The forest ecosystem is often a key item in discussions on global warming and sustainable development goals (SDGs). In order to evaluate and discuss the functions of forests (ecosystem services) in the world, a universal research method is required, in which remote sensing data should be used as the base data. For forest remote sensing, there are continuous technological developments in areas such as multi-scaling and indexing. Additionally, sophisticated evaluation methods related to forest quality, such as indexing of biodiversity and ecosystem stress, have been reported. The practical applications for forest remote sensing are currently being promoted by integrating various field data. At the global level, Forest Resource Assessment by the Food and Agriculture Organization (FAO) assesses forest resources in the world, while for specific forest problems, for example in the tropics, the USA, Europe, northern forests, and Japan, remote sensing is being researched and used according to the characteristics of each ecosystem along with wide-area factors such as global warming. Therefore, various papers on forest remote sensing have been reported all over the world. This report focuses on such reports in 2020, and word clouds were used to scan the contents of the cited references in Japanese.

Much attention has been paid in recent years to forest biomass, due to the significance of forests in the context of climate change. The Committee on Earth Observation Satellites (CEOS) has been accelerating its forest biomass monitoring activities. Many spaceborne sensors suitable for aboveground forest biomass estimation, such as the Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), are currently in operation, and more are planned in coming years. As part of the CEOS biomass monitoring activities, maps of aboveground forest biomass are being generated by international research groups, many of which are using Japanese radar (SAR) satellite data as the key input dataset. Map validation constitutes an important part of this CEOS effort. In this study, we present results from the initial validation of an above-ground biomass map against two field-plots datasets in Japan. Our results indicate a general tendency of underestimation in the biomass map compared to ground-based measurements, in particular in high biomass forest areas. We therefore believe that it is crucial to establish a dedicated, accurate Japanese forest biomass mapping technology, adapted to take into account the special characteristics of the types of forest in Japan.

The Forestry Agency revised afforestation grant program rules to allow to use various remote sensing data in application and inspection of the grant procedure in March, 2020. Since then, utilization of remote sensing data has been expanding, for example for area measurement and on-site situation check.

In this paper, we propose a method for efficiently conducting more accurate forest resource surveys by combining the advantages of wide-area measurement from the sky with those of direct measurement of standing trees in the forest. Specifically, we focus on the positional relationships among standing trees, and introduce a collation method for the positions of tree apexes by measuring the sky drone and the position of the standing tree by measuring OWL in the forest.

In this study, the advantage of the state-of-the-art sensor TROPOspheric Monitoring Instrument (TROPOMI) for air pollution research in Indian subcontinent is examined by comparing it with the conventional sensor Ozone Monitoring Instrument (OMI), which has been utilized for more than 15 years since its launch in 2004. The OMI nitrogen dioxide (NO₂) dataset was used for comparison, namely, version 4.0 of the standard product developed by NASA (named OMNO2). As our focus is the application of satellite sensors to the study of air pollution, only the areas with high NO₂ concentration were extracted for the analysis. A one-year comparison between July 2018 and June 2019 showed strong positive correlation between TROPOMI and the OMI product, with Pearson correlation coefficient of 0.76. The difference between OMI and TROPOMI was generally random. Compared with OMNO2 version 4.0, the annually averaged difference of TROPOMI was found to be (−0.8±1.1)×10¹⁵ (1σ) molecules cm⁻², which is −22 %±24 % (1σ) as a relative value. The good agreement between TROPOMI and OMI confirmed the compatibility of the observed values. The high resolution of TROPOMI enables the observation of small-scale sources of NO₂ that cannot be detected by OMI, which allowed the identification of some examples of NO₂ hotspots over power plants in India. The recent identification of a rapid decrease in NO₂ after the COVID-19 lockdown in March 2020 in India using TROPOMI data demonstrates the potential of this sensor to detect rapid changes in anthropogenic activities. Our analysis demonstrates usefulness of the NO₂ data from TROPOMI, and fruitful scientific results are expected in the future.

Due to the strong demand for renewable energy resources, the number of newly built photovoltaic cells has increased dramatically. However, these photovoltaic cells are sometimes subject to disasters such as floods and mudflow. To keep such facilities safe from disasters, a method that can monitor the locations and extent of photovoltaic cells in hazardous zones in cost-effective and less time-consuming ways is necessary. In this study, we developed a multi-temporal and multi-source machine -learning-based method for photovoltaic cell detection . The Sentinel-1 and Sentinel-2 datasets were used as data sources, and the random -forest classification method was applied to classify the land use and land cover (LULC) of the study area. Various combinations of inputs to the classifier were compared based on their performance of the LULC classification. After this process, the combination of optical -data, coherence -data, and the average of the coherence -data was selected as the best classification method. The photovoltaic cell detection process was carried out by combining the multi-temporal classification results to improve detection accuracy. This photovoltaic cell detection method achieved high -overall accuracy, high user accuracy, and a high-kappa coefficient.