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Estimating landslide dynamics is vital for the prevention of landslide disasters. Differential interferometric synthetic aperture radar (DInSAR) based on L-band SAR satellites is an effective tool for estimating the dynamics of forested landslides that occur in Japan. High-temporal L-band SAR observations have been planned for the future. Thus, it is necessary to further investigate the specific advantages of high-temporal L-band SAR observations for estimating landslide dynamics. In this study, we used DInSAR data with different time windows to identify active landslides in Hokkaido, Japan. This study is the first attempt to demonstrate the advantages of high-temporal L-band SAR observations for estimating active landslide dynamics. We successfully observed the dynamics of two active landslides, Kounai-1 and Kounai-2, using DInSAR over a time window of 14 days. We present the first spatial observation of the dynamics of Kounai-1 and Kounai-2. In addition, we discuss the dynamics of Kounai-1 and Kounai-2 based on interferograms, and our results suggest that both landslides are subunits of the same landslide, called the Kounai landslide. These results indicate that high-temporal L-band SAR observations can mitigate cycle slips and enable the estimation of active landslide dynamics. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%). [ABSTRACT FROM AUTHOR]

In order to realize effective monitoring of Zhixincun landslide, this paper selected 27 sentinel-1A data in 2017, and conducted deformation monitoring of Zhixincun landslide based on small baseline radar interferometry technology (SBAS-InSAR), and analyzed its temporal evolution situation. Using ALOS-2 data from 2016 and 2017, differential radar interferometry (D-InSAR) was used to monitor the characteristics of the landslide variant. SBAS-InSAR monitors the temporal evolution situation of landslide deformation, while D-InSAR mainly monitors the deformation of specific landslide shape and variation. Moreover, the penetration of L-band ALOS-2 data is stronger than that of C-band sentinel-1A data, which can obtain more complete interference information. The monitoring results of both can be cross-verified. Improve the reliability of the results. The SBAS-InSAR monitoring results showed that the slope end of the landslide catchment area in Zhixincun had subsidence during the monitoring period, and the surface subsidence at the landslide end reached 12.47mm from July 5 to July 29, with an average subsidence rate of 2.88mm/a during the monitoring period. Uplift occurred in the threatened residential areas in the valley, with an average cumulative uplift of 19.59mm on December 8 and an average uplift rate of 19.99mm/a during the monitoring period. The D-InSAR results showed that there were five major deformations on the slope of Zhixincun landslide catchment area. The largest deformations with an area of 17 973m2 were located on the west side of the slope, and the most unstable deformations were located on the east side of the slope. The average cumulative shape variable reached 49.9mm during the monitoring period. Both monitoring methods showed that the threat of landslide disaster mainly came from the west slope with poor vegetation cover, and the rainy season was the key period of landslide disaster prevention and control in Zhixincun.

This study aims to reveal the relationships between understory coverage (principally, weed growth) beneath the canopies of Sumatran eucalyptus plantations and L-band backscatter data gathered by the PALSAR-2 Synthetic Aperture Radar (SAR) onboard the ALOS-2 satellite. Statistical analysis in conjunction with in situ measurements of forest structural parameters reveals that the SAR polarization ratio is significantly negatively correlated with understory coverage in forests taller than or equal to 10 m or above 2 years of age. Our field investigations confirm that a decrease in canopy coverage takes place some 2 years after transplanting, which in turn permits increased penetration (decreased attenuation) of microwave signals thereby enabling the SAR to detect changes in understory coverage. In addition, both growth and unmanaged remnants of the understory vegetation in older forests reduce evaporation from the ground, which in turn stabilizes soil-moisture levels throughout the year and allows co-polarized backscatter data (which might otherwise be dominated by soil-moisture changes) to contain significant information on the underlying vegetation. No correlation between the understory and SAR backscatter was recorded in younger forests undergoing programs of intensive weed management. This is presumably because strong canopy and trunk backscatter obscured the response from understory vegetation and because artificial changes were induced in vegetation water content as a result of the application of herbicides which in turn reduced the accuracy of some of our field measurements of understory coverage. This research nevertheless demonstrates an important potential application of active satellite microwave remote sensing. We show that satellite-based L-band SAR can be used as a tool to investigate coverage percentages of understory layers. This synoptic technique reduces the need for repeated field surveys across large areas of forest. It can enhance ecosystem assessment and improve understory coverage estimates in sparse forests and in various types of plantations.

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data. [ABSTRACT FROM AUTHOR]

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Frequent occurrence of earthquakes and the massive exploitation of underground resources make Songyuan City an area prone to surface deformation. Surface deformation causes damage to roads, pipelines and other public facilities, and have a negative impact on the ecological protection and economic construction. Based on the SBAS-InSAR and using Sentinel-1 images as the data source, this study obtained the time series surface deformation results of Songyuan City from August 2015 to August 2019. Furthermore, through the consistency check of deformation results obtained by different methods or data, and the cross-validation between InSAR and leveling results, the reliability of time series InSAR applied to surface deformation monitoring in Songyuan City was proved. The results showed that the surface deformation of Songyuan City was dominated by slow subsidence, the average deformation rate was − 0.6 mm/year, and the deformation showed an annual cyclical "subsidence-uplift" change. The maximum cumulative subsidence reached − 100.6 mm, occurred in the oil production area in the east of Qian'an County. Finally, starting from natural influencing factors, this study analyzed the differences in deformation results of different land cover areas, studied the correlation between time series deformation and meteorological data, and discussed the relationship between deformation and earthquake distribution. By monitoring and analyzing the surface deformation results based on time series InSAR, it can provide data reference for urban planning and ecological environment improvement of Songyuan City, improve the scientific level of surface deformation disasters monitoring and prevention, and lay a good foundation for sustainable development. [ABSTRACT FROM AUTHOR]

Estimating landslide dynamics is vital for the prevention of landslide disasters. Differential interferometric synthetic aperture radar (DInSAR) based on L-band SAR satellites is an effective tool for estimating the dynamics of forested landslides that occur in Japan. High-temporal L-band SAR observations have been planned for the future. Thus, it is necessary to further investigate the specific advantages of high-temporal L-band SAR observations for estimating landslide dynamics. In this study, we used DInSAR data with different time windows to identify active landslides in Hokkaido, Japan. This study is the first attempt to demonstrate the advantages of high-temporal L-band SAR observations for estimating active landslide dynamics. We successfully observed the dynamics of two active landslides, Kounai-1 and Kounai-2, using DInSAR over a time window of 14 days. We present the first spatial observation of the dynamics of Kounai-1 and Kounai-2. In addition, we discuss the dynamics of Kounai-1 and Kounai-2 based on interferograms, and our results suggest that both landslides are subunits of the same landslide, called the Kounai landslide. These results indicate that high-temporal L-band SAR observations can mitigate cycle slips and enable the estimation of active landslide dynamics.

The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%).

On 11 January 2021, an Mw 6.7 earthquake, which involved a complex rupture mechanism, occurred in Lake Hovsgol, Mongolia. This study measured the earthquake’s 3-D coseismic surface displacement fields using interferometric synthetic aperture radar (InSAR) pairs of three radar look directions: Sentinel-1B from the descending path and Advanced Land Observing Satellite-2 from the ascending and descending paths. The three DInSARs showed that the maximum coseismic surface displacement appeared east of the Northern Hovsgol Fault (NHF), where the displacement components were 18 cm, 5 cm, and 33 cm in the east–west, north–south, and up–down directions, respectively (precision: 1.7 cm, 20.4 cm, and 5.2 cm). However, the 3-D displacements indicated that although the earthquake induced a combination of normal faulting with strike slip motions, the displacements in the north–south direction had very large uncertainty owing to the similar geometry of the InSARs in the descending path. Later, we performed inversions of the DInSAR-measured coseismic surface displacement fields in the line-of-sight direction, which assumes a fault plane’s uniform and distributed slips in analyzing the slip mechanism. We conducted slip distribution estimations on the ruptured fault plane in accordance with the optimal fault geometry and source parameters determined by the uniform slip model. Investigations revealed that, with a correlation of 95.3%, the simulated displacements from the best-fitted distributed slip model were consistent with the observed displacements from DInSARs. Besides, our slip distribution model showed two distinctive slip patches, which include differences in their magnitudes and directions on the fault plane. We also observed ruptured faults experiencing a predominant right-lateral strike slip with a significant dip slip, according to the slip distribution, which caused two distinct slips due to the dramatic bending of the fault strike. Then, by analyzing the Coulomb stress change, our findings proposed that the seismic risk potential of active faults in the Hovsgol Basin increased after the earthquake. Overall, the great potential of multi-track DInSAR observations in the identification of complex slip mechanisms was demonstrated.

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future. [ABSTRACT FROM AUTHOR]

Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues. This research aims to improve the potential for estimating forest aboveground biomass (AGB) by feature selection based on a scattering mechanism and sensitivity analysis and utilizing a non-parametric model that combines the advantage of dual-frequency SAR data. By employing GF-3 and ALOS-2 data, this study explores the scattering mechanism within a coniferous forest by using results of target decomposition and the pixel statistics method. By selecting an appropriate feature (backscatter coefficients and polarization parameters) and using stepwise regression models and a non-parametric model (the random forest adaptive genetic algorithm (RF-AGA)), the results revealed that the RF-AGA model with feature selection exhibited excellent AGB estimation performance without obvious saturation (RMSE = 10.42 t/ha, R2 = 0.93, leave-one-out cross validation). The σ H V , σ V H , Pauli three-component decomposition, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of thee C-band and σ H V , σ V H , σ H H , Yamaguchi three-component decomposition, and VanZyl3 component decomposition of the L-band are suited for estimating the AGB of coniferous forests. Volume scattering was the dominant mechanism, followed by surface scattering, while double-bounce scattering had the smallest proportion. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in the C- and L-band SAR data for improved forest AGB estimation. [ABSTRACT FROM AUTHOR]

We developed a GNSS-assisted InSAR phase unwrapping algorithm for large-deformation DInSAR data processing in coal mining areas. Utilizing the Markov random field (MRF) theory and simulated annealing, the algorithm derived the energy function using MRF theory, Gibbs distribution, and the Hammersley–Clifford theorem. It calculated an image probability ratio and unwrapped the phase through iterative calculations of the initial integer perimeter matrix, interference phase, and weight matrix. Algorithm reliability was confirmed by combining simulated phases with digital elevation model (DEM) data for deconvolution calculations, showing good agreement with real phase-value results (median error: 4.8 × 10−4). Applied to ALOS-2 data in the Jinfeng mining area, the algorithm transformed interferometric phase into deformation, obtaining simulated deformation by fitting GNSS monitoring data. It effectively solved meter-scale deformation variables between single-period images, particularly for unwrapping problems due to decoherence. To improve calculation speed, a coherence-based threshold was set. Points with high coherence avoided iterative optimization, while points below the threshold underwent iterative optimization (coherence threshold: 0.32). The algorithm achieved a median error of 30.29 mm and a relative error of 2.5% compared to GNSS fitting results, meeting accuracy requirements for mining subsidence monitoring in large mining areas. [ABSTRACT FROM AUTHOR]

Abstract A major source of error in interferometric synthetic aperture radar (InSAR), used for mapping ground deformation, is the delay caused by changes in the propagation velocity of radar microwaves in the troposphere. Correcting this tropospheric delay noise using numerical weather models is common because of their global availability. Various correction methods and tools exist; selecting the most appropriate one by considering weather models, delay models, and delay calculation algorithms is essential for specific applications. We compared the performance of two tropospheric delay correction methods applied to Advanced Land Observing Satellite-2 (ALOS-2) data acquired over Japan, where the atmospheric field is complex with significant seasonal variation. We tested: (1) a method of delay integration along the slant radar line-of-sight (LOS) path using the mesoscale model (MSM) provided by the Japan Meteorological Agency and (2) the Generic Atmospheric Correction Online Service (GACOS) for InSAR, which estimates delay using the high-resolution forecast (HRES)-European Centre for Medium-Range Weather Forecasts (ECMWF) products along with an iterative decomposition approach. The results showed that the tropospheric delay correction using the slant-delay integration approach with MSM, which has a finer temporal and spatial resolution, performed slightly better than GACOS. We further found that the differences in the refractivity models would have limited significance, suggesting that the difference in performance mainly originates from differences in the numerical weather models being used. This study highlights the importance of using the best-available numerical weather model data for tropospheric delay calculations. Graphical Abstract

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

در این پژوهش، قابلیت داده‌های راداری قطبش کامل ماهوارۀ ALOS-2 در برآورد زی‌تودۀ چوبی روی‌زمینی جنگل‌های آمیختۀ هیرکانی بررسی شد. به‌منظور آماربرداری ابتدا با جنگل‌گردشی مناطق مناسب با شیب کم شناسایی و 127 قطعه نمونۀ مربع‌شکل با مساحت 900 متر مربع در آن پیاده شد. قطر برابرسینه و ارتفاع درختان قطورتر از 5/7 سانتی‌متر اندازه‌گیری و حجم هر درخت با استفاده از این دو مشخصه محاسبه شد. برای محاسبۀ زی‌توده در هر قطعه نمونه از رابطۀ تبدیل حجم به زی‌توده (با ضرب حجم در چگالی بحرانی چوب) استفاده شد. میانگین زی‌تودۀ روی‌زمینی محاسباتی در منطقۀ پژوهش 04/318 تن در هکتار است. به‌منظور پردازش داده‌های ALOS-2، کانال‌های قطبشی کالیبراسیون و چنددیده شدند. برای کاهش نویز، فیلتر باکس‌کار با ابعاد پنجرۀ 5×5 پیکسل اعمال شد و در نهایت متغیرهای استخراج‌شده تصحیح هندسی شدند. شدت بازپخش در فرم گامانات برای قطبش‌های مختلف، مؤلفه‌های تجزیۀ هدف Freeman-Durden و مشخصه‌های حاصل از روابط ریاضی بین کانال‌های قطبشی متغیرهای استخراج‌شده در این تحقیق بودند. به‌منظور مدل‌سازی برآورد زی‌توده با استفاده از متغیرهای ذکرشده، سه الگوریتم ناپارامتریک شاملk نزدیک‌ترین ‌همسایه (k-NN)، جنگل‌تصادفی (RF) و رگرسیون بردار پشتیبان (SVR) اجرا شد. نتایج مدل‌سازی با استفاده از الگوریتم جنگل تصادفی نشان داد که زی‌تودۀ روی‌زمینی می‌تواند با ضریب تبیین 38/0 و جذر میانگین مربعات خطای نسبی 77/23 درصد برآورد شود؛ درحالی که الگوریتم‌های k نزدیک‌ترین همسایه (k-NN) با ضریب تبیین 38/0 و جذر میانگین مربعات خطای 38/24 درصد و رگرسیون بردار پشتیبان با مقادیر ضریب تبیین 35/0 و جذر میانگین مربعات خطای 2/25 درصد صحت کمتری در برآورد زی‌توده داشتند. به‌طورکلی نتایج این پژوهش نشان‌دهندۀ قابلیت متوسط داده‌های قطبش کامل راداری ALOS-2 در برآورد زی‌تودۀ چوبی روی‌زمینی در شرایط جنگل‌های آمیختۀ هیرکانی است.

Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately assess the damage characteristics. The buildings identified as Major damage in the Japanese damage certification survey contain damage with various characteristics. If Major damage is treated as a single class, the parameters of SAR images will vary greatly, and the relationship between building damage and SAR images would not be properly evaluated. Therefore, it is necessary to divide Major damage buildings into more detailed classes. In this study, the Major damage buildings were newly classified into five damage classes, to correctly evaluate the relationship between building damage characteristics and SAR imagery. The proposed damage classification is based on Japanese damage assessment data and field photographs, and is classified according to the dominant damage characteristics of the building, such as collapse and damage to walls and roofs. We then analyzed the backscattering characteristics of SAR images for each classified damage class. We used ALOS-2 PALSAR-2 images observed before and after the 2016 Kumamoto earthquake in Mashiki Town, where many buildings were damaged by the earthquake. Then, we performed the analysis using two indices, the correlation coefficient R and the coherence differential value γdif, and the damage class. The results indicate that the backscattering characteristics of SAR images show different trends in each damage class. The R tended to decrease for large deformations such as collapsed buildings. The γdif was likely to be sensitive not only to collapsed buildings but also to damage with relatively small deformation, such as distortion and tilting. In addition, it was suggested that the ground displacement near the earthquake fault affected the coherence values. [ABSTRACT FROM AUTHOR]

Fault mapping is done to observe the possibility of fault-driven groundwater flow into the fault systems. Fault detection analysis on PALSAR-1/2 data has been implemented through a series of adaptive and gradient filters to derive a Relief Image. This method was applied to delineate good-resolution faults distribution and to revise faults mapping derived from existing geology maps over Djibouti. Study areas are located in the central and southwest of Djibouti, where faults exist because of continental plate movements, contact of geological formation, and tectonics activities. Results show that the processed Relief Image could delineate the fault lines and remove artifacts from PALSAR-1/2 data. Furthermore, the Geology Scoring Index was introduced in this study to analyze the correlation of groundwater data with the geology setting of borehole sites such as geological formation, fault system from Relief Image, distance to rivershed, type of aquifer, etc. It is shown that the occurrence of fault systems possibly increases the groundwater volume of boreholes in study areas. The proposed study is useful to locate the potential area for groundwater resources in Djibouti. [ABSTRACT FROM AUTHOR]

Snow covered crevasses in the Siachen glacier (Karakoram Range) cause great danger during glacier movement, and the knowledge of their spatial distribution is important for the safe travel. In the present study, crevasses in the Siachen glacier have been detected and further categorized as permanent open/hidden and seasonal open/hidden using a combination of optical (Landsat-8 and Sentinel-2) and microwave (ALOS-2) satellite data. The study is carried out for the year 2018. Initially, the locations of crevasses are manually marked using ALOS-2 data and further, their categorization in open and hidden is done using Sentinel-2 data. Apart from manual marking, the band ratio method is applied on Landsat-8 data to detect the permanent open crevasses in an automatic manner. Gray Level Co-occurrence Matrix (GLCM) technique has also been successfully attempted for the automatic classification of the crevasse and non-crevasse zones. The open crevasse locations using band ratioing are compared with manually marked and a good correlation is found having an accuracy of ~ 93%. A total of 140 crevasse zones have been found within the study area, with 32% as permanent open, 29% as permanent hidden, and 39% as seasonal open/hidden. Manual digitization is done for estimating crevasse dimension (length and width) as this is important input required before the start of any movement. The study reveals that the highly varying nature of crevasses in terms of changes from hidden to open in lower altitude regions of the glacier is mainly observed between July and September. The present methodology of crevasse detection and categorization leads to crevasse information system, which will be used in future to monitor the opening and closing of crevasses in the Siachen glacier on regular basis. [ABSTRACT FROM AUTHOR]

A major source of error in interferometric synthetic aperture radar (InSAR), used for mapping ground deformation, is the delay caused by changes in the propagation velocity of radar microwaves in the troposphere. Correcting this tropospheric delay noise using numerical weather models is common because of their global availability. Various correction methods and tools exist; selecting the most appropriate one by considering weather models, delay models, and delay calculation algorithms is essential for specific applications. We compared the performance of two tropospheric delay correction methods applied to Advanced Land Observing Satellite-2 (ALOS-2) data acquired over Japan, where the atmospheric field is complex with significant seasonal variation. We tested: (1) a method of delay integration along the slant radar line-of-sight (LOS) path using the mesoscale model (MSM) provided by the Japan Meteorological Agency and (2) the Generic Atmospheric Correction Online Service (GACOS) for InSAR, which estimates delay using the high-resolution forecast (HRES)-European Centre for Medium-Range Weather Forecasts (ECMWF) products along with an iterative decomposition approach. The results showed that the tropospheric delay correction using the slant-delay integration approach with MSM, which has a finer temporal and spatial resolution, performed slightly better than GACOS. We further found that the differences in the refractivity models would have limited significance, suggesting that the difference in performance mainly originates from differences in the numerical weather models being used. This study highlights the importance of using the best-available numerical weather model data for tropospheric delay calculations. [ABSTRACT FROM AUTHOR]

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between −4.86 mm/yr and −23.58 mm/yr and −9.50 and −27.77 mm/yr for InSAR and GNSS, respectively.

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar interferometry (InSAR) is an important method to obtain surface deformation information. Due to the limitations of InSAR data acquisition and the accuracy errors produced in the data processing, the identification of hidden dangers also needs to be combined with the analysis of geological hazards themselves, so a method based on InSAR technology combined with the disaster-pregnancy background in the study area is proposed. This study took the Badong section of the Three Gorges Reservoir area as the study area, and ALOS-2 PALSAR radar images were used to obtain the spatial distribution and change rate of deformation in the study area by using time-series InSAR technology. Combining the disaster-prone background of the study area, four indicators of the susceptibility level, slope, engineering rock group and distance from the disaster catalogue point are used as indicators for the identification of hidden dangers of geological disasters. As a result, 19 suspected hidden disaster areas were identified comprehensively, and then the suspected hidden geological disaster areas were verified in the field one by one. The success rate of verification and identification was 78.9%, proving that the method combining the disaster pregnancy background and InSAR results is feasible and can play an important role in regional disaster identification.

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future.

Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues. This research aims to improve the potential for estimating forest aboveground biomass (AGB) by feature selection based on a scattering mechanism and sensitivity analysis and utilizing a non-parametric model that combines the advantage of dual-frequency SAR data. By employing GF-3 and ALOS-2 data, this study explores the scattering mechanism within a coniferous forest by using results of target decomposition and the pixel statistics method. By selecting an appropriate feature (backscatter coefficients and polarization parameters) and using stepwise regression models and a non-parametric model (the random forest adaptive genetic algorithm (RF-AGA)), the results revealed that the RF-AGA model with feature selection exhibited excellent AGB estimation performance without obvious saturation (RMSE = 10.42 t/ha, R2 = 0.93, leave-one-out cross validation). The σHV, σVH, Pauli three-component decomposition, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of thee C-band and σHV, σVH,σHH, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of the L-band are suited for estimating the AGB of coniferous forests. Volume scattering was the dominant mechanism, followed by surface scattering, while double-bounce scattering had the smallest proportion. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in the C- and L-band SAR data for improved forest AGB estimation.

The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to discriminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwestern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algorithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an overall accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on dual-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests. [ABSTRACT FROM AUTHOR]

This work presents a technique to merge two Sentinel-1 image products of complementary polarimetric information (HH/HV and VH/VV) to derive pseudo-polarimetric features, such as polarimetric covariance, but also model-based and eigenvalue-based decompositions and an unsupervised Wishart classification of scattering types. The images were acquired within a 6-day period over Southern Germany and have been processed to mimic an actual quad-pol product. This was analyzed statistically, visually and within several classification processes to get an understanding of how well such a dataset depicts scattering mechanisms and other polarimetric features as inputs for land use and land cover mapping. A systematic comparison with the original dual-polarization product showed an increase in information content and largely feasible polarimetric features. Yet, especially the average Alpha angle was found to be biased and too high for some of the compared surfaces. Despite these inaccuracies, the polarimetric features turned out to improve potential land cover mapping as compared with backscatter intensities and dual-polarization features of the input products alone. Among the most significant variables related to land use and cover reported by an independent dataset, Entropy, the co-polarization ratio and the C22 element of the covariance matrix generated the strongest impact on the class separability, although misclassifications between physically related classes remain. Yet, the findings are encouraging concerning further investigation of the polarimetric potential to combine repeat-pass acquisitions of Sentinel-1 for a better description of more specific types of land cover.

Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition ( Δ t ). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI ≤ 0.7 and NDVI > 0.7 ). The main results showed that when the vegetation is moderately developed (NDVI ≤ 0.7 ), the C-band temporal variation remains sensitive to the soil moisture dynamics and the irrigation events could be detected. The C-VV signal decreases due to the drying out of the soil when the time difference between the S1 image and irrigation event increases. For well-developed vegetation cover (NDVI > 0.7 ), the C-band sensitivity to irrigation events becomes dependent on the crop type. For well-developed Gramineae grass with longs stalks and seedheads, the C band shows no correlation with Δ t due to the absence of the soil contribution in the backscattered signal, contrary to the legume grass type, where the C band shows a good correspondence between C-VV and Δ t for NDVI > 0.7. In contrast, analysis of the L-band backscattering coefficient shows that the L band remains sensitive to the soil moisture regardless of the vegetation cover development and the vegetation characteristics, thus being more suitable for irrigation detection than the C band. The L-HH signal over Gramineae grass or legume grass types shows the same decreasing pattern with the increase in Δ t , regardless of the NDVI-values, presenting a decrease in soil moisture with time and thus high sensitivity of the radar signal to soil parameters. Finally, the co-polarizations for both the C and L bands (L-HH and C-VV) tend to be more adequate for irrigation detection than the HV cross-polarization, as they show higher sensitivity to soil moisture values. [ABSTRACT FROM AUTHOR]

Many SAR satellites such as the ALOS-2 satellite and Sentinel-1A satellite can be used in Interferometric Synthetic Aperture Radar (InSAR) to identify landslides. As their wavelengths are different, they can perform differently in the same area. In this study, we selected the alpine canyon heavy forest area of the Baishugong–Shangjiangxiang section of the Jinsha River with a strong uplift of faults and folds as the study area. The Small Baseline Subset (SBAS)–InSAR was used for landslide identification to compare the reliability and applicability of L-band ALOS-2 data and C-band Sentinel-1A data. In total, 13 potential landslides were identified, of which 12 potential landslides were identified by ALOS-2 data, two landslides were identified by Sentinel-1A data, and the Kongzhigong (KZG) landslide was identified by both datasets. Then, the field investigation was used to verify the identification results and analyze the genetic mechanism of four typical landslides. Both the Duila (DL) and KZG landslides are bedding slip, while the Jirenhe (JRH) and Maopo (MP) landslides are creep–pull failure. Then, the difference between ALOS-2 and Sentinel-1A data on KZG landslide was compared. A total of 35,961 deformation points on the KZG landslide were obtained using ALOS-2 data, which are relatively dense. Meanwhile, a total of 7715 deformation points were obtained by Sentinel-1A data, which are relatively scattered and seriously lacking, especially in areas with dense vegetation coverage. Comparing the advantages of ALOS-2 and Sentinel-1A data and the identification results of potential landslides, the reliability and applicability of ALOS-2 data in the identification of potential landslides in areas with dense vegetation cover and complex geological conditions were confirmed from the aspects of vegetation cover, topography, field investigation, and comparative analysis of typical landslides. [ABSTRACT FROM AUTHOR]

Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately assess the damage characteristics. The buildings identified as Major damage in the Japanese damage certification survey contain damage with various characteristics. If Major damage is treated as a single class, the parameters of SAR images will vary greatly, and the relationship between building damage and SAR images would not be properly evaluated. Therefore, it is necessary to divide Major damage buildings into more detailed classes. In this study, the Major damage buildings were newly classified into five damage classes, to correctly evaluate the relationship between building damage characteristics and SAR imagery. The proposed damage classification is based on Japanese damage assessment data and field photographs, and is classified according to the dominant damage characteristics of the building, such as collapse and damage to walls and roofs. We then analyzed the backscattering characteristics of SAR images for each classified damage class. We used ALOS-2 PALSAR-2 images observed before and after the 2016 Kumamoto earthquake in Mashiki Town, where many buildings were damaged by the earthquake. Then, we performed the analysis using two indices, the correlation coefficient R and the coherence differential value γdif, and the damage class. The results indicate that the backscattering characteristics of SAR images show different trends in each damage class. The R tended to decrease for large deformations such as collapsed buildings. The γdif was likely to be sensitive not only to collapsed buildings but also to damage with relatively small deformation, such as distortion and tilting. In addition, it was suggested that the ground displacement near the earthquake fault affected the coherence values.

With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world's largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007–2009), 73 scenes of ALOS-2 data (2015–2020) and 284 scenes of Sentinel-1 data (2017–2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km2 in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from −20 to +20 mm/year during 2007–2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from −30 to 30 mm/year during 2015–2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV < −10 mm/year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters. [ABSTRACT FROM AUTHOR]

Objectives: The Sichuan Tibet Railway crosses the Jinsha River specifically the section from Xiongsong Town to Shadong Town, Gongjue County, Tibet Autonomous Region, in which exist strong topography variations and many potential geohazards, and hence it urgent to detect the potential geohazards in this region to ensure the railway safety. Methods: In this paper, 61 Sentinel -1 ascending, 53 Sentinel -1 descending and 7 advanced land observing satellite 2 (ALOS 2) ascending images are used to derive the annual mean deformation rates in the satellite radar line of sight (LOS) with two advanced InSAR (interferometric synthetic aperture radar) approaches, namely generic atmospheric correction online service for InSAR (GACOS) assisted InSAR Stacking and LiCSBAS. The three LOS annual mean deformation rate maps are then used to determine 2D surface movements (one along the slope and the other perpendicular to the slope) in the study region. Results: The comparison between GACOS assisted InSAR Stacking and LiCSBAS results show that they agree with each other with correlation coefficients over 0.92 for the LOS deformation rates and over 0.85 for the 2D deformation rates, suggesting the reliability of GACOS assisted InSAR Stacking. The maximum annual deformation rate of 163 mm/a can be observed in the slope direction and seven landslides (A, B, C, D, E, F, G) can be clearly identified, which in turn lays the foundation for future real-time monitoring. Based on the detailed analysis of the seven regions by using InSAR and optical interpretation results, it is found that the seven landslides had relatively obvious deformation. Landslides A, B, C and D are active landslides, which might cause the result of river blocking due to overall instability. At present, landslides E, F and G are still in the stage of slow deformation. Conclusions: This study also find that GACOS assisted InSAR Stacking can effectively remove long band and topographic related atmospheric delay error with the help of GACOS, which has the advantages of being simple, effective, fast, and easy to popularize and apply. GACOS assisted InSAR Stacking technology can be used to quickly identify potential landslide hazards. [ABSTRACT FROM AUTHOR]

Abstract We applied differential InSAR analysis to the Shiretoko Peninsula, northeastern Hokkaido, Japan. All the interferograms of long temporal baseline (~ 3 years) processed from SAR data of three L-band satellites (JERS-1, ALOS, ALOS-2) commonly indicate remarkable phase changes due to the landslide movement at the southeastern flank of Mt. Onnebetsu-dake, a Quaternary stratovolcano. The area of interferometric phase change matches to known landslide morphologies. Judging from the timing of the SAR image acquisitions, this landslide has been moving at least from 1993 to the present. Successive interferograms of 1-year temporal baseline indicate the temporal fluctuation of the landslide velocity. Especially for the descending interferograms, the positive line-of-sight (LOS) length change, which indicates large subsidence relative to the horizontal movement, is observed in the upslope section of the landslide during 1993–1998, while the negative LOS change is observed in the middle and the downslope section after 2007 indicating less subsidence. The landslide activity culminates from 2014 to 2017: the eastward and the vertical displacement rates reach ~ 6 and ~ 2 cm/yr, respectively. Utilizing high spatial resolution of ALOS and ALOS-2 data, we investigated velocity distribution inside the landslide. During 2007–2010, the eastward component of surface displacement increases toward the east, implying that the landslide extends toward the east. During 2014–2017, the vertical displacement profile exhibits spatially periodic uplift and subsidence consistent with surface gradient, which indicates the ongoing deformation driven by gravitational force. Heavy rainfall associated with three typhoons in August 2016 might have brought about an increase in the landslide velocity, possibly due to elevated pore-fluid pressure within and/or at the base of the landslide material. Also, annual rainfall would be an important factor that prescribes the landslide velocity averaged over 3 years.

Abstract Interferograms pertaining to large earthquakes typically reveal the occurrence of elastic deformations caused by the earthquake along with several complex surface displacements. In this study, we identified displacement lineaments from interferograms; some of which occur on the shallow section of seismogenic faults. However, such displacements are typically located away from the hypocenter, and they are considered as triggered shallow slips. We found that the triggered shallow slips had a varied nature, as follows. (1) No evidence has yet been obtained regarding the generation of large seismic motion via triggered shallow slips; thus, their occurrence is seldom considered a cause of major earthquakes. However, we found that a movement of triggered shallow slip associated with an M 6-class earthquake occurred on an active fault that has previously caused an M 7-class earthquake. (2) At certain locations, several parallel displacement lineaments have been discovered. During the Kumamoto earthquake sequence in 2016, the strain created by the main shock and the motion of triggered shallow slips coincided at a specific location, resulting in the creation of parallel triggered shallow slips by the main shock. Conversely, at another location, the movements of the main shock and triggered shallow slips did not match, since the main shock was simply a trigger, whereas the latter parallel triggered shallow slips are likely a means for releasing previously accumulated strain. (3) The fault scaling law—which states that the length and displacement of a fault are proportional—does not hold true for triggered shallow slips. However, the parallel triggered shallow slips show a relationship between horizontal spacing and its displacement. This may be attributed to immobility in deep locations. These results lead to the following conclusions. Large earthquakes tend to trigger shallow slips on the pre-existing faults. Subsequently, these triggered shallow slips release accumulated strain by causing fault motions, which in turn result in displacement lineaments. The occurrence of such passive faulting creates weak, mobile fault planes that repeatedly move at the same location. These triggered shallow slips cause the diversity among active faults as a result.

Abstract The cloud-free, wide-swath, day-and-night observation capability of synthetic aperture radar (SAR) has an important role in rapid landslide monitoring to reduce economic and human losses. Although interferometric SAR (InSAR) analysis is widely used to monitor landslides, it is difficult to use that for rapid landslide detection in mountainous forest areas because of significant decorrelation. We combined polarimetric SAR (PolSAR), InSAR, and digital elevation model (DEM) analysis to detect landslides induced by the July 2017 Heavy Rain in Northern Kyushu and by the 2018 Hokkaido Eastern Iburi Earthquake. This study uses fully polarimetric L-band SAR data from the ALOS-2 PALSAR-2 satellite. The simple thresholding of polarimetric parameters (alpha angle and Pauli components) was found to be effective. The study also found that supervised classification using PolSAR, InSAR, and DEM parameters provided high accuracy, although this method should be used carefully because its accuracy depends on the geological characteristics of the training data. Regarding polarimetric configurations, at least dual-polarimetry (e.g., HH and HV) is required for landslide detection, and quad-polarimetry is recommended. These results demonstrate the feasibility of rapid landslide detection using L-band SAR images.

Abstract Determining the fault parameters of an earthquake is fundamental for studying the earthquake physics, understanding the seismotectonics of the region, and forecasting future earthquake activities in the surrounding area. Dense crustal deformation data such as Interferometric Synthetic Aperture Radar (InSAR) are useful for fault parameter determination, but determining the dipping direction of a blind fault is often challenging when the size of the earthquake is not large (M

Built-up areas extraction and characterization from remote sensing images is essential for monitoring urbanization and the associated challenges. This work presents a novel integrated classification framework building on the symbolic machine learning classifier and fully polarimetric Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) to derive both the extent and vertical components of built-up areas from the same scene. It also explores the complementarity between ascending and descending orbits of PALSAR-2 for built-up areas detection. The experimental results in Chicago and Tokyo cities with different landscape and characteristics of built-up areas demonstrate that the proposed generic method can achieve three main challenges of urban remote sensing: 1) enabling automated delineation of built-up areas at a spatial resolution of 5 m with a balanced accuracy of 85% using globally available low-resolution training data, 2) assessing the density of building height class with a root mean square error of 0.25, 0.034, and 0.032 for the low-rise, mid-rise, and high-rise building density class, respectively, and 3) dealing with the scattering components of buildings with different orientation angles by combining data from ascending and descending orbits for enhanced mapping of built-up areas.

Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from synthetic aperture radar (SAR) data generally use airborne laser scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multitemporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using the three-parameter water cloud model (WCM) trained with field data from relatively small (0.1 ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a subtropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multitemporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions; however, using multitemporal data, these effects are mitigated. Using a multitemporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multitemporal acquisitions spanning 5 years are sufficient for improving AGB retrieval accuracy over the considered test site. Furthermore, we use allometry from colocated field plots and Global Ecosystem Dynamics Investigation (GEDI) L2A height metrics to produce GEDI-derived AGB estimates. Despite the limited co-location of GEDI and field data over our study area, within the period of interest, the preliminary analysis shows the potential of jointly using the GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval.

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.

The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to discriminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwestern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algorithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an overall accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on dual-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests.

SAR Interferometry (InSAR) has been proven to be effective for measuring landslides deformation. However, the InSAR application of landslide mapping is limited by the blind observation areas caused by SAR geometric distortion or vegetation decorrelation. We proposed an approach of landslide detection and mapping with a priori assessment of InSAR applicability, which can not only guide the selection of SAR data, but also prejudge the observable areas. Meanwhile, it can assist to analyze the reliability of landslide detection and help to reveal the reason of detection failure of occurred landslides. We employed this approach to evaluate the InSAR applicability over the whole Guizhou Province at first. Then, we took Shuicheng County as an example to conduct time-series InSAR landslide detection combined with a priori applicability. Six large active landslides were identified from the results of three SAR data stacks (ALOS-2 PALSAR-2 and ascending/descending Sentinel-1 from April 2017 to July 2019). The reliabilities of landslide detection results were analyzed and the possible reasons for the detection failure of Jicangzhen landslide were revealed. We inferred that the Jichangzhen landslide suddenly happened induced by short-term heavy rainfalls and presented no precursors, which cannot be captured by SAR satellites with intermittent observations. [ABSTRACT FROM AUTHOR]

Based on satellite images of two different wavelengths acquired by ALOS - 2 and Sentinel - 1, the authors used DInSAR two - track method and stacking technology to analyze and delineate the active landslide that is undergoing surface deformation in the central part of Maoxian County, Sichuan Province. First, in view of the long time baseline of the ALOS - 2 satellite and the small amount of image data, the two - track DInSAR method was used to detect landslide deformation. Secondly, on the basis of the short - time baseline multi - scene Sentinel -1 data, the stacking technology was used to detect landslide deformation. Finally, comprehensive analysis of the deformation rate results of the two data sets was conducted, and the potential landslide area in the middle of Maoxian County of Sichuan was delineated. The results show that the suspected active landslides in Baibu Village of Maoxian County and other places have obvious surface deformation, and the maximum absolute amount of radar line - of - sight deformation rate reaches 200 mm/a; combined with optical image characteristics and existing historical survey data, 8 active landslides were delineated, and the exploration result of two kinds of data shows that the detection results of the data can correspond to each other in the spatial distribution of 7 active landslides. Field surveys were conducted on 6 active landslides, and signs of ground deformation were found. The work in this paper shows that using a small number of long - band ALOS - 2 images and the DInSAR two - track method can detect more obvious landslide surface deformation in mountainous areas with certain vegetation coverage; when C - band Sentinel - 1 images are applied, accumulation of continuous multi - scene data is required; in addition, by applying time series analysis method, the detection effect is better than the two - track method using ALOS - 2 images. [ABSTRACT FROM AUTHOR]

Abstract We identified and analyzed surface displacements associated with the 2018 Hokkaido Eastern Iburi Earthquake in northern Japan using satellite radar interferograms from the Advanced Land Observing Satellite 2. The data generally show elastic deformation caused by the main earthquake as well as numerous complex surface displacements that cannot be explained by the motion of the seismic source fault. We identified three distinct phenomena: linear surface displacements representing secondary earthquake faults west of the epicenter, surface deformation caused by liquefaction in urban and coastal areas, and coherence changes in the interferograms due to landslides in mountainous areas and liquefaction in urban areas. The linear surface displacements show reverse fault motion with low dip angles and appear to be a geographic extension of known active faults; however, it is unlikely that these displacements were directly connected to the source fault of the main earthquake. Although there is no evidence that they generated strong seismic waves at the time of the main earthquake, there is a possibility that they represent active fault traces and could be the sources of large earthquakes in the future. Therefore, such linear surface displacements can be used to identify potentially dangerous hidden active faults. The interferograms reveal that liquefaction in urban areas occurred in low areas artificially filled during past residential development. Coherence-change maps drawn from the interferograms were useful for detecting liquefaction, but their high sensitivity limited their application for landslide detection in mountainous areas; the phase noise deviation method was more practical for purposes such as rapid response or mitigation. Our methods have the potential to allow improved mapping of local hazards in other areas and can be applied to urban planning and/or safety assessments.

Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition (Δt). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI ≤0.7 and NDVI >0.7). The main results showed that when the vegetation is moderately developed (NDVI ≤0.7), the C-band temporal variation remains sensitive to the soil moisture dynamics and the irrigation events could be detected. The C-VV signal decreases due to the drying out of the soil when the time difference between the S1 image and irrigation event increases. For well-developed vegetation cover (NDVI >0.7), the C-band sensitivity to irrigation events becomes dependent on the crop type. For well-developed Gramineae grass with longs stalks and seedheads, the C band shows no correlation with Δt due to the absence of the soil contribution in the backscattered signal, contrary to the legume grass type, where the C band shows a good correspondence between C-VV and Δt for NDVI > 0.7. In contrast, analysis of the L-band backscattering coefficient shows that the L band remains sensitive to the soil moisture regardless of the vegetation cover development and the vegetation characteristics, thus being more suitable for irrigation detection than the C band. The L-HH signal over Gramineae grass or legume grass types shows the same decreasing pattern with the increase in Δt, regardless of the NDVI-values, presenting a decrease in soil moisture with time and thus high sensitivity of the radar signal to soil parameters. Finally, the co-polarizations for both the C and L bands (L-HH and C-VV) tend to be more adequate for irrigation detection than the HV cross-polarization, as they show higher sensitivity to soil moisture values.

Synthetic aperture radar interferometry (InSAR) is the technique capable of monitoring active landslide in all-weather conditions and in a near real-time. In this study, ALOS-2 (L-band) and Sentinel-1 (C-Band) images were used to map potential areas prone to the occurrence of large-scale landslide disasters. Differential InSAR (D-InSAR) and multi-temporal techniques were applied to detect landslides in a part of Maoxian(a typical mountainous county in Southwest China), allowing the identification of 8 active landslides. The integration of local historical geological hazard data and local field exploration data allowed to conclude that the detected landslides found in Baibu village and other places were in the development stage. Deformation rate reached 200mm/yr along the line of sight direction. The results obtained by exploiting the two datasets correspond to each other in terms of spatial distribution and are consistent with the local field exploration results. L-band ALOS-2 SAR data proved to be very effective for detecting short-term and severe ground deformation, which is suitable for monitoring deformation in mountainous areas with a certain degree of vegetation coverage. [ABSTRACT FROM AUTHOR]