Continuous Time-Series Land Cover Maps for South Korea using Google Earth Engine

• Published in: Geo Data Vol. 5; no. 4; pp. 304 - 313
• Main Authors: Choi, Chulhuyn, Jang, Inyoung, Han, Sanghak, Kang, Sungryong
• Format: Journal Article
• Language: English
• Published: GeoAI Data Society 31.12.2023
• Subjects: land cover map; landsat, google earth engine; random forest, lulucf
• ISSN: 2713-5004; 2713-5004
• DOI: 10.22761/GD.2023.0044

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.