https://repository.kopri.re.kr/handle/201206/13419 2026-04-16T00:08:34Z 2026-04-16T00:08:34Z Kim Jun Lim Jiseon Kim, Moonkyo Lee, Yoo Kyung https://repository.kopri.re.kr/handle/201206/16190 2025-10-24T02:47:45Z 2024-01-01T00:00:00Z

Title: Whole-genome sequencing of 13 Arctic plants and draft genomes of Oxyria digyna and Cochlearia groenlandica Authors: Kim Jun; Lim Jiseon; Kim, Moonkyo; Lee, Yoo Kyung Abstract: To understand the genomic characteristics of Arctic plants, we generated 28-44 Gb of short-read sequencing data from 13 Arctic plants collected from the High Arctic Svalbard. We successfully estimated the genome sizes of eight species by using the k-mer-based method (180-894 Mb). Among these plants, the mountain sorrel (Oxyria digyna) and Greenland scurvy grass (Cochlearia groenlandica) had relatively small genome sizes and chromosome numbers. We obtained 45 x and 121 x high-fidelity long-read sequencing data. We assembled their reads into high-quality draft genomes (genome size: 561 and 250 Mb; contig N50 length: 36.9 and 14.8 Mb, respectively), and correspondingly annotated 43,105 and 29,675 genes using similar to 46 and similar to 85 million RNA sequencing reads. We identified 765,012 and 88,959 single-nucleotide variants, and 18,082 and 7,698 structural variants (variant size >= 50 bp). This study provided high-quality genome assemblies of O. digyna and C. groenlandica, which are valuable resources for the population and molecular genetic studies of these plants.

2024-01-01T00:00:00Z Lee, Yoo Kyung https://repository.kopri.re.kr/handle/201206/14630 2023-09-21T07:06:21Z 2023-02-20T00:00:00Z

Title: Development of multi-spectral image analysis technique, and RAD-seq and metabolites analysis techniques to understand the response of Svalbard plants to climate change Authors: Lee, Yoo Kyung

2023-02-20T00:00:00Z 양준영 Lee, Yoo Kyung 지준화 https://repository.kopri.re.kr/handle/201206/15001 2023-12-14T16:38:05Z 2023-01-01T00:00:00Z

Title: Spectral Unmixing-based Arctic Plant Species Analysis using a Spectral Library and Terrestrial Hyperspectral Imagery: A Case Study in Adventdalen, Svalbard Authors: 양준영; Lee, Yoo Kyung; 지준화 Abstract: Remote sensing is an invaluable tool for understanding and monitoring the rapid changes in the distribution and composition of Arctic vegetation caused by global warming. Although hyperspectral data consisting of contiguous spectral bands enables the quantitative analysis of remote sensing data, monitoring studies on the Arctic vegetation based on hyperspectral remote sensing have rarely been discussed because of the difficulty in acquiring data from the Arctic region. To address this limitation, we collected hyperspectral information on the dominant vegetation species in Adventdalen Valley, Svalbard, and investigated various approaches for mapping Arctic vegetation using these data. First, labeled datasets were constructed for Arctic vegetation species by extracting pixel data from ground-based hyperspectral images. We then quantitatively and qualitatively compared the classification performances of three machine learning-based classifiers, random forest (RF), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN), using the datasets and hyperspectral images. The 1D-CNN classifier combined with the first-order derivative of smoothed reflectance (RS,FD) achieved the highest statistical accuracies of 0.9892 (Kappa = 0.9880) and 0.9352 (Kappa = 0.9280) for the two independent test sets and produced the most accurate vegetation maps. Additionally, a spectral library was developed using the mean specta per class of the labeled datasets. The spectral library using the RS,FD spectrum showed high spectral discriminability and potential for estimating the abundance of classes from mixed pixels, which is commonly formed in low-spatial resolution images. Accordingly, the spectral library developed in this study can serve as a fundamental reference for large-scale mapping studies using remote sensing. The proposed approaches in this study can be widely applied to various remote sensing datasets, leading to effective monitoring of Arctic vegetation changes in response to climate change.

2023-01-01T00:00:00Z