Band Synthetic Aperture Radar Research Articles

Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.

Impact of DEMs for Improvement Sentinel 2 Lithological Mapping Utilizing Support Vector Machine: A Case Study of Mineralized Fe-Ti-Rich Gabbroic Rocks from the South Eastern Desert of Egypt

Fused remote sensing datasets have greatly contributed to enhancing lithological targets and providing significant information for mineral exploration. For instance, multispectral datasets can discriminate rock units through their unique spectral signatures. Digital Elevation Models (DEMs) could be an effective tool boosting lithological discrimination based mainly on their topographic variations. Consequently, the current study applied the power of the support vector machine (SVM) algorithm and the integrated Phased Array L-type band Synthetic Aperture Radar (PALSAR) DEM and multispectral Sentinel 2 data to: 1—Create an updated lithological map of a poorly mapped arid terrain (Khashir-Ras El-Kharit district, Eastern Desert of Egypt), and 2—Compare and assess the distribution of ferrogabbros with the aim of improving the localization of these rock bodies and investigating their mineral content. Our results enhanced the lithological mapping and revealed a better generalization of mineralized ferrogabbros when the input was a fused DEM with Sentinel 2 compared to the salt and pepper effect observed when adopting the Sentinel 2 solely as the input data. Additionally, the current research strongly recommends detailed exploration programs for Fe-Ti oxide ores within the gabbroic rocks delineated through the resultant thematic map. The ferrogabbros were subjected to a comprehensive analysis involving whole rock geochemistry, XRD, EDX, and light-reflecting investigations, which revealed the existence of magnetite, titanomagnetite, and hematite. The titanomagnetite crystals display subhedral morphology and exhibit fine- to medium-grained surfaces with irregular shapes. X-ray diffraction (XRD) analysis revealed the presence of magnetite and hematite in the concentrated Fe-Ti oxides. Additionally, the whole rock geochemistry highlighted the origin of the ferrogabbros and their proposed tectonic setting as an alkaline to calc-alkaline magma type that developed in a continental arc setting.

Interoperability of -Band Sentinel-1 SAR and GRACE Satellite Sensors on PSInSAR-Based Urban Surface Subsidence Mapping of Varanasi, India

The Sentinel-1 is an active synthetic aperture radar (SAR) satellite with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -band SAR sensor operating at a center frequency of 5.405 GHz and a wavelength of 5.55 cm. With the availability of freely available SAR datasets from Sentinel-1, the persistent scatterer SAR interferometry (PSInSAR)-based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over-exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the Gravity Recovery and Climate Experiment (GRACE) satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface line of sight (LOS) displacement from Sentinel-1, with the average groundwater fluctuations from the GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values to be 0.63 and 0.65 for VV and VH polarizations, respectively, with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of a gravimetric anomaly due to a decrease in groundwater level was carried out for Varanasi city. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> , mean absolute error (MAE), and root-mean-square error (RMSE) were observed to be 0.84, 1.23, and 2.4, respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.

Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover

Abstract. The European Space Agency SnowSAR instrument is a side-looking, dual-polarised (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from various sizes of aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland, for a single flight mission in March 2011 and 12 missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones representing a montane valley, Alpine tundra and a glacier environment. In Canada, a total of two missions were flown in March and April 2013 over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data and coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record, the datasets described here are deposited in a permanent data repository (https://doi.org/10.1594/PANGAEA.933255, Lemmetyinen et al., 2021).

Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region

Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests’ AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (&lt;1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RIRMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests.

Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images

Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010.

SAR Image Fusion Classification Based on the Decision-Level Combination of Multi-Band Information

Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach.

Contribution of L band SAR data for identification of buried/paleochannels in Jaisalmer region of Rajasthan, India

Imaging of the subsurface in the arid area is one of the well-known application of the synthetic aperture radar (SAR) technology due to the deeply penetration of the longer wavelengths in the sand. In this study, we have investigated the buried/paleochannels signatures in the Jaisalmer, Rajasthan, India using the penetration capabilities of the Microwave data particularly at L band. High resolution Advanced Land Observing Satellite (ALOS) data has been used for the subsurface understanding in the desert area of the Jaisalmer, India. Here, around 20 km northeast direction from the main city, we traced the channels concealed in the Aeolian sediments around Jaisalmer. The paleochannel system drains in the southwest while the main channel approach northward. After the identification of the paleochannel though SAR technology, a ground base technique called Ground penetrating radar (GPR) was used for the verification of the subsurface penetration results. Authentication has been done by using the systematic ground investigation to collect scientific evidence in the Jaisalmer area. Moreover, these results offer new insight about the identification of buried channels in the paleoenvironment context in the Thar desert area.

Identifying Soil Freeze/Thaw States Using Scattering and Coherence Time Series of High-Resolution C-Band Synthetic Aperture Radar in the Qinghai-Tibet Plateau

The soil freeze/thaw (F/T) cycles play an important role in the climate system and human activities. However, the harsh environment in the Qinghai-Tibet Plateau (QTP) poses great challenges for both in-situ observation and remote-sensing monitoring of the soil F/T process. In this article, the time series of scattering and coherence of the high-resolution Sentinel-1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-</i> band synthetic aperture radar (SAR) is analyzed to identify the soil F/T state. The time series of scattering, including intensity and decomposition parameters, and coherence, are analyzed based on three typical landcover types (i.e., desert, grassland, and meadow) in the QTP. They are given the mathematical description by second-order and fourth-order Fourier functions, respectively. Based on Fourier functions, the initial F/T time points of the soil are detected in each pixel to draw the F/T map of the entire study area. The experiment results are cross-validated with the initial F/T time points of the soil calculated from the MODIS land surface temperatures, showing that the differences in days are less than one revisit cycle of Sentinel-1 (i.e., 12 days). Furthermore, the possible impacts of environmental factors acquired from the Wudaoliang meteorological station, including air temperature, ground surface temperature, snow depth, and precipitation, on scattering and coherence are discussed. This study explores that Sentinel-1 has great potential for soil F/T monitoring in the QTP, which can indicate F/T states of the surface soil as well as F/T information of the deeper soil with a high spatial–temporal resolution.

Comparison of SAR and CYGNSS Surface Water Extent Metrics

Many sensors are suitable for accurate delineation of open water extent, but in vegetated environments, the vegetation canopy can obscure the presence of standing water from detection. Detecting inundation extent in these vegetated environments is especially critical for identifying flooding extent where surface water may exceed flood boundaries and extend into forests surrounding nearby lakes and streams. Regular and timely observations of water surfaces by optical sensors can be impeded by both cloud cover and by vegetation. Here, two microwave techniques for identifying inundation extent will be investigated and compared: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band global navigation satellite systems reflectometry (GNSS-R) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> - and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> -band synthetic aperture radar (SAR); and will confirm that there are correspondences between metrics derived from GNSS reflected signals and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band SAR to inundated area, including wetlands covered by vegetation.

Preliminary analysis of experimental cross-polarization images from polarimetric aircraft synthetic aperture radar of P-band

An analysis of polarimetric synthetic aperture radar (SAR) space observations usage for measuring Faraday rotation angle of plane-polarized electromagnetic radiation on the satellite-Earth-satellite route and calibrating the radar is performed. It is shown that technical characteristics of SAR, the radar movement during the survey and features of electromagnetic waves reflection from the rough Earth’s surface, as well as small-scale ionospheric inhomogeneities affect the results of comparison the radar images at HV and VH cross-polarizations. As a result, there is a problem of SAR signal polarization plane Faraday rotation angle measuring accuracy, which is needed for proper calibration of linearly polarized polarimetric long-wave band SAR using methods proposed by soviet and foreign specialists. Currently, there are no studies of Earth’s surfaces reflection properties carried out by means of space-based long-wave band polarimetric synthesized aperture radars at HV and VH polarizations. At the same time, the authors are not aware of any works, in which the reciprocity principle of cross-polarized HV and VH radar images is analyzed. An attempt to use aircraft P-band radar images at HV and VH polarizations for studying the reciprocity principle is performed in this work.

A Method to Assess the Performance of SAR-Derived Surface Soil Moisture Products

Synthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C -band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C -band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C -band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged.

Surface Soil Moisture Retrievals Under Forest Canopy for $L$-Band SAR Observations Across a Wide Range of Incidence Angles by Inverting a Physical Scattering Model

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

Ionospheric Phase Compensation for InSAR Measurements Based on the Faraday Rotation Inversion Method.

The ionospheric error can significantly affect the synthetic aperture radar (SAR) signals, particularly in the case of L band and lower frequency SAR systems. The ionospheric distortions are mixed with terrain and ground deformation signals, lowering the precision of the interferometric measurements. Moreover, it is often difficult to detect the small-scale ionospheric structure due to its rapid changes and may have more influence on ionospheric phase compensation for InSAR measurements. In this paper, we present a Faraday rotation (FR) inversion method and corresponding procedure to compensate the ionospheric error for SAR interferograms and to detect the variations of small-scale ionospheric disturbances. This method retrieves the absolute total electron content (TEC) based on the FR estimation and corrects the ionospheric error for synthetic aperture radar interferometry (InSAR) measurements by transforming the differential TEC into the ionospheric phase. In two selected study cases, located in high latitude and equatorial regions where ionospheric disturbances occur frequently, we test the method using the Phased Array L-band Synthetic Aperture Radar (PALSAR) full-polarimetric SAR images. Our results show that the proposed procedure can effectively compensate the ionospheric phase. In order to validate the results, we present the results of ionospheric phase compensation based on the split-spectrum method as a comparison to the proposed method. To analyze the ability of our proposed method in detecting small-scale ionospheric disturbances, TEC derived from FR estimation are also compared with those derived from the global ionosphere maps (GIM). Our research provides a robust choice for the correction of ionospheric error in SAR interferograms. It also provides a powerful tool to measure small-scale ionospheric structure.

Synergistic use of ASTER, L-band ALOS PALSAR, and hyperspectral AVIRIS-NG data for exploration of lode type gold deposit – A study in Hutti Maski Schist Belt, India

In this study, we have explored the utility of advanced hyperspectral, dual-polarized microwave, and advanced multispectral data to delineate potential areas of mineralization associated with the lode type gold deposit in Hutti-Maski Schist Belt Karnataka, India. We derived different image-enhanced products using visible-near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate major rock types of gold-bearing greenstone belt such as metabasalt, amphibolite, younger alkali feldspar rich granite, granodiorite, and tonalitic-granodioric gneiss (TTG). The principal component (PC) image composite of ASTER VNIR-SWIR bands was not only used to distinguish different granitoids but also confirmed their sharp instructive nature of contact with the greenstone rocks. We also used an index image composite using ASTER SWIR bands to discriminate granitoid, basalt and amphibolite. We could demarcate few lineaments extending through the mineralized zone (e.g., Hutti, Heera-Buddine mines, etc.) using the fused products of ASTER and two polarization channels of the Phased-array L band Synthetic Aperture Radar (PALSAR) sensor of the Advanced Land Observation Satellite (ALOS). All these image-enhanced products are validated using the reference geological map of the study area and field data collected along selected field -traverses. The mineralogical and petrographical data of the altered rocks from the study site revealed the formation of calcite, scapolite, and other minerals. Represenattive reflectance spectrum of the altered rock was used to spatially delineate the surface distribution of the patchy and small alteration zones using airborne hyperspectral data of the Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) sensor. In this regard, a unified spectral map of altered rocks was derived by integrating two alteration maps prepared independently using two different partial sub-pixel algorithms (constrained energy minimization and matched filtering). Occurrences of altered rocks in the vicinity to favorable geological lineaments and their spatial proximity to metavolcanic-younger felsic intrusive contacts suggest their importance as the potential sites for detailed exploration.

The Tangential Velocity MTI Algorithms in Space-borne Systems for Remote Sensing of the Earth

In this paper, the problem of moving target indication (MTI) using synthetic aperture radar (SAR) is considered. The focus of the article is the tangential component of velocity. Two tangential velocity MTI algorithms are considered. The first algorithm uses two apertures with various synthetic time of the radar image (AVST algorithm), and the second uses two apertures displaced along trajectory (ADAT algorithm). The structure of the MTI system based on the analysis of phase and amplitude radar images is considered. For S band and X band SAR, the phase change in the trajectory signal of a moving target, the effects of shift and bifurcation of target responses on the radar image are analyzed in detail. It was found that the AVST algorithm has a small working range of unambiguous velocity estimate (up to ±10 m/s). It is shown that the ADAT algorithm has a higher quality of work in a wide velocity range and can effectively suppress the signals of stationary objects by 20…30 dB. The obtained characteristics allow us to make demands on the parameters of space-borne systems for remote sensing of the Earth and processing systems.

Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

Priority Analysis of Remote Sensing and Geospatial Information Techniques to Water-Related Disaster Damage Reduction for Inter-Korean Cooperation

The social and economic harm to North Korea caused by water-related disasters is increasing with the increase in the disasters worldwide. Despite the improvement of inter-Korean relations in recent years, the issue of water-related disasters, which can directly affect the lives of people, has not been discussed. With consideration of inter-Korean relations, a government-wide technical plan should be established to reduce the damage caused by water-related disasters. Therefore, the purpose of this study was to identify remote sensing and GIS techniques that could be useful in reducing the damage caused by water-related disasters while considering inter-Korean relations and the disasters that occur in North Korea. To this end, based on the definitions of disasters in South and North Korea, water-related disasters that occurred during a 17-year period from 2001 to 2017 in North Korea were first summarized and reclassified into six types: typhoons, downpours, floods, landslides, heavy snowfalls, and droughts. In addition, remote sensing- and GIS-based techniques in South Korea that could be applied to water-related disasters in North Korea were investigated and reclassified according to applicability to the six disaster types. The results showed that remote sensing and other monitoring techniques using spatial information, GIS-based database construction, and integrated water-related disaster management have high priorities. Especially, the use of radar images, such as C band images, has proven essential. Moreover, case studies were analyzed within remote sensing- and GIS-based techniques that could be applicable to the water-related disasters that occur frequently in North Korea. Water disaster satellites with high-resolution C band synthetic aperture radar are scheduled to be launched by South Korea. These results provide basic data to support techniques and establish countermeasures to reduce the damage from water-related disasters in North Korea in the medium to long term.

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

New quantitative and semiautomated methods for analyzing oil slick evolution using a time series of $L$ -band synthetic aperture radar (SAR) images with short repeat time are developed and explored. In this study, two methods that are complementary in terms of identifying temporal changes within an oil slick are presented. The two methods reflect two ways of evaluating the oil slicks. The first method identifies regions within the slick that show persistently high damping ratio (the contrast between clean sea and oil intensity), using higher damping values as a proxy for increasing oil thickness. This method also weights the age of the scenes as the algorithm incorporates new images. The second method outputs the short-term drift pattern and the changes in the damping ratios and copolarization ratios between two scenes, proxies for thickness, and emulsification. Both methods can aid in identifying regions of high priority for oil recovery. Due to the simplicity of the methods, they can be adapted to time-series data from different types of sensors, e.g., optical and SAR imagery. The methods are demonstrated on three $L$ -band uninhabited aerial vehicle SAR UAVSAR time series acquired in November 2016 over a persistent seep in the Mississippi Canyon Block 20 of the Gulf of Mexico. The results of the two methods clearly show the movement and the weathering of the oil as a function of both time and location.

Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

The European Space Agency’s (ESA) “SAR for REDD” project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C- band SAR can compensate for the L-band’s deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4–5 percent points and 1–2 percent points for C- and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C- and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program’s and JAXA’s ALOS-1/2 based global FNF maps.