Advanced Land Observing Satellite Phased Array Type L-band Synthetic Aperture Radar Research Articles

Deformation Signals at Mud Volcano in Kambing Island Detected by L-band InSAR

Abstract – Mud volcanoes are generated by fluid and solid material extrusion from their mud reservoir and are predominantly found at convergent plate margins. Kambing Island is a small island in Eastern Timor, Indonesia, where the Australian continental plate collides with the Banda Sea plate. The previous geological study identified active mud volcano on this island, but it is still unclear whether and to what extent they are active. In this work, we attempt to detect surface deformation due to the mud volcanoes activism in Kambing island using the Advanced Land Observing Satellite/ Phased Array Type L-band Synthetic Aperture Radar (ALOS/PALSAR) Interferometry Synthetic Aperture Radar (InSAR). The ALOS/PALSAR datasets consist of 12 ascending and 5 descending Synthetic Aperture Radar (SAR) data from 2006 to 2011. Although the results indicate surface deformation signals are hardly detected in the interferogram pairs due to lost coherence and atmospheric noise, we could highlight the episodic occurrence of surface deformation signals between April and November 2008 reaches 6.4 cm moving away from the satellite line of sight. In the future, to back up our findings, further field surveys or long-term InSAR are needed. Keywords: mud volcano, InSAR, deformation.

Optimizing Image Scale and Resolution to Obtain Lineament Related Structures using ALOS PALSAR Dual Orbit Data

Geological structures identification using conventional optical remote sensing encountered some problems due to atmospheric and surface conditions. Therefore, we exploited the Synthetic Aperture Radar (SAR) in Ascending and Descending orbits to characterize lineaments related to geological structures. We have analyzed two images from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS). The adjustment of their resolution in the multilooking process was applied to obtain the best understanding between detected lineament and reference fault. The cross-polarized data of ALOS PALSAR level SLC were multilooked with variation of resolution from 15 to 120 m. Obtaining the optimum detected lineament-related faults, which applied the Yamaguchi criterion to extract the lineament visually in the multilooked images. The Kamojang-Guntur Geothermal Fields in West Java were selected as a study area because there is a challenge to confirm geological structure in the area with high alteration and high weathering conditions. According to the proposed criterion, we have obtained those images with resolution of 75 to 90 from the analysis images can represent surface geological structure conditions, and the correlation with existing published geological map. The field verifications have been confirmed that the geological structure indication from images has been proved with the existence of slickenside and morphological conditions such as drainage of rivers, ridges, and valleys. Based on lineaments, stress regimes, and field verifications, it was concluded that Yamaguchi criterion analysis with image resolution 75 to 90 m can describe the presence of regional structures and interpreted structures.

A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA

Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.

Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

Synthetic Aperture Radar Interferometry: Utilizing Radar Principles

One of the most important applications of synthetic aperture radar interferometry (SARIF) is making a geometrical plot of observed points on the ground surface. For that purpose, we derive the two points' distances using a radar principle. Mathematically, this problem is a two-unknown-variables problem. To solve, it we need only two equations. Due to limitations of the measured distances by radar, we require one more equation. The availability of the data is carefully investigated. Then, we obtain a very clear expression for the parallel and perpendicular components of the distance. The perpendicular component is expressed by the difference of the measured phase differences with an extremely large amplifier. We discuss the conversion of the measured phase differences, the plot of which is an interferogram, to distance. We present a numerical example that verifies the likelihood of obtaining the difference of the phase differences. We also show the phase differences and calculate the perpendicular component for the Advanced Land Observing Satellite-Phased Array-Type L-Band Synthetic Aperture Radar (ALOSPALSAR). The plot of ground points is very noisy; therefore, we must improve our calculation by reconsidering the coregistration calculation.

Assessment of oil palm yield and biophysical suitability in Indonesia and Malaysia

ABSTRACT The most crucial technical challenge facing the Malaysian and Indonesian oil palm industry is that the actual yield in the form of Fresh Fruit Bunch (FFB; unit in tonne per hectare (t ha−1)) are well below of potential levels and have stagnated over last two decades. Closing this wide yield gap would have a positive impact on the revenue as it increases productivity per hectare and it eventually leads to less pressure on opening new land and mitigates environmental costs of production. With respect to the indispensable need for closing this gap for future prosperity of this industry and sustainable production of palm oil, this study assessed oil palm yield, considering the potential growth of oil palm dependent on the site qualities and actual yield. Firstly, we mapped oil palm plantations combining yearly Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and ALOS-2 mosaics of L-band backscatter, Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance (MOD13Q1), and the MODIS Vegetation Continuous Field canopy cover product (MOD44B); where 10.3 and 6.68 million ha (Mha) of oil palm plantations were mapped, respectively, in Indonesia and Malaysia in 2017. Secondly, the age after planting was estimated at detected plantations using time series of MODIS canopy cover with correlation coefficient (r) of 0.68 and Root Mean Square Error (RMSE) of 4.7 years. Thirdly, the biophysical suitability of detected plantations was evaluated considering the spatial-temporal variation of different biophysical criteria. Combining information from second and third steps, we estimated the potential yield at 250 m spatial resolution. The average potential yield in Malaysia ranges between 13.8 t ha−1 and 19.3 t ha−1 in 2017, where in Indonesia it ranges between 17.8 t ha−1 and 21.7 t ha−1 in the same year. The actual yield in next step, has been quantified by HH-HV attribute of ALOS PALSAR and ALOS-2 mosaics, where the average actual yield in Malaysia ranges between 14.48 t ha−1 and 20.63 t ha−1 and in Indonesia it ranges between 8.49 t ha−1 and15.40 t ha−1 in 2017. Finally, comparing estimated potential and actual yields, we evaluated oil palm industries’ performances where distinct differences were found between two countries. In most of the Malaysian states quantified actual yields were above or at the level of estimated potential yields, whereas in all Indonesian provinces quantified actual yields were well below the potential level. Considering the favourability of environment, among all provinces/states, Sabah, and Sarawak states in Malaysia and Aceh and North Kalimantan provinces in Indonesia distinctly differ due to their poor performances from rest of provinces/states. The information on different yields provided in this study are indispensable needs for efficient and accountable policies as it enables governors to directly target specific objectives such as subsidies on fertilizers, productive cultivars, and new technologies for the plantations suffering from low yield. Also, this study provides benchmarks for each province/state for scopes of actual yield improvements for long-term planning.

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.

Flood Susceptibility Modeling of Megech River Catchment, Lake Tana Basin, North Western Ethiopia, Using Morphometric Analysis

The present study intended to flood susceptibility modeling by using drainage morphometric investigations of the Megech River catchment, Lake Tana Basin, North Western Ethiopia. Drainage morphometric criterion study performs a critical aspect in recognizing the factual aspects of river catchment with concern to floods. In the present study, we depicted the Megech River catchment into four sub-watersheds, followed by clipping off the drainage grid by using ALOS-PALSAR (Advanced Land Observing Satellite-Phased Array-Type L-Band Synthetic Aperture Radar) digital elevation model, toposheets, and Landsat-8 Operational Land Imager coupled with the Geographic Information System (GIS) program. The drainage morphometric parameters, such as basic, linear, areal and relief, were calculated in the current research by using the standard formula. The morphometric parameters, such as bifurcation ratio, drainage density, length of overland flow and drainage frequency, have a direct connection with flood susceptibility. Hence, rank 1 assigned to the highest values of the above-mentioned parameters followed by second-rank to second-highest value and rank third given the lowest value of the above parameters. The morphometric parameters, such as circulatory ratio, form factor, elongation ratio, drainage texture and compactness coefficient, have a reverse relation with flood proneness. Hence, rank 1 assigned to the lowest values of those parameters, followed by rank two to the second-lowest value and rank three given to the highest value of the above parameters. The compound factor is computed by aggregating the assigned ranks of the morphometric drainage parameters mentioned above and then dividing by the number of morphometric criteria used for sub-watersheds prioritization. The results of the present study displayed sub-watershed 3 scored very high prioritization (Ist), including a compound factor value of 1.89, and sub-watershed 2 got the highest compound factor value of 3.11, and it scored the lowest rank (IVth). The found sub-watershed 3 in the current research area was acquired as a high-priority grade one, and it demands urgent flood regulation remedies for competent water budget devising and administration in the Megech catchment area. The current study demonstrates the capability of sub-watershedwise drainage morphometric investigations in the flood susceptibility analysis using toposheets, optical remote sensing data and digital elevation model associated with GIS tools.

Correction of amplitude scintillation effect in fully polarimetric SAR coherency matrix data

In this paper, we investigate the correction of ionospheric amplitude scintillations appearing as image artifacts in low frequency synthetic aperture radar (SAR) data. A method has been proposed to separate amplitude and phase components of fully polarimetric SAR (POLSAR) coherency matrix [T], and then implement a combination of multi-level, two-dimensional Discrete Wavelet Transform and Fast Fourier Transform (DWT-FFT) to correct the amplitude scintillation-induced stripes in [T] matrix. In the methodology, [T] is decomposed into component matrices of amplitude and relative phases. To the amplitude [T] matrix, a combination of 2-D DWT is performed, using the Haar wavelet, followed by a non-linear 2-D FFT correction strategy to remove amplitude scintillation stripes. Using DWT, each amplitude [T] matrix element is decomposed into low- and high-frequency components known as approximate and detailed coefficients respectively. On applying a 2-D FFT on the detailed coefficients, amplitude scintillation-induced stripes are easily identified in the frequency spectrum and then removed using 2-D FFT correction strategy of averaging-post-thresholding. The proposed method is tested on the [T] matrix of several POLSAR datasets acquired from Advanced Land Observation Satellite/Phased Array type L-band synthetic aperture radar (ALOS/PALSAR) and Advanced Land Observation Satellite-2/Phased Array type L-band synthetic aperture radar-2 (ALOS-2/PALSAR-2) satellites. The performance of the correction technique is analysed both, qualitatively and quantitatively. While improvements in the post-correction datasets are visually observed, a supervised Wishart classification also performed on the [T] matrices of undisturbed (reference), disturbed and corrected data indicates significant increase in overall accuracy (OA) and kappa coefficient (k̂) parameters. The classification accuracies of the disturbed data (OA = 69.31%, k̂=0.54) improved to OA = 76.49%, k̂=0.68 after applying the proposed correction approach, which is comparable to the reference data values (OA = 81.57%, k̂=0.71). The proposed method performs significantly better than the existing technique of a simple 2-D FFT approach (OA = 73.49%, k̂=0.62). The contribution of dominant scattering powers, obtained from the seven-component scattering power decomposition of POLSAR coherency matrix (7SD) model, also demonstrates post-correction improvements. The dominant scattering power for targets increased by ~3–9% using the proposed approach, shows an improvement of ~3% over the existing 2-D FFT technique.

Use of optical and radar remote sensing satellites for identifying and monitoring active/inactive landforms in the driest desert in Saudi Arabia

Observing Earth from the space is key for monitoring land-surface dynamics and probing near-surface geomorphic and geological features, including those obscured by sand sheets/dunes and human activity. The hyper-arid area of the Arabian Peninsula was selected for detecting and extracting obscure paleohydrological systems and modeling the spatio-temporal dynamics of sand dunes using synthetic aperture radar (SAR) and optical high-resolution remote sensing images. Interferometric synthetic aperture radar (InSAR) coherence change detection (CCD) using Sentinel-1 imagery was performed, revealing subtle changes in the surficial landforms of the study area. These images also discriminated between active and inactive geomorphic features. In addition, low-frequency L-band Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and Shuttle Radar Topography Mission (SRTM) data revealed the details of paleochannels in the basins that drain into the Arabian Gulf. These ephemeral rivers are the source of sand that was later reshaped into dunes by aeolian processes. The results also revealed that the morphology and extension of dunes were clearly influenced by topography, slope, and wind velocity and direction, rather than human activities. Knowledge gained by exploring desert features is essential for planning settlements and development, and integrated approaches using a variety of remote sensing datasets are key for enhancing Earth observation across a range of spatial and temporal scales.

Multispectral and RADAR images integration for geologic, geomorphic, and structural investigation in southwestern Arabian Shield, Al Qunfudhah area, Saudi Arabia

In this article, Western Saudi Arabia underwent remote sensing investigation via Operational Land Imager (OLI), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), Sentinal-1 and Shuttle Radar Topography Mission (SRTM). Image-transformation and enhancement techniques such as Band ratios, principal component analysis (PCA), and Minimum Noise Fraction (MNF) were utilized. Applying Sentinel-1 and SRTM DEMs enhanced the perspective views of the terrain. The main results are highlighting of geologic rock units, geomorphic and structural features, and extraction and visualization of dominant lineaments. N–S foliations that are associated with folds N–S tight, isoclinal, and asymmetric (F1), NW-SE trending Najd system strike-slip faults and NNE–SSW, NE–SW NW–SE, and E–W Cenozoic faults are the major identified structural lineaments, which were analyzed and synthesized via rose diagrams. Visible Near Infra-Red/Short Wave Infra-Red (VNIR/SWIR) radar data are very important data set for water resources applications.

Improved POLSAR Model-Based Decomposition Interpretation Under Scintillation Conditions

Low-frequency synthetic aperture radar (SAR) sensors are prone to ionospheric irregularity structures that affect the amplitude and/or phase of the radar signal. Azimuthal striping caused by amplitude scintillation in SAR data is an initial observation of such effects. In the absence of any physical model and/or technique to mitigate scintillation stripes, we have applied a 2-D fast Fourier transform (FFT) approach for improved interpretation and target identification in fully polarimetric SAR (POLSAR) model-based decomposition scattering powers. Few scenes of fully polarimetric Advanced Land Observation Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and ALOS-2/PALSAR-2 under different ionospheric conditions are acquired and used in this study. As a reference, data sets over the same areas are also acquired on different dates with negligible ionospheric activity (ionospheric quiet day). All the data sets are corrected for the Faraday rotation angle ( $\Omega$ ), which is estimated using the Bickel–Bates approach. Three correction strategies of scintillation-affected model-based scattering power decomposition images are presented. The strategies (zero masking, thresholding, and averaging-post-thresholding) are implemented on 2-D FFT of scattering power images. An intercomparison of corrected decomposition results from the correction strategies demonstrates the capability of 2-D-FFT-based correction strategies to improve the dominant scattering component by 3%–5% for homogeneous terrains. The correction method based on averaging-post-thresholding gives the best results that are further tested by performing a supervised classification. The overall accuracy and kappa coefficient ( ${OA}, \hat {k}$ ) of the averaging-post-thresholding technique (72.87%, 0.59) is comparable to those of the reference data (78.74%, 0.69).

Advanced Applications of Synthetic Aperture Radar (SAR) Remote Sensing for Detecting Pre- and Syn-eruption Signatures at Mount Sinabung, North Sumatra, Indonesia

DOI:10.17014/ijog.6.2.123-140Mount Sinabung was re-activated at August 28th, 2010 after a long repose interval. The early stage of a phreatic eruption was then followed by magmatic eruptions at September 15th, 2013 for years until now. To understand the ground surface changes accompanying the eruption periods, comprehensive analyses of surface and subsurface data are necessary, especially the condition in pre- and syn-eruption periods. This study is raised to identify ground surface and topographical changes before, intra, and after the eruption periods by analyzing the temporal signature of surface roughness, moisture, and deformation derived from Synthetic Aperture Radar (SAR) data. The time series of SAR backscattering intensity were analyzed prior to and after the early eruption periods to know the lateral ground surface changes including estimated lava dome roughness and surface moisture. Meanwhile, the atmospherically corrected Differential Interferometric SAR (D-InSAR) method was also applied to know the vertical topographical changes prior to the eruptions. The atmospheric correction based on modified Referenced Linear Correlation (mRLC) was applied to each D-InSAR pair to exclude the atmospheric phase delay from the deformation signal. The changes of surface moistures on syn-eruptions were estimated by calculating dielectric constant from SAR polarimetric mode following Dubois model. Twenty-one Phased Array type L-band SAR (PALSAR) data on board Advanced Land Observing Satellite (ALOS) and nine Sentinel-1A SAR data were used in this study with the acquisition date between February 2006 and February 2017. For D-InSAR purposes, the ALOS PALSAR data were paired to generate twenty interferograms. Based on the D-InSAR deformation, three times inflation-deflation periods were observed prior to the early eruption at August 28th 2010. The first and second inflation-deflation periods at the end of 2008 and middle 2009 presented migration of magma batches and dike generations in the deep reservoir. The third inflation-deflation periods in the middle of 2010 served as a precursor signal presenting magma feeding to the shallow reservoir. The summit was inflated about 1.4 cm and followed by the eruptions. The deflation of about 2.3 cm indicated the release pressure and temperature in the shallow reservoir after the early eruption at August 28th, 2010. The last inflation-deflation period was also confirmed by the increase of the lava dome roughness size from 5,121 m2 on July to 6,584 m2 on August. The summit then inflated again about 1.1 cm after the first eruption and followed by unrest periods presented by lava dome growth and destruction at September 15th, 2013. The volcanic products including lava and pyroclastics strongly affected the moisture of surface layer. The volcanic products were observed to reduce the surface moisture within syn-eruption periods. The hot materials are presumed responsible for the evaporation of the surface moisture as well.

Analysis of the relationship between volcanic eruption and surface deformation in volcanoes of the Alaskan Aleutian Islands using SAR interferometry

The Alaskan Aleutian Islands form one of the world’s largest volcanic island chains. The islands are exposed to both direct and indirect damage from continuous volcanic eruptions. Surface deformation is mostly observed before volcanic eruption, but with some volcanoes, such as Ontake Volcano, deformations cannot be detected. In this study, we analyzed volcanic eruptions in the Alaskan Aleutian Islands, which is a region of frequent volcanic eruptions. Based on our results, we predicted the type of eruption that would occur on Baekdusan Volcano according to the presence or absence of surface deformation. For this purpose, 10 sites were selected from areas where recent volcanic activity had occurred in the Aleutian Islands. Additionally, Advanced Land Observing Satellite Phased Array-type L-band Synthetic Aperture Radar (ALOS-PALSAR) and European Remote Sensing (ERS)-1/2 satellite data were obtained from 10 experimental sites. Based on the radar satellite data, the volcanic surface deformations were identified, and the characteristics of the volcanic eruption were quantitatively calculated by determining the presence of surface deformation. The results of this study should facilitate the process of correlation between volcanic eruption and surface deformation.

The Total Electron Content From InSAR and GNSS: A Midlatitude Study

The total electron content (TEC) measured from the interferometric synthetic aperture radar (InSAR) and from a dense network of global navigation satellite system (GNSS) receivers are used to assess the capability of InSAR to retrieve ionospheric information, when the tropospheric contribution to the interferometric phase is reasonably negligible. With this aim, we select three night-time case studies over Italy and investigate the correlation between TEC from advanced land observing satellite-phased array type L-band synthetic aperture radar (ALOS-PALSAR) and from the Rete Integrata Nazionale GPS (RING) network, the latter considered as the reference true ionospheric TEC. To retrieve the TEC variability from ALOS-PALSAR, we first investigate the correlation between the integral of the azimuth shifts and the interferometric phase in the absence of ground motions (e.g., earthquakes) and/or heavy rain events. If correlation exists (as in two out of three case studies under investigation), we can assume the tropospheric contribution to the interferometric phase as negligible and the TEC variability from L-band InSAR can be retrieved. For these two case studies, the comparison between the TEC from the InSAR images and from the RING network is quite encouraging as the correlation coefficient is R ∼ 0.67 in the first case and R ∼ 0.83 in the second case. This result highlights the potential to combine InSAR and GNSS experimental measurements to investigate small-scale spatial variability of TEC, in particular over regions scarcely covered by ground-based GNSS receivers.

A multi-sensor approach for improving biodiversity estimation in the Hyrcanian mountain forest, Iran

ABSTRACTModelling tree biodiversity in mountainous forests using remote-sensing data is challenging because forest composition and structure change along elevation. Topographic variations also affect vegetation’s spectral and backscattering behaviour. We demonstrate the potential of multi-source integration to tackle this challenge in a mountainous part of the Hyrcanian forest in Iran. This forest is a remnant of a deciduous broadleaved forest with heterogeneous structure affected by natural and anthropogenic factors. The multi-source approach (i.e. Landsat Enhanced Thematic Mapper Plus (ETM +), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), and topographic variables) allows us to propose a biodiversity estimation model using partial least square regression (PLSR) calibrated and validated with limited field data. The effective number of species was calculated based on field measurements of the biodiversity in the study area. In order to model species diversity in more homogeneous extrinsic environmental conditions, we divided data into two groups with relatively uniform slope values. In each slope group, we modelled the correlation between observed biodiversity and satellite-derived data. For that, we followed three scenarios: (A) multispectral Landsat ETM + alone, (B) ALOS/PALSAR alone, and (C) inclusion of both sensors. In each scenario, elevation and slope data were also considered as predictors. We observed that in all scenarios, coefficient of determination (R2) in gentler slopes was higher than that in areas with steeper slopes (average difference in R2: ∆R2 = 0.21). The highest correlation was achieved by inclusion of synthetic aperture radar (SAR) and ETM + (R2 = 0.87). The results clearly confirm that the multi-source remote-sensing approach can provide a practical estimate of biodiversity across the Hyrcanian forest and potentially in other deciduous broadleaved forests in complex terrain.

Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data

Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.

Preliminary study of near surface detections at geothermal field using optic and SAR imageries

Current remote sensing technologies shows that surface manifestation of geothermal system could be detected with optical and SAR remote sensing, but to assess target beneath near the surface layer with the surficial method needs a further study. This study conducts a preliminary result using Optic and SAR remote sensing imagery to detect near surface geothermal manifestation at and around Mt. Papandayan, West Java, Indonesia. The data used in this study were Landsat-8 OLI/TIRS for delineating geothermal manifestation prospect area and an Advanced Land Observing Satellite(ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) level 1.1 for extracting lineaments and their density. An assumption was raised that the lineaments correlated with near surface structures due to long L-band wavelength about 23.6 cm. Near surface manifestation prospect area are delineated using visual comparison between Landsat 8 RGB True Colour Composite band 4,3,2 (TCC), False Colour Composite band 5,6,7 (FCC), and lineament density map of ALOS PALSAR. Visual properties of ground object were distinguished from interaction of the electromagnetic radiation and object whether it reflect, scatter, absorb, or and emit electromagnetic radiation based on characteristic of their molecular composition and their macroscopic scale and geometry. TCC and FCC composite bands produced 6 and 7 surface manifestation zones according to its visual classification, respectively. Classified images were then compared to a Normalized Different Vegetation Index (NDVI) to obtain the influence of vegetation at the ground surface to the image. Geothermal area were classified based on vegetation index from NDVI. TCC image is more sensitive to the vegetation than FCC image. The later composite produced a better result for identifying visually geothermal manifestation showed by detail-detected zones. According to lineament density analysis high density area located on the peak of Papandayan overlaid with zone 1 and 2 of FCC. Comparing to the extracted lineament density, we interpreted that the near surface manifestation is located at zone 1 and 2 of FCC image.

The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts

Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A (SIR-A) L-band mission in 1982, radar images have been exploited to map subsurface features beneath a sandy cover of extremely low loss and low bulk humidity in some hyper-arid regions such as from the Japanese Earth Resources Satellite 1 (JERS-1) and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR). Therefore, we hypothesise that a Digital Elevation Model (DEM) derived by InSAR in hyper-arid regions is likely to represent a subsurface elevation model, especially for lower frequency radar systems, such as the L-band system (1.25 GHz). In this paper, we compare the surface appearance of radar images (L-band and C-band) with that of optical images to demonstrate their different abilities to show subsurface features. Moreover, we present an application of L-band InSAR to measure penetration depths in the eastern Sahara Desert. We demonstrate how the retrieved L-band InSAR DEM appears to be of a consistently 1–2 m lower elevation than the C-band Shuttle Radar Topography Mission (SRTM) DEM over sandy covered areas, which indicates the occurrence of penetration and confirms previous studies.

Remote sensing of the Grand Ethiopian Renaissance Dam: a hazard and environmental impacts assessment

ABSTRACTThe Grand Ethiopian Renaissance Dam (GERD) is constructed in a mountainous area where limited information has been collected on key environmental factors and which is lacking of information about site condition, hazards and environmental impacts. To deal with problems, the Shuttle Radar Topographic Mission (SRTM) DEM and Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (ALOSPALSAR) were used to map factors that can lead to the GERD failure and predict the flooded area in Sudan and seawater intrusion in the Nile Delta, Egypt. The results showed that the Nubian Block and the GERD site are structurally controlled by sets of faults. Their trends are in the NNE–SSW, NE–SW, and NNW–SSE directions and share the similar trends with the shear stress and African Rift, which created some alarm. The results indicated that the maximum extent of the Sudanese inundation area, as estimated from a DEM using a flood basin model, was about 667,228 km2 along the Blue Nile River and was at high risk. The results also showed that one-third of the Nile Delta will experience seawater intrusions when the groundwater table depletes 5 m below sea level.