Polarimetric synthetic aperture radar (PolSAR) data show good performance in near-real-time earthquake/tsunami damage assessment. In this paper, an improved damage level index for earthquake/tsunami damage level mapping of urban areas based on distance metric learning using full PolSAR data has been proposed. First, urban areas are extracted by our proposed classification method, which conjunctively uses multiresolution segmentation and support vector machine. Then, a double-bounce scattering power-based damage level index is adopted for damage level mapping. To solve the problem that the primary result does not match with the truth damage level in heavily damaged areas, distance metric learning method is introduced to calculate the Mahalanobis metric to compose an improved index. Extensive experimental comparison and analysis on L-band ALOS PALSAR data of Tohoku earthquake demonstrate the effectiveness of the proposed index. Both the analysis about damage level assessment map and sample areas’ linear fitting with truth damage level indicate the improved damage level index achieves high accuracy. The same analysis was applied to evaluate the damage induced in Kumamoto earthquake event. The analysis result verified the robustness of the proposed damage level index.

Intense urbanization due to population growth and the increase in anthropogenic activities has transformed the world’s landscapes and urban ecosystem rather actively in the last few decades. Increasing trends in industrialization and urbanization are the extreme factors that are influencing the land surface transformation.¹ Urbanization is the major activity that is contributing dynamically to change the land use/land cover (LULC),² 3. ^{– 4} modifying the energy balance and making the city areas warmer than the rural areas. Modern transportation and industries are adding to the greenhouse gases in the atmosphere. The activity of the replacement of the natural vegetation area into buildings at a fast rate is threatening. This is because of the usage of low albedo materials, leading to higher heat absorption in urban centers and causing the significantly higher surface temperature of the urban boundary than its surroundings. These hot areas are just like an island in a sea of relatively cooler areas and termed as the urban heat islands (UHI).⁵ UHI not only cause a change in biodiversity but are also responsible for human discomfort by spreading different diseases causing disease and mortality.⁶ UHIs increase the energy consumption to generate cooling, hence emitting ${\mathrm{CO}}_2$.⁷

Analyses of single-post-event polarimetric synthetic aperture radar (PolSAR) data permit fast and convenient post-disaster damage assessment work. By analyzing valid features, damaged and undamaged buildings can be quickly classified. However, the presence of oriented buildings in the disaster area makes the classification work more challenging. Many previous works extract the damage information of the disaster area by considering oriented buildings and undamaged parallel buildings as survived buildings. However, after-effect debris may create structures with random orientation angles. In our study on the Tohoku earthquake/tsunami disaster event, we found that some damaged buildings with large building orientation angles (with respect to the satellite flight path) are grouped as oriented buildings (undamaged buildings). In this paper, we propose a new earthquake/tsunami damage assessment method, particularly for urban areas, that takes this complex situation into consideration. The proposed method solves the problems of both urban-area extraction and damaged-building identification. For urban-area extraction, the proposed combined thresholding and majority voting method can accurately discriminate between urban and foreshortening mountain areas. Meanwhile, for damaged-building identification, the proposed new unsupervised damage assessment method classifies the buildings in a disaster area according to four conditions, and it outperforms the techniques used in existing works. The analysis results and the comparison with the supervised support vector machine (SVM) classification technique show that our proposed method can produce more accurate results for damage assessment using single-post-event PolSAR data.

This paper was aimed at estimating the forest aboveground biomass (AGB) in the Central Kalimantan tropical peatland forest, Indonesia, using polarimetric parameters extracted from RadarSAT-2 images. Six consecutive acquisitions of RadarSAT-2 full polarimetric data were acquired and polarimetric parameters were extracted. The backscattering coefficient ( ${\sigma }_o$ ) for HH, HV, VH, and VV channels was computed respectively. Entropy (H) and alpha ( $\alpha$ ) were computed using eign decomposition. In order to understand the scattering behavior, Yamaguchi decomposition was performed to estimate surface scattering ( ${\gamma }_{surf}$ ) and volume scattering ( ${\gamma }_{vol}$ ) components. Similarly following polarimetric indices were computed; Biomass Index (BMI), Canopy Structure Index (CSI), Volume Scattering Index (VSI), Radar Vegetation Index (RVI) and Pedestal Height ( $p_h$ ). The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence, and suitability for forest aboveground biomass estimation across rainy and dry conditions. Regression analysis was performed between referenced biomass measurements and polarimetric parameters; VSI, H, RVI, $p_h$ , and ${\gamma }_{vol}$ were found significantly correlated with AGB. Biomass estimation was carried out using significant models. Resultant models were validated using field-based AGB measurements. Validation results show a significant correlation between measured and referenced biomass measurements with temporal consistency over the acquisition time period.

We use passive optical high-resolution GeoEye-1 imagery and active synthetic aperture radar (SAR) Advanced Land Observing Satellite (ALOS-1) phased array type L-band synthetic aperture radar (PALSAR) L-band horizontal–horizontal-polarization imagery to estimate forest aboveground biomass (AGB) of the tropical mountainous forest test site in Kayar Khola watershed, Chitwan district, Nepal. Object-based tools were used to delineate tree crowns from the orthorectified pan-sharpened GeoEye-1 optical imagery. AGB modeling with crown projection area extracted from the optical imagery shows a good linear relationship with $R2=0.76$. The terrain-corrected, radiometrically calibrated, and speckle-filtered ALOS-1 PALSAR backscatter image was utilized for AGB modeling; the nonlinear modeling of AGB with the SAR backscatter (dB) shows $R2=0.52$. The validation $R2$ values for AGB estimates from GeoEye-1 and ALOS-1 PALSAR are 0.83 and 0.44, respectively. The direct comparison of AGB estimates from both sensors is made possible by the utilization of the same set of ground survey points for both training and validation of the statistical models for both datasets. The final AGB output maps from both sensors show that the spatial patterns of AGB are in reasonable agreement at lower elevation, while SAR seems to underestimate AGB values as compared with optical-based estimates in the higher elevation zones.

Silica is one of the mineral needs of industry, especially in base materials for the primary or auxiliary raw materials. Silica sand on Rupat Island was identified on the northern coastline. Selected samples were measured to get the percentage of minerals/elements using X-ray fluorescence. Two adjacent scenes of ALOS PALSAR full-polarimetry data were used. The dielectric constant of samples was measured using a dielectric probe kit in the frequency range from 0.3 to 3.0 GHz and also used for calculating the backscattering coefficient. Freeman-Durdeen and Yamaguchi techniques were used to obtain the scattering decomposition of physical scattering from the incoherent object model base. Surface scattering is a very clear decomposition used to show the silica sand identification compared with other decompositions. From surface scattering, the backscattering coefficient value of silica sand resulted from −59 dB to −52 dB. Thickness estimation from satellite and field measurements show the minimum is 59 cm and the maximum is 74 cm, with the average differentiation between these measurements being 4.88 cm. The condition supported by the grain size of silica sand particles that have almost the same size and shape were conducted by using microscopic photograph testing. These values were caused by a slightly rough surface.