Synthetic Aperture Radar Datasets Research Articles

MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection.

In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.

Change detection in SAR images based on superpixel segmentation and image regression

Change detection (CD) is one of the most important application in remote sensing domain. The difference image (DI) generated by traditional change detection methods are sensitive to several factors, such as atmospheric condition changes, illumination variations, sensor calibration, and speckle noise, greatly affecting the detection performance. To avoid the aforementioned problem, in this paper, a novel approach based on superpixel segmentation and image regression is proposed to detect changes between bitemporal synthetic aperture radar (SAR) images. Specifically, the bitemporal images are firstly divided into a number of superpixel pairs under the guidance of segmentation result of a pre-DI. Next, each pixel in pre-event image is reconstructed utilizing its nearest neighbor to reduce the influence of noise. Then, a set of preselected unchanged sample are selected to learn the local regression model and to estimate the post-event image. After that, the final DI can be obtained by measuring the difference between estimated post-event image and the actual one. Finally, the fuzzy c-means (FCM) clustering algorithm is adopted to generate the binary change map. Adequate experiments on four SAR datasets have been tested, and the experimental results compared with the state-of-the-art methods have proved the superiority of the proposed method.

Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing

The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

SAR Target Classification Based on Sample Spectral Regularization

Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.

Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.

An Automatic High Confidence Sets Selection Strategy for SAR Images Change Detection

Change detection result is usually obtained by clustering or classifying; however, the spatial information of pixels is rarely considered during the classification process. In this letter, we propose a practical method to improve the performance of existing change detection algorithms on remote-sensing images without prior information. First, the existing detection result is regarded as an initial result. Second, it takes advantage of this initial result with neighborhood information of pixels to select the training data, then a random forest classifier is trained for precise classification. Finally, the median filtering is used to eliminate singular points for further improvement of detection performance. Corresponding experiments on three real synthetic aperture radar (SAR) data sets demonstrate the effectiveness of the proposed method.

A saliency-guided neighbourhood ratio model for automatic change detection of SAR images

ABSTRACT In this paper, a new approach to the saliency-guided neighbourhood ratio method has been proposed to change-detection of Synthetic Aperture Radar (SAR) images. Two methods, the saliency-guided and the neighbourhood ratio generate a vector as a feature to classify the image difference of SAR images into changed and unchanged classes by using a clustering method such as k-means clustering. Therefore, in this study, the vectors obtained from the saliency-based neighbourhood ratio method was used as separate properties in the k-means clustering algorithm to classify the area into two classes, changed and unchanged region. Furthermore, the saliency-guided method uses a threshold value to generate a saliency map that is selected by trial and error for each data set. In this study, the Otsu threshold histogram method was used to calculate the threshold, which leads to higher accuracy of the method and the automatic selection of the threshold at a higher rate. Moreover, in the neighbourhood ratio model, the probability distribution function image was used instead of the average ratio image. Next, the proposed method has been implemented in three SAR data sets of Bern, Ottawa, and Yellow River China as ground truth data to evaluate the accuracy of the model. The results of the proposed model show that the kappa coefficient (κ) of the Berne data set is increased from 0.8705 in the saliency-guided model and 0.8590 in the neighbourhood ratio method to 0.8821. Also, for the Ottawa data set, the κ of the proposed model has been raised by 0.1 and 0.3190 compared to the saliency-guided and neighbourhood ratio methods, respectively. Finally, the accuracy of the proposed model in the Yellow River data set, respectively, 0.45 and 0.57, is improved than saliency-guided and neighbourhood ratio models.

Feature-Enhanced Speckle Reduction via Low-Rank and Space-Angle Continuity for Circular SAR Target Recognition

With the development of synthetic aperture radar (SAR) system, automatic target recognition (ATR) has attracted wide attention in many decision-making tasks, in which an enhanced feature of SAR image is a powerful tool to improve the recognition accuracy. However, the presence of speckle noise and natural clutter inevitably contaminates SAR images and, thus, degrades image features. In this article, we explicitly address the speckle reduction problem for the circular SAR system, in which the motion of aircraft platform causes continuous angular variations so that different SAR images can be captured with the high interrelationship. By exploiting the underlying low-rank and continuous properties among different SAR images, a method called the $\ell _{p}$ -regularized low-rank and space-angle continuity extraction ( $\ell _{p}$ -LSCE) is proposed to suppress the noise and enhance the target feature. Taking into account the interrelationship between SAR images, we arrange the images in a 3-D tensor to investigate the space-angle continuity of the targets. Furthermore, we develop a robust $\ell _{p}$ -regularized scheme to incorporate the low-rank property of targets. Then, the joint optimization problem is solved via the framework of augmented Lagrange multiplier (ALM) with efficient computation of each ALM subproblem. The experimental results of circular SAR data sets of the moving and stationary target acquisition and recognition (MSTAR) and the VideoSAR demonstrate that the proposed method can efficiently despeckle SAR images with well-preserved target features, which is conducive to the improvement of ATR performance.

Characterizing the Great Lakes Coastal Wetlands with InSAR Observations from X-, C-, and L-Band Sensors

We investigated the potential of using Synthetic Aperture Radar (SAR) imagery from three different frequencies: X-, C-, and L-band, to characterize coastal wetlands in the Great Lakes. Three sets of SAR data acquired over the Bay of Quinte, Ontario, Canada between 2016 and 2018 from Radarsat-2, 2016 from TerraSAR-X, and 2018 from ALOS-2 satellites were processed using small baseline subset (SBAS) Interferometric SAR (InSAR) techniques to provide maps of surface changes in marshes and swamps. Results showed that SAR backscatter and coherence were sensitive to sensor characteristics (frequency, polarization, incidence angle, acquisition interval), changes in water level, and phenology. InSAR time series observations were evaluated using measurements from water level loggers based on correlation and root mean square error (RMSE) from a linear regression model. Correlation between InSAR measurements and water level changes in the field varied from −1 to 1 depending on the site, type of wetland vegetation, and incidence angle. Although results from some sensor modes provided good correlation (0.77–1) at a few locations, the low fringe rate and large RMSE between 4 and 64 cm indicated that InSAR observations of water level changes in the dynamic wetland environment were generally underestimated.

Recent Ground Subsidence in the North China Plain, China, Revealed by Sentinel-1A Datasets

Groundwater resources have been exploited and utilized on a large scale in the North China Plain (NCP) since the 1970s. As a result of extensive groundwater depletion, the NCP has experienced significant land subsidence, which threatens geological stability and infrastructure health and exacerbates the risks of other geohazards. In this study, we employed multi-track Synthetic Aperture Radar (SAR) datasets acquired by the Sentinel-1A (S1A) satellite to detect spatial and temporal distributions of surface deformation in the NCP from 2016 to 2018 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. During the InSAR monitoring period, the temporal pattern of land subsidence was dominated by a decreasing tendency and the spatial pattern of land subsidence in the coastal plain exhibited an expansion trend. Validation results show that the S1A datasets agree well with levelling data, indicating the reliability of the InSAR results. With groundwater level data, we found that the distribution of subsidence in the NCP is spatially consistent with that of deep groundwater depression cones. A comparison with land use data shows that the agricultural usage of groundwater is the dominant mechanism responsible for land subsidence in the whole study area. Through an integrated analysis of land subsidence distribution characteristics, geological data, and previous research results, we found that other triggering factors, such as active faults, precipitation recharge, urbanization, and oil/gas extraction, have also impacted land subsidence in the NCP to different degrees.

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multi-acquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3–11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Explainability of Deep SAR ATR Through Feature Analysis

Understanding the decision-making process of deep learning networks is a key challenge that has rarely been investigated for synthetic aperture radar (SAR) images. In this article, a set of new analytical tools is proposed and applied to a convolutional neural network (CNN) handling automatic target recognition on two SAR datasets containing military targets. First, an analysis of the respective influence of target, shadow, and background areas on classification performance is carried out. The shadow appears to be the least used portion of the image affecting the decision process, compared to the target and clutter, respectively. Second, the location of the most influential features is determined with classification maps obtained by systematically hiding specific target parts and registering the associated classification rate relative to the images to be classified. The location of the image areas without which classification fails is target type and orientation specific. Nonetheless, a strong contribution of specific parts of the target, such as the target top and the areas facing the radar, is noticed. Finally, results show that features are increasingly activated along the CNN depth according to the target type and its orientation, even though target orientation is absent from the loss function.

A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas

Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.

Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey)

This study presents a successful combination of different remote sensing data used in a long-term retrospective investigation of a large and destructive deep-seated, slow-moving landslide reactivated on 16 July 2015 in Devrek District (Zonguldak, Turkey). To this aim, Synthetic Aperture Radar (SAR) data were used for Interferometric SAR (InSAR) time-series analysis together with unmanned aerial vehicle (UAV) images and aerial photographs for digital photogrammetric analysis. The SAR dataset was divided into three sub-periods: 1) 1992–2001 for ERS-1 and ERS-2 satellites; 2) 2003–2010 for Envisat ASAR; and 3) 2014–2015 for Sentinel-1. Persistent Scatterers Interferometry (PSI) was applied for each sub-period. In total, 20 aerial photographs, dating from as early as 1944, were obtained, along with data from a UAV flight mission conducted on 23 June 2018. The historical aerial photographs revealed that the region has had a landslide problem since the 1940s. Between 1944 and 2018, a noticeable expansion of the settlement area towards the toe of the landslide was also observed. Aerial photographs (1984 and 2011) and UAV images (2018) were used to map landslide deformations using the M3C2 algorithm. Due to the high number of modelling errors, the 1984 and 2011 aerial photographs did not allow mapping of the landslide deformations. However, it was possible to determine them for the periods of 2011 and 2018. The M3C2 results between 2011 and 2018 were also compared to the PSI results, which were quite compatible with those obtained via photogrammetric methods. Moreover, two orthophotos belonging to 2011 and 2018 were used to reveal the horizontal displacement of buildings caused by the landslide. As a result, the complete investigation of the landslide performed in this study may serve to facilitate additional plans and strategies for prevention and mitigation of potential reactivations in the future.

Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies

Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.

Collaborative Attention-Based Heterogeneous Gated Fusion Network for Land Cover Classification

Existing land cover classification methods mostly rely on either the optical or synthetic aperture radar (SAR) features alone, which ignore the mutual complementary effects between optical and SAR sources. In this article, we compare the distribution histograms of deep semantic features extracted from optical and SAR modalities within land cover categories, which intuitively demonstrates that there are the large complementary potentials between the optical and SAR features. Therefore, we propose a novel collaborative attention-based heterogeneous gated fusion network (CHGFNet), which hierarchically fuses both optical and SAR features for land cover classification. More specifically, the CHGFNet consists of three main components: two-stream feature extractor, multimodal collaborative attention module (MCAM), and the gated heterogeneous fusion module (GHFM). Given optical and SAR patch pairs, two-stream feature extractor introduces multistage feature learning methodology to acquire discriminative optical and SAR features. Then, to explore the inherent complementarity between optical and SAR features, MCAM is embedded into CHGFNet, which provides an efficient stage to capture the correlation between optical and SAR features by jointly calculating the collaborative attention in joint feature space. Finally, to automatically learn the varying contributions of both optical and SAR features for classifying different land categories, GHFM is used to fuse both optical and SAR features. Extensive comparative evaluations demonstrate the advantages of CHGFNet within land cover classification over the state-of-the-art methods on three co-registered optical and SAR data sets.

The feasibility of assessing swell-based bathymetry using SAR imagery from orbiting satellites

Remote sensing instruments, especially synthetic aperture radar (SAR), have become valuable tools for monitoring coastal bathymetry in both space and time. In this study, we investigate the viability of using data from spaceborne SAR satellites to detect shallow water depths from swell patterns. Using data from four Sentinel-1A SAR images, we employed numerical simulations to quantify the sensitivity of the bathymetry results to different initial input parameters such as wavelength, swell period, and initial water depth. Our sensitivity experiments indicate that the swell period is most sensitive to the water depth, while the initial water depth has the lowest impact on the shallow water depth estimation. Once we quantified the sensitivity of the estimated water depth to these parameters, we calculated the minimum and maximum detectable shallow water depth ranges of six SAR orbiting satellites (L-band: ALOS-2, C-band: GF-3, RADARSAT-2 and Sentinel-1, X-band: TerraSAR-X and COSMO-SkyMed). Because of its single-polarization SAR data, the TerraSAR-X satellite is ultimately the best source of SAR data for swell-based bathymetry analyses. With an incidence angle of 20° and significant wave height of 0.6 m, the TerraSAR-X satellite has the largest theoretical depth detection range (with a minimum detectable depth of less than 2 m, and a maximum detectable depth of more than 200 m) for swell waves with a period of 15 s. The results of this study demonstrate that for low to moderate wave height, satellites with lower slant range to platform velocity ratios (RRV) can be successfully applied to the task of large-scale bathymetric mapping at shallow depths where there are weak tidal currents or where no recent or accurate ground truth data is available. Based on these results, we can make recommendations that will enable future users to get reliable, robust bathymetry information from their SAR data sets.

Monitoring active open-pit mine stability in the Rhenish coalfields of Germany using a coherence-based SBAS method

With the recent progress in synthetic aperture radar (SAR) technology, especially the new generation of SAR satellites (Sentinel-1 and TerraSAR-X), our ability to assess slope stability in open-pit mines has significantly improved. The main objective of this work is to map ground displacement and slope instability over three open-pit mines, namely, Hambach, Garzweiler and Inden, in the Rhenish coalfields of Germany to provide long-term monitoring solutions for open-pit mining operations and their surroundings. Three SAR datasets, including Sentinel-1A data in ascending and descending orbits and TerraSAR-X data in a descending orbit, were processed by a modified small baseline subset (SBAS) algorithm, called coherence-based SBAS, to retrieve ground displacement related to the three open-pit mines and their surroundings. Despite the continuously changing topography over these active open-pit mines, the small perpendicular baselines of both Sentinel-1A and TerraSAR-X data were not affected by DEM errors and hence could yield accurate estimates of surface displacement. Significant land subsidence was observed over reclaimed areas, with rates exceeding 500 mm/yr, 380 mm/yr, and 310 mm/yr for the Hambach, Garzweiler and Inden mine, respectively. The compaction process of waste materials is the main contributor to land subsidence. Land uplift was found over the areas near the active working parts of the mines, which was probably due to excavation activities. Horizontal displacement retrieved from the combination of ascending and descending data was analysed, revealing an eastward movement with a maximum rate of ∼120 mm/yr on the western flank and a westward movement with a maximum rate of ∼ 60 mm/yr on the eastern flank of the pit. Former open-pit mines Fortuna-Garsdorf and Berghein in the eastern part of Rhenish coalfields, already reclaimed for agriculture, also show subsidence, at locations reaching 150 mm/yr. The interferometric results were compared, whenever possible, with groundwater information to analyse the possible reasons for ground deformation over the mines and their surroundings.

Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan.

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120-140 tons/ha, with the region of high sensitivity around 50-130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.

A tutorial on tomographic synthetic aperture radar methods

Synthetic Aperture Radar (SAR) is a type of radar that is mounted on an airborne platform and aims to increase the resolution of the acquisitions by traveling over the target area. The signals acquired by SAR are two dimensional, but it is possible to create three dimensional models using signal processing methods. Tomography is the method of creating three dimensional models from multiple two dimensional signals. This method can be applied to SAR acquisitions to create three dimensional models of the landscape. The goal of this study is to create a comprehensive tutorial on how to work with SAR data, what toolbox to use for analysis, how to create a tomographic SAR dataset, and how to use different methods of spectral estimation for SAR tomography. In this work, first, we focus on the main problem of SAR tomography followed by SAR preprocessing steps, a brief description of data levels, and finally, we discuss the different spectral estimation methods used in SAR tomography along with examples at every step.