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Article

Park, E. ORCID: https://orcid.org/0000-0002-0442-7621, Jo, H.-W. ORCID: https://orcid.org/0000-0001-6127-883X, Biging, G.S., Chun, J.A., Jeon, S.W., Son, Y., Kraxner, F. & Lee, W.-K. (2024). Advancement of a diagnostic prediction model for spatiotemporal calibration of earth observation data: a case study on projecting forest net primary production in the mid-latitude region. GIScience & Remote Sensing 61 (1), e2401247. 10.1080/15481603.2024.2401247.

Kim, J., Jo, H.-W., Kim, W., Jeong, Y., Park, E., Lee, S., Kim, M. & Lee, W.-K. (2024). Application of the domain adaptation method using a phenological classification framework for the land-cover classification of North Korea. Ecological Informatics 81, e102576. 10.1016/j.ecoinf.2024.102576.

Jo, H.-W. ORCID: https://orcid.org/0000-0001-6127-883X, Krasovskiy, A. ORCID: https://orcid.org/0000-0003-0940-9366, Hong, M., Corning, S. ORCID: https://orcid.org/0009-0001-5277-5380, Kim, W., Kraxner, F. & Lee, W.-K. (2023). Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach. Remote Sensing 15 (5), e1446. 10.3390/rs15051446.

Cha, S., Jo, H.-W., Kim, M., Song, C., Lee, H., Park, E., Lim, J., Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Shvidenko, A. & Lee, W.-K. (2022). Application of deep learning algorithm for estimating stand volume in South Korea. Journal of Applied Remote Sensing 16 (02), e024503. 10.1117/1.JRS.16.024503.

Monograph

Jo, H.-W. ORCID: https://orcid.org/0000-0001-6127-883X (2022). Optimization of the IIASA’s FLAM model to represent forest fires in South Korea. IIASA YSSP Report. Laxenburg, Austria: IIASA

Conference or Workshop Item

Jo, H.-W. ORCID: https://orcid.org/0000-0001-6127-883X, Corning, S. ORCID: https://orcid.org/0009-0001-5277-5380, Kiparisov, P. ORCID: https://orcid.org/0000-0003-1223-7964, San Pedro, J. ORCID: https://orcid.org/0000-0001-9317-7275, Krasovskiy, A. ORCID: https://orcid.org/0000-0003-0940-9366, Kraxner, F. & Lee, W.-K. (2024). Integrating Human Domain Knowledge into Artificial Intelligence for Hybrid Forest Fire Prediction: Case Studies from South Korea and Italy. DOI:10.5194/egusphere-egu24-12320. In: EGU General Assembly 2024, 14-19 April 2024, Vienna.

Other

Maximo, Y.I., Hassegawa, M., Nabau, J., Pecurul-Botines, M., Aquilué, N., Kraxner, F., Johnstone, C., Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Krasovskiy, A. ORCID: https://orcid.org/0000-0003-0940-9366, Kindermann, G.E. ORCID: https://orcid.org/0000-0003-4297-1318, Park, E. ORCID: https://orcid.org/0000-0002-0442-7621, Jo, H.-W., Nordström, E.-M., Pezdevšek Malovrh, Š. & Verkerk, P.J. (2025). Report on Improved Forest models with enhanced representation of behavior and behavioral change of forest owners and conservation managers. BIOCONSENT (Deliverable 3.1). Zenodo 10.5281/zenodo.14801477.