Article
Engineering, Multidisciplinary
Razieh Darang, Saeed Nasri, Mansoor Zeinali
Summary: This paper presents an iterative approach to minimize phase difference error in interferometric synthetic aperture radar (InSAR) systems. The proposed method describes the interferometric phase relationships, formulates the phase unwrapping problem as an optimization issue, and converts the cost function into a submodular convex Markov random field function. Numerical studies confirm the efficiency and superiority of the proposed method in terms of elevation estimation.
Article
Environmental Sciences
Mamoru Ishikawa, Ram Avtar, Shixin Mo
Summary: This study used interferometric synthetic aperture RADAR (InSAR) technique to detect topographic deformation related to the irreversible changes in ground ice. The overall deformation of the ground surface was found to be in the range of -3 to 3 cm, mainly influenced by thawing and growing ground ice.
LAND DEGRADATION & DEVELOPMENT
(2023)
Article
Geochemistry & Geophysics
Cheng Wang, Wu-Long Guo, Qing-He Zhang, Hai-Sheng Zhao, Le Cao
Summary: This article proposes a bi-iteration algorithm that integrates GPS, PALSAR, and ionosonde data to improve the precision of ionospheric tomography. Experimental verification demonstrates that the reconstruction accuracy of the algorithm is significantly higher compared to using GPS alone or combining GPS and ionosonde data, indicating the effectiveness of combining these three kinds of data in improving the precision of CIT.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Sang-Eun Park, Yoon Taek Jung, Hyun-Cheol Kim
Summary: This study explores the possibility of using combined interpretation of optical and SAR data to identify and understand the spatiotemporal changes in the permafrost active layer. The results show a significant correlation between winter changes observed in SAR data and summer land cover changes observed in optical data. Additional data from independent sources also support this relationship.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Engineering, Electrical & Electronic
Wenfei Mao, Guoxiang Liu, Xiaowen Wang, Rui Zhang, Wei Xiang, Shuaiying Wu, Bo Zhang, Jiawen Bao, Jialun Cai, Saeid Pirasteh
Summary: An integrated InSAR ionospheric correction method is proposed in this study, utilizing Helmert variance component estimation to allocate weights for azimuth offset-based and RSS techniques. The method effectively removes long-wavelength ionospheric delay and mitigates local ionospheric disturbance simultaneously.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Wei Chen, Qihui Zheng, Haibing Xiang, Xu Chen, Tetsuro Sakai
Summary: This study utilized PolInSAR technology to estimate forest canopy height based on full-polarized ALOS/PALSAR data, proposing multiple algorithms such as differential DEM, coherent amplitude, coherent phase-amplitude, and RVoG_3 for comparison. By introducing change rate and slope, the estimation model was optimized to improve accuracy in forest canopy height estimation.
Article
Environmental Sciences
Jin Deng, Keren Dai, Rubing Liang, Lichuan Chen, Ningling Wen, Guang Zheng, Hong Xu
Summary: Landslides frequently occur in the mountainous area of southwest China, and the use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular for identifying potential landslides in steep mountainous regions. This study focused on the Mao County region in China and utilized InSAR technology with Sentinel-1 and ALOS-2 data to identify potential landslides and analyze their applicability, providing insights for future landslide identification.
Article
Geosciences, Multidisciplinary
Yoshihiro Iijima, Takahiro Abe, Hitoshi Saito, Mathias Ulrich, Alexander N. Fedorov, Nikolay I. Basharin, Alexey N. Gorokhov, Victor S. Makarov
Summary: Thermokarst is causing extensive landscape changes and surface subsidence in Central Yakutia. The study used GIS and remote sensing to analyze the spatial extent and rate of subsidence, revealing activated surface subsidence of about 2 cm/year in disturbed areas. Some historically deforested areas have likely recovered without further thermokarst development.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Geochemistry & Geophysics
Zhiyang Chen, Yuanhao Li, Cong Li, Yan Liu, Xichao Dong, Cheng Hu
Summary: This study models the geometric decorrelation in SAR observation geometry and derives an accurate analytical model. The simulation results confirm the validity of the model. It is found that unparallel tracks introduce an extra geometric decorrelation factor and worsen the geometric decorrelation compared to parallel tracks.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Remote Sensing
Anthony Carpenter, James A. Lawrence, Richard Ghail, Philippa J. Mason
Summary: Interferometric synthetic aperture radar (InSAR) is used to measure Earth surface and structural deformation, and drone InSAR offers improved spatial-temporal data resolutions. This study presents the design, simulation, fabrication, and testing of lightweight and inexpensive copper clad laminate/printed circuit board horn antennas for C-band radar deployed on a drone. The antennas achieved high gain and met the performance criteria, demonstrating the potential of CCL/PCB/FR-4 as a lightweight and inexpensive material for custom antenna production in drone radar and other antenna applications.
Article
Chemistry, Multidisciplinary
Shuai Yang, Jinmin Zhang, Lei Fu, Chunhua Chen, Zijing Liu, Wenlong Zhang
Summary: Due to the complex terrain, intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, landslides pose a serious threat to local residents. InSAR has been widely used for landslide detection, but its effectiveness is challenged by the steep terrain and dense vegetation in the area. This study compared ALOS/PALSAR-2 and Sentinel-1A data for landslide detection and found that ALOS/PALSAR-2 is more suitable for detecting landslides in areas with high vegetation coverage, meeting over 90% of the monitoring needs. The study provides important scientific support for future landslide monitoring in the area and the accuracy evaluation methods of InSAR monitoring.
APPLIED SCIENCES-BASEL
(2023)
Article
Geochemistry & Geophysics
Simon Zwieback, Franz J. Meyer
Summary: The study found pervasive deviations from Gaussianity in repeat-pass radar interferometric data, especially in areas with naturally heterogeneous surfaces. Permanent texture has a significant impact on intensity heterogeneity related to phase heterogeneity. Accounting for heterogeneity has a moderate impact on phase estimates and estimated uncertainty in deformation analyses, but can improve phase estimation accuracy and uncertainty estimates in inherently heterogeneous areas.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Muhammad Fulki Fadhillah, Arief Rizqiyanto Achmad, Chang-Wook Lee
Summary: In this study, a new algorithm called ICOPS was developed to monitor surface deformation by combining persistent scatterers (PSs) and distributed scatterers (DSs). The algorithm was improved through a machine learning process and showed promising results in terms of accuracy. The advantage of this method is the increased density of measurement points and the spatial clustering information it provides about surface deformation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Gert Mulder, Freek J. van Leijen, Ramon F. Hanssen
Summary: The observed phase in InSAR products is a combination of various components, including differential topography, line-of-sight displacements, and differential atmospheric delays. Isolating the atmospheric component has been challenging due to its dynamic and superposed nature. In this study, we propose a method to characterize the stochastic properties of atmospheric delays as a means to define the atmospheric signal. By using structure functions, we can describe the spatiotemporal variability of InSAR atmospheric delays and quantify atmospheric noise in deformation time series.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Shinki Cho, Haoyi Xiu, Masashi Matsuoka
Summary: Most research on earthquake-caused building damage extraction using SAR images rely on inaccurate assessment methods. This study proposes a more detailed classification of Major damage buildings based on Japanese assessment data and field photographs. The backscattering characteristics of SAR images were analyzed for each damage class, and it was found that the correlation coefficient R decreased for large deformations such as collapsed buildings, while the coherence differential value gamma(dif) was sensitive to not only collapsed buildings but also damage with relatively small deformation. The study also suggests that ground displacement near the earthquake fault affected the coherence values.
Article
Environmental Sciences
Hongtak Lee, Joong-Sun Won, Wook Park
Editorial Material
Green & Sustainable Science & Technology
Hyung-Sup Jung, Saro Lee, Biswajeet Pradhan
Article
Engineering, Multidisciplinary
Won-Kyung Baek, Hyung-Sup Jung
Article
Environmental Sciences
Kwan-Young Oh, Hyung-Sup Jung, Sung-Hwan Park, Kwang-Jae Lee
Editorial Material
Environmental Sciences
Kwang-Jae Lee, Tae-Byeong Chae, Hyung-Sup Jung
Article
Chemistry, Multidisciplinary
Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Saro Lee
APPLIED SCIENCES-BASEL
(2020)
Article
Environmental Sciences
Sung-Hwan Park, Hyung-Sup Jung, Sunmin Lee, Eun-Sook Kim
Summary: The study proposed a method for estimating the vertical structure of forests based on FW LiDAR data, achieved a high accuracy through unsupervised classification algorithm applied to tree point density maps.
Article
Environmental Sciences
Won-Kyung Baek, Hyung-Sup Jung
Summary: The study compared the classification performance of single- and dual-polarized SAR images using support vector machine, random forest, and deep neural network models. The results indicated that dual-polarized images achieved some performance improvement in marine target classification, but it was not remarkable.
Article
Environmental Sciences
Hyung-Sup Jung, Saro Lee
Article
Chemistry, Multidisciplinary
Sung-Hwan Park, Hyung-Sup Jung, Sunmin Lee, Heon-Seok Yoo, Nam-Wook Cho, Moung-Jin Lee
Summary: In Korea, environmental impact assessments (EIA) are conducted to evaluate the impact of development projects on the environment, using EIA Big Data and GIS to analyze indicators related to air, soil, and water. A study from 2007-2016 showed an increasing trend in the environmental impact of development projects in South Korea, emphasizing the need for management agencies to apply calculation models to reduce these impacts.
APPLIED SCIENCES-BASEL
(2021)
Article
Environmental Sciences
Sung-Hwan Park, Jeseon Yoo, Donghwi Son, Jinah Kim, Hyung-Sup Jung
Summary: This study proposed a new method using artificial neural networks to calibrate ASCAT-based wind speed estimates. By training a DNN model, biases and root mean squared error between ASCAT-based and in-situ wind speeds were reduced, especially improving the quality of low and high wind speeds.
Article
Environmental Sciences
Jin-Woo Yu, Young-Woong Yoon, Won-Kyung Baek, Hyung-Sup Jung
Summary: Research has shown that utilizing two seasonal UAV optic images and LiDAR-derived DSM, in conjunction with optimal machine learning approaches, can achieve a classification performance of 0.9 for forest vertical structure, indicating that seasonal variation is more important than spatial resolution. Using two seasonal images can significantly improve the classification performance.
Article
Environmental Sciences
Joon Hyuk Choi, Joong-Sun Won
Summary: This paper presents a unique approach for reducing azimuth ambiguities or ghosts in SAR images using a simple rotation matrix. By concentrating the energy of vessels onto a single axis and dispersing ghost signal powers onto three axes, the method effectively suppresses ghosts while preserving vessels. Application results demonstrate high performance in ghost suppression and improved ship detection capabilities.
Article
Environmental Sciences
Eu-Ru Lee, Won-Kyung Baek, Hyung-Sup Jung
Summary: With the increasing importance of forests, monitoring and managing forest ecology information has become essential. Tree species are important indicators of forest ecosystems, and studies have been conducted using remote sensing data and machine learning algorithms for tree species classification. The classification accuracy varies based on the characteristics and quantity of data used, so applying different classification models is necessary. This study analyzed the accuracy of tree classification using convolutional neural networks (CNNs)-based deep learning models and examined the impact of data augmentation methods. The results showed that the CNN-3 model demonstrated the best classification performance.
Article
Environmental Sciences
Jin-Woo Yu, Hyung-Sup Jung
Summary: With the acceleration of global warming, research on forests has become important. In our previous study, we mapped the vertical forest structure using machine learning techniques and multi-seasonal remote sensing data, and improved the classification performance. However, this approach had tree location errors. To overcome this, we used modified U-Net models with multi-seasonal UAV optic and LiDAR data, and achieved better mapping performance.