Climate change is increasing extreme rainfall and landslides in mountain regions, raising concerns about accelerated erosion. Our study in Taiwan’s Laonong Basin combined satellite-derived elevation data with local Lidar data to examine how rapidly the landscape is changing. We found that decadal erosion rates are about 5 millimeters per year, similar to geological rates measured over thousands to millions of years. This suggests that sediment produced by storms and landslides is often temporarily stored in valleys as we have found before being transported downstream, helping maintain long-term erosion balance. Our study also identified smaller areas with much higher erosion rates, highlighting localized landslide hazards and demonstrating the value of enhanced elevation differencing data for monitoring landscape change.
氣候變遷讓極端降雨和山崩變得更頻繁，也讓山區侵蝕速度更受關注。我們以臺灣荖濃溪流域為例，結合衛星高程資料與在地光達資料，來看近幾十年的地形變化。結果發現，流域平均侵蝕速率大約是每年 5 毫米，和長時間（幾千到幾百萬年）的地質估算其實很接近。這表示雖然暴雨和崩塌會帶來大量土砂，但這些物質常會先暫時堆在河谷或沖積扇，再慢慢被搬運出去，所以長期來看侵蝕還是維持平衡。不過在一些子流域，侵蝕速度明顯更高，顯示局部仍有較強的山崩風險。這也說明改良後的高程差分資料，在監測山區地形變化和災害評估上很有幫助。
This study quantifies geothermal radiative heat flux and heat loss at the Dayoukeng crater, Tatun Volcanic Group, Taiwan, using UAV thermal imagery and Landsat thermal infrared data. While Landsat efficiently captures regional geothermal heat loss, UAV observations resolve fine-scale thermal variability with much higher spatial detail. Both approaches produce comparable geothermal heat loss estimates (210–217 MW), demonstrating their complementary strengths. This work represents the first UAV-based geothermal heat loss assessment at Dayoukeng and provides a valuable framework for geothermal exploration and volcanic monitoring.
本研究結合無人機熱影像與 Landsat 熱紅外資料，量化台灣大屯火山群大油坑地熱輻射熱通量與熱損失。結果顯示衛星可有效評估區域地熱熱損失，無人機則可解析局部熱異質性，兩者具互補優勢，可應用於地熱探勘與火山監測。
This study examines sediment erosion in Taiwan's Zhoukou River Basin over the past 30 years, focusing on the impact of extreme rainfall. Traditional methods measure erosion over different time scales, but we used a unique approach for decade-long calculations with global and regional digital elevation models (DEMs). Our new method, applying Fourier analysis, reduces vertical bias in DEMs. Spectral analysis helps correct vertical offsets. Erosion rates calculated through DEMs of Difference (DoD) show a significant drop in sediment export from 1990-2010 to 2011-2020 due to reduced extreme rainfall. The denudation rate decreased from 14.19 mm/yr to 10.46 mm/yr in the Zhoukou River Basin. Our method effectively estimates sediment transport rates using underutilized DEMs.
This study focuses on detecting and monitoring slow-moving landslides in Taiwan using a sophisticated radar technique. Identifying over 2500 pre-existing landslides is crucial for assessing their activity, especially before typhoon seasons. The proposed method, "multi-snap2stamps," effectively analyzes nine slow-moving landslides, revealing seasonal patterns in two sites and accelerated movement in one. The study showcases the potential of the method for large-scale landslide detection and monitoring.
This research focuses on Kueishantao (also known as Turtle Island), an active and iconic volcanic island situated off the northeastern coast of Taiwan. The island is famous for its extreme underwater hot springs and active sulfur gas vents, making it a highly dynamic geological environment to study. We utilized thermal satellite imagery to measure and track the heat patterns on the surface of the island over an extended period. By carefully analyzing this satellite temperature data, we were able to map the specific thermal pattern of the volcano and observe exactly how its heat output fluctuated over time. The primary significance of this research is that it establishes a long-term thermal baseline for Kueishantao and the potential relations to the subsurface structures. Using satellites provides a continuous, and comprehensive view of its activity, helping us understand the volcano's hidden behaviors.
A long-term landslide on the Huafan University campus in Taiwan, observed since 1990, lacks reliable monitoring data post-2018 due to equipment maintenance issues. This study employs multitemporal interferometry (MTI) using Sentinel-1 SAR images from 2014–2019 to monitor the landslide. MTI reveals consistent slow-moving areas with previous studies, indicating gravity-induced deformation and seasonal surface fluctuations linked to precipitation. This technique compensates for the lack of data and aids in evaluating and monitoring landslides for potential early warnings.
