GEO DATA

Data Article

Land Use in the Surrounding Area of Lake-type Wetland Protection Areas: Yeon Hui Jang, Jong-Hak Yun; GEO DATA. 2024;6(4):305-311. Published online December 27, 2024; DOI: https://doi.org/10.22761/GD.2024.0032

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Abstract PDF: In this study, we defined the range of surrounding areas that may impact the ecological environments of three lake-type inland wetland protection areas: Upo Wetland, Mungyeong Doline Wetland, and Duung Wetland. We analyzed the land cover types of each surrounding area to determine the proportion of anthropogenic environmental factors affecting the wetland protection area. The analysis revealed that agricultural areas and urbanized/construction areas were identified as the most significant anthropogenic threats among the land cover types. A comparison of these types across the wetlands showed that in Mungyeong Doline Wetland, there was a high proportion of agricultural and urbanized/construction areas in the zone adjacent to the buffer area (A-2). Therefore, it is necessary to manage this area to prevent the influx of external soil and plant/animal resources, and to establish management strategies for waste and emissions resulting from agricultural activities. For Upo Wetland, the overall high proportion of agricultural areas is expected to have a significant impact. In particular, the northeastern zone (B-2), where Saji-po is located, exhibited the highest proportions of both types. Thus, it is recommended that the agricultural areas surrounding Upo Wetland implement measures to prevent pesticide runoff and contamination from soil, as well as manage domestic wastewater generated from residences. Similarly, in Duung Wetland, a high proportion of agricultural and urbanized/construction areas was observed in the zone adjacent to the buffer area (C-2). This area is particularly vulnerable to issues such as ecosystem fragmentation within the wetland and the inflow of external resources, necessitating effective management strategies. The results of this study can serve as foundational resources for the sustainable conservation and management of the wetland protection area and its surrounding area.

Original Papers

Continuous Time-Series Land Cover Maps for South Korea using Google Earth Engine: Chulhuyn Choi, Inyoung Jang, Sanghak Han, Sungryong Kang; GEO DATA. 2023;5(4):304-313. Published online December 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0044

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Abstract PDF Supplementary Material: In this study, we utilized Google Earth Engine to construct, for the first time in South Korea, a long-term (1986-2021) continuous time series of land cover maps with a spatial resolution of 30 meters. Derived from the surface reflectance data of the Landsat satellite series, a total of 44 input variables were generated, including various spectral bands and indices related to land cover. For accuracy verification of the maps, 4,824 reference data were established using areas where land cover remained unchanged, identified by comparing the most recent (2018) and historical (1988) land cover maps from the Ministry of Environment. The Random Forest model was employed to classify seven land cover types (settlements, cropland, forest land, grassland, wetlands, bare land, and water bodies), with an overall accuracy of 0.97 and a Macro F1-score of 0.91, indicating a generally high performance of the model. However, considering the annual variability, potentially due to unidentified or untraceable errors, a composite land cover map dataset, integrated in five-year intervals, was suggested to ensure the generation of stable data.

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

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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.

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