GEO DATA

Data Article

Unmanned Aerial Vehicle Photogrammetry Based Dataset of Halophyte Distribution in Jujin Estuary: Donguk Lee, Yeongjae Jang, Joo-Hyung Ryu, Hyeong-Tae Jou, Keunyong Kim; GEO DATA. 2024;6(4):505-511. Published online December 4, 2024; DOI: https://doi.org/10.22761/GD.2024.0012

255 View
37 Download

Abstract PDF: The importance of blue carbon is significant in terms of climate change mitigation and marine ecosystem conservation, and halophyte acts as a crucial reservoir for this blue carbon. Accordingly, this study utilized unmanned aerial vehicle (UAV) optical sensors to create a distribution map of vegetation in the natural salt marsh of the Jujin estuary. The optical images captured from a UAV at an altitude of 50 m provide ultra-high-resolution optical information with a ground sampling distance of 0.6 cm. Based on these images, a U-Net model was trained to classify Phragmites communis and Suaeda maritima, generating a classification map of the mixed habitats of salt marsh plants. The areas of Phragmites communis and Suaeda maritima in the Jujin- Cheon region were found to be 6,653.23 m² and 1,409.08 m², respectively. The classification results were validated using field control point data, confirming an approximate classification accuracy of 92%.

Original Paper

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data: Jaese Lee, Dukwon Bae, Young Jun Kim, Jungho Im; GEO DATA. 2023;5(3):119-125. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0025

1,310 View
82 Download

Abstract PDF: Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

First
Prev
Page of 1
Next
Last

Search