https://repository.kopri.re.kr/handle/201206/11937 2026-04-06T23:29:14Z https://repository.kopri.re.kr/handle/201206/12988 Title: Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction Authors: Chi, Junhwa; Bae, Jihyun; Kwon, Young-Joo Abstract: Arctic sea ice plays a significant role in climate systems, and its prediction is important for coping with global warming. Artificial intelligence (AI) has gained recent attention in various disciplines with the increasing use of big data. In recent years, the use of AI-based sea ice prediction along with conventional prediction models have drawn attention. This study proposes a new deep learning (DL)-based Arctic sea ice prediction model with a new perceptual loss function to improve both statistical and visual accuracy. The proposed DL model learned spatiotemporal characteristics of Arctic sea ice for sequence-to-sequence predictions. The convolutional neural network-based perceptual loss function successfully captured unique sea ice patterns, and the widely used loss functions could not use various feature maps. Furthermore, the input variables that are essential to accurately predict Arctic sea ice using various combinations of input variables were identified. The proposed approaches produced statistical outcomes with better accuracy and qualitative agreements with the observed data. 2021-09-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/12992 Title: Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models Authors: Choe, Hyeyeong; Chi, Junhwa; Thorne, James H. Abstract: The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obsta-cles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using MODIS products and modeled potential spe-cies richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial pat-terns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized dif-ference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually de-veloped models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data. 2021-07-01T00:00:00Z