Abstract: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.
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.Index Terms-Four-component decomposition, polarimetric synthetic aperture radar (SAR), polarization orientation angle (POA), urban-area extraction.
The garnet‐perovskite phase transformation in CaGeO3 was investigated in the pressure‐temperature region to 6.5 GPa and 1200°C using a cubic anvil type of high‐pressure apparatus combined with synchrotron radiation. In‐situ measurements with an energy dispersive x‐ray diffraction system enable us to carry out dynamical observation of the transformation. The equilibrium phase boundary between the garnet and perovskite phases was determined as P(GPa)= 6.9 ‐ 0.0008T(°C). The negative P‐T slope definitely established in the present study is in reasonable agreement with the value, −0.0023(8) GPa/°C, that was calculated from the thermochemical data on the enthalpy of transition. The molar volume change accompanied with this transformation was estimated to be about 13% at about 6 GPa and 1000°C.
Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013October ( to 2016, in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R 2 = 0.77) to determine the water clarity (2013)(2014)(2015)(2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R 2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg dry weight (DW) m −2 . The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m −2 ) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.
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 radar's 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 component's 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.
Phase boundaries for the reactions CaGeO3 (wollastonite → garnet) and CaGeO3 (garnet → perovskite) were determined at high pressure and temperature. The enthalpies of these transitions were measured by high temperature calorimetry. These studies indicate a positive dP/dT for the wollastonitegarnet transition and a negative dP/dT for the garnet‐perovskite transition. These PT slopes are further supported by calculations of lattice vibrational entropies based on Kieffer's model and the infrared and Raman spectra of the three polymorphs.
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