Junichi Susaki

The effect of pressure on the thermal conductivity of silicate rocks up to 12 kbar

Abstract The effect of high pressure up to 12 kbar on thermal conductivity of silicate rocks was determined. Measurements were made by the transient hot wire method on 23 samples. With the exception of one sedimentary rock, one meteorite and manufactured fused and crystalline quartz, the samples were igneous and metamorphic rocks of the oceanic and the continental lithospheres. The samples were of cylindrical shape, 24 mm long and 12 mm in diameter, containing a heater of 0.1 mm thick chromel wire along their axis and a thermocouple at the center. They were encased in cubes of 41 mm-edge-long pyrophyllite and then placed between slide-type cubic anvils of the IHI high-pressure apparatus, which transmitted quasi-hydrostatic pressure of more than 2 kbar to the sample through the solid pyrophyllite medium. The validity of the method was confirmed by comparing the conductivity of standard materials measured using the present method with literature values. The results show that the thermal conductivity of all samples increases with increasing pressure. The most rapid increase in the range below 2 kbar can be attributed to the closure of microcracks in the sample, and uniform, less pronounced increases above 2 kbar should be intrinsic to the material. The effect of temperature was also studied on a small number of selected samples. In the temperature range from 300 to 700 K, the thermal conductivities of crystalline rocks under quasi-hydrostatic compressive stresses of 4 and 10 kbar showed a monotonic decrease of thermal conductivity. The thermal conductivity of fused quartz, however, increased with temperature. Pressure appeared to have no appreciable effect on the temperature dependence of silicate thermal conductivity.

Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation

A filtering algorithm is proposed that accurately extracts ground data from airborne light detection and ranging (LiDAR) measurements and generates an estimated digital terrain model (DTM). The proposed algorithm utilizes planar surface features and connectivity with locally lowest points to improve the extraction of ground points (GPs). A slope parameter used in the proposed algorithm is updated after an initial estimation of the DTM, and thus local terrain information can be included. As a result, the proposed algorithm can extract GPs from areas where different degrees of slope variation are interspersed. Specifically, along roads and streets, GPs were extracted from urban areas, from hilly areas such as forests, and from flat area such as riverbanks. Validation using reference data showed that, compared with commercial filtering software, the proposed algorithm extracts GPs with higher accuracy. Therefore, the proposed filtering algorithm effectively generates DTMs, even for dense urban areas, from airborne LiDAR data.

Validation of MODIS Albedo Products of Paddy Fields in Japan

A study was conducted in Chiba, Japan, to validate Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products by taking the field measurements of shortwave band albedos in paddy fields. A large difference in spatial scale, from field-measured point data to 1-km resolution, complicates the validation process. To assess such effect of different spatial scales, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Enhanced Thematic Mapper Plus (ETM+) data were used. Spatial scale effects on the albedo were examined from three viewpoints: 1) comparison between point-based albedo and mean of albedo in homogeneous area; 2) comparison between point-based albedo and 1-km aggregated albedo; and 3) assessment of semivariogram of albedo in homogeneous area. In implementation of viewpoint 2), Liangs regression model was applied to convert ASTER reflectance into shortwave band albedo. The 1-km ASTER albedo was estimated using the point spread function, and in the same manner, 1-km ETM+ albedo was estimated. All results represent that an area around the measurement site can be assumed to be homogeneous, indicating negligible effects of spatial resolution difference during most of the periods. Comparison of ground-point-based albedos with MODIS actual albedo, estimated from MODIS black-sky albedo, white-sky albedo, and a fraction of diffuse skylight, showed that the accuracy of MODIS albedo products for paddy fields in Japan is within approximately 0.026 by absolute value (root-mean-square error) and 15.1% by relative value

Urban-Area Extraction From Polarimetric SAR Images Using Polarization Orientation Angle

In this letter, an algorithm is proposed that robustly extracts urban areas from polarimetric synthetic aperture radar images. Polarization orientation angle (POA), volume scattering power (Pv) derived by four-component decomposition, and total power (TP) are utilized in the proposed algorithm. The dependence of the four decomposition components on POA can be lessened by rotating the elements of the coherency matrix by the POA. However, a level of POA dependence remains even after the correction. The proposed algorithm utilizes POA-corrected components, but pixels are grouped into several categories according to POA. First, urban and farmland training data are selected for each category in a study area. Then, urban and mountain areas are separated from farmland, bare ground, and sea by utilizing the Pv-TP scattergram. Finally, a measure of the POA randomness between neighboring pixels is used to discriminate between urban areas with nearly homogeneous POA and mountain areas with randomly distributed POAs. When performing classification on more than one study area, thresholds manually selected for one of the study areas are used to automatically estimate thresholds for the other areas. An accuracy assessment demonstrates that POA-based categorization and utilization of POA randomness contribute to improving classification accuracy.

Knowledge-Based Modeling of Buildings in Dense Urban Areas by Combining Airborne LiDAR Data and Aerial Images

In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas, in which houses are typically located close to each other and their heights are similar. This makes it difficult to separate point clouds into individual buildings. A combination of airborne LiDAR and aerial images can be an effective approach to resolve this issue. Information about individual building boundaries, derived by segmentation of images, can be utilized for modeling. However, shadows cast by adjacent buildings cause segmentation errors. The algorithm proposed in this paper uses an improved segmentation algorithm (Susaki, J. 2012.) that functions even for shadowed buildings. In addition, the proposed algorithm uses assumptions about the geometry of building arrangement to calculate normal vectors to candidate roof segments. By considering the segmented regions and the normals, models of four common roof types—gable-roof, hip-roof, flat-roof, and slant-roof buildings—are generated. The proposed algorithm was applied to two areas of Higashiyama ward, Kyoto, Japan, and the modeling was successful even in dense urban areas.

Urban Density Estimation From Polarimetric SAR Images Based on a POA Correction Method

In this paper, an algorithm for estimating urban density from polarimetric synthetic aperture radar (SAR) images is proposed. Polarization orientation angle (POA) and four power components derived by four-component decomposition are used in the algorithm. In particular, in urban areas, SAR data are generally affected by factors such as the interval between buildings, building height, and building azimuth angle. Here, building azimuth (orientation) angle means the relative azimuth between the wall normal and the radars ground range direction. The interval between buildings and building height are used for building density calculation such as the building-to-land ratio and the floor area ratio. However, building azimuth angle which depends on satellite orbit has almost no relation with building density. The scattering intensity of microwaves emitted from SAR has a strong dependence on this building azimuth angle. Therefore, the main part of this paper is focused on the correction of this angular effect. The first step in the POA correction method is the extraction of homogeneous-POA city districts. In the second step, each power components scattering intensity is normalized for all pixels in a particular POA interval separately for different POA types of districts. In the case of Tokyo metropolitan area, Japan, estimated urban density from ALOS/PALSAR data has correlation coefficients of nearly 0.7 with the building-to-land ratio and 0.5 with the floor area ratio on the scale of hundreds of meter. In the areas where strong POA dependence is seen, the improvement of the correlation coefficient runs up to approximately 0.2.

Rice-Planted Area Mapping Using Small Sets of Multi-Temporal SAR Data

A rice-planted area map is a basic information resource for rice production management. Synthetic aperture radar (SAR) is an appropriate technique for rice mapping and so far is mostly based on extracting time series changes of backscattering (σ0) in a rice-planted area. However, sometimes there is not enough data to extract the σ0 curve for the area. To overcome this problem of a lack of data, we propose a method to detect rice-planted area by using small sets of multi-temporal SAR data. This method also addresses the fluctuation of σ0 values between SAR measurements. We have applied the method using multi-temporal ALOS/PALSAR data acquired over five years during the dry season. The rice-planted area was well detected and the viability of this method was demonstrated.

Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs

Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough textures, digital numbers (DNs) are quantized into several quantum values. Quantization using several interval widths is applied during segmentation, and for each quantization, areas with homogeneous values are labeled in an image. Edges determined from the homogeneous areas obtained at each quantization are then merged, and frequently observed edges are extracted. By using a “rectangular index”, regions whose shapes are close to being rectangular are thus selected as buildings. Experimental results show that the proposed algorithm generates more practical segmentation results than an existing algorithm does. Therefore, the main factors in successful segmentation of shadowed roofs are (1) combination of different quantization results, (2) selection of buildings according to the rectangular index, and (3) edge completion by the inclusion of non-edge pixels that have a high probability of being edges. By utilizing these factors, the proposed algorithm optimizes the spatial filtering scale with respect to the size of building roofs in a locality. The proposed algorithm is considered to be useful for conducting building segmentation for various purposes.

CdGeO3-phase transformations at high pressure and temperature and structural refinement of the perovskite polymorph

Four polymorphs of CdGeO3 were synthesized at high temperatures (600 ∼ 1200° C) and high pressures up to 12 GPa. The pyroxenoid phase synthesized under ambient pressure transforms to garnet, ilmenite and perovskite phases with increasing pressure. The phase boundary of ilmenite-perovskite had a slightly negative P-T slope in contrast to the positive P-T slopes of the pyroxenoid-garnet and garnet-ilmenite transition boundaries. CdGeO3III has the ilmenite structure with hexagonal lattice parameters, a=5.098 Å and c =14.883 Å. The c/a ratio of 2.919 is greater than that of any other ilmenite. CdGeO3IV has a distorted perovskite structure with orthorhombic lattice parameters a = 5.209 Å, b = 5.253 Å and c = 7.434 Å. Synthesis of a CdGeO3IV single crystal was successful and structural refinement revealed that the structure is isomorphic to GdFeO3 with the space group Pbnm. The increase of density with the CdGeO3III→CdGeO3IV transformation is the largest (9.8%) for any ilmenite-perovskite transition studied so far.

Automatic GCP Extraction of Fully Polarimetric SAR Images

This paper presents a method for automatic extraction of ground control points (GCPs) of fully polarimetric synthetic aperture radar (SAR) (PolSAR) images obtained from various satellites with different viewing angles. The scale-invariant feature transform (SIFT) algorithm is applied to extract candidate GCPs, where two-way keypoint matching eliminates improbable correspondence keypoints. Minimizing the root-mean-square error (rmse) also removes matching points with large rmse through a pseudoaffine transformation. In addition, information entropy and spatial dispersion quality constraints enable quantification of the spatial distribution of the GCPs. In accordance with full polarization, applying the SIFT-OCT algorithm (SIFT algorithm with the first scale-space octave skipped) to PolSAR data is examined. The total power (TP) image represents a combination of the characteristics of all four polarization images [horizontal transmitting and horizontal receiving (HH), horizontal transmitting and vertical receiving (HV), vertical transmitting and horizontal receiving (VH), and vertical transmitting and vertical receiving (VV)]. Therefore, GCP extraction using a TP image rather than each polarization image is proposed in order to maximize the accuracy of GCP extraction for all of the polarization data, as the TP image generates the highest signal-to-noise ratio (SNR) value. The SNR in conjunction with the matching correlation surface is used as an indicator of the reliability and accuracy of GCP extraction. After successfully applying the method to Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar and Japanese Earth Resources Satellite-1 SAR images, the GCP matching accuracy is further improved by using geometric calibration.