The expansion of abandoned bamboo forests is a significant issue in Japan. Remote sensing is an effective technique for mapping land cover over large areas. In order to accurately estimate the distribution of bamboo forests using the MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite, four classification algorithms (random forest, support vector machine, multilayer perceptron, and gradient boosting decision tree) were evaluated, with a particular focus on the seasonality of classification accuracy and the importance of the spectral band. A land cover map of Kochi City was generated for each month using MSI data and reference maps. The performance of the algorithms in the bamboo forests was assessed based on the seasonal profiles of precision, recall, and F-measure. Permutation feature importance was used to measure the importance of spectral bands. The classification accuracy of the random forest classifier was found to be slightly superior to that of the other algorithms, although the difference was smaller in the case of the gradient boosting decision tree. Classification accuracy was high in May and remained relatively high from May through August. All algorithms considered bands 11 and 12 in the short-wavelength infrared region to be significant. In addition, bands 6 and 7 in the red-edge spectral region were of nearly equal importance in the support vector machine, multilayer perceptron, and gradient boosting decision tree algorithms. The study also created seasonal profiles of misclassification of bamboo forests as other land cover types, such as evergreen broadleaf forests or deciduous broadleaf forests. These findings could be used to improve the accuracy of bamboo forest mapping using optical remote sensors.

In recent years, excessive overgrowth of floating aquatic plants has become an issue in a number of inland lakes in Japan. However, the relationship between this overgrowth and meteorological and water quality factors remains unclear. In this study, we used PlanetScope, which is provided by a commercial optical satellite constellation with high temporal and spatial resolution, to extract the activity level of floating aquatic plants in Lake Inbanuma in Chiba Prefecture and Lake Suwako in Nagano Prefecture, which are representative eutrophic lakes in Japan where floating aquatic plants are overgrown. We conducted correlation and causal analyses using meteorological data from the Japan Meteorological Agency (JMA) and water quality observation data for public water bodies, and examined the relationship between plant overgrowth and the monthly median of the normalized aquatic vegetation index (NDAVI) of floating aquatic plants. The results showed that in both lakes, there was a statistically significant relationship between temperature and the growth of floating aquatic plants, and that dissolved oxygen concentrations decreased when the plants were rooting. In Lake Inbanuma, there was a positive relationship between water quality indicators related to organic matter, such as chemical oxygen demand (COD) and suspended solids (SS), and the growth of floating aquatic plants, but this relationship was not found in Lake Suwako.

Monitoring of cherry flowering phenology at multiple points over a wide area is important to deepen our understanding of the influence of climate change on plant phenology and ecosystem services. Toward this aim, we developed a simple quantitative monitoring method using a digital camera and GIS software on a personal computer to minimize limitations of observation location, time, and budget. The method allows us to evaluate changes over time in the flowering phenology of Cerasus×yedoensis by examining the canopy gap fraction extracted from binarized daily images in photographs taken upwards from the floor. To extend the utility of this method, here we explain the results of 11 years of observations in Yokohama, Japan, as an example and discuss our perspectives of monitoring flowering phenology with this method.