To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.

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In scenario studies of biodiversity and ecosystem services, the population distribution is one of the key driving forces. In this study, we developed a coupling method for narrative scenarios and spatially explicit residential and working population designs for all of Japan as a common data set for ecosystem scenario analysis implemented by 5-year project entitled "Predicting and Assessing Natural Capital and Ecosystem Services (PANCES)". Four narrative scenarios were proposed by the PANCES project by using two axes as major uncertainties: the population distribution and the capital preference. The residential population and the working population in primary industries were calculated using a gravity-based allocation algorithm in a manner consistent with the storylines of the PANCES scenarios. By using the population distribution assumption by scenario, the population was overlaid with the natural capital and the supply potential of ecosystem services. The results supported to understand the gaps between natural capital and maintainability, the potential of ecosystem services and realizability. The spatially explicit population distribution data products are expected to help design the nature conservation strategy and governance option in terms of both social system and ecological system.

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Sharing successful practices with other stakeholders is important for achieving SDGs. In this study, with a deep-learning natural language processing model, bidirectional encoder representations from transformers (BERT), the authors aimed to build (1) a classifier that enables semantic mapping of practices and issues in the SDGs context, (2) a visualizing method of SDGs nexus based on co-occurrence of goals (3) a matchmaking process between local issues and initiatives that may embody solutions. A data frame was built using documents published by official organizations and multi-labels corresponding to SDGs. A pretrained Japanese BERT model was fine-tuned on a multi-label text classification task, while nested cross-validation was conducted to optimize the hyperparameters and estimate cross-validation accuracy. A system was then developed to visualize the co-occurrence of SDGs and to couple the stakeholders by evaluating embedded vectors of local challenges and solutions. The paper concludes with a discussion of four future perspectives to improve the natural language processing system. This intelligent information system is expected to help stakeholders take action to achieve the sustainable development goals.