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Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.
Shuo Chen; Kefei Zhang; Yindi Zhao; Yaqin Sun; Wei Ban; Yu Chen; Huifu Zhuang; Xuewei Zhang; Jinxiang Liu; Tao Yang. An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation. Agriculture 2021, 11, 420 .
AMA StyleShuo Chen, Kefei Zhang, Yindi Zhao, Yaqin Sun, Wei Ban, Yu Chen, Huifu Zhuang, Xuewei Zhang, Jinxiang Liu, Tao Yang. An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation. Agriculture. 2021; 11 (5):420.
Chicago/Turabian StyleShuo Chen; Kefei Zhang; Yindi Zhao; Yaqin Sun; Wei Ban; Yu Chen; Huifu Zhuang; Xuewei Zhang; Jinxiang Liu; Tao Yang. 2021. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation." Agriculture 11, no. 5: 420.
Ground deformation caused by underground coal mining has a large subsidence gradient and high non-linearity and may cause continuous destruction to the surface structure after mining. Synthetic aperture radar interferometry (InSAR) is a powerful method for measuring and reconstructing ground displacement. In this paper, a total of 19 descending orbit X-band TerraSAR-X and 16 ascending orbit C-band Sentinel-1 images were integrated to monitor the ground displacement of a coal mining area in Yu County (Hebei Province, China) using a small baseline subset (SBAS-InSAR) algorithm. We found that the SBAS-InSAR technology can obtain reliable results in regions without high gradient deformation by combining the deformation rates derived from both the TerraSAR-X and Sentinel-1 datasets. The maximum subsidence rate was approximately 125 mm/y between 17 June 2015 and 07 January 2016. In the case of large gradient deformation, it is indeed difficult to obtain mining-induced surface subsidence information accurately using conventional multi-temporal (MT-InSAR) techniques owing to the limitations of SAR wavelength. In response to this problem, a new decision-making fusion method based on SBAS-InSAR and offset tracking was developed to monitor large gradient settlement in mining areas. The results show that the method developed compensates for the shortcomings of traditional MT-InSAR technologies in the field of large-gradient deformation and obtains reliable results.
Yu Chen; Yunxiao Tong; Kun Tan. Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 6077 -6087.
AMA StyleYu Chen, Yunxiao Tong, Kun Tan. Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):6077-6087.
Chicago/Turabian StyleYu Chen; Yunxiao Tong; Kun Tan. 2020. "Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 6077-6087.
Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.
Yuan Zhang; Kun Tan; Xue Wang; Yu Chen. Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data. Remote Sensing 2020, 12, 2239 .
AMA StyleYuan Zhang, Kun Tan, Xue Wang, Yu Chen. Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data. Remote Sensing. 2020; 12 (14):2239.
Chicago/Turabian StyleYuan Zhang; Kun Tan; Xue Wang; Yu Chen. 2020. "Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data." Remote Sensing 12, no. 14: 2239.
Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.
Moyang Wang; Kun Tan; Xiuping Jia; Xue Wang; Yu Chen. A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images. Remote Sensing 2020, 12, 205 .
AMA StyleMoyang Wang, Kun Tan, Xiuping Jia, Xue Wang, Yu Chen. A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images. Remote Sensing. 2020; 12 (2):205.
Chicago/Turabian StyleMoyang Wang; Kun Tan; Xiuping Jia; Xue Wang; Yu Chen. 2020. "A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images." Remote Sensing 12, no. 2: 205.
Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance.
Kun Tan; Zengfu Hou; Donglei Ma; Yu Chen; Qian Du. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint. Remote Sensing 2019, 11, 1578 .
AMA StyleKun Tan, Zengfu Hou, Donglei Ma, Yu Chen, Qian Du. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint. Remote Sensing. 2019; 11 (13):1578.
Chicago/Turabian StyleKun Tan; Zengfu Hou; Donglei Ma; Yu Chen; Qian Du. 2019. "Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint." Remote Sensing 11, no. 13: 1578.
Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed.
Yu Chen; Kun Tan; Shiyong Yan; Kefei Zhang; Xiaoyang Liu; Huaizhan Li; Yaqin Sun. Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry. Remote Sensing 2019, 11, 1494 .
AMA StyleYu Chen, Kun Tan, Shiyong Yan, Kefei Zhang, Xiaoyang Liu, Huaizhan Li, Yaqin Sun. Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry. Remote Sensing. 2019; 11 (12):1494.
Chicago/Turabian StyleYu Chen; Kun Tan; Shiyong Yan; Kefei Zhang; Xiaoyang Liu; Huaizhan Li; Yaqin Sun. 2019. "Monitoring Land Surface Displacement over Xuzhou (China) in 2015–2018 through PCA-Based Correction Applied to SAR Interferometry." Remote Sensing 11, no. 12: 1494.
Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others.
Kun Tan; Zengfu Hou; Fuyu Wu; Qian Du; Yu Chen. Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation. Remote Sensing 2019, 11, 1318 .
AMA StyleKun Tan, Zengfu Hou, Fuyu Wu, Qian Du, Yu Chen. Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation. Remote Sensing. 2019; 11 (11):1318.
Chicago/Turabian StyleKun Tan; Zengfu Hou; Fuyu Wu; Qian Du; Yu Chen. 2019. "Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation." Remote Sensing 11, no. 11: 1318.
This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery.
Depin Ou; Kun Tan; Qian Du; Jishuai Zhu; Xue Wang; Yu Chen. A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction. Remote Sensing 2019, 11, 654 .
AMA StyleDepin Ou, Kun Tan, Qian Du, Jishuai Zhu, Xue Wang, Yu Chen. A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction. Remote Sensing. 2019; 11 (6):654.
Chicago/Turabian StyleDepin Ou; Kun Tan; Qian Du; Jishuai Zhu; Xue Wang; Yu Chen. 2019. "A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction." Remote Sensing 11, no. 6: 654.
In the process of underground coal gasification (UCG), there have been doubts or controversies if it is possible to control pollution of heavy metals such as arsenic and cadmium. To eliminate this suspicion, this paper first investigated the control method and design flow of reducing heavy metal pollution in the process of underground coal gasification (UCG) without shaft based on the analysis of the potential arsenic and cadmium pollution forms. The relationships between the development height of underground gasification mining-induced fractures and the parameters of gasifiers was revealed. The findings indicated that: 1) By rationally designing the parameters of the gasifier, it can be possible to control the development of mining-induced fractures, thus ensuring that the underground combustion space area does not penetrate the aquifer, surface water and atmospheric environment. Consequently, the pollution of arsenic and cadmium caused by UCG without shaft can be controlled from the source. Meanwhile, the validity of this method was verified from an engineering example. 2)When the gasifier height was constant and the isolated coal pillar was stable, the gasifier width was linearly positively correlated with the development height of the mining-induced fracture; When the widths of the gasifier and the isolated coal pillar were constant, the gasifier height and the development height of the mining-induced fracture was also linearly positively associated. As the contents of arsenic and cadmium in coal are high in China and the United States, the arsenic and cadmium control methods during the underground coal gasification proposed in this paper have valuable application prospects in these two major coal countries.
Huaizhan Li; Nanshan Zheng; Guangli Guo; Yu Chen. Control measures for reduction of arsenic and cadmium contamination during underground coal gasification without shaft. Journal of Cleaner Production 2019, 219, 960 -970.
AMA StyleHuaizhan Li, Nanshan Zheng, Guangli Guo, Yu Chen. Control measures for reduction of arsenic and cadmium contamination during underground coal gasification without shaft. Journal of Cleaner Production. 2019; 219 ():960-970.
Chicago/Turabian StyleHuaizhan Li; Nanshan Zheng; Guangli Guo; Yu Chen. 2019. "Control measures for reduction of arsenic and cadmium contamination during underground coal gasification without shaft." Journal of Cleaner Production 219, no. : 960-970.
With the exploitation of coalfields, the eco-environment around the coalfields can become badly damaged. To address this issue, “mine greening” has been proposed by the Ministry of Land and Resources of China. The sustainable development of mine environments has now become one of the most prominent issues in China. In this study, we aimed to make use of Landsat 7 ETM+ and Landsat 8 OLI images obtained between 2005 and 2016 to analyze the eco-environment in a coalfield. Land cover was implemented as the basic evaluation factor to establish the evaluation model for the eco-environment. Analysis and investigation of the eco-environment in the Yuxian coalfield was conducted using a novel evaluation model, based on the biological abundance index, vegetation coverage index, water density index, and natural geographical factors. The weight of each indicator was determined by an analytic hierarchy process. Meanwhile, we also used the classic ecological footprint to calculate the ecological carrying capacity in order to verify the effectiveness of the evaluation model. Results showed that the eco-environment index illustrated a slowly increasing tendency over the study period, and the ecological quality could be considered as “good”. The results of the evaluation model showed a strong correlation with the ecological carrying capacity with a correlation coefficient of 0.9734. In conclusion, the evaluation method is a supplement to the time-series quantitative evaluation of the eco-environment, and also helps us to explore the eco-environment in the mining area.
Xue Wang; Kun Tan; Kailei Xu; Yu Chen; Jianwei Ding. Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China. International Journal of Environmental Research and Public Health 2019, 16, 511 .
AMA StyleXue Wang, Kun Tan, Kailei Xu, Yu Chen, Jianwei Ding. Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China. International Journal of Environmental Research and Public Health. 2019; 16 (3):511.
Chicago/Turabian StyleXue Wang; Kun Tan; Kailei Xu; Yu Chen; Jianwei Ding. 2019. "Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China." International Journal of Environmental Research and Public Health 16, no. 3: 511.
The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods.
Kun Tan; Yusha Zhang; Xue Wang; Yu Chen. Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sensing 2019, 11, 359 .
AMA StyleKun Tan, Yusha Zhang, Xue Wang, Yu Chen. Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sensing. 2019; 11 (3):359.
Chicago/Turabian StyleKun Tan; Yusha Zhang; Xue Wang; Yu Chen. 2019. "Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis." Remote Sensing 11, no. 3: 359.
As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data.
Shanchuan Guo; Xuyu Bai; Yu Chen; Shaoliang Zhang; Huping Hou; Qianlin Zhu; Peijun Du. An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images. Remote Sensing 2019, 11, 349 .
AMA StyleShanchuan Guo, Xuyu Bai, Yu Chen, Shaoliang Zhang, Huping Hou, Qianlin Zhu, Peijun Du. An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images. Remote Sensing. 2019; 11 (3):349.
Chicago/Turabian StyleShanchuan Guo; Xuyu Bai; Yu Chen; Shaoliang Zhang; Huping Hou; Qianlin Zhu; Peijun Du. 2019. "An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images." Remote Sensing 11, no. 3: 349.
The authors wish to make the following corrections to this paper
Yu Chen; Kefei Zhang; Kun Tan; Xiaojun Feng; Huaizhan Li. Correction: Chen, Y., et al. Long-Term Subsidence in Lava Fields at the Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sens. 2018, 10, 597. Remote Sensing 2018, 11, 30 .
AMA StyleYu Chen, Kefei Zhang, Kun Tan, Xiaojun Feng, Huaizhan Li. Correction: Chen, Y., et al. Long-Term Subsidence in Lava Fields at the Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sens. 2018, 10, 597. Remote Sensing. 2018; 11 (1):30.
Chicago/Turabian StyleYu Chen; Kefei Zhang; Kun Tan; Xiaojun Feng; Huaizhan Li. 2018. "Correction: Chen, Y., et al. Long-Term Subsidence in Lava Fields at the Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sens. 2018, 10, 597." Remote Sensing 11, no. 1: 30.
Ground surface subsidence is a universal phenomenon in coal mining areas which can cause serious damage to the surrounding environment. In this paper, we consider the use of differential interferometric synthetic aperture radar (D-InSAR), multi-temporal InSAR (MT-InSAR), and the pixel offset tracking technique to monitor the surface deformation of a coal mining area. In this study, we use the two-pass D-InSAR method to generate 19 interferometric image pairs from 20 TerraSAR-X SpotLight images. The results show that D-InSAR can be used to obtain high accuracy surface deformation in the mining areas where there is no high gradient deformation, and the pixel offset tracking method offers advantages in those areas where high gradient deformation is found, but its performance is not stable. This means that the unilateral use of these technologies cannot obtain reliable subsidence information in mining areas. Therefore, it is essential to find a new way to integrate the respective advantages of these different methods. In this paper, a new fusion method combining the D-InSAR result with the offset tracking result based on a spatial decorrelation distribution map is proposed to obtain the subsidence results in a mining area. To ensure the reliability of the results, a decision rule is proposed for the spatial decorrelation distribution map, which is generated manually by union analysis in ArcGIS. In the experiments, the mean absolute error of the fusion result is 0.0748 m, while that of D-InSAR is 0.1890 m, and that of offset tracking is 0.1358 m. It is therefore clear that the proposed fusion method is more reliable and more accurate than the use of individual methods, and it may be able to serve as a reference in mining subsidence monitoring.
Depin Ou; Kun Tan; Qian Du; Yu Chen; Jianwei Ding. Decision Fusion of D-InSAR and Pixel Offset Tracking for Coal Mining Deformation Monitoring. Remote Sensing 2018, 10, 1055 .
AMA StyleDepin Ou, Kun Tan, Qian Du, Yu Chen, Jianwei Ding. Decision Fusion of D-InSAR and Pixel Offset Tracking for Coal Mining Deformation Monitoring. Remote Sensing. 2018; 10 (7):1055.
Chicago/Turabian StyleDepin Ou; Kun Tan; Qian Du; Yu Chen; Jianwei Ding. 2018. "Decision Fusion of D-InSAR and Pixel Offset Tracking for Coal Mining Deformation Monitoring." Remote Sensing 10, no. 7: 1055.
Long-term deformation often occurs in lava fields at volcanoes after flow emplacements. The investigation and interpretation of deformation in lava fields is one of the key factors for the assessment of volcanic hazards. As a typical Hawaiian volcano, Piton de la Fournaise volcano’s (La Réunion Island, France) main eruptive production is lava. Characteristics of the lava flows at Piton de la Fournaise, including the geometric parameters, location, and elevation, have been investigated by previous studies. However, no analysis focusing on the long-term post-emplacement deformation in its lava fields at a large spatial extent has yet been performed. One of the previous studies revealed that the post-emplacement lava subsidence played a role in the observed Eastern Flank motion by conducting a preliminary investigation. In this paper, an InSAR time series analysis is performed to characterize the long-term deformation in lava fields emplaced between 1998 and 2007 at Piton de la Fournaise, and to conduct an in-depth investigation over the influence of post-emplacement lava subsidence processes on the instability of the Eastern Flank. Results reveal an important regional difference in the subsidence behavior between the lava fields inside and outside of the Eastern Flank Area (EFA), which confirms that, in addition to the post-lava emplacement processes, other processes must have played a role in the observed subsidence in the EFA. The contribution of other processes is estimated to be up to ~78%. The spatial variation of the observed displacement in the EFA suggests that a set of active structures (like normal faults) could control a slip along a pre-existing structural discontinuity beneath the volcano flank. This study provides essential insights for the interpretation of the Eastern Flank motion of Piton de la Fournaise.
Yu Chen; Kefei Zhang; Jean-Luc Froger; Kun Tan; Dominique Remy; José Darrozes; Aline Peltier; Xiaojun Feng; Huaizhan Li; Nicolas Villeneuve. Long-Term Subsidence in Lava Fields at Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sensing 2018, 10, 597 .
AMA StyleYu Chen, Kefei Zhang, Jean-Luc Froger, Kun Tan, Dominique Remy, José Darrozes, Aline Peltier, Xiaojun Feng, Huaizhan Li, Nicolas Villeneuve. Long-Term Subsidence in Lava Fields at Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion. Remote Sensing. 2018; 10 (4):597.
Chicago/Turabian StyleYu Chen; Kefei Zhang; Jean-Luc Froger; Kun Tan; Dominique Remy; José Darrozes; Aline Peltier; Xiaojun Feng; Huaizhan Li; Nicolas Villeneuve. 2018. "Long-Term Subsidence in Lava Fields at Piton de la Fournaise Volcano Measured by InSAR: New Insights for Interpretation of the Eastern Flank Motion." Remote Sensing 10, no. 4: 597.
Yu Chen; Dominique Remy; Jean-Luc Froger; Aline Peltier; Nicolas Villeneuve; José Darrozes; Hugo Perfettini; Sylvain Bonvalot. Long-term ground displacement observations using InSAR and GNSS at Piton de la Fournaise volcano between 2009 and 2014. Remote Sensing of Environment 2017, 194, 230 -247.
AMA StyleYu Chen, Dominique Remy, Jean-Luc Froger, Aline Peltier, Nicolas Villeneuve, José Darrozes, Hugo Perfettini, Sylvain Bonvalot. Long-term ground displacement observations using InSAR and GNSS at Piton de la Fournaise volcano between 2009 and 2014. Remote Sensing of Environment. 2017; 194 ():230-247.
Chicago/Turabian StyleYu Chen; Dominique Remy; Jean-Luc Froger; Aline Peltier; Nicolas Villeneuve; José Darrozes; Hugo Perfettini; Sylvain Bonvalot. 2017. "Long-term ground displacement observations using InSAR and GNSS at Piton de la Fournaise volcano between 2009 and 2014." Remote Sensing of Environment 194, no. : 230-247.