ALOS PALSAR Images Research Articles

Investigation of Ionospheric Disturbance in ALOS PALSAR Images Using Machine Learning

Investigation of Ionospheric Disturbance in ALOS PALSAR Images Using Machine Learning

Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information

Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity.

Deriving Mining-Induced 3-D Deformations at Any Moment and Assessing Building Damage by Integrating Single InSAR Interferogram and Gompertz Probability Integral Model (SII-GPIM)

It is necessary to timely and accurately estimate the surface deformations in mining areas, especially the three-dimensional (3D) deformations during surface movement. At present, nearly all mining-induced 3D deformations retrieved by interferometric synthetic aperture radar (InSAR) pertain to the SAR imaging interval. Research on progressive 3D deformations during surface movement is limited, and the existing approaches are unsatisfactory in practical engineering. Aiming at these challenges, we proposed a novel method for deriving mining-induced 3D surface deformations at any moment by integrating single InSAR interferogram (SII), the Gompertz time function, and the probability integral model (PIM), named the SII-GPIM method. We established an inversion approach for GPIM parameters and derived the mining-induced 3D surface deformations at any moment. Subsequently, we conducted experiments considering two ALOS PALSAR images in the Huaibei mining area. The accuracy of the proposed method was evaluated in subsidence, tilt, curvature, horizontal displacement, and horizontal strain. Compared with existing methods, the SII-GPIM method is state of the art. Additionally, we assessed the building damage, performance of parameter inversion, and method generality. The results demonstrated that the proposed method can accurately determine the mining-induced 3D surface deformations and deformation level at any moment under different geological mining conditions. Moreover, accurate GPIM parameters can be acquired with only two SAR images and traditional measurement is nearly not required. Consequently, the SII-GPIM method owns great value for improving economic efficiency, assessing building damage, and restoring the ecological environment in the mining area.

Combining Microwave and Optical Sensor to Improve Post Cyclone Affected Area Mapping

Storm surge was adversely affected the life of people and property of coastal region of Bangladesh due to cyclone Roanu on 22 May 2016. It is important to delineate this surge extent and pattern which helps in the vulnerability assessment for relief and rehabilitation purposes. Optical sensor is not enough for outlining the affected areas. Because during cyclone, bad weather (cloud) usually prevails over the swamped area. Microwave remote sensing, can solve the problem because the radar pulse can penetrate cloud cover and it has ability to sharply distinguish between land and water. Combination of microwave and optical sensor found very useful to mark out storm surge inundated area. A change detection approach was implemented through the analysis of pre and post cyclone period. In this approach Landsat8-OLI image have been used for before cyclone and ALOS-PALSAR image have been used in after cyclonic period. Combining the PALSAR and OLI imagery using fusion method allows for the accurate depiction of such kind of violence surge extend. The study reveals that about 497698 (ha) areas have been affected due to storm surge during cyclone Roanu.
 Environ. Sci. & Natural Resources, 12(1&2): 1-9, 2019

RATIO-BASED SIMILARITY CRITERIA FOR POLARIMETRIC SAR IMAGE

Abstract. Dealing with multi-look polarimetric synthetic aperture radar (PolSAR) images requires averaging several independent looks to generate a sample covariance matrix of similar target scattering vectors. Along this, estimation of optimal similarity between target scattering vectors is still an open issue. In the literature, this intrinsic task has been mainly addressed in the information-based, geometric-based and detection-based frameworks. However, the derived measures mainly rely on the model assumption such as fully developed speckle and circular complex Gaussian distribution of the scattering vectors, which may not be held in high-resolution images of urban environments. To cope with this possible issue a discriminative model-free measure is proposed, where the similarity of target scattering is computed in the framework of non-local or patch based algorithm. In particular, the discriminative measure is constructed using the ratio between two pre-estimated covariance matrices of the scattering vectors. Experimental validation of the proposed measure is provided using ALOS-PALSAR image and compared with existing criterions in the literature.

Synergistic utilization of optical and microwave satellite data for coastal bathymetry estimation

The present study adopts a multi-sensor approach that utilizes optical remote sensing and Synthetic Aperture Radar (SAR) data complimentarily for coastal bathymetry estimation. The depths between 0–15 m are derived from Landsat 8 OLI imageries, by using the machine learning approach of Support Vector Regression (SVR); and high accuracies with RMSEs ranging between 0.17 m−0.95 m are obtained for the chosen study regions. Further, ALOS PALSAR images are used to obtain depths beyond 15 m, by applying the Wave Based Approach (WBA) which is based on the wave transformation and linear dispersion principles. The estimated depths are compared with the bathymetry obtained from admiralty charts or GEBCO and RMSEs between 0.83–1.34m are observed. Finally, the outputs of the SVR and WBA are combined to derive depths between 0–50 m at 75 m spatial resolution. The resultant depth maps agree with those obtained from GEBCO, with an additional advantage of higher accuracy in the near-shore region.

Carbon Stocks Mapping of Mangrove Forest in North Buton Indonesia using Combination of Landsat and ALOS PALSAR Images in the Perspective of Climate Change Mitigation

Mapping of carbon stocks, distribution and its changes become one of the important strategies in climate change mitigation. This study aims to map the carbon stocks of mangrove forests and analyze the changes using a combination of Landsat and ALOS PALSAR images in North Buton, South East Sulawesi Province, Indonesia. Carbon stocks in mangrove forests are known stores several times more than forests in the mainland. Mangrove forest at coast of North Buton is one of the largest area of mangrove forest in south east Sulawesi. The research conducted using backscatter coefficient of HH and HV polarization from ALOS PALSAR and Normalized Difference Vegetation Index (NDVI) from Landsat. The results showed that Landsat image provided information about the distribution, density and change of the carbon stocks during year 2001-2017 while ALOS PALSAR provided the volume of carbon stocks in this region.

Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

ABSTRACTThe complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.

Application of Remote Sensing and Ground Penetrating Radar for Evaluating the Groundwater Potentiality of West Qena Area, Egypt

Mapping the structural lineaments/fractures is of great importance for evaluating the groundwater potentiality, especially in hard rock terrains where they play as conduits and control the hydrogeological processes. This study demonstrates the efficiency of remote sensing imagery for mapping the lineaments in the Western Limestone Plateau and the low desert alluvial floodplain of the Nile west of Qena City. Different remote sensing data were used, including the SRTM DEM, Landsat-8, ALOS/PALSAR, and Sentinel-1 InSAR coherence images. They were processed to enhance the edge linear features by applying the hillshade algorithm on the DEM using ArcMap, the Sobel directional filter on the Landsat-8 image using ENVI, and the Pauli classification on the ALOS PALSAR image using SNAP. The lineaments were extracted from the processed images through on-screen digitizing in the ArcMap to produce the final lineament map. The lineaments have been confirmed from the Sentinel-1 InSAR coherence image. Moreover, GPR profiles have been acquired and carried out at discrete localities at the low desert alluvial floodplain. The extracted lineaments were statistically analyzed, and the results were displayed in the form of a rose diagram and a lineaments density map. The rose diagram has indicated that the area is affected by fractures of different lengths and structural trends; mostly NW-SE and some NE-SW. All of them occupy the plateau, while the low desert floodplain is free of lineaments. The plateau has a lineament density ranging from 0 to 1.21 km/km2. The high-density zones are of a high fracturing degree reflecting their high groundwater potentiality compared to the low-density zones. The study led to the classification of the area into zones of different groundwater potentiality ranging from very poor to very good. The potentiality of the plateau increases eastward as the lineament density increases

Subsidence of sinkholes in Wink, Texas from 2007 to 2011 detected by time-series InSAR analysis

West Texas’ Permian Basin, where the Wink sinkholes are located, is underlain by evaporite rocks that have been exposed to the dissolution due to natural processes and/or anthropogenic activities. We used the time-series interferometric synthetic aperture radar technique to process 16 ALOS PALSAR images from 01/01/2007 to 02/27/2011 for inspecting ground stability. Our results clearly show that two major sinkholes (Wink Sinks 1, 2), formed in 1980 and 2002, are both still subsiding, but the maximum subsidence for the 4-year period (2007–2011) occurred over an area about 1 km northeast of Wink Sink 2. The peak subsidence rate reached ∼40 cm/year during 2007–2010 and rose to ∼50 cm/year after 2010 when the area was hit by a record drought. Continuous monitoring of the subsidence in the vicinity of the Wink sinkholes is required for preventing and mitigating catastrophic outcomes of long-term developing geohazards to the area’s oil production facilities, infrastructure, and human safety.

Ground deformation and fissure activity in Datong basin, China 2007–2010 revealed by multi-track InSAR

abstarctThe Datong basin is one of the complex geologic environments in China. Several faults are distributed in the basin, and a number of hidden faults have been discovered within the basin. Ground fissures (GFs) and land subsidence occur in this region. In the past, ground subsidence at Datong basin has been studied using groundwater level records, intermittent small-scale levelling measurements and field investigations. However, due to the low spatial resolution and temporal sampling of these records, detailed ground deformation within the basin remains poorly understood. In this study, we exploited Envisat ASAR and ALOS PALSAR images to map the ground deformation between 2007 and 2010. Most of the deformation within the basin occurred at rates of <20 mm/year. In addition to the subsidence due to coal mining in the western mountainous areas, at least four large subsidence zones could be detected. Our analysis indicates that the ground subsidence is affected by groundwater extraction and the distribution of faults. Additionally, the vertical deformation is much larger than the deformation in the east–west direction. Obvious deformation gradients occurred on individual GFs and the activity of the GFs varied spatially. Finally, Interferometric synthetic aperture radar (InSAR) observations related to groundwater exploitation and GFs were modelled and discussed.Abbreviations: GFs: ground fissures; SAR: Synthetic Aperture Radar; ASAR: Advanced Synthetic Aperture Radar; ALOS: Advanced Land Observing Satellite; PALSAR: Phased Array type L-band Synthetic Aperture Radar; InSAR: Interferometric Synthetic Aperture Radar; ESA: European Space Agency; JAXA: Japan Aerospace Exploration Agency; SBAS: Small Baseline Subsets; SRTM: Shuttle Radar Topography Mission; DEM: Digital Elevation Model; DORIS: Dopper Orbitography and Radiopositioning Integrated by Satellite instrument; MCF: minimum cost flow; LOS: line-of-sight2D/3D: two-dimensional/three-dimensional

Salt pond analysis using ALOS PALSAR case study Sampang, Madura-Indonesia

Indonesia is a country that has a large sea area of about 96,079.15 km2. With this condition we can utilize the natural resources that are infested in the sea, such as fish, and salt. Indonesia has exceptional salt pond commodities from various areas such as Cirebon, Sampang, Indramayu Madura, and Rembang. These resources need to be monitor and inventory in proper way. The development of science and technology, there is an effective and efficient way to identify, monitoring and inventory of salt ponds using remote sensing technology. In this last period the saltwater pond infiltration is still inclined to utilize optical sensor system to monitor and infuse. The method used is visual interpretation through multipolarization composite radar image by using interpretation key. The weakness of passive sensors is affected by the presence of cloud cover. The location of salt ponds in Indonesia is mostly located on the coastal regions that is visually affected cloud cover. In addition the use of active sensor is become a challenging for this aspect. This study was conducted to analysis of hues, patterns, and textures in order to recognize the salt pond object using Alos PALSAR Image (Active Sensor). The salt pond object in multi-polarized composite HH-HH / HV composite radar images is recognizable in dark blue with regular pattern plots with characterized by brighter / clear / bright pink ridges.

Application of Multi-parametric AHP for Flood Hazard Zonation in Coastal Lowland Area of Central Vietnam

Flooding is one of the most commonly occurring natural hazards worldwide. Multiple geomorphic factors such as elevation, slope, topographic wetness index and distance from the river channel impact on flood susceptibility. Determining the effect of each factor and integration of their influence on flood hazard zonation has become an essential need in flood management. This study attempts to develop a hierarchical model for flood hazard zonation in the coastal lowland of Central Vietnam by using a multi-parametric approach. Five factors including elevation based flood inundation, distance from the river channel, land use, slope and topographic wetness index have been taken into consideration for developing flood hazard zonation. The Analytical Hierarchy Process (AHP) method was applied to assign weights according to the pair-wise comparisons of the parameters. These weights were used for calculation of Flood Hazard Index which was then evaluated and classified into four zones of flood hazard susceptibility. The result of flood hazard zonation was compared with field survey flood pillar points and flood inundation map extracted from ALOS PALSAR image in 2007 for validation. The comparison with field flood pillars indicated good correspondence with demarcated zones and estimated depth. Further, nearly 80 percent of inundated areas observed in ALOS PALSAR data fall in the high and very high flood hazard zones. The excellent agreement between flood hazard zonation map and observed inundation demonstrate the effectiveness AHP based method in flood hazard assessment.

Karakterisktik Backscatter Citra Alos Palsar Polarisasi Hh dan Hv terhadap Parameter Biofisik Hutan di Sebagian Taman Nasional Kerinci Seblat

Synthetic Aperture Radar (SAR) is one kind of an active remote sensing system, utilizing microwave to records the earth surface. One of the SAR’s satellites is ALOS PALSAR which is capable of penetrating clouds so that the underlying objects can be identified. ALOS PALSAR has a polarization system operating with single beam polarization (HH or HV), dual polarization (HH + HV or VV + VH) and full polarization (HH + HV + VH + VV). This study aims to use the backscatter value of ALOS PALSAR's image for monitoring and mapping the forest. The Utilization of ALOS PALSAR imagery to measure forest biophysical parameters and saturation levels identified based on backscatter sensitivity. Image extraction is done through image calibration where the digital value is converted to sigma naught backscatter value then the result of calibration unit is changed to decibels. Backscatter sensitivity depends on forest structure which can affect scattering mechanism. The backscatter value is also influenced by humidity and weather dynamics which can also affect the dielectric constant. The result of accurate classification of land use polarization fall in HH accuracy of 62,4%. Whereas for HV polarization accuracy obtained equal to 74,88%.

Estimation of Above Ground Biomass in Forests Using Alos Palsar Data in Kericho and Aberdare Ranges

Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.

Multimodal registration of remotely sensed images based on Jeffrey’s divergence

Entropy-based measures (e.g., mutual information, also known as Kullback-Leiber divergence), which quantify the similarity between two signals, are widely used as similarity measures for image registration. Although they are proven superior to many classical statistical measures, entropy-based measures, such as mutual information, may fail to yield the optimum registration if the multimodal image pair has insufficient scene overlap region. To overcome this challenge, we proposed using the symmetric form of Kullback-Leiber divergence, namely Jeffrey’s divergence, as the similarity measure in practical multimodal image registration tasks. Mathematical analysis was performed to investigate the causes accounting for the limitation of mutual information when dealing with insufficient scene overlap image pairs. Experimental registrations of SPOT image, Landsat TM image, ALOS PalSAR image, and DEM data were carried out to compare the performance of Jeffrey’s divergence and mutual information. Results indicate that Jeffrey’s divergence is capable of providing larger feasible search space, which is favorable for exploring optimum transformation parameters in a larger range. This superiority of Jeffrey’s divergence was further confirmed by a series of paradigms. Thus, the proposed model is more applicable for registering image pairs that are greatly misaligned or have an insufficient scene overlap region.

Above ground biomass estimation of mangroves located in Negombo - Muthurajawela wetland in Sri Lanka using ALOS PALSAR Images

In Sri Lanka, mangrove forests are scattered along the north-western, north eastern, Jaffna Peninsula and eastern coastal belt. The total estimated extent of mangroves in the country is about 87 km 2 . Estimation of Above Ground Biomass (AGB) of mangroves is a challenging task due to field sampling difficulties. Use of satellite based remote sensing technologies is becoming popular for estimation of AGB for different vegetation types. To overcome the limitations during field sampling and to identify the possibility of using SAR data for AGB estimation, ALOS PALSAR satellite data were used to estimate AGB of mangroves and associated vegetation in Muthurajawela- Negombo wetland in Sri Lanka. Diameter at Breast Height (DBH) measurements over 5cm of eighteen (18) sampling plots (10x10 m) were collected and the relevant allometric equation was used to estimate the AGB. Backscatter coefficient values of HH and HV polarization of ALOS –PALSAR images were used to estimate the AGB of mangroves using a previously derived model. Finally, an AGB map of mangrove associated vegetation was developed for the study area using ALOS PALSAR data as a method of minimizing field work while saving time and cost. According to the results, the average AGB is observed as 65t/ha from the field sampling method (28 -135 t/ha) while it was estimated as 76 t/ha (33-155 t/ha) using PALSAR which shows an overestimation by 17%. A significant overestimation by the remote sensing method is occurred when the tree height is more than 5 m. Though it shows an overestimation, the map developed using this approach is helpful to understand the distribution of AGB within mangrove associated vegetation systems where field sampling is a challenging task.

Comparison and Analysis of Geometric Correction Models of Spaceborne SAR.

Following the development of synthetic aperture radar (SAR), SAR images have become increasingly common. Many researchers have conducted large studies on geolocation models, but little work has been conducted on the available models for the geometric correction of SAR images of different terrain. To address the terrain issue, four different models were compared and are described in this paper: a rigorous range-doppler (RD) model, a rational polynomial coefficients (RPC) model, a revised polynomial (PM) model and an elevation derivation (EDM) model. The results of comparisons of the geolocation capabilities of the models show that a proper model for a SAR image of a specific terrain can be determined. A solution table was obtained to recommend a suitable model for users. Three TerraSAR-X images, two ALOS-PALSAR images and one Envisat-ASAR image were used for the experiment, including flat terrain and mountain terrain SAR images as well as two large area images. Geolocation accuracies of the models for different terrain SAR images were computed and analyzed. The comparisons of the models show that the RD model was accurate but was the least efficient; therefore, it is not the ideal model for real-time implementations. The RPC model is sufficiently accurate and efficient for the geometric correction of SAR images of flat terrain, whose precision is below 0.001 pixels. The EDM model is suitable for the geolocation of SAR images of mountainous terrain, and its precision can reach 0.007 pixels. Although the PM model does not produce results as precise as the other models, its efficiency is excellent and its potential should not be underestimated. With respect to the geometric correction of SAR images over large areas, the EDM model has higher accuracy under one pixel, whereas the RPC model consumes one third of the time of the EDM model.

Detection of coal-mining-induced subsidence and mapping of the resulting deformation using time series of ALOS-PALSAR data

ABSTRACTLand subsidence caused by extracted void collapses due to coal mining activities is a major geohazard in northern China. Differential SAR interferometry (DInSAR) has been adopted as an effective tool for coal mining subsidence detection and deformation mapping. However, previous studies were mostly focused on the derivation of linear deformation rates in coal mines, while the spatial–temporal pattern of coal mining subsidence and its interaction with the fault systems have rarely been investigated. In this study, ALOS-PALSAR images acquired from 19 June 2007 to 12 November 2010 were analysed with DInSAR and small baselines subset (SBAS) techniques to characterize the distribution and magnitude of subsidence in central Henan province, which is one of the leading coal producers in China. More than 40 funnel-shaped subsidence areas distributed within the coalfields have been detected in both Dengfeng and Xinmi. The maximum annual mean deformation can be as high as 8 cm in the line-of-sight (LOS) direction. Deformation maps were generated in the study area, showing an extended subsidence of 2009 compared with that of 2007. We also analysed the deformation profiles perpendicular to the faults, and found an asymmetric subsidence pattern along the profiles. Such a pattern may be an indicator of local fault activities, and it may help understanding of coal mine subsidence near faults.

Evaluation of methods for deriving mountain glacier velocities with ALOS PALSAR images:A case study of Skyang glacier in central Karakoram

Evaluation of methods for deriving mountain glacier velocities with ALOS PALSAR images:A case study of Skyang glacier in central Karakoram