In this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced ...Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.
Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from ...drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user’s accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of “with deer” and “without deer” were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use.
•MODIS cloud properties and surface solar-irradiance data are analyzed simultaneously.•Relation of cloud properties to variation in surface solar irradiance is analyzed.•A method for estimating the ...variability of surface solar irradiance is proposed.
The variation in surface solar irradiance (SSI) on short timescales has been investigated previously in relation to ground-based observations. Such results are limited to the locality of the observation stations, leading to insufficient knowledge about the spatial distribution of variation features. We propose a method for characterizing variations in SSI using cloud properties obtained from satellite observations. Datasets of cloud properties from satellite observation and SSI from ground-based observation are combined at simultaneous observation points to investigate their relations. The SSI variations are classified statistically into six categories. The cloud properties related to the categorized variation features are then analyzed. From such relations, a statistical discriminant method is used to design a classifier to assign a category to the SSI variation over an area from the cloud properties obtained by satellite observation. The accuracy of classification and feature selection is discussed.
•We developed abnormal potato plant detection system considering the growth stage.•We developed explainable deep classification models, then we applied one of them.•We developed new pipeline to ...compare with the surrounding plants.•The required accuracy for the system with near-real-time processing was achieved.•We are currently developing means of practical implementation using the system.
Potatoes are the world’s most important root and tuber crop. A diseased seed potato can produce approximately 10 potato tubers, and the disease can propagate through the seed potato production cycle. To promote stable potato production, quality seed potatoes that are healthy and disease-free should be supplied. The Japanese government established a propagation system for the production and distribution of seed potatoes. Experienced laborers are required in the fields for visual inspection and removal of abnormal plants during seed potato production. To aid visual detection, reduce labor effort, and improve assessment time, we developed an automated abnormal potato plant detection system that utilizes a portable video camera and deep learning models. The proposed system detects abnormal plants or leaves considering the stage of growth. It detects three cases: (i) abnormal potato plants in the early growth stage, (ii) abnormal potato plants in comparison to the surrounding plants, and (iii) abnormal potato leaves. For the abnormal and healthy potato plant classification, the accuracy was ~90%, and the average precision (AP) for detection was 78.2%. Furthermore, the classification accuracy of the abnormal and healthy potato leaf classification was 96.7%, and the AP for detection was 90.5%. Therefore, the proposed system can be used to detect abnormal potato plants.
Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it ...is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.
We have proposed and demonstrated a novel approach for generating high-energy extreme-ultraviolet (XUV) continuum radiation. When a two-color laser field consisting of a sub-10-fs fundamental and its ...parallel-polarized second harmonic was applied to high-order harmonic generation in argon, a continuum spectrum centered at 30 nm was successfully obtained with an energy as high as 10 nJ. This broadband emission indicates the possibility of generating intense single attosecond pulses in the XUV region.
Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation ...(TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.
At the J-PARC muon facility (MUSE), new beamlines started operation recently. H-line is a high-intensity pulsed muon beamline for fundamental physics experiments. The first beam of the H-line was ...delivered to its first branch (H1 area) in January 2022, where a precise measurement of the muonium hyperfine structure and a search for μ-e conversion will be conducted. Further extension of the second branch of the H-line for a muon g-2/EDM experiment and a transmission muon microscope project is also ongoing. In addition, the second branch of the surface muon beamline (S2 area of the S-line) was opened for a muonium 1S-2S spectroscopy experiment in FY2021. In this paper, the recent upgrade and present status of the J-PARC muon facility and its prospects are presented.
The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in October 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. ...Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO
2
and CH
4
have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO
2
emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO
2
and CO by TANSO-FTS-2.
Muonic hydrogen is a bound state of a proton and a negative muon. Its Bohr radius is 200 times smaller than that of an electronic hydrogen atom. Therefore, a spectroscopy of the muonic hydrogen is ...highly sensitive to the finite size effect of proton. Recent years, the proton charge radius was determined by the laser spectroscopy of the Lamb shifts in muonic hydrogen atom. The experiment determined the proton charge radius significantly smaller than the results of past measurements. This anomaly is called "proton radius puzzle" and it has been an important unsolved problem in subatomic physics. Towards solving the puzzle, a new measurement of the ground-state hyperfine splitting in muonic hydrogen was proposed. The hyperfine splitting of muonic hydrogen derives the proton Zemach radius, which is defined as a convolution of the charge distribution with the magnetic moment distribution. This experiment aims to determine the proton Zemach radius with 1% precision by a measurement of the decay electron angular asymmetry. In order to test the feasibility of the laser spectroscopy, a preliminary experiment to measure the hyperfine quenching rate was proposed.
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