Stable food production has become difficult due to global warming and abnormal weather patterns, and in the agricultural sector, damage caused by plant diseases has been increasing. To address the plant diseases, technologies involving unmanned aerial vehicles (UAVs) to monitor the growth status of plants are being investigated. However, when the initial disease symptoms of plants are small and light in color (e.g., downy mildew), it is difficult to detect the disease at sites such as trellis vineyards by simply having the UAV take aerial photographs. It is also necessary to evaluate grapevines from the side views in addition to the aerial view. Another challenge concerns how to obtain high-quality images during the daytime, because of the interference of backlight and excessive brightness from sunlight. To address these challenges, we have investigated a chlorophyll (Chl)a fluorescence imaging technique that allows imaging measurements at night by providing stable lighting. We hypothesized that this new technique could be used to clarify the state of impaired photosynthetic reactions caused by plant diseases. The present study was conducted to calculate and visualize the values of a photosynthetic function index (PFI) in grapevine images. We also evaluated the effectiveness of the proposed technique by comparing its results with those obtained using the conventional pulse amplitude modulation (PAM) fluorescence measurement method, which has been traditionally used for measuring Chla fluorescence. Our findings demonstrated the potential of the new technique using PFI images for visualizing diseased areas on trellis-grown plants.

Understanding land use and land cover (LULC) is crucial for various purposes such as sustainable land use, agriculture, and environmental conservation. In Japan, the Japan Aerospace Exploration Agency (JAXA) develops and periodically releases High-Resolution Land Use and Land Cover maps of the entire Japanese territory (HRLULC-Japan). However, JAXA’s existing LULC maps have certain limitations, and some categories with unique characteristics and importance are oversimplified or aggregated into broader classes. This lack of detail hinders the map’s ability to accurately depict diverse LULC patterns. To address this issue, this study tried to add seven new categories (Wetland, Greenhouse, Deciduous orchard, Evergreen orchard, Rice intercropping zone, Lotus field, Tea farm) to HRLULC-Japan version 21.11 that was the latest version available at the start of this research, which has 12 categories. Since accuracy generally decreases as the number of categories increases, this study incorporated ensemble learning to stabilize classification results and reduce misclassification, while enhancing the overall level of detail on the LULC map. Using the Kanto area as a test site, data from Sentinel-2/MSI, Sentinel-1/GRD and ALOS-2/PALSAR-2, as well as from some other ancillary datasets such as Digital Surface Model, were used as input data for the classification algorithm “SACLASS2,” followed by ensemble learning. This method produced an LULC map with an overall accuracy of 93.5 ± 2.0 % across the 19 categories, demonstrating not only that the inclusion of new categories can improve classification detail without sacrificing high accuracy, but also that ensemble learning is effective in achieving these results.

Atmospheric aerosols, produced by anthropogenic activities or natural phenomena, often cause serious air pollution with impacts on human health and the environment. However, the distribution of ground-based aerosol observation network sites such as SKYNET is not necessarily dense and uniform. The Nara Basin, located in the northwestern part of Nara Prefecture in the center of the Kinki region, is surrounded on all sides by mountains and hills. Thus, although it is easy for pollutants to remain in the area, few studies have been conducted at this site compared to other basins. Nara Women’s University is located in the northeastern part of the Nara Basin, and we have conducted long-term ground-based observations of aerosols there using a portable sunphotometer (Microtops II). In this study, we report the regional characteristics of aerosols in the Nara Basin by comparing the results of Microtops II observations there with those of SKYNET observations in Sendai. We first conducted synchronous observations of Microtops II and SKYNET in Sendai. The results showed that the values of aerosol optical thickness (AOT) from Microtops II and SKYNET were consistent. Based on this, the AOT and Ångström exponents in the Nara Basin and Sendai were compared using Microtops II and SKYNET. The results suggested that the aerosols in the Nara Basin tended toward a lower aerosol loading and a predominance of coarser particles than in Sendai. Thus, Microtops II is generally capable of capturing aerosol behavior, and has the potential to enable long-term ground-based observations in areas such as Nara where there are currently no network sites, and thus further our understanding of aerosol distribution in more diverse areas.