Su-Jin Kim's research while affiliated with National Institute of Ecology and other places

Deforestation is a global issue; however, each deforestation phenomenon occurs within its own local context. Elucidating this context in detail is important to prevent deforestation and maintain sustainable ecosystem management. In this study, we analyzed the land cover changes, forest characteristics, and modeled the forest decline over the last two decades to reveal the pattern and affecting factors of deforestation in the Honam-Jeongmaek mountain range. Forests less than 50-years-old dominate the study area, indicating they were mainly regenerated after the 1970s. Reforestation policies such as planting trees have helped forest regeneration. In the study region, as deforestation occurred, agricultural and residential areas decreased, and barren and grassland increased. We applied the Weibull regression model to determine forest survivorship and covariates. The deforestation risks are significantly different among regions; protected areas lose less forest than non-protected areas but the losses in protected areas were also significant, with approximately 5% from 2000 to 2020. Areas of higher elevation and steep slopes experience less deforestation, whereas areas closer to the mountain ridge are at greater risk. With survival analysis, it is possible to assess the risk of deforestation quantitatively and predict long-term survival of forests. The findings and methods of this study could contribute to better forest management and policymaking.

Forests are composed of complex socio-ecological systems. In the scientific management of forest resources, it is essential to understand the characteristics of villages within forest net-works. This study classifies resources and characterizes the forest networks with neighbouring vil- lages using unsupervised learning algorithms: self-organizing maps (SOM) and geographic self-organizing maps (Geo-SOM). Considering ecological, economic, and socio-cultural indi- cators, we analyzed 18 variables for 379 villages in the Nakdong and Naknam Jeongmaek mountain regions, Republic of Korea. The map size to visualize the multidimensional data was examined considering quantization and topographic errors, and the number of clusters was determined by comparing k-means and hierarchical clustering techniques. The optimal map size was fixed with 17×12 girds, and the mapped data were grouped into six clusters. The common characteristics of villages were identified using SOM, whereas the geographical characteristics were explored using Geo-SOMs. The approach introduced in this study emphasizes the connectivity of forests through the convergence of human, social and ecological data and presents sustainable forest management directions. This study will reference to policy designers and scholars for setting goals linking remote sensing and geographic information system. Keywords: Forest inventory, Forest management, Socio-ecological system, Nakdong & Naknam Jeongmaek mountain, Conservation Funding Source: This work was supported by a grant (no. FE0100-2023-01-2023) from the National Institute of Forest Science, Republic of Korea.

Understanding the village characteristics linked to forest networks is essential for the scientific management of forest resources. Forests are complex socio-ecological systems. This study classifies the resources and characteristics of forest networks and neighboring villages using unsupervised learning algorithms: self-organizing maps (SOM) and geographic-self-organizing maps (Geo-SOMs). Considering ecological, economic, and socio-cultural indicators, 18 covariates of 379 villages in two forest networks of the Baekdudaegan Mountain Range in South Korea were analyzed. The data visualizing map size was fixed based on changes in quantization and topographic errors of the same grid maps, and the number of clusters was determined by comparing K-means and hierarchical clustering techniques. An optimal map size of 17 × 12 grids and six clusters was used for further classification of the input data for both SOM and Geo-SOM analyses. The common characteristics of villages were identified using SOM classification, whereas geographically bounded characteristics were identified using Geo-SOM. The approach introduced in this study can be applied to socio-ecological classification and the design of sustainable forest management policies that link the remote sensing and geographic information systems.