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This study uses ozone in the atmosphere column collected by the Aura satellite's ozone monitoring instrument (OMI), to evaluate the ozone pollution status of Japan. Mann–Kendall and slope trend analysis, Hurst index analysis, the potential source contribution factor algorithm, GTWR (geographically and temporally weighted regression model), and the random forest regression model were used in this paper to investigate ozone column concentrations in Japan from 2010 through 2021. The results showed that ozone column concentrations had a significant latitudinal trend over the past 12 years, i.e., concentrations increased with increasing latitude. And the slope has showed a small upward trend over the years (slope = 0.01). The interannual maximum and minimum ozone column concentrations are in 2021 (387.57 DU) and 2015 (241.27 DU), respectively. The monthly maximum and minimum values occur in March (361.60 DU) and October (286.96 DU), with seasonal concentrations in the order of Spring (352.94 DU) > Winter (336.01 DU) > Summer (306.78 DU) > Autumn (296.30 DU). Column ozone concentrations have increased in 69.82% of Japan over the past 12 years. In 2022, the maximum, minimum, and mean ozone column concentrations based on random forest forecasts are 372DU, 278DU, and 334DU, respectively. Pollution sources in Japan are largely derived from pollutants that are transported across borders from the seas around the country or from other countries in the world, with the largest potential source areas being located in the Kanto region. Relative humidity, lift index, and air temperature (in all three seasons except winter) have a negative effect on ozone column concentrations. The positive effects of precipitable water and nitrogen dioxide on ozone column concentrations in the Hokkaido locality and southern Japan were more significant. The contribution of population density and GDP to ozone is small. [ABSTRACT FROM AUTHOR]

Recent advancements in remotely sensed techniques have markedly expanded data acquisition potential in riverine studies, but the techniques' applicability must be validated and improved because of uncertainties associated with diverse field conditions. This study is the first experimental evidence of using a newly designed unmanned aerial vehicle (UAV)‐borne green lidar system (GLS) and deep learning‐automated space–time image velocimetry (STIV) for remote investigation of hydraulic and vegetation quantities of the gravel‐bed Asahi River in Okayama Prefecture, Japan. In addition to identifying bed deformation in waters shallower than 2 m, the GLS point clouds characterized the submerged infrastructure with block detailing patterns, thereby identifying positional displacement and severely damaged parts. This paper also presents a noncontact method of estimating incremental river discharge. Compared to benchmarked flow model estimates, remotely sensed discharges for three transects covering shallower, deeper, and partially submerged woody vegetation areas were overestimated by 1–11%, with 4% underestimation for another cross‐section. The STIV analysis also showed complicated flow patterns that were reasonably confirmed by flow vectors from depth‐averaged modeling. Ultimately, depth‐averaged flow model estimates validated hydraulic parameters derived remotely from GLS and STIV, and vice versa. In addition to approximating vegetation growth rates, the study using GLS attributes accurately identified riparian vegetation types as herbaceous (70%), woody (86%), and bamboo groves (65%). Finally, our findings provide insight into the management of shallow clear‐flowing vegetated rivers and remote sensing of streamflow to validate hydrodynamic‐numerical methods. [ABSTRACT FROM AUTHOR]

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]

Image segmentation plays an important role in image understanding and region‐based applications. Many image segmentation algorithms have been proposed, but in this paper, we enhance the segmentation performance of deep learning using attention models that extract important features from the target images. The structure of the segmentation network is an encoder–decoder model that can combine position features and channel features, where the attention mechanisms refine both position and channel features. In the experiments, the proposed method is applied to satellite image analysis, where synthetic aperture radar images are analyzed to detect landslide areas after heavy rain occurred. The experimental results show that the proposed method obtains higher segmentation accuracy comparing with some conventional methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [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]

Between 2007 and 2010, Japan's Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) captured dual polarization HH and HV data across the tropics and sub-tropics. A pan tropical dataset of Japanese Earth Resources Satellite (JERS-1) SAR (HH) data was also acquired between 1995 and 1998. The provision of these comparable cloud-free datasets provided an opportunity for observing changes in the extent of coastal mangroves over more than a decade. Focusing on nine sites distributed through the tropics, this paper demonstrates how these data can be used to backdate and update existing baseline maps of mangrove extent. The benefits of integrating dense time-series of Landsat sensor data for both validating assessments of change and determining the causes of change are outlined. The approach is evaluated for wider application across the geographical range of mangroves in order to advance the development of JAXA's Global Mangrove Watch (GMW) program. [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]

This paper reports seasonal data regarding leaf litter for 14 deciduous broad-leaved species and one evergreen coniferous species as well as leaf area index (LAI) data for the 14 deciduous broad-leaved species in a cool-temperate deciduous broad-leaved forest in Japan. The seasonal leaf biomass of various tree species is important for accurately evaluating ecosystem functions such as photosynthesis and evapotranspiration under climate change. However, there is a lack of freely available, long-term data. We collected litterfall every 1 to 4 weeks from September or October to November or December each year from 2005 to 2014 in Takayama, Japan (36°08′46″N, 137°25′23″E, 1420 m a.s.l.). After sorting the litter into leaves (according to species categories), stems + branches, and 'other', we dried and weighed the litter groups. We also collected seasonal leaf data (number of leaves and leaf length and width) for each broad-leaved species, which we recorded every 1 to 4 weeks from April or May to October or November using multiple target shoots. To estimate the LAI in autumn for each deciduous broad-leaved species, we used a semi-empirical model of the vertical integration of leaf dry mass per unit leaf area. To estimate the LAI in spring and summer, we used the relationship between the LAI in autumn and the seasonal leaf data. Our data provide input, calibration, and validation parameters for determining LAI based on satellite remote-sensing observations or radiative transfer models and for use in ecosystem models. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to assess the vulnerability of the Kesennuma area in Japan to a tsunami disaster and to map the area of inundation. Design/methodology/approach – Digital elevation model (DEM) data and ALOS image were used to create maps of the parameters of this study area: elevation, slope, coastal proximity, river, and land use. An analytical hierarchy process was used to assign weights to each parameter and a spatial multi-criteria analysis was applied through cell-based modelling for vulnerability mapping. Findings – The vulnerability map shows that 17.679 km2 of the area could be inundated by a tsunami. High vulnerability areas were mostly found in coastal areas with a sloping coast and a cape area. A low elevation and the presence of rivers or water channels are factors that increase the impact of tsunamis. Inundation areas were predicted to spread in areas identified as having either high vulnerability or slightly high vulnerability. Research limitations/implications – Because of the limited geospatial data, the authors encourage further studies using DEM data with a high spatial resolution. Practical implications – The results of this research can be used as basic information for disaster mitigation and urban planning in coastal areas. Originality/value – This research creates a new approach for assessing which areas could be inundated by tsunamis, based on the vulnerability map generated through remote sensing and spatial multi-criteria analysis. Moreover, the parameters used are very close to those of actual inundation maps. [ABSTRACT FROM AUTHOR]

The earthquake and tsunami of March 11, 2011 killed more than 15 000 people in Eastern Japan. The importance of remote sensing in understanding the damage caused by natural disasters is quite significant, and many data sets were acquired after the events. In this paper, we demonstrate the importance and the potential of full polarimetric synthetic aperture radar (SAR) images for damage assessment. Full polarimetric SAR images acquired by the spaceborne ALOS/PALSAR system from the Japan Aerospace Exploration Agency (JAXA) on November 21, 2010 and April 8, 2011 and acquired by the airborne Pi-SAR2 system from the National Institute of Information and Communications Technology (NICT) on March 12 and 18, 2011 are used for this analysis. Model-based decomposition is applied and clearly shows the scattering mechanism changes at the seriously damaged downtown of Ishinomaki city and the flooded areas near the main stream of the Kitakami River. Polarization orientation angle is estimated to provide additional information to understand the damage effect in the built-up areas. Eigenvalue-eigenvector-based decomposition analysis is also employed to further confirm the scattering mechanism changes of the flooded areas. ALOS/PALSAR does not have fine enough resolution; however, the difference of the scattering mechanisms is sufficient to identify the damaged and flooded areas. In addition, the Pi-SAR2 data sets are used to analyze the flooded paddy fields in Natori city. The relative backscattering values are compared with the multitemporal images and the cross-polarization component (HV) is observed to be more sensitive to the flooded boundary. The automatically detected flooding maps using the cross-polarization component were found to provide relatively accurate results. [ABSTRACT FROM AUTHOR]

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn. [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]

Hyperspectral imagery has become increasingly available in recent years and this has necessitated the evaluation of its potential for crop monitoring and precision agriculture applications. The potential of using airborne hyperspectral imagery to develop yield prediction models for citrus fruits was examined in this paper. Hyperspectral images in 72 visible and near-infrared (NIR) wavelengths (407-898 nm) were acquired over a citrus orchard in Japan by an Airborne Imaging Spectrometer for Applications (AISA) Eagle system. The canopy spectral features of individual trees were identified using pixel-based average spectral reflectance values at various wavelengths from the acquired images, which were then used to develop yield prediction models. Yield prediction models were developed using three different techniques: (i) three commonly employed vegetation indices, i.e. the normalized difference vegetation index (NDVI), simple ratio (SR) and photochemical reflectance index (PRI); (ii) a few significant wavelengths; and (iii) partial least squares (PLS) regression factors. Greater prediction accuracy was obtained with PLS models than with the models based on NDVI, SR or PRI, or the significant wavelengths. PLS models showed a significant correlation between hyperspectral imagery data and actual citrus yield for data acquired in 2003 and 2004. These results confirmed the hypothesized correlation between canopy spectral features and citrus yield. This information is valuable for forecasting yields, planning harvest schedules and generating prescription maps for the application of tree-specific alternate bearing control measures and management practices. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a multi-scale solution based on mathematical morphology for extracting the building features from remotely sensed elevation and spectral data. Elevation data are used as the primary data to delineate the structural information and are firstly represented on a morphological scale-space. The behaviors of elevation clusters across the scale-space are the cues for feature extraction. As a result, a complex structure can be extracted as a multi-part object in which each part is represented on a scale depending on its size. The building footprint is represented by the boundary of the largest part. Other object attributes include the area, height or number of stories. The spectral data is used as an additional source to remove vegetation and possibly classify the building roof material. Finally, the results can be stored in a multi-scale database introduced in this paper. The proposed solution is demonstrated using the data derived from a Light Detection And Ranging (LiDAR) surveying flight over Tokyo, Japan. The results show a reasonable match with reference data and prove the capability of the proposed approach in accommodation of diverse building shapes. Higher density LiDAR is expected to produce better accuracy in extraction, and more spectral sources are necessary for further classification of building roof material. It is also recommended that parallel processing should be implemented to reduce the computation time. [Copyright &y& Elsevier]

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]

This study investigates the impact of scattered greenery (street trees and yard bushes), rather than cohesive greenery (parks and forests), on housing prices. We identify urban green space from high-resolution satellite images and combine these data with data on both condominium sales and rentals to estimate hedonic pricing models. We find that scattered urban greenery within 100 m significantly increases housing prices, while more distant scattered greenery does not. Scattered greenery is highly valued near highways, and the prices of inexpensive and small for-sale and for-rent properties are less affected by scattered greenery. These results indicate that there is significant heterogeneity in urban greenery preferences by property characteristics and location. This heterogeneity in preferences for greenery could lead to environmental gentrification since the number of more expensive properties increases in areas with more green amenities. [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]

With the increasing of extreme rainfall frequency, landslides accompanied by mudslides often lead to serious casualties and property damage. On 3rd July 2021, a debris flow occurred in Izu Mountain area, Shizuoka County, Japan, and then resulted in 26 deaths and 131 houses destroyed, where houses were mainly built along the banks of the creek. In order to analyse the landslide state and distribution, a two-dimensional debris flow dynamic model (Massflow) was selected to simulate the process of the landslide-generated debris flow. When the model results are considered together with remote sensing images, the volume distribution of the unstable landslide is also able to be determined. The results show that (1) the affected areas are mainly concentrated at the outfall of the gully and on both sides of the streets. (2) The pore pressure ratio is an important factor affecting the damage range of this debris flow. (3) The increase of the pore pressure ratio in the landslide make the movement distance of debris flow increase significantly. [ABSTRACT FROM AUTHOR]

Repetitive volcanoseismic signals, including very‐long‐period signals (VLP) and long‐period signals (LP), provide a unique probe of fluid transport processes inside magmatic‐plumbing system. While syneruptive signals are often detected and analyzed with regional or/and global seismic networks to retrieve eruption location and mechanism, repetitive noneruptive volcanoseismic signals are generally small, and they are typically detected with in situ stations near the volcanic edifices. Here, we show that repetitive VLP and synchronous deformation events in Aso volcano, Japan, can be detected in the high (15–30 s) and low (50–100 s) VLP bands, respectively, at seismic stations located ∼30–1,000 km away from their sources. Changes in the polarities, phases, and amplitudes of VLP and synchronous deformation events observed at the in situ stations can be verified by the seismic waves in the two VLP bands, respectively, at distant stations up to 150 km. Forward modeling of the amplitude decay in the two VLP bands against epicentral distance corroborates the source locations previously determined by the in situ data, whereas the joint data analysis of in situ and distant stations at high VLP band suggests the presence of single‐force component (i.e., force/moment ratio of 10−4 m−1) in the source of VLPs. We advocate that not only can systematic data mining against established global and regional seismic networks potentially expand the detection capability of repetitive volcanoseismic signals backward in time when in situ observations were unavailable, but it could also substantially improve the detection and monitoring capacity in otherwise uninstrumented volcanoes, complementary to remote sensing of ground deformation. Plain Language Summary: Seismic signals emitted from volcanic regions carry invaluable information on the transport of magma or/and hydrothermal fluid beneath the volcanic edifice. During explosive eruptions, seismic signal can be detected and analyzed using regional or global seismic networks. On the other hand, during the intereruption interval, repetitive volcanoseismic signals directly linked to fluid transport in the magma plumbing system rely on in situ seismic stations near the volcano edifice. Using repetitive seismic signals from one of the most active volcanoes in Aso as an example, we demonstrate that synchronous seismic signals emitted from a shallow crack‐like conduit and a deep chamber roof can be detected at seismic stations even hundreds of kilometers away from their sources. The inclusion of data at distant stations from established global and regional seismic networks improves source characterization, expands the detection capability of repetitive volcanoseismic signals backward in time and increases the monitoring capacity of uninstrumented volcanoes. Key Points: Repetitive noneruptive volcanoseismic signals are often monitored by in situ seismic networks but rarely detected in distant stationsSeismic data at distant stations validate source properties of very‐long‐period signal and synchronous deformation events in Aso volcanoRemote monitoring using established regional seismic networks helps provide insights into in otherwise uninstrumented volcanoes [ABSTRACT FROM AUTHOR]

The increasing popularity of the Internet and user-friendly Web-based GIS applications such as Google Maps/Earth and Microsoft Bing Maps Platform have made GIS an integral part of life today for finding the nearest facilities, driving routes, and so on. However, choosing an ecofriendly walking route is a big challenge for local residents because of the lack of GIS analytical functions and environmental data available online. Although analysis of route paths has been widely used in GIS applications, the integration of green factors with the analysis of the route path is still lacking in the GIS arena. In this paper, we have presented an integrated methodology, remote sensing, GIS and spatial Web technology, for identifying an ecofriendly walking route (Smart Eco-path Finder) by providing Web-based GIS analytical functions using Tsukuba City in Japan as a case study. This Web-based, ecofriendly route pathfinder enables users to choose a walking route for green exercise by using their smartphones or ultramobile PCs (UMPCs). [ABSTRACT FROM AUTHOR]

Abstract: The paper is related to the study of the VLF (very low frequency) signal transmitted from the radio-navigation station in Komsomolsk-na-Amure at frequencies 11.9kHz and 14.8kHz during seismic activity in Japan. First, using data collected with the electric field experiment on board the DEMETER satellite, global maps of electromagnetic signal at the frequencies of the transmitter have been obtained. Second, particular analysis of the VLF signals have been done at the time of two powerful earthquakes which took place in the region of Honshu Island on 2005/08/16 and on 2007/07/16. Comparison with averaged background data revealed disturbances in the signal intensity transmitted from the Komsomolsk-na-Amure station and received by the satellite. Discussions to understand why such changes can be associated with seismic activity are developed. [Copyright &y& Elsevier]

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]

In recent years, several studies have been performed on methods of investigating the periphery of power lines using remote sensing. Among the survey methods using unmanned aerial vehicles (UAVs), those methods are being studied wherein the environment around power lines is photographed by a UAV, and separation measurements are performed by reconstructing the three‐dimensional (3D) point cloud between power lines and the vegetation. Such methods currently rely on reconstructing the power line, making it necessary to acquire images from a position close to the power line. However, by using information available from the power company on the position and structure of pylons and the sag of power lines, it is possible to calculate the position of the power line without 3D reconstruction. In this study, time series management of vegetation around power lines was enabled using the data obtained by UAVs at different times and information on the transmission equipment. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [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]

A series of earthquakes struck Kumamoto City area, SW Japan, including a magnitude 7.3 (mainshock) at 01:25 (Japan Standard Time: JST) on April 16 and a foreshock with a magnitude 6.5 at 21:26 JST on April 14, 2016. The earthquakes tremendously damaged the vulnerable structures and left a huge amount of disaster waste. Immediately after the earthquake, the Kumamoto Prefectural Government designated a number of locations for the erection of temporary waste storage facilities essentially in the vicinity of the most affected zones for the purpose of removing of the disaster waste from the damaged areas and their accumulation in the properly managed sites. The facilities provided sufficient ground for the temporary storage, handling, early sorting, and crushing of metallic objects, wood and its derivatives, glass, soil and rock, concrete, and household appliances. Mashiki Machi (town) and Nishihara Village were the nearest settlements to the epicenters of Kumamoto earthquakes that were severely damaged. The estimated amount of the disaster waste generated in Kumamoto Prefecture was about 2.89 million tons, the recycling and disposal of which must be completed within two years from the date of earthquake. Among various waste disposal facilities, the Nishihara Village storage facility was selected as the target for in-depth site investigation and disaster waste estimation. Drone survey was also conducted at different intervals at Nishihara Village facility with emphasis on wood debris. The drone technique employed in this survey demonstrated a reliable method for capturing high-resolution images from the selected site in a remarkably short period of time, and a relatively quick measure for estimating the amount of the disaster waste immediately after the disaster occurred. [ABSTRACT FROM AUTHOR]

As elsewhere in Indonesia, local inhabitants in the Pangkajene and Kepulauan (PANGKEP) Regency, Spermonde Archipelago area and along the south-west coast of Sulawesi traditionally regard the coral reefs as their livelihood source. Since human activities as well as natural disturbances pose major threats to the coral reefs, these livelihoods may also be at risk. Currently, no comprehensive information on the status and condition of coral reefs in this area is available for this resource management. We determined the changes of coral reef habitat over a period of 20 years from 1994 to 2014 using a satellite Landsat multi-temporal image substantiated with in situ measurement data collected in 2014. The spectral value of coral reefs was extracted from multi-temporal Landsat imagery data, while the diffuse attenuation coefficient of water was obtained by using statistical analysis between the ratio of live coral cover and the spectral value of the visible bands. By using the unsupervised classification integrated with the data ground truth, it is stated that there has been a decline in live coral cover over a period of 20 years from 7716 ha in 1994 to 4236 ha in 2014, with a degradation rate of 174 ha/year. Based on the results, the ratio of the coral cover in the coral reef transects varied from the average of 24% for live corals to 96% for coral rubbles, implying the degraded status of coral reefs in the study area. [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]

In 2007, high-temperature-induced mass coral mortality was observed in a well-developed fringing reef area on the southeastern coast of Ishigaki Island, Japan. To analyze the response of the corals to thermal stress, the coral cover was examined using Quickbird data, taken across the reef flat just before and after the bleaching event and performing a reef scale horizontal 2-dimensional thermal model simulation. The Quickbird data consisted of multispectral (MSS) imagery, which had a spatial resolution of 2.4 m, and panchromatic (PAN)-fused multispectral imagery, which had a 0.6-m spatial resolution. The observed changes in coral cover implied that the delineation of partially bleached coral was more precise with PAN + MSS. The classification accuracy achieved using PAN + MSS (93%) was superior to that obtained using MSS (88%). The in situ water temperature observations and 2-dimensional thermal model simulation results indicated that the water temperature fluctuated greatly in the inner reef area in late July 2007. Different thermal stress indices, including daily average temperature, daily maximum excess temperature, and daily accumulated temperature, were examined to define a suitable index that represented the severity of the thermal stress on coral cover. The results suggested that the daily accumulated temperature that occurred during the maximum sea surface temperature period of the bleaching season provided the best predictor of bleaching. The changes in water temperature, bathymetry, and coral patch size affected the severity of bleaching; therefore, the spatial dependence of these variables was examined using Moran's I and Lagrange multiplier tests. An investigation of the effect of coral patch sizes on coral bleaching indicated that large coral patches were less affected than the small patches, which were more likely to suffer bleaching and coral mortality. [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]

Remote sensing surveys for estimating forest cover may be divided into two approaches: wall-to-wall and sampling. Sampling approaches offer a practical alternative to wall-to-wall mapping, but estimates of forest cover may be affected by the sampling rate of the estimation area. This study aimed to obtain stable estimates of forest cover from satellite data using object-oriented classification at the national level. We investigated a suitable value for the scale parameter in object-oriented classification using eCognition software to identify land cover types, and we evaluated the sampling rate for estimating forest cover at the national level. We used eight different scale parameters when applying object-oriented classification to Landsat data for a set of forty-six 10 km × 10 km sampling tiles centered at each degree of latitude and longitude in Japan. The scale parameter of 10 or less was found suitable for obtaining objects with areas of about 5 ha. Overall accuracy in classification was greater than 75% and greatest when the scale parameter was between 6 and 10. We then analyzed the entire land area of Japan using 10 km × 10 km tiles to evaluate the optimum sampling rate for estimating forest cover. A sampling rate greater than 20% was required to stably estimate forest cover in Japan. Les inventaires effectués au moyen de la télédétection pour estimer le couvert forestier peuvent être classés selon deux approches : systématique et par échantillonnage. Les approches par échantillonnage offrent une alternative pratique à la cartographie systématique mais les estimations du couvert forestier peuvent être influencées par le taux d'échantillonnage et la zone d'estimation. Cette étude visait à obtenir des estimations stables du couvert forestier à partir de données satellitaires en utilisant une classification orientée objet à l'échelle nationale. Nous avons cherché une valeur appropriée pour le paramètre d'échelle dans la classification orientée objet à l'aide du logiciel eCognition pour identifier les types de couvert et nous avons évalué le taux d'échantillonnage pour estimer le couvert forestier à l'échelle nationale. Nous avons utilisé huit paramètres d'échelle différents lorsque nous avons appliqué la classification orientée objet aux données Landsat pour un ensemble de 46 tuiles échantillons de 10 km × 10 km centrées à l'intersection de chaque degré de latitude et de longitude au Japon. Nous avons trouvé qu'un paramètre d'échelle de 10 ou moins était approprié pour obtenir des objets avec une superficie de 5 ha. La précision globale de la classification était plus grande que 75 % et maximale lorsque le paramètre d'échelle était situé entre 6 et 10. Nous avons ensuite analysé le territoire du Japon au complet en utilisant des tuiles de 10 km × 10 km pour évaluer le taux optimal d'échantillonnage pour estimer le couvert forestier. Un taux d'échantillonnage supérieur à 20 % était nécessaire pour obtenir des estimations stables du couvert forestier au Japon. [ABSTRACT FROM AUTHOR]

Abstract  Scallop aquaculture has grown to become the most successful marine shellfish farming venture in Japan. To ensure long-term sustainability of scallop production, suitable sites need to be selected. This study was conducted to identify the most suitable sites for hanging culture of Japanese scallop (Mizuhopecten yessoensis) based on measured biophysical parameters and modeled using geographic information system (GIS) models. Four biophysical parameters that influence Japanese scallop aquaculture in Funka Bay were selected: sea temperature, food availability (chlorophyll-a), suspended solid, and bathymetry. The SeaWiFS and MODIS images were used to extract most of these parameters. A series of GIS models was developed to identify suitable sites for scallop culture using a multi-criteria evaluation (MCE) known as weighted linear combination. Suitability scores were ranked on a scale from 1 (least suitable) to 8 (most suitable), and about 83% of the total potential area with bottom depths less than 60 m had scores of 7 and 8. The final model outputs were compared with field verification data and found to be consistent. Local sensitivity analysis was used to test the robustness of the model output. Based on this analysis, the order of importance of the variables affecting the model was as follows: suspended solid > chlorophyll-a > sea temperature. [ABSTRACT FROM AUTHOR]

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]