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Imanpour, Fatemeh, Saadi Esfangareh, Ramin, Saradjian Maralan, Mohammad Reza. (1405). Improving ALOS Digital Elevation Model Using ICESat-2 Data and Random Forest. , 9(1), 137-143. doi: 10.22059/eoge.2026.409807.1207
Fatemeh Imanpour; Ramin Saadi Esfangareh; Mohammad Reza Saradjian Maralan. "Improving ALOS Digital Elevation Model Using ICESat-2 Data and Random Forest". , 9, 1, 1405, 137-143. doi: 10.22059/eoge.2026.409807.1207
Imanpour, Fatemeh, Saadi Esfangareh, Ramin, Saradjian Maralan, Mohammad Reza. (1405). 'Improving ALOS Digital Elevation Model Using ICESat-2 Data and Random Forest', , 9(1), pp. 137-143. doi: 10.22059/eoge.2026.409807.1207
Imanpour, Fatemeh, Saadi Esfangareh, Ramin, Saradjian Maralan, Mohammad Reza. Improving ALOS Digital Elevation Model Using ICESat-2 Data and Random Forest. , 1405; 9(1): 137-143. doi: 10.22059/eoge.2026.409807.1207

Improving ALOS Digital Elevation Model Using ICESat-2 Data and Random Forest

Earth Observation and Geomatics Engineering
دوره 9، شماره 1، شهریور 2025، صفحه 137-143    اصل مقاله (723.41 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/eoge.2026.409807.1207
نویسندگان
Fatemeh Imanpour1؛ Ramin Saadi Esfangareh1؛ Mohammad Reza Saradjian Maralan* 2
1School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran
2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
چکیده
Digital Elevation Models (DEMs) are fundamental analytical tools in environmental studies, urban development, natural change monitoring, and spatial analysis. These models are typically generated using remote sensing technologies, satellite imagery, and LiDAR measurements. ICESat-2, a state-of-the-art satellite developed by NASA, employs advanced LiDAR sensors and a unique orbital design to provide high-resolution elevation data. In this study, an integrated approach is presented to enhance the accuracy of ALOS-derived DEMs by incorporating ICESat-2 elevation data (ATL08 product) and the Random Forest machine learning algorithm. The research was conducted over Avajiq County in West Azerbaijan Province, using data from 2018 to 2023. ICESat-2 data were first used as a reference dataset. By extracting topographic features and applying the Random Forest model, the original DEM errors were corrected. Results indicate a significant improvement in elevation accuracy, with an RMSE of 0.0319, R² of 0.9727, and MAE of 0.0198. A comparison with elevation data from the National Cartographic Center of Iran further validated the performance of the proposed method, where RMSE, R², and MAE were reported as 2.35, 0.98, and 1.05, respectively.
کلیدواژه‌ها
Digital Elevation Model (DEM)؛ ICESat-2؛ ALOS؛ ATL08؛ Random Forest
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تعداد مشاهده مقاله: 66
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