The 2011 off the Pacific coast of Tohoku Earthquake occurred, and coastal forests were severely damaged by a huge tsunami. Since the disaster, coastal forest restoration projects have been underway by the Forestry Agency and local governments. Detailed time-series monitoring of the regeneration process of coastal forests is important in order to proceed with regeneration appropriately. The Normalized Difference Vegetation Index (NDVI), which uses near-infrared and visible red images obtained from optical satellite observations, has been widely used to survey trees and vegetation. However, it has been reported that NDVI tends to be saturated depending on the observation period and vegetation type. In addition, there is a tendency for index values to be overestimated on the soil surface. In particular, in the case of coastal forest regeneration, the influence of the soil surface is even greater because the complex mixture of soil surface and afforestation is assessed from observation images. To date, many improvement vegetation indices have been proposed to reduce soil surface effects and more appropriately evaluate vegetation activity. However, the applicability of improvement indexes using higher-resolution satellite images for evaluating the regeneration of tsunami-affected coastal forests has not yet been sufficiently investigated.
COVID-19 rapidly spread worldwide in 2020, leading to a pandemic. In Japan, measures like stay-at-home requests and remote work were implemented, shifting towards an internet-based lifestyle. Strict restrictions were imposed on high-risk industries such as restaurants, resulting in a 10-15% GDP decrease and significant societal and economic impacts. Nighttime light is known to correlate with various social and economic indicators. This observational information is expected to be used in the future, as it will be possible to easily estimate economic indicators from satellite night light observations in areas where statistical information research and maintenance is insufficient. This study utilized nighttime observation images from the Suomi-NPP satellite, which has improved spatial resolution and observation range compared to conventional night light observation satellite. Using these images observation information, the social situation and living environment of Tokyo's 23 wards were investigated, and changes in people's lives under COVID-19 were considered. Time-series changes in nighttime light distribution before, during, and after the COVID-19 outbreak were investigated. Different trends in the distribution of night light were observed in each of Tokyo's 23 wards. These trends were analyzed by overlaying them with various spatial information, such as land use and transportation networks like railways and highways. As a result, in commercial areas with large bustling commercial districts, radiance decreased, while in areas with a high concentration of residences, radiance increased. From this study, trends in changes in radiance values related to land use and transportation infrastructure were considered.
In developing countries such as Southeast Asia, there are many areas where urbanization is progressing. In developing countries, detailed data on land use and other matters are often insufficient, making it difficult to understand the status of urbanization, the effects of projects, and the extent of their impact. First, in order to evaluate the effectiveness and accuracy of the urban area extraction method targeting Tokyo and its surrounding areas, we considered land cover classification processing using clustering using optical observation information from Landsat satellites and polarization information from Sentinel. did. The results showed that the accuracy of urban area extraction was improved by using optical satellites and SAR satellites in combination. Subsequently, this method was applied to areas with social infrastructure development in developing countries in Southeast Asia, and the state of urbanization was investigated in detail, and characteristics such as the state of urbanization and the scope of influence associated with social infrastructure development were effectively evaluated.
The 2011 tsunami caused by the Tohoku Region Pacific Offshore Earthquake caused severe damage. In particular, the city of Rikuzentakata, Iwate Prefecture, a coastal city in the Tohoku district, sustained enormous damage, and long-term revitalization process is in progress. For this disaster, several satellite observations and remote disaster investigations by aerial photography were conducted, and various damage situations were reported. Although there are many interpretations of images, few continuously evaluate the recovery process after the disaster. Here, land cover was investigated using image information observed time-sequentially by high-resolution satellite remote sensing. In addition, the re-urbanization process of the disaster area was evaluated from evaluation index values obtained from several types of filtering analysis. The distribution of debris caused by the tsunami, the change in the characteristics of the bare ground due to debris removal, and the characteristics of the land cover shape were quantitatively evaluated from the regularity and identity of the land cover distribution. In addition, the early stages of re-urbanization, which is still in progress, were effectively evaluated.
The Mw 9.0 earthquake that struck Japan in 2011 was followed by a large-scale tsunami in the Tohoku region. The damage in the coastal plane was extensively displayed through many satellite images. Furthermore, satellite imaging is requested for the ongoing evaluation of the restoration process. The reconstruction of the urban structure, farmlands, grassland, and coastal forest that collapsed under the large tsunami requires effective long-term monitoring. Moreover, the post-tsunami land cover dynamics can be effectively modeled using time-constrained satellite data to establish a prognosis method for the mitigation of future tsunami impact. However, the remote satellite capture of a long-term restoration process is compromised by accumulating spatial resolution effects and seasonal influences. Therefore, it is necessary to devise a method for data selection and dataset structure. In the present study, the restoration processes were investigated in four years following the disaster in a part of the Sendai plain, northeast Japan, from same-season satellite images acquired by different optical sensors. Coastal plains struck by the tsunami are evaluated through land-cover classification processing using the clustering method. The changes in land cover are analyzed from time-series optical images acquired by Landsat-5/TM, 7/ETM+, 8/OLI, EO-1/ALI, and ALOS-1/AVNIR-2. The study reveals several characteristics of the change in the inundation area and signs of artificial and natural restoration.
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