Estimation of Precipitable Water Using Numerical Prediction Data

Precipitable water (PW) is an important variable in the climate system. Interferometric synthetic aperture radar (InSAR) is a powerful remote sensing technique for measuring the topography and deformation of the Earth’s surface. However, variations in atmospheric water vapor content affect the accuracy of InSAR measurements. Therefore, it is important to understand the distribution of PW to mitigate atmospheric effects on remote sensing data. Herein, we estimated the PW distribution with high spatial resolution using numerical prediction data and digital elevation model (DEM) data from the Kanto region of Japan. We estimated the PW distribution at a resolution of 90 m from mesoscale model grid point value data while accounting for the difference in surface elevation within pixels using DEM data with a resolution of 90 m. The PW distribution at 90-m resolution could be estimated using the proposed method with good accuracy (root-mean-square difference within 4.0 mm) throughout the year. The proposed method provides high-resolution information on atmospheric water vapor content and its variation at 3-h intervals. This method is expected to be applicable in climate research and for the atmospheric correction of remote sensing data, which can improve the accuracy of remote sensing measurements

Estimation of Land Surface Albedo from GCOM-C/SGLI Surface Reflectance

XXIV ISPRS Congress “Imaging today, foreseeing tomorrow, ” Commission III2021 edition, 5–9 July 2021This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C)/Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research

Region-based automatic mapping of tsunami-damaged buildings using multi-temporal aerial images

After a disaster, prompt distribution of information is critical for national or local governments to plan the disaster response and recovery measures. In case of a tsunami, information about buildings destroyed by the waves is required. Here, we present a method that identifies individual damaged buildings by using aerial images obtained pre- and post-tsunami. The method utilizes significant height changes in building regions to assess the damage. Stereo aerial images are used to generate a digital surface model (DSM) of the area. We assume two cases: if geographic information system (GIS) data (building region data) are available, we use them and if GIS data are unavailable, we instead use segmented results and a filtered DSM. In each case, regions corresponding to buildings are identified in the pre-tsunami image. Damaged regions are then extracted by considering the height change within a building region between the pre- and post-disaster images. Horizontal shifts resulting from land deformation caused by the earthquake are automatically estimated by an existing algorithm such as scale-invariant feature transform (Lowe in Int J Comput Vis, 60(2):91–110, 2004). Validation showed that the proposed method extracted damaged buildings with high accuracy (94–96 % in number and 96–98 % in area) when GIS data are available and with lower accuracy (69–79 % in area) when GIS data are unavailable. In addition, we found that horizontal shifts between pre- and post-disaster should be considered to extract the damaged buildings. We conclude that our method can automatically generate effective maps of buildings damaged not only by tsunamis but also by other disasters

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

Sensitivity Analysis for L-Band Polarimetric Descriptors and Fusion for Urban Land Cover Change Detection

A fully polarimetric synthetic aperture radar (PolSAR) image allows the generation of a number of polarimetric descriptors. These descriptors are sensitive to changes in land use and cover. Thus, the objective of this study is twofold: first, to identify the most effective descriptors for each change type and ascertain the best complementary pairs from the selected polarimetric descriptors; and second, to develop an information fusion approach to use the unique features found in each polarimetric descriptor to obtain a better change map for urban and suburban environments. The effectiveness of each descriptor was assessed through statistical analysis of the sensitivity index in selected areas and through change detection results obtained by using the supervised thresholding method. A good agreement was found between the statistical analysis and the performance of each descriptor. Finally, a polarimetric information fusion method based on the coupling of modified thresholding with a region-growing algorithm was implemented for the identified complementary descriptor pairs. The mapping accuracy, as measured by the Kappa coefficient, was improved by 0.09 (from 0.76 to 0.85) with a significant reduction of false and missing alarm rates compared to using single PolSAR images

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 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

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

Analysis of scattering components from fully polarimetric SAR images for improving accuracies of urban density estimation

[XXIII ISPRS Congress, Commission VII] 12-19 July 2016, Prague, Czech RepublicIn this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index T[v+c] obtained by normalizing the sum of volume and helix scatterings P[v+c]. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why T[v+c] is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of P[v+c] are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that T[v+c] works most effectively because of its similarity to the normal distribution

Detection of Differential Settlement of Man-Made Structures Coupled with Urban Development by Using Persistent Scatterer Interferometry (PSI)

Many cities are prone to land subsidence, particularly due to the overuse of ground water. However, because man-made structures are normally built upon foundations that are stiffer than the surrounding ground, such structures react to land subsidence to a lesser extent. This settlement mismatch between ground and buildings, also known as differential settlement (DS), may cause serious problems in urban management, such as foundation overloading due to down-drag forces and damage to underground pipelines. Here, we present a technique for determining DS from multi-temporal satellite synthetic aperture radar (SAR) images. Permanent scatterers originating from ground and man-made structures are extracted using the differential interferometric SAR (DInSAR) technique, whereupon the DS is obtained by subtracting the settlement of the former from that of the latter. For validation purposes, we demonstrate that the estimated DS in Bangkok is consistent with field observations