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Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima nuclear reactor explosion, an unmanned helicopter capable of obtaining overhead images, gathering radiation measurements, and mapping both the structural and radiation content of the environment would have given the response team invaluable data early in the disaster, thereby allowing them to understand the extent of the damage and areas where dangers to personnel existed. With this motivation, the Unmanned Systems Lab at Virginia Tech has developed a remote sensing system for radiation detection and aerial imaging using a 90 kg autonomous helicopter and sensing payloads for the radiation detection and imaging operations. The radiation payload, which is the sensor of focus in this paper, consists of a scintillating type detector with associated software and novel search algorithms to rapidly and effectively map and locate sources of high radiation intensity. By incorporating this sensing technology into an unmanned aerial vehicle system, crucial situational awareness can be gathered about a post-disaster environment and response efforts can be expedited. This paper details the radiation mapping and localization capabilities of this system as well as the testing of the various search algorithms using simulated radiation data. The various components of the system have been flight tested over a several-year period and a new production flight platform has been built to enhance reliability and maintainability. The new system is based on the Aeroscout B1-100 helicopter platform, which has a one-hour flight endurance and uses a COFDM radio system that gives the helicopter an effective range of 7 km. [ABSTRACT FROM AUTHOR]

Typhoon-induced P-wave microseisms can be observed using seismological arrays and analyzed for the seismic monitoring of ocean storms. This paper presents a frequency-domain beamforming (FB) method that integrates a three-dimensional (3-D) Earth model to better capture the heterogeneities in the subsurface structure, and therefore yield more accurate ray-tracing and travel-time predictions. This method is applied to the Super Typhoon Lupit (2009) using seismological array observations from the Northeast China Extended Seismic Array (NECESSArray) and high-sensitivity seismograph network in Japan (Hi-net). The results show that the localized P-wave microseism source regions based on the 3-D model are in better agreement with the theoretical source regions and typhoon centers than those based on a conventional one-dimensional (1-D) model. The significance of using a 3-D model instead of a 1-D model in the FB method is further investigated by comparing the consistency of the localization results for the two different arrays, with the localized source regions being more mutually concordant when using the 3-D model. The results demonstrate that integrating the 3-D model into the FB method improves the accuracy of locating the typhoon-induced P-wave microseism source regions. [ABSTRACT FROM AUTHOR]

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud. [ABSTRACT FROM AUTHOR]

In recent years, the population of sika deer in Japan has been increasing, causing animal damage. As for sika deer in the vicinity of the village of Yamanakako, Yamanashi Prefecture, although the total number of individuals is on a downward trend due to the controlled capture that has been conducted throughout the year since 2009, the number of individuals (population) that stay in the vicinity of residential areas (villa areas) has been increasing in recent years. Unmanned aerial vehicle (UAV) or drone has been proven as an effective tool in wildlife research. In particular, thermal images are useful for relatively large mammals such as deer. In this paper, the utility of drone remote sensing using RGB and thermal video is tested for controlled capture of sika deer as a case study in the village of Yamanakako. We develop a methodology to acquire data and to visually identify sika deer specifically for this mission. As a result, the drone remote sensing was considered successful completing within 1 hour and identifying 25 sika deer individuals. Thermal video format was effective to visually identify sika deer on site in terms of time savings, compared to RGB video or RGB/thermal still image format. To have flight area grided and hovering 3 seconds at the centre of a grid during the flight was essential both for visually identifying the number and location of sika deer and for sharing such information later with beaters and shooters using a map. [ABSTRACT FROM AUTHOR]

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. [ABSTRACT FROM AUTHOR]

After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner's lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms. [ABSTRACT FROM AUTHOR]

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable. [ABSTRACT FROM AUTHOR]

Wind retrieval from Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) using an L-band model function in coastal regions is evaluated. It is known that JERS-1 SAR has excessive ambiguities. This paper also gives a quantitative evaluation of excessive ambiguities in coastal scenes of JERS-1 SAR. First, focusing on the cases where wind blows from the shore in Sagami Bay, we investigate phenomena of wind speed increase with offshore distance using European Remote sensing Satellite-1 (ERS-1) SAR-derived wind speeds. The relation between wind speed and offshore distance is well formulated, which indicates the transition of the atmospheric boundary layer from land to sea surface. Wind speeds derived from JERS-1 SAR should be overestimated due to the excessive ambiguity. Then, for observation time of each JERS-1 SAR capturing the cases that wind blows from the shore in Sagami Bay, the expected wind speed growth profile is derived from the wind speed growth formula and an in situ wind observation of Hiratsuka Experiment Station. We convert the wind-speed profile into the sigma-0 profile by an L-band model function. Finally, the profiles of JERS-1 SAR-observed and the estimated sigma-0 are compared, and the excessive ambiguity is estimated as the difference between them. As a result, the dynamic range of first azimuth ambiguity is as large as that of the wind-relating signal from the ocean surface. Moreover, higher order azimuth ambiguities and range ambiguity also may have a significant impact on near-shore wind retrieval. Ambiguities, Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR), L-band model function, synthetic aperture radar (SAR) wind retrieval. [ABSTRACT FROM AUTHOR]

In mid-May 2020, a coccolithophore bloom of Gephyrocapsa oceanica Kamptner, 1943 was observed in Sagami Bay, Japan, possibly for the first time in 25 years. This species is a common yet elusive taxon in the waters around Japan, but there has only been one report of a bloom observation in Sagami Bay in 1995. The G. oceanica bloom in 2020 was observed by the combined approach of the JAXA ocean color remote sensing satellite "Shikisai" and field surveys. The true-color RGB images from the satellite showed the initial bloom in Tokyo Bay appears to have advected into Sagami Bay on May 5. On May 15, ad hoc field sampling was conducted based on visual confirmation from the shore. Nitrate, nitrite and phosphate concentrations at the sea surface were 3.28 µM, 0.11 µM and 0.29 µM, respectively, and higher than the median of May from 1997 to 2019. Chlorophyll a concentration was 2.23 µg L-1 at the sea surface, and cell density was 5.3 × 10³ cells mL-¹. The large bloom appears to have reached the western shorelines of the bay, and dispersed counterclockwise within the bay. The bloom reached an estimated 1119.02 km² surface expression on May 17, and completely disappeared by May 24. Further, the median value of 19'-hexanoyloxyfucoxanthin (a signature pigment of haptophytes, including coccolithophores) concentration has been increasing from 2017 to 2020, and the coastal area of Sagami Bay may have changed to a region favorable for haptophytes. [ABSTRACT FROM AUTHOR]

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8's classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making. [ABSTRACT FROM AUTHOR]

Featured Application: Application of the SWAT model at an hourly scale for flood simulation in a coastal basin. Hourly scale hydrological modeling holds pivotal significance for flood-related research, as watershed floods often occur within a few hours of intense rainfall. Although the SWAT model's hourly simulation has found application in a few watersheds, the relatively short usage history and limited scope inhibit a comprehensive grasp of its potential. In this study, a coastal watershed in Fukuyama City, Japan was selected as a case study for conducting hourly simulations. The study highlights the hourly simulation's proficiency in capturing diverse flood trends while also accurately replicating baseflow and flood peaks within the 0 to 200 m3/s range. However, there is an underestimation of peak flows in calculations exceeding 200 m3/s. Moreover, empirical summaries have been generated to outline the parameterization process of hourly simulation, revealing how distinct parameters influence simulated runoff. For instance, it outlines how BFLO_DIST significantly affects baseflow simulation when operating in hourly mode. Our findings provide an empirical summary of the advantages, disadvantages, and parameterization process for hourly simulations of the SWAT model. [ABSTRACT FROM AUTHOR]

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time. [ABSTRACT FROM AUTHOR]

Urbanization has led to rapid growth in energy consumption and CO2 emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing data. However, there is still a lack of research on estimating long-term BCEs by integrating multi-source remote sensing data and applications in different regions. We selected the Beijing–Tianjin–Hebei (BTH) urban agglomeration and the National Capital Region of Japan (NCRJ) as research areas for this study. We also built multiple linear regression (MLR) models between prefecture-level BCEs and multi-source remote sensing data. The prefecture-level BCEs were downscaled to grid scale at a 1 km2 resolution. The estimation results verify the method's difference and accuracy at different development stages. The multi-scale BCEs showed a continuous growth trend in the BTH urban agglomeration and a significant downward trend in the NCRJ. The decrease in energy intensity and population density were the main factors contributing to the negative growth of BCEs, whereas GDP per capita and urban expansion significantly promoted it. Through our methods and analyses, we contribute to the study of estimating greenhouse gas emissions with remote sensing and exploring the environmental impact of urban growth. [ABSTRACT FROM AUTHOR]

In the 2011 Tohoku tsunamigenic earthquake, ionospheric anomalies generated by tsunami-induced gravity waves were observed by many types of instruments. Three digisondes and one Doppler receiver located in East Asia were applied to investigate the far-field ionospheric response to the westward-propagating gravity waves generated by the earthquake. Based on time–period spectrum analysis, oscillations between 20 and 36 min on the fo\textF2 (critical frequency of F2 layer) curves and Doppler variations of each observation location were identified. The horizontal group speed, generation time, and source location estimated with the ray-tracing method all indicated that the periodic disturbances recorded by the four radio systems were gravity waves induced by the tsunami following the 2011 Tohoku earthquake. The plasma frequency variations at five fixed altitudes in the ionospheric F2 layer over I-Cheon were used to investigate the vertical propagation of the tsunami-associated gravity waves. The measured phase progression in the vertical direction was opposite that of the energy transport, which further confirmed that the recorded waves were atmospheric gravity waves. The use of multisite remote sensing for examining tsunami-induced waves in the ionosphere may open new perspectives in oceanic monitoring and future tsunami warning systems. [ABSTRACT FROM PUBLISHER]

In our previous regression model for estimating forest biomass, it was shown that non-Gaussian amplitude fluctuations in high-resolution polarimetric synthetic aperture radar (SAR) data of coniferous forests can accurately be described by the K-distribution and that the order parameter of the K-distribution can be useful in estimating the tree biomass of coniferous forests from L-band cross-polarization amplitude images in a wider range than the conventional method using the radar cross section alone. The result was based on the analysis of the "ground-truth" biomass data of 19 forest stands and airborne polarimetric interferometric SAR L-band data over the Tomakomai forests in Hokkaido, Japan. From this relation, an empirical regression model was developed to estimate forest biomass from SAR data. In this paper, we report the results on further analyses of this regression model. The validity of the K-distribution is first reconfirmed using the Akaike information criterion, followed by the description on the accuracy of the model. To examine model accuracy, we carried out further field measurements on 22 forest stands in 2005, and the ground survey was made in 2006 to find out the causes of several anomalous data. Based on a comparison of the model-based biomass and the ground-truth data, the accuracy of the model was found to be approximately 86%. The regression model was then updated for practical application in estimating the biomass of the Hokkaido forests by including the ground-truth data of all 41 forest stands. [ABSTRACT FROM AUTHOR]

Combined NO2 and CO2 observations have the potential to constrain the identification of the locations and strength of urban CO2 emissions, in particular, point sources such as power plants. We report the first results of airborne spectroscopic NO2 and CO2 observations over an urban area in Japan in February 2018. Inversed emission rates of two stacks of the coal‐fired power plant for CO2 showed relatively good agreement with those estimated by a bottom‐up inventory—the Regional Emission inventory in ASia (REAS) v3.1—within −7% to 40% because the plume shapes were well identified due to constraint by NO2 measurements. The estimated NOx emission rates showed discrepancies more than 80% with those estimated by the REAS v3.1, mainly due to the uncertainties in activity data and emission factors, or in the greatly varying NO/NO2 ratios in fresh plumes, which warrant further investigations when estimating NOx emissions from satellite NO2 observations on km‐scales. Plain Language Summary: Burning of fossil fuels at high temperatures constitutes a major anthropogenic source of nitrogen oxides (NOx) and carbon dioxide (CO2). While CO2 stays in the atmosphere for hundreds of years, thereby being a well‐mixed gas, NO2 has a much shorter lifetime of only a few hours. This substantial difference in lifetime between NO2 and CO2 means that concurrent NO2 and CO2 observations obtained by the same platform can be used to identify the locations and strength of CO2 emissions from point sources such as power plants. In February 2018, for the first time, we obtained concurrent airborne spectroscopic NO2 and CO2 observations over an urban area, to demonstrate the traceability of NO2 to CO2. The plumes of co‐emitted NO2 and CO2 were derived from measured spectra. The plumes of NO2 and CO2 co‐emitted from the stacks of power plants were well identified owing to constraint by NO2. Uncertainties of inversed emission rates were statistically derived. For CO2, the results were within 40% in agreement with a bottom‐up emission inventory known as REAS v3.1. For NOx, however, a disagreement of 80% was identified, likely due to the uncertainties of the inventory data or in the NOx partitioning in fresh plumes. Key Points: Concurrent observations of NO2 and CO2 in fresh plumes from a large single point source using airborne spectrometers were reportedColumnar enhancements of NO2 and CO2 due to power plant emissions were up to 3.8 × 1016 molec.cm−2 and 75 ppm, respectivelyPlume shapes of CO2 emitted from power plants were well identified and constrained by NO2 plume shapes [ABSTRACT FROM AUTHOR]

A phreatic eruption suddenly occurred at Motoshirane (Kusatsu-Shirane volcano, Japan) at 10:02 JST on January 23, 2018. A member of the Japan Self-Defense Force was killed by volcanic blocks during training in Motoshirane, and 11 people were injured by volcanic blocks or fragments of broken glass. According to a field survey, ash fall was confirmed in Minakami, about 40 km east-northeast from Motoshirane. Although the eruption was not captured by a distant camera, the eruption plume/cloud was captured by three of the Japan Meteorological Agency's operational weather radars. These radars observed the echo propagated to the northeast in the lower troposphere, and to the east in the middle troposphere. This is generally consistent with the observed ash fall distribution. Using the modified probabilistic estimation method, the maximum plume height was estimated to be about 5580 ± 506 m (1σ) above sea level. Estimates of the erupted mass based on the range of plume heights from radar observations and the duration of volcanic tremor during the eruption (about 8 min) do not match that obtained from a field survey (3.0–5.0 × 107 kg). This discrepancy confirms that estimates of erupted mass based on plume heights must account for eruption style parametrically, which can only be constrained by case studies of varied eruption styles. [ABSTRACT FROM AUTHOR]

Ionospheric total electron content (TEC) is one of the key parameters for users of radio-based systems, such as the Global Navigation Satellite System, high-frequency communication systems, and space-based remote sensing systems, since total ionospheric delay is proportional to TEC through the propagation path. It is important to know extreme TEC values in readiness for hazardous ionospheric conditions. The purpose of this study is to estimate extreme TEC values with occurrences of once per year, 10 years, and hundred years in Japan. In order to estimate the extreme values of TEC, a cumulative distribution function of daily TEC is derived using 22 years of TEC data from 1997 to 2018. The extreme values corresponding to once per year and 10 years are 90 and 110 TECU, respectively, in Tokyo, Japan. On the other hand, the 22-year data set is not sufficient to estimate the once-per-100-year value. Thus, we use the 62-year data set of manually scaled ionosonde data for the critical frequency of the F-layer (foF2) at Kokubunji in Tokyo. First, we study the relationship between TEC and foF2 for 22 years and investigate the slab thickness. Then the result is applied to the statistical distribution of foF2 data for 62 years. In this study, two methods are applied to estimate the extreme TEC value. In the first method, the distribution of slab thickness is artificially inflated to estimate extreme TEC values. In the second method, extreme slab thicknesses are applied to estimate extreme TEC values. The result shows that the once-per-100-year TEC is about 150–190 TECU at Tokyo. The value is also estimated to be 180–230 TECU in Kagoshima and 120–150 TECU in Hokkaido, in the southern and northern parts of Japan, respectively. [ABSTRACT FROM AUTHOR]

Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake, and the effect of seasons on the quality, is important. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of the water quality parameters. To obtain maps that are in good agreement with the experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM), and considered the effect of specific disturbances and uncertainties on the remote sensing data. Furthermore, FCM using only remote sensing data creates estimation maps in which relative water surface conditions are classified. Therefore, determining the relationship between FCM results and water quality facilitates the creation of low-cost, high-frequency water quality estimation maps. Our results indicated that FCM was particularly effective in determining the presence of suspended solids (SS) during water quality analysis. However, the relationship between FCM results and water quality has not been determined in detail. In this study, we analyzed the water quality conditions of Lake Hachiroko with FCM using the data collected by the Advanced Space-borne Thermal Emission and Reflection Radiometer on Terra and, the Operational Land Imager on Landsat-8. In addition, FCM results were compared with the maps created by fuzzy regression analysis and the actual conditions of water pollution. The results indicated that (i) the maps created using FCM are effective in determining the water surface conditions, (ii) the FCMmaps using data obtained during August and September have a strong relationship with biochemical oxygen demand (BOD) and SS, and (iii) the FCM maps using data obtained during May and June have a strong relationship with chemical oxygen demand (COD), SS, and total nitrogen (TN). [ABSTRACT FROM AUTHOR]

To accurately evaluate the responses of spatial and temporal variation of ecosystem functioning (evapotranspiration and photosynthesis) and services (regulating and cultural services) to the rapid changes caused by global warming, we depend on long-term, continuous, near-surface, and satellite remote sensing of phenology over wide areas. Here, we review such phenological studies in Japan and discuss our current knowledge, problems, and future developments. In contrast with North America and Europe, Japan has been able to evaluate plant phenology along vertical and horizontal gradients within a narrow area because of the country's high topographic relief. Phenological observation networks that support scientific studies and outreach activities have used near-surface tools such as digital cameras and spectral radiometers. Differences in phenology among ecosystems and tree species have been detected by analyzing the seasonal variation of red, green, and blue digital numbers (RGB values) extracted from phenological images, as well as spectral reflectance and vegetation indices. The relationships between seasonal variations in RGB-derived indices or spectral characteristics and the ecological and CO flux measurement data have been well validated. In contrast, insufficient satellite remote-sensing observations have been conducted because of the coarse spatial resolution of previous datasets, which could not detect the heterogeneous plant phenology that results from Japan's complex topography and vegetation. To improve Japanese phenological observations, multidisciplinary analysis and evaluation will be needed to link traditional phenological observations with 'index trees,' near-surface and satellite remote-sensing observations, 'citizen science' (observations by citizens), and results published on the Internet. [ABSTRACT FROM AUTHOR]

Japan, with over 75% forest cover, is one of the most heavily forested countries in the world. Various types of climax forest are distributed according to latitude and altitude. At the same time, human intervention in Japan has historically been intensive, and many forest habitats show the influence of various levels of disturbance. Furthermore, Japanese landscapes are changing rapidly, and a system of efficient monitoring is needed. The aim of this research was to identify major historical trends in Japanese landscape change and to develop a system for identifying and monitoring patterns of landscape change at the national level. To provide a base for comparison, Warmth Index (WI) climatic data was digitalized and utilized to map potential climax vegetation for all of Japan. Extant Land Use Information System (LUIS) data were then modified and digitalized to generate national level Land Use/Land Cover (LU/LC) distribution maps for 1900, 1950 and 1985. In addition, MODIS data for 2001 acquired by the Tokyo University of Information Sciences were utilized for remote LU/LC classification using an unsupervised method on multi-temporal composite data. Eight classification categories were established using the ISODATA (cluster analyses) method; alpine plant communities, evergreen coniferous forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, arable land (irrigated rice paddy, non-irrigated, grassland), urban area, river and marsh. The results of the LUIS analyses and MODIS classifications were interpreted in terms of a Landscape Transformation Sere model assuming that under increasing levels of human disturbance the landscape will change through a series of stages. The results showed that overall forest cover in Japan has actually increased over the century covered by the data; from 72.1% in 1900 to 76.9% in 2001. Comparison of the actual vegetation and the potential vegetation as predicted by WI, however, indicated that in many areas the climax vegetation has been replaced by secondary forests such as conifer timber plantations. This trend was especially strong in the warm and mid temperate zones of western Japan. This research also demonstrated that classification of moderate resolution remote sensing data, interpreted within a LTS framework, can be an effective tool for efficient and repeat monitoring of landscape changes at the national level. In the future, the authors plan to continue utilizing this approach to track rapidly occurring changes in Japanese landscapes at the national level. [ABSTRACT FROM AUTHOR]

CO2 sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO2 sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO2/yr for coniferous, 0.77 MtCO2/yr for deciduous broadleaf, and 2.25 MtCO2/yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO2/yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO2/yr (coniferous) and 0.31 MtCO2/yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO2 sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources. [ABSTRACT FROM AUTHOR]

Radar scattering mechanisms over landslide areas were studied using representative full polarimetric parameters: Freeman-Durden decomposition, and eigenvalue-eigenvector decomposition. Full polarimetric ALOS (Advanced Land Observation Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) datasets were used to examine landslides caused by the 2008 Iwate-Miyagi Nairiku Earthquake in northern Japan. The Freeman-Durden decomposition indicates that areas affected by large-scale landslides show dominance of the surface scattering component in both ascending and descending orbit data. The polarimetric parameters of eigenvalue-eigenvector decomposition, such as entropy, anisotropy, and alpha angle, were also computed over the landslide areas. Unsupervised classification based on the H-α plane explicitly distinguishes landslide areas from others such as forest, water, and snow-covered areas, but does not perform well for farmland. A landslide area is difficult to recognize from a single-polarization image, whereas it is clearly extracted on the full polarimetric data obtained after the earthquake. From these results, we conclude that 30-m resolution full polarimetric data are more useful than 10-m resolution single-polarization PALSAR data in classifying land coverage, and are better suited to detect landslide areas. Additional information, such as pre-landslide imagery, is needed to distinguish landslide areas from farmland or bare soil. [ABSTRACT FROM AUTHOR]

Quantitative real-time observations of a tsunami have been limited to deep-water, pressure-sensor observations of changes in the sea surface elevation and observations of sea level fluctuations at the coast, which are essentially point measurements. Constrained by these data, models have been used for predictions and warning of the arrival of a tsunami, but to date no system exists for local detection of an actual incoming wave with a significant warning capability. Networks of coastal high frequency (HF)-radars are now routinely observing surface currents in many countries. We report here on an empirical method for the detection of the initial arrival of a tsunami, and demonstrate its use with results from data measured by fourteen HF radar sites in Japan and USA following the magnitude 9.0 earthquake off Sendai, Japan, on 11 March 2011. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends on the bathymetry: the wider the shallow continental shelf, the greater this time. We compare arrival times at the radars with those measured by neighboring tide gauges. Arrival times measured by the radars preceded those at neighboring tide gauges by an average of 19 min (Japan) and 15 min (USA) The initial water-height increase due to the tsunami as measured by the tide gauges was moderate, ranging from 0.3 to 2 m. Thus it appears possible to detect even moderate tsunamis using this method. Larger tsunamis could obviously be detected further from the coast. We find that tsunami arrival within the radar coverage area can be announced 8 min (i.e., twice the radar spectral time resolution) after its first appearance. This can provide advance warning of the tsunami approach to the coastline locations. [ABSTRACT FROM AUTHOR]

Quantitative real-time observations of a tsunami have been limited to deep-water, pressure-sensor observations of changes in the sea surface elevation and observations of sea level fluctuations at the coast, which are essentially point measurements. Constrained by these data, models have been used for predictions and warning of the arrival of a tsunami, but to date no detailed verification of flow patterns nor area measurements have been possible. Here we present unique HF-radar area observations of the tsunami signal seen in current velocities as the wave train approaches the coast. Networks of coastal HF-radars are now routinely observing surface currents in many countries and we report clear results from five HF radar sites spanning a distance of 8,200 km on two continents following the magnitude 9.0 earthquake off Sendai, Japan, on 11 March 2011. We confirm the tsunami signal with three different methodologies and compare the currents observed with coastal sea level fluctuations at tide gauges. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends on the bathymetry: the wider the shallow continental shelf, the greater this time. Data from these and other radars around the Pacific rim can be used to further develop radar as an important tool to aid in tsunami observation and warning as well as post-processing comparisons between observation and model predictions. [ABSTRACT FROM AUTHOR]

Radiarta, I N., Saitoh, S-I., and Yasui, H. 2011. Aquaculture site selection for Japanese kelp (Laminaria japonica) in southern Hokkaido, Japan, using satellite remote sensing and GIS-based models. – ICES Journal of Marine Science, 68: 773–780.Japanese kelp (Laminaria japonica) is an important species cultured and harvested in Japan. The most suitable areas for hanging culture in southern Hokkaido were determined using geographic information system (GIS) models and a multicriteria evaluation approach. Analyses of physical parameters (sea surface temperature and suspended solid from SeaWiFS and MODIS) and available bathymetric data indicated that some 74% (1139 km2) of the total potential area with bottom depths <60 m had the two highest suitability scores. A local sensitivity analysis indicated that suspended solids were more important than temperature in affecting model output. This study demonstrates that GIS databases of different formats and sources can be used effectively to construct spatial models for kelp aquaculture. [ABSTRACT FROM PUBLISHER]

The dielectric properties of volcanic ash from five volcanoes in Japan were measured by using the transmission! reflection coaxial line method of a network analyzer, in a frequency range from 3 to 13 GHz. The measured permittivity of powdered ash was then converted to that of solid ash (dense rock equivalent) with the aid of effective medium theory. The values of solid-ash relative permittivities for the five volcanoes revealed that they were in a relatively narrow range. Their real part was generally in a range from five to six in magnitude, while their imaginary part was in a range from 0.08 to 0.18. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are prolonged discrete events of anomalously warm seawater observed around the world. The occurrence of MHWs in the Japan/East Sea (JES), a middle latitude marginal sea with abundant aquatic resources, has severe impacts on marine ecosystems and human society. We used satellite remote sensing products spanning 1982–2020 to investigate the mean states and variations of JES MHWs. The results show that the JES MHWs occurred twice per year. More than 40% of the MHWs were shorter than the mean duration of 12.6 days and weaker than the mean intensity of 2.4 °C. Frequent MHWs were observed in August, while high mean intensities were observed in winter and spring in the western JES. In recent years, the area's averaged yearly sum of MHW intensity in the JES has increased dramatically, with a positive trend of 29.62 °C days per decade—over twice the average global trend (12.37 °C days per decade), which is further confirmed by its first empirical orthogonal function mode. The strengthening trend in MHWs may be explained by oceanic dynamics rather than atmospheric forcing, and may result in increased surface latent heat flux from the ocean to the atmosphere. [ABSTRACT FROM AUTHOR]

Reliable prediction of precipitation by numerical weather prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, to retrieve reasonable cloud distributions, a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a four-stream fast microwave radiative transfer model in the atmosphere as an observation operator, and a global minimization method that is known as the shuffled complex evolution. IMDAS assimilates the satellite microwave radiometer data set of the Advanced Microwave Scanning Radio- meter for the Earth Observing System (AMSR-E) and retrieves integrated water vapor and integrated cloud liquid water content. This new method successfully introduces heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan. It has significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions. [ABSTRACT FROM AUTHOR]

The relationship between alder ( Alnus japonica) distribution and surrounding land use in Kushiro Mire was spatially assessed using remotely sensed imagery. From the result, it was found out that the expanding area of alder trees in Kushiro Mire was affected by the agricultural land area in the upper course of the river basin and flooding in the lower course of the river. The soil sediments flowing into the Kushiro Mire from the agricultural land resulted in heavy sedimentation that favors the growth of alder trees. Consequently, the number and density of alder trees has increased. The future distribution of alder trees was predicted based on the mechanism of expansion of the alder-tree area in Kushiro Mire, and it was found that large vegetation areas in Kushiro Mire will be changed to areas with alder trees. [ABSTRACT FROM AUTHOR]

This research investigates the applicability of combining spatial filter's algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll- α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC's centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll- α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio's centerline extracted from heat flux, chlorophyll- α , and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal environment of the ocean after a heavy rain disaster is critical for satellite remote sensing research and disaster prevention. We attempted to reproduce changes in marine debris distributions using multidate data of Landsat-8 spectral reflectance acquired immediately after a heavy rain disaster in western Japan in July 2018. Data from cleaning ships were used for screening the marine debris area. As most of the target marine debris consisted of plant fragments, a method based on the corrected floating algae index (cFAI) was applied to Landsat-8 data. Data from cleaning ships clarify that most of the marine debris accumulated in the waters in the northern part of Aki Nada, a part of the Seto Inland Sea. The spectral characteristics of the corresponding marine debris spectral reflectance obtained from the Landsat-8 data were explained by the FAI with band 5 (central wavelength: 865 nm) as the maximum value. Unlike traditional FAI, cFAI eliminated the effect of background water turbidity. The Otsu method was effective for the automatic threshold determination for cFAI. Although Landsat-8 data have limited spatial resolution and observation frequency, these data were useful for understanding marine debris distribution after a heavy rain disaster. [ABSTRACT FROM AUTHOR]

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model's efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model's ability to represent the ocean currents near the coast accurately. [ABSTRACT FROM AUTHOR]

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response. [ABSTRACT FROM AUTHOR]

Ground-based remote sensing equipment have the potential to be used for the nowcasting of the tephra hazard from volcanic eruptions. To do so raw data from the equipment first need to be accurately transformed to tephra-related physical quantities. In order to establish these relations for Sakurajima volcano, Japan, we propose a methodology based on high-resolution simulations. An eruption that occurred at Sakurajima on 16 July 2018 is used as the basis of a pilot study. The westwards dispersal of the tephra cloud was ideal for the observation network that has been installed near the volcano. In total, the plume and subsequent tephra cloud were recorded by 2 XMP radars, 1 lidar and 3 optical disdrometers, providing insight on all phases of the eruption, from plume generation to tephra transport away from the volcano. The Weather Research and Forecasting (WRF) and FALL3D models were used to reconstruct the transport and deposition patterns. Simulated airborne tephra concentration and accumulated load were linked, respectively, to lidar backscatter intensity and radar reflectivity. Overall, results highlight the possibility of using such a high-resolution modelling-based methodology as a reliable complementary strategy to common approaches for retrieving tephra-related quantities from remote sensing data. [ABSTRACT FROM AUTHOR]

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy ( PA ), user accuracy ( UA ), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models. [ABSTRACT FROM AUTHOR]

The current generation of geostationary weather satellite instruments, such as the Advanced Baseline Imagers (ABIs) on board the US NOAA GOES 16 and 17 satellites and the Advanced Himawari Imagers (AHIs) on board the Japanese Himawari-8/9 satellites, have six channels located in the visible to shortwave IR (SWIR) spectral range. These instruments can acquire images over both land and water surfaces at spatial resolutions between 0.5 and 2 km and with a repeating cycle between 5 and 30 min depending on the mode of operation. The imaging data from these instruments have clearly demonstrated the capability in detecting sediment movements over coastal waters and major chlorophyll blooms over deeper oceans. At present, no operational ocean color data products have been produced from ABI data. Ocean color data products have been operationally generated from AHI data at the Japan Space Agency, but the spatial coverage of the products over very turbid coastal waters are sometimes lacking. In this article, we describe atmospheric correction algorithms for retrieving water leaving reflectances from ABI and AHI data using spectrum-matching techniques. In order to estimate aerosol models and optical depths, we match simultaneously the satellite-measured top of atmosphere (TOA) reflectances on the pixel by pixel basis for three channels centered near 0.86, 1.61, and 2.25 μm (or any combinations of two channels among the three channels) with theoretically simulated TOA reflectances. We demonstrate that water leaving reflectance retrievals can be made from ABI and AHI data with our algorithms over turbid case two waters. Our spectrum-matching algorithms, if implemented onto operational computing facilities, can be complimentary to present operational ocean versions of atmospheric correction algorithms that are mostly developed based on the SeaWiFS type of two-band ratio algorithm. [ABSTRACT FROM AUTHOR]

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively. [ABSTRACT FROM AUTHOR]

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%. [ABSTRACT FROM AUTHOR]