Phased Array L-band Synthetic Aperture Radar Research Articles

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

Earth observation approach for targeting stratiform deposit of manganese in central India

The stratiform manganese deposits, hosted within the Sausar Group of rocks, in Madhya Pradesh state of India, has undergone polyphase deformation and metamorphism. The region is an enterprising geosite to understand the lithological and tectonic control on manganese mineralization. We have carried out a conjugate remote sensing based analysis using Visible, Near Infrared and Short Wave Infrared bands (VIS-NIR-SWIR) of optical imagery from multiple sensors supplemented with analysis of L-band microwave data over the Balaghat district of Madhya Pradesh to delineate prospective areas for manganese exploration, supported by the lab and field-based evidence.The enhanced image products (i.e.band ratios, principal components) and spectral anomaly map derived from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2B multispectral bands are used to delineate the lithological contrast of the host rock and indicate key locales of mineralization. The geological structures interpreted using the merged product of Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR −1), Landsat 8 PC bands (2 & 3) and the processed Horizontal Gradient Magnitude (HGM) from advanced satellite gravity model (EIGEN-6C4), illustrates the structural control on the mineralization. The petrological details derived from the host rock samples shows microstructures like micro-folding, foliations, quartz grain elongation etc., thus representing the microscale deformations within the rock units occurring in the area.The lab-based spectral signatures of manganese-bearing host rock was resampled to image spectra and spectral anomaly map using ConstrainedEnergyMinimization(CEM), which shows good correlation with the existing mine locations.The information from remote sensing and lab analysis has been integrated thematically using the knowledge-based Weighted Sum Method (WSM) with existing mine locations as the training dataset. The derived mineral prospect map indicates several new areas of manganese prospectivity which can serve as a guide for detailed future explorations in the region .

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa

Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R2 (RMSE) = 0.39 (36.04%), and in 2015, R2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.

A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB) prediction from multiple satellite data and identify important predictors, but the role of FS in AGB estimation has not received sufficient attention. Here, we aimed to quantify the degree to which FS can benefit forest AGB prediction. To this end, we extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features. We then proposed an ensemble FS method that takes inro account the stability of an individual FS algorithm with respect to different training datasets used; the heterogeneity or diversity of different FS methods; the correlations between features and forest AGB; and the multicollinearity between the selected features. We further investigated the performance of the proposed stability-heterogeneity-correlation-based ensemble (SHCE) method for AGB estimation. The results showed that selected features by SHCE provided a more accurate prediction of forest AGB than existing state-of-the-art FS methods, with R2 = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg ha−1, MAE = 9.34 ± 0.09 Mg ha−1, and bias = 1.67 ± 0.11 Mg ha−1 at 90 m resolution. Boruta yielded comparable prediction accuracy of forest AGB, but could not identify the importance of features, which led to a slightly greater bias than the proposed SHCE method. SHCE not only ranked selected features by importance but provided feature subsets that enabled accurate AGB prediction. Moreover, SHCE provides a flexible framework to combine FS results, which will be crucial in many scenarios, particularly the wide-area mapping of land-surface parameters from various satellite datasets.

Estimating the Interferometric Vertical Wavenumber From Range Shifts

The estimation of the interferometric vertical wavenumber over sloped terrain is an integral step for many interferometric and tomographic synthetic aperture radar (SAR) applications. The state of the art for this estimation is to calculate the angle of incidence and the local terrain slope after geometric corregistration has been performed. In this letter, an alternative approach for estimating the vertical wavenumber is proposed that requires only the estimation of the range corregistration shifts. This considerably simplifies the calculation effort without compromising the estimation performance. The proposed approach is demonstrated on Advanced Land Observing Satellite (ALOS) phased array L-band synthetic aperture radar (PALSAR) data and compared against the conventional methodology.

InSAR-derived present-day crustal movement of Daliangshan on the southeastern margin of the Qinghai–Tibetan Plateau

SUMMARY The Daliangshan area of Sichuan, China, is located on the southeastern margin of the Qinghai–Tibetan Plateau. It marks the central section of the left-lateral Xianshuihe–Xiaojiang Fault System (XXFS), which plays a crucial role in accommodating clockwise vertical-axis rotation of the expanding Qinghai–Tibetan Plateau relative to the South China Block. The area is seismically and tectonically active, and three major active faults—the Anninghe fault (ANHF), Zemuhe fault (ZMHF) and Daliangshan fault (DLSF)—together accommodate a significant amount of left-lateral strike-slip motion. Here, we present a geodetic study of the Daliangshan area mainly based on satellite interferometric synthetic aperture radar (InSAR). We processed Advanced Land Observing Satellite-2 (ALOS2) Phased-Array L-band Synthetic Aperture Radar-2 (PALSAR2) imagery to reconstruct the present-day interseismic deformation field. We developed an optimal atmospherics phase correction strategy to overcome the significant artefacts caused by ionospheric and tropospheric. By applying a multitemporal analysis on corrected interferograms, for the first time we generated a spatially continuous deformation rate field for the Daliangshan area. The satellite line-of-sight (LOS) rate field agrees with Global Positioning System (GPS) and levelling data to ∼2 mm yr–1, confirming that our processing approach is suitable for use in heavily vegetated areas. The InSAR rate map reveals displacements along the central and southern segments of the DLSF and ZMHF. By applying an inversion based on the Okada model, we quantitatively estimated the kinematic parameters of fault segments. Assuming a simple rectangular fault plane for each fault segment and constrained with the prior knowledge of the left-lateral slip, we determined that the ZMHF has a slip rate of 5.06$\pm 0.99{\rm{\ mm}}\,{\rm{yr}}^{-1}$ with a rake angle of ∼$38^\circ $ gently dipping to the SW. The southern section of the DLSF has a rake of $- 48^\circ \,\,{\rm to}\,\, - 41^\circ $ with the total slip rate of $4.93 \pm 0.4{\rm{\ mm}}\,{\rm{yr}}^{-1}$, dipping towards to the NE at $63.50^\circ \pm 2.31^\circ $. The modelled 3-D rate field has been validated with GPS and levelling measurements. Additionally, the inversion model and strain field suggest that the middle section of the DLSF undergoes strong deformation. We considered the implications of three factors—oblique convergence, gravity-driven movement, and the ELIP beneath the Sichuan–Yunnan area—for complex 3-D velocities in the transitional Daliangshan area.

Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped-random volume over ground, the terrain-compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L-Band Synthetic aperture Radar (PALSAR) dataset.

A study of the serious conflicts between oil palm expansion and biodiversity conservation using high-resolution remote sensing

ABSTRACT Sustainable oil palm production is a key issue in global biodiversity conservation and sustainable development. As one of the world’s major vegetable oil crops, oil palm has expanded exponentially to meet increased demand over the past decades. However, previous monitoring and assessments of oil palm plantations were hampered because of the lack of high-resolution annual maps at the global scale. We produced annual oil palm plantation maps in 4 major producer countries (Indonesia, Malaysia, Thailand and Papua New Guinea) in Asia-Pacific from 2007 to 2018 at 100-m resolution using advanced remote sensing techniques with Phased Array L-band Synthetic Aperture Radar (PALSAR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. We uncover the global patterns of oil palm expansion and find that global oil palm expansion has a very high degree of potential conflict with local biodiversity. Globally, 99.9% of oil palm plantations overlapped with Conservation Priority Zones (CPZs) and oil palm plantations encroached on 231 protected areas. We suggest to incorporate the related issues into the Post-2020 Global Biodiversity Framework.

Ionospheric Sounding Based on Spaceborne PolSAR in P-Band

The signal of spaceborne low-frequency full-polarization synthetic aperture radar (full-pol SAR) contains abundant ionospheric information. Phased Array L-band Synthetic Aperture Radar (PALSAR) working in the L-band has been verified as an emerging ionospheric sounding technology. Aiming for a future P-band SAR system, this paper investigates the ability of the P-band SAR system in ionospheric one-dimensional and two-dimensional detection. First, considering different systematic error levels, the total electron content (TEC) retrieval in L/P-band is studied by using three typical full-pol SAR data sets based on a circular polarization algorithm. Second, the TEC data retrieved by SAR are fused with the ionosonde, and the joint retrieval of ionospheric electron density is performed. Results show that the P-band TEC retrieval is approximately twice as accurate as the L-band retrieval under the same conditions, and possesses excellent robustness. In addition, the TEC obtained by L/P-band SAR can be used to correct the electron density of the topside on the ionosonde. Results also show that compared with the topside correction accuracy of L-band SAR, that of the P-band SAR is improved by more than 20%. SAR has natural high-resolution characteristics and the P-band signal contains more obvious ionospheric information than the L-band signal. Therefore, future spaceborne P-band SAR has many advantages in two-dimensional fine ionospheric observation and one-dimensional electron density retrieval.

Addressing critical influences on L-band radar backscatter for improved estimates of basal area and change

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.

Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.

PolInSAR decorrelation-based decomposition modelling of spaceborne multifrequency SAR data

ABSTRACT The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features.

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.

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

BackgroundAboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.MethodsHere, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.ResultsThe proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha− 1, the mean absolute error ranged from 6.54 to 32.32 t∙ha− 1, the bias ranged from − 2.14 to 1.07 t∙ha− 1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha− 1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.ConclusionsValidation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

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.

A New Method for Constructing 3-D Crustal Deformation Field From Single InSAR-LOS Data

We propose a new method for retrieving 3-D deformation maps by combining the single Interferometric Synthetic Aperture Radar (InSAR)-line of sight (LOS) data and the physical property of crustal deformation, where the spatial coherence of the crustal deformation direction in elastic senses caused by fault slip is considered. First, fault slip and geometry parameters are inverted based on a simplified elastic dislocation model using the InSAR-LOS data. The continuous 3-D surface deformation direction vectors are then estimated by the model parameters. Finally, with the constraints of direction, InSAR-LOS is transformed into the 3-D components, and the 3-D coseismic deformation field is obtained. We implement the L-band SAR data from Advanced Land Observing Satellite (ALOS)/Phased Array L-band SAR (PALSAR) for the 2008 Wenchuan earthquake to testify our method. For the purpose of comparison, four representative models are constructed, ranging from rough to fine. The experimental results explain that the spatial consistency of our method agrees well with the field investigation and Global Navigation Satellite System (GNSS) observations and shows more application potentials than forward modeling.

Accuracy validation and bias assessment for various multi-sensor open-source DEMs in part of the Karakoram region

ABSTRACT The present study evaluate horizontal and vertical accuracy of seven open-source digital elevation models (DEMs) having moderate-to-high resolutions viz. 30 m Shuttle Radar Topography Mission (SRTM1), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), Advanced Land Observing Satellite World 3D (AW3D30), and Cartosat DEM (CartoDEM), 90 m TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), 12.5 m terrain corrected Phased Array L-band Synthetic Aperture Radar (PALSAR) from Advanced Land Observing Satellite (ALOS) and 8 m High Mountain Asia (HMA) over the rugged mountainous terrain of the Karakoram region. Horizontal accuracy (specified in x and y) assessed by referring photogrammetrically generated master DEM from Cartosat-1 revealed AW3D30 as the most consistent DEM with a slight shift of +2.80 m and −4.89 m in x and y direction, respectively. However, vertical accuracy analysis showed that both HMA and AW3D30 DEMs are quite close to each other with MAE of 3.01 m and 3.46 m, RMSE of 5.6 m and 7.5 m, and NMAD of 4.09 m and 5.5 m, respectively. We also examined the influence of slope on DEM errors and associated elevation-dependent bias over non-glaciated surface which can be valuable input for geodetic mass balance estimations.

Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

Mangrove forests in the Niger Delta are very valuable, providing ecosystem services, such as carbon storage, fish nurseries, coastal protection, and aesthetic values. However, they are under threat from urbanization, logging, oil pollution, and the proliferation of the invasive Nipa Palm (Nypa fruticans). However, there are no reliable data on the current extent of mangrove forest in the Niger Delta, its rate of loss, or the rate of colonization by the invasive Nipa Palm. Here, we estimate the area of Nipa Palm and mangrove forests in the Niger Delta in 2007 and 2017, using 567 ground control points, Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat and the Shuttle Radar Topography Mission Digital Elevation Model 2000 (SRTM DEM). We performed the classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) methods. The classification results showed SVM (overall accuracy 93%) performed better than ML (77%). Producers (PA) and User’s accuracy (UA) for the best SVM classification were above 80% for most classes; however, these were considerably lower for Nipa Palm (PA—32%, UA—30%). We estimated a 2017 mangrove area of 801,774 ± 34,787 ha (±95% Confidence Interval) ha and Nipa Palm extent of 11,447 ± 7343 ha. Our maps show a greater landward extent than other reported products. The results indicate a 12% (7–17%) decrease in mangrove area and 694 (0–1304)% increase in Nipa Palm. Mapping efforts should continue for policy targeting and monitoring. The mangroves of the Niger Delta are clearly in grave danger from both rapid clearance and encroachment by the invasive Nipa Palm. This is of great concern given the dense carbon stocks and the value of these mangroves to local communities for generating fish stocks and protection from extreme events.

Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images

Soil organic carbon (SOC) is the largest carbon pool and a key property of ecosystems. Compared with traditional field surveys, remote sensing (RS) represents a more efficient approach to mapping SOC, especially in larger-scale areas. Hyperspectral imagery provides a great potential for SOC prediction, but predicting the SOC content at a regional scale for a given year remains a great challenge. Multispectral RS images (e.g., Landsat images) with middle spatial resolution can be used as an alternative to predict SOC amongst other images. However, multispectral images are commonly affected by cloud cover and lack information on the shallow soil surface and soil surface with vegetation. By contrast, synthetic aperture radar (SAR) images can capture shallow surface information, penetrate through vegetation and are not affected by cloud cover. In this study, a new approach was evaluated for the prediction of SOC content by integrating Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) images. Mainland Spain (hereafter Spain) was used as the study site. Soil samples from the Land Use and Coverage Area frame Survey (LUCAS) European topsoil dataset in 2009 were utilized to fit a random forest (RF) model of the relationships between SOC content and several covariates in Spain. SOC prediction using only the Landsat TM image and only the ALOS PALSAR image was established as benchmarks to verify the efficiency of the proposed approach. The results for Spain showed that the integrated approach holds the highest prediction accuracy, with R2, RPD and RMSE of 0.59, 1.98 and 9.27 g kg−1, respectively. The correlations between the SOC content and various covariates were investigated and discussed, and the derived indices presented high relationships with SOC. The proposed method can accurately map the SOC content covering a large area by combining spectral information on land cover from the Landsat TM and topsoil information from the ALOS PALSAR. Land covers and elevation are closely related to SOC prediction, where lands with high vegetation canopy density have high SOC content, and the SOC content in areas with slopes within 15° is mainly concentrated between 3 and 26 g kg−1.

Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data

Landfast sea ice (fast ice) is an important feature prevalent around the Antarctic coast, which is affected by climate change and energy exchanges with the atmosphere and ocean. This study proposed a method for detection of the West Antarctic fast ice using the Advanced Land Observing Satellite Phased Array L-band SAR (ALOS PALSAR) images. The algorithm has combined image segmentation, image correlation analysis, and machine learning techniques (i.e., random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)). We used SAR images with a baseline of 5 days that are not in the same orbit but overlap each other as overlaps between swaths in adjacent orbits are often available in the polar regions. The underlying assumption for the proposed fast ice detection algorithm is that fast ice regions in SAR images with a time interval of 5 days are highly correlated. The object-based approach proposed in this study was well suited to high-resolution SAR images in deriving spatially homogeneous fast ice regions. The image segmentation results using the optimized parameters showed a distinct difference in the backscatter temporal evolution between fast ice and pack ice regions. Correlation and STD of backscattering coefficients were found to be the most significant variables for the object-based fast ice detection from two temporally separated images. In overall, the quantitative and qualitative evaluation demonstrated that the algorithm was an effective approach to detect fast ice with high accuracies. The models well detected various fast ice regions in the West Antarctica but misclassified some objects. The misclassifications occurred toward the edge of fast ice regions with relatively rapid changes in backscattering between both data acquisitions. On the other hand, few fast ice objects were misclassified as uniform backscattering over time occurred by chance on very small objects far from the coast. Very old multi-year fast ice regions with high backscattered signals were also a source for some misclassifications. This may be due to the sensitivity of L-band to snow structure to some extent and a thinner ice over the region with either ice growth (no deformation) or closing (slight deformation) between both images. Heavy snow load on the ice could be another error source for some misclassification as well. The approach allowed for the reliable detection of fast ice regions by using L-band SAR images with a small local incidence angle difference.