter and height growth were significantly inhibited near the edge of the openings. However, the effect of growth inhibition near the edge of the openings was mitigated with age. The most important factor in the mitigation of the growth inhibition effect with age was the height of the subject tree. We conclude that, in the long term, the influence of growth inhibition near the edges of the openings is small. Our study demonstrates that the inhibition of height and diameter growth by the residual trees is gradually mitigated. This highlights that long-term monitoring is important to assess the growth of individuals within openings when implementing forest management using group selection systems, as previous studies evaluating the effect of tree position in group selection openings have focused on young or juvenile trees only, which is of limited value when evaluating the overall silvicultural system.

The urgency to conserve and restore forests for their multifaceted benefits is escalating. We spotlight Japan's new Forest Environment Tax, a novel fiscal measure crafted to finance public‐beneficial ecosystem services through enhanced forest management. To convey the expert perceptions of the policy, we present the results of a survey targeting individuals immersed in Japan's forest policies, which aimed to assess attitudes toward the various benefits that forests provide. We classified forest functions into five core areas: wood production, soil and water conservation, mitigation of anthropogenic global warming, wildlife conservation and cultural utilities. We found that stakeholders closely involved in forest policies in Japan prioritize soil and water conservation as the paramount function over the mitigation of anthropogenic global warming. The results of the survey underscore the necessity of evaluating forest management practices and the importance of recognizing the multiple values that can be derived from forests. While there has been much attention to the carbon benefits of forests in the region and beyond, we emphasize the need to avoid an excessive focus on this single ecosystem service and to ensure that the other important multifunctional values of forests are not overlooked. Policy implications : We call for a more holistic approach that recognises the interdependence of the different functions of forests and the importance of valuing forests as natural capital in all their dimensions.

井上修吾・太田徹志・溝上展也:衛星コンステレーションを用いた竹林分布把握における最適撮影時期の探索,森林計画誌57:45~51,2024 衛星リモートセンシングデータから竹林を抽出する際の最適撮影時期を検証した。対象地は福岡県旧立花村周辺とし,2022年1月から12月までの合計11枚のPlanetScopeデータを入手した。11枚のデータに機械学習モデルを適用して竹林と竹林以外の2クラスに分類し,その精度を比較した。その結果,全体精度は6月で最も高い91.2%,4月で最も低い74.6%だった。全体精度の1年間の推移としては,1月ごろに全体精度が低く,徐々上昇した後5〜6月をピークとなる山型となった。6月時点のデータを用いた竹林の分類においては,可視赤色光の重要度が最も高く,可視赤色光や可視緑色光や近赤外光を用いた植生指数の重要度も高かった。以上のことから,竹林の抽出には可視赤色光や可視緑色光や近赤外光を含む衛星により5〜6月に取得したデータを用いるべきと結論づけた。

Attribution of forest disturbance types using satellite remote sensing is practicable and several methods have been developed to automate the procedure. However, limited by commonly used data and the methodology, achieving accurate and rapid attribution of forest disturbance types over broad spatial extents remains challenging. In this study, we developed a method for attributing forest disturbance types using Dynamic World class probability data (i.e., probabilities for Dynamic World land use land cover types). Specifically, we first obtained a high-quality probability time series by pre-processing the class probability data. Then, we segmented the entire time series into several subseries and classified them according to the hypothetical trajectories. Finally, we completed the attribution of forest disturbance types using the variables derived from the probability time series and the results of the subseries classification. We used the developed method to investigate the forest disturbance types in Myanmar from 2017 to 2023 and validated its effectiveness by conducting unbiased accuracy assessment. The overall accuracy of the type for the acquired map was approximately 93.3%, and the overall accuracy of the year was approximately 96.7%, proving that the method is feasible. This method is based on the Google Earth Engine, which allows users to attribute forest disturbance types in different areas rapidly by simple parameter adjustments. Even if available classes do not satisfy users’ needs, the method can facilitate more detailed attribution of disturbance types.

The most practical method for monitoring forest change over large areas is using remotely sensed data. However, given that current techniques are somewhat weak for monitoring small-scale forest disturbances, achieving accurate monitoring remains challenging, especially in tropical areas where selective and illegal logging occurs frequently. To further improve the ability to monitor forest changes, we estimated tree canopy cover (TCC) using Sentinel-1 and Sentinel-2 data. We developed an approach to monitor forest change on the obtained TCC time series. This approach was applied to monitor forest change in the Bago Mountains of Myanmar from 2017 to 2021. We then completed accuracy assessments and area estimation using reference data obtained from stratified random sampling and unbiased estimators. The final results indicated that: (1) in TCC estimation, Sentinel-1 played a limited role; the red-edge bands of Sentinel-2 achieved slightly different results to the other bands, and superior results were obtained by using all bands; (2) our method successfully mapped forest change with the overall accuracy of 93%. Furthermore, compared with the most widely used and the most recent approaches, our method was better at capturing forest disturbances.

Landslides cause significant economic, social, and environmental impacts worldwide. However, selecting the most suitable model and factors for landslide susceptibility mapping (LSM) remains challenging due to the diverse factors influencing landslides and the unique environmental settings in which they occur. Here, we conducted a systematic literature review from 2001 to 2021 to identify the main core-base factors and models used in LSM and highlight areas for future research. We found that there is a need for increased research collaboration with leading knowledge-producing countries and research efforts in underrepresented regions such as Africa, Central America, and South America. Of the 31 most used landslide susceptibility factors, we identified the core-base factors slope, elevation, lithology, land use/land cover, and distance from road, which were the most used, top-ranked predictors and commonly used together when mapping landslide susceptibility. Although aspect was the third most used factor, it ranked among the eight least effective predictors of LSM. Among the core-base factors of LSM, road density, elevation, and slope exhibited the least ranking variability as LSM predictors. The most used methods in LSM were random forest, logistic regression, support vector machine, and artificial neural network, with hybrid, ensemble, and deep learning methods currently trending. Random forest was the most accurate of the four most commonly used models, followed by artificial neural networks. However, artificial neural networks exhibited the least performance variability, followed by support vector machines. This comprehensive review provides valuable insights for researchers in selecting appropriate factors and models for LSM and identifies potential areas for future collaboration and research.

Unsustainable land use practices have led to increased forest loss rates. Implementing cacao agroforestry can reduce forest loss by preventing the clear-cutting of forests for monoculture plantations. However, research is needed on its effectiveness in preventing forest loss and the factors influencing its adoption between full-time and part-time farmers. Here, we address these gaps in the Maya Golden Landscape, Belize, by using Mahalanobis distance matching to compare forest loss in cacao agroforestry concession, forest reserve, and de-reserve areas and analyzing social data of 187 households. The results suggest that the odds of forest loss in the cacao agroforestry concession area are approximately 16% higher than in the Maya Mountain North Forest Reserve. In comparison, they are 85% lower than in the de-reserved areas. We also report differences in the factors influencing agroforestry adoption between part-time and full-time farmers. Successful cacao agroforestry adoption requires considering the differences that exist between farmers' categories.

Community forestry is a regime of forest management that engages local communities to conserve forests and improve their livelihoods. As the number of community-conserved forests grows, a growing body of evidence indicates the positive effects of community forests in reducing deforestation. However, there is little analysis encompassing the comprehensive effectiveness of community forests (CFs) in terms of deforestation, forest degradation, forest cover change and forest increase. Here, we conducted a comprehensive analysis to investigate the influence of CFs on these aspects between 2015 and 2019 in two watershed conservation forests in Myanmar. We used visual interpretation of very high-resolution satellite imagery and applied propensity score matching to ensure a balanced distribution of covariates. When compared directly, deforestation inside CFs (5.08 %) were higher than those outside CFs (3.89 %), while forest degradation (23.73 %) and forest increase (11.86 %) inside CFs were lower than those outside CFs (24.9 % and 16.34 %, respectively). However, these differences were not significant, and the matching results showed that CFs did not exhibit significant control over deforestation, forest degradation, forest cover change, and improvements in forest cover compared to areas outside CFs. We conclude that establishing community forests alone does not guarantee forest conservation in the short term. Therefore, community-based forest management practices are needed to address deforestation and forest degradation and achieve effective forest conservation aligned with local needs.

Community forests (CFs) have been widely established in tropical countries as a tool to achieve forest conservation. Many studies have shown that CFs can contribute to the reduction of deforestation, yet studies that evaluate the contribution of CFs to reducing forest degradation and facilitating forest recovery remain scarce. We investigated the ability of CFs to prevent deforestation and forest degradation and to facilitate forest recovery by using a country-scale longitudinal tree canopy cover and forest cover data set in Cambodia. We found that CFs can prevent both forest degradation and deforestation, but we did not observe a forest recovery effect. We also found that recently established CFs are not effective for forest conservation compared with older CFs. We conclude that, to date, CFs are an effective forest conservation tool; however, this does not necessarily mean that new CFs will be as effective as established ones.

Semi-captive Asian elephants (Elephas maximus) engaged in forestry activities in Myanmar account for 20% of all captive and semi-captive Asian elephants in the world, and are important for both forestry and the conservation of Asian elephant populations. Understanding moving behavior of the semi-captive elephants is required to sustain them. Our specific goals are 1) to determine the moving range during free time, and 2) to determine the hourly moving distance during skidding and when off duty. Three elephants were fitted with handheld global navigation satellite systems with the signals of global positioning system to collect data on their movements. The elephants were generally located between 0.534 and 0.875 km from the camp with temporary housing of the elephant handler when not skidding (i.e., free time) and between 1.365 and 1.372 km when skidding (i.e., work time). The hourly moving distance during free time (0.622–0.655 km) and work time (1.522 and 1.629 km) did not differ greatly from the hourly moving distance of wild Asian elephants (0.010–1.500 km). The elephants remained within 0.875 km of the camp of the elephant handler, and some variation in movements among individuals was observed during free time. Thus, the conservation of forest in areas near the camp is important for the well-being of these elephants.

Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.

Assessing the impact of forest type and age on shallow landslide susceptibility is important for managing protective functions of forests. Previous studies have examined the correlation between forest types and ages, and shallow landslide susceptibility, but causal effects of forest type and age on shallow landslide susceptibility are yet not clear. This study investigated the causal effect of forest type and age on shallow landslide susceptibility using a propensity score method by combining existing geo-data sets. Here, we focus on shallow landslides caused by an extreme rainfall event on 5-6 July 2017, in the mountainous areas of Asakura City and Toho Village, Fukuoka Prefecture, Japan. Randomly located samples of coniferous forest, broadleaved forest, and young forest exposed to shallow landslide events were analyzed. The inverse probability of treatment weighting with the propensity score was applied to the samples to compare the differences in shallow landslide susceptibility among the three forest classes. Young forest had increased shallow landslide susceptibility, compared with coniferous forest and broadleaved forest, by 3.70 x 10(-2) and 4.12 x 10(-2), respectively, which corresponded to a two to three times increase in shallow landslide susceptibility when the forest changes from coniferous or broadleaved forest to young forest. No significant difference in shallow landslide susceptibility between coniferous forest and broadleaved forest was observed. These differences in shallow landslide susceptibility should be considered when implementing forest management schemes.

Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.

Background Wildland fires are part of the ecology of forests in Central America. Nevertheless, limited understanding of fire probability and the factors that influence it hinder the planning of intervention strategies. Aims This research combined climatic, anthropogenic and vegetation factors to identify wildland fire probability and determine the most relevant factors. Methods We performed an exploratory analysis to identify important factors and integrated them with fire observations using random forest. We then used the most relevant factors to predict wildland fire occurrence probability and validated our results using different measures. The results demonstrated satisfactory agreement with the independent data. Key results Central regions of Honduras, northern Guatemala and Belize have a very high probability of wildland fire occurrence. Human imprint and extreme climatic conditions influence wildland fire probability in Central America. Conclusions Using random forest, we identified the major influencing factors and areas with a high probability of wildland fire occurence in Central America. Implications Results from this research can support regional organisations in applying enhanced strategies to minimise wildland fires in high-probability areas. Additional efforts may also include using future climate change scenarios and increasing the time frame to evaluate the influence of teleconnection patterns.