The Port of Yokohama, in conjunction with the Port of Kawasaki, is currently conducting studies to achieve the status of carbon-neutral port. To provide a baseline for these studies, we mapped vessels and oil tank groups in the Port of Yokohama and the Port of Kawasaki on high-resolution optical images taken by the Pleiades-1B satellite on September 24, 2021, in order to then estimate the total greenhouse gas emissions from all the vessels in the port, as well as to examine potential areas for a conversion to hydrogen facilities in the future. The mapping showed 322 maneuvering vessels and 2682 moored vessels. More than 90 % of the moored vessels were located in the Port of Yokohama. Additionally, there were 68 and 78 oil tank groups in the Port of Yokohama and Port of Kawasaki, respectively. The City of Yokohama manages the Port of Yokohama as its port administrator and has a general understanding of information on vessels and oil tank groups. However, we do not have information on the exact number of vessels in the entire port, nor do we have accurate information on the oil tank groups, which are mostly on private property. Furthermore, little information is available on the adjacent Port of Kawasaki, whose administrator is the City of Kawasaki. The use of high-resolution optical satellite images provided detailed information that could not be obtained previously. This study approach was useful in studying the potential use of hydrogen, fuel ammonia, carbon-neutral gas, etc. as part of the effort to achieve carbon-neutral port status in the Ports of Yokohama and Kawasaki.
In this paper, an example of displacement monitoring of landslides using interferometric SAR analysis is reported. The displacement of the mountain landslides was separated into vertical displacement and east-west displacement by 2.5-dimensional analysis using ALOS-2 with multi-temporal and multi-orbital data. The direction and the boundary of displacement were consistent with the results of displacement measured by airborne laser profiler between 4 years and also with the field survey.
The unmanned aerial vehicle (UAV) green laser uses laser light in the green wavelength band, which passes through water. Therefore, it is a measurement technology that can efficiently measure point clouds in water areas such as rivers and lakes and in land areas on the surface of the earth at the same time. Currently, it is being used in fields such as river surveying and harbor structure maintenance.
To date, there has been almost no use of this technology at construction sites, and there has been little progress in the selection of types of work that can make effective use of this technology or in the development of measurement skills on site.
In order to utilize the integrated data of water and land areas measured by UAV green laser at construction sites, the measurable conditions must be clarified, as must the measurement accuracy in groundbreaking survey and finished survey in each area. Therefore, in this study, we first determined the measurement accuracy of the UAV green laser in land and water areas, and then verified its applicability to the groundbreaking survey and the finished survey in construction areas.
On August 13, 2021, there was an eruption of Fukutoku-Oka-no-Ba submarine volcano, located between Iwo Jima and Minami Iwo Jima in the southernmost part of the Ogasawara Islands, approximately 1,300 km from Tokyo. The volcanic plume reached a height of 16 km and ejected a large amount of pumice, which floated over the Pacific Ocean.
Some of the floating pumice drifted to the Ryukyu Islands, reaching the main island of Okinawa and Amami-Oshima in early October 2021. As of June 2022, floating pumice was still affecting marine traffic, fishing, and tourism in the area. This article presents satellite images of the eruption and the drifting floating pumice stone.
Land use and land cover (LULC) maps provide essential data for ecosystem service assessment, agriculture, resource management, disaster management, etc. We have developed a multi-temporal LULC classification algorithm called "SACLASS2" based on a convolutional neural network (CNN) in a two-dimensional space spanned by a temporal axis and a feature axis. This allows for better generalizability and lowers computational costs through a simple treatment of the characteristics of time-series remote sensing data. Moreover, it can keep fine spatial patterns that may be lost when CNN is used in a geographic space. Using this algorithm in combination with a well-qualified training dataset, we took data from Sentinel-2 and ALOS-2/PALSAR-2 together with some ancillary data as input and created a new LULC map of all of Japan for the period from 2018 to 2020. The Japan Aerospace Exploration Agency (JAXA) has released this product free of charge under the title of "JAXA HRLULC version 21.11". It has been greatly improved in terms of both number of categories (12 categories, 88.85 %) and overall accuracy from the earlier version (JAXA HRLULC v18.03; 10 categories, 81.62 %), which used a previous algorithm, SACLASS, based on a kernel density estimator. Compared to other LULC maps of Japan (made by the European Space Agency, Esri, the Ministry of the Environment, and the Ministry of Land, Infrastructure, Transportation and Tourism), HRLULC-Japan v21.11 has the multiple advantages of high-spatial resolution, description of the most recent situation, suitable categories for typical LULC maps of Japan (rice paddy fields, solar panels, bamboo forests, etc.), and overall accuracy.