The Watarase Retarding Basin, located 60 km north of Tokyo, is the largest retarding basin in Japan. The retarding basin was constructed at the beginning of the twentieth century to store toxic pollution from the nearby Ashio Copper Mine. The copper mine is located in the upper stream of the Watarase River and is the best known example of environmental pollution in Japan. We analyzed land use changes over the past 100 years in the area. At the time construction began in 1907, ponds, grassland, and marshes were evident. By 1979, the Watarase Retarding Basin was a wetland or water area. A large part of the retarding basin had become arid after that. In 2001, the land use in the retarding basin was a mosaic of wetlands with common reed (Phragmites australis) and amur silver grass (Miscanthus sacchariflorus). Most of the area became a Ramsar site in 2012 because the area is representative of a reed-dominated low moor wetland in Japan and has a high level of diversity of wetland flora and fauna. Typhoon Hagibis struck eastern Japan on 12–13 October 2019. The Watarase Retarding Basin stored 250 million m3 of water and prevented flooding downstream in the Edo River.

The Oriental White Stork (Ciconia boyciana) is one of the species threatened by mainly anthropogenic factor and their habitats are considered to be conserved. The first reintroduction of the species in Japan occurred in 2005. But as the species' natural wetland habitats are declining, the birds now prefer to forage in rice paddies. Thus, restoring the paddy-dominated landscape is key for further success in the reintroduction program. In addition, a quantitative method is urgently needed to assess how much suitable habitat is available and where it is located. In this study, we identified environmental factors that affect the distribution of the Oriental White Stork and produced the first predictive spatial distribution map using 2-year satellite tracking data of reintroduced individuals. The maximum entropy (MaxEnt) approach was used to model the species' distribution at the landscape scale (1 kmx1 km grid cells). We identified six relevant environmental variables. Our results highlight the proportion of area of rice paddies as alternative wetland habitat as the most influential variable affecting the distribution positively. Landscape diversity represented by a complex mosaic of paddies and forest is also important for the species, as total length of paddy-forest edge also had a positive effect on habitat suitability. Our predictive distribution map cannot entirely provide distribution; however, it may be valuable information when considering where should be conserved as habitat to maintain the Oriental White Stork population in Japan.

We assessed aerial hyperspectral imagery with high spatial 1.5 m and spectral 8.9 nm resolutions for detecting and mapping the early invasion by Solidago altissima of understory vegetation in moist tall grassland. Generalized linear models GLMs were constructed to predict S. altissima occurrence using 1.5 m pixels from hyperspectral data collected during the spring when understory vegetation was directly observable from above. A data set of presence–absence derived from percentage cover data was used for the analyses. The values of the area under the receiver operating characteristic ROC curve AUC ranged from 0.77–0.87 in the validation data set. Three minimum noise fraction MNF bands differentiated S. altissima in the best-performing model selected based on Akaike's information criterion for the occurrence of S. altissima. The results suggest that the aerial hyperspectral images obtained during spring before the seasonal development of the grass canopy are useful for the early detection and mapping ofS. altissima invading moist tall grassland.

Weeping love grass (Eragrostis curvula) has become a well-established invasive species along the Kinu River, Japan and is now considered a problematic invasive weed species. The aim of this study was to map the probability of the establishment of this invasive grass in the Shore of the Kinu River using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probable presence/absence of weeping love grass. This study tried entering two types of input variables, original reflectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model. No available variable of original reflectance data was selected, but two bands of MNF were selected in the regression analysis. The final classification, using the selected MNF bands, has distinguished weeping love grass from pseudo-absence pixels with user's and producer's accuracies of 100% and 66.7% respectively. The kappa coefficient was 0.74. These results indicate that the MNF transformed hyperspectral bands are more suitable than the original reflectance data to estimate the distribution of invasive weeping love grass in the Shore of the Kinu River.

Weeping love grass (Eragrostis curvula) has become a well-established invasive species along the Kinu River, Japan and is now considered a problematic invasive weed species. The aim of this study was to map the probability of the establishment of this invasive grass in the shore of the Kinu River using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probable presence/absence of weeping love grass. This study tried entering two types of input variables, original reflectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model. No available variable of original reflectance data was selected, but two bands of MNF were selected in the regression analysis. The final classification, using the selected MNF bands, has distinguished weeping love grass from pseudo-absence pixels with user's and producer's accuracies of 100% and 66.7% respectively. The kappa coefficient was 0.74. These results indicate that the MNF transformed hyperspectral bands are more suitable than the original reflectance data to estimate the distribution of invasive weeping love grass in the shore of the Kinu River.

The dominant grasses in a wetland are of critical concern for the wetland’s ecological integrity, because these species provide the habitats for many small plants and animals. In this study, we used hyperspectral imagery to map the distributions of two dominant tall grasses (Miscanthus sacchariflorus (Maxim.) Benth and Phragmites australis (Cav.) Trin. ex Stend) in the Watarase wetland, in central Japan. Stepwise multiple linear regression analysis was applied to the hyperspectral data to predict the shoot density and biomass of the two grasses. The independent data sets included original reflectance, band ratios, significant components identified by principal components analysis (PCA), and significant components identified by decision boundary feature extraction (DBFE). The coefficient of determination (R2) and the root-mean-square error (RMSE) of model calibration and validation were used to evaluate the models. The significant DBFE components showed better ability at predicting shoot density of the two grasses than the other variables in the validating areas. The RMSE values were 7.40/m2 for M. sacchariflorus and 13.09/m2 for P. australis, which amounted to errors of around 10.0% and 12.6%, respectively, of the maximum shoot density measured during our surveys. All variables showed similar performance at predicting biomass, but the results were less accurate than those for shoot density. Considering the performance of the DBFE components for both shoot density and biomass prediction, we suggest that these are the best indicators for estimating the abundance of the two grasses.

We examined the capability of hyperspectral imagery to map habitat types of under-storey plants in a moist tall grassland dominated by Phragmites australis and Miscanthus sacchariflorus , using hyperspectral remotely-sensed shoot densities of the two grasses. Our procedure (1) grouped the species using multivariate analysis and discriminated habitat types (species groups) based on P. australis and M. sacchariflorus shoot densities, (2) used estimated shoot densities from hyperspectral data to draw a habitat type map, and (3) analyzed the association of threatened species with habitat types. Our identification of four habitat types, using cluster analysis of the vegetation survey coverage data, was based on P. australis and M. sacchariflorus shoot density ratios and had an overall accuracy of 77.1% (kappa coefficient = 0.71). Linear regression models based on hyperspectral imagery band data had good accuracy in estimating P. australis and M. sacchariflorus shoot densities (adjusted R 2 = 0.686 and 0.708, respectively). These results enabled us to map under-storey plant habitat types to an approximate prediction accuracy of 0.537. Among the eight threatened species we examined, four exhibited a significantly biased distribution among habitat types, indicating species-specific habitat use. These results suggest that this procedure can provide useful information on the status of potential habitats of threatened species.

Dragonfly and damselfly (Odonata) species were surveyed from May to October 2002 on 38 small irrigation ponds in the north part of Awaji Island. The investigation was conducted nine times on each pond. Totally 1568 individuals of 28 species were recorded for six months. We selected nine species recorded more than 40 individuals and analyzed the relationships of individual number of these species with environmental factors, including conductivity, NO2-, NO3-, NH4-, PO43-, COD, surrounding land uses within 50 meters from the edge of pond, the number of aquatic water plant species, and autocovariates explaining spatial autocorrelation, using Generalized Linear Models (GLM). The result showed that NO3-, COD, surrounding grassland, woodland and the number of water plant species were critical factors for the distribution of some Odonata species.

Technologies to identify people and determine their positions are developing rapidly day by day. Many means exist to estimate a person's position. Mainly, wireless sensors are used. Nevertheless wireless sensors have problems in accuracy. Because of multipath problem, wireless sensors cannot actually measure one's position as the level of their specifications. This causes some fatal problems to estimate an area where person is. In this paper, we present a way to improve position information by filtering data which was obtained by a wireless location awareness system. We also show designed filter which is based on particle filter is effective by simulations and experiments.

兵庫県淡路市黒谷において，棚田に作られた38箇所のため池に生育する水草の分布様式と環境要因との関係を解析した。本調査地では，絶滅危惧種および外来種を含む16種の水草が確認された。水草相は入れ子分布を示し，絶滅危惧種は種数の多いため池に出現した。期待された分布に対する谷地形の影響はほとんど認められず，GLMを用いた解析によって，種数に対して硝酸イオン濃度と平均水位が影響することが明らかになった。

More than 40 endangered plants listed in the national red list are growing in moist tall grasslands of Watarase wetland, the largest lowland wetland, in Japan. The dominant plants in grasslands are Miscanthus sacchariflorus and Phragmites australis, and each endangered species is associated with the habitats where either grass species is predominate, or both are mixed more evenly. Therefore, estimation of the relative dominance of M. sacchariflorus or P. australis may help in evaluation of the potential habitat area of the individual endangered species. In this study, matched filtering (MF), a specialized type of spectral mixture analysis, was used to estimate the abundance and distribution of M. sacchariflorus and P. australis from Airborne Imaging Spectrometer for Applications (AISA) data. Correlation analysis of the MF results and ground truth data was conducted to determine the accuracy of the estimates. Overall performance of MF for estimating amount of M. sacchariflorus was good with correlation coefficient of 0.89 for shoot density, and 0.78 for total stem volume. However, a poor estimate was obtained for P. australis with correlation coefficient of 0.43 for shoot density and 0.58 for total stem volume. Possible reasons for the difference in the accuracy estimates were discussed in this paper.

More than 40 endangered plants listed in the national red list are growing in moist tall grasslands of Watarase wetland, the largest lowland wetland, in Japan. The dominant plants in grasslands are Miscanthus sacchariflorus and Phragmites australis, and each endangered species is associated with the habitats where either grass species is predominate, or both are mixed more evenly. Therefore, estimation of the relative dominance of M. sacchariflorus or P. australis may help in evaluation of the potential habitat area of the individual endangered species. In this study, matched filtering (MF), a specialized type of spectral mixture analysis, was used to estimate the abundance and distribution of M. sacchariflorus and P. australis from Airborne Imaging Spectrometer for Applications (AISA) data. Correlation analysis of the MF results and ground truth data was conducted to determine the accuracy of the estimates. Overall performance of MF for estimating amount of M. sacchariflorus was good with correlation coefficient of 0.89 for shoot density, and 0.78 for total stem volume. However, a poor estimate was obtained for P. australis with correlation coefficient of 0.43 for shoot density and 0.58 for total stem volume. Possible reasons for the difference in the accuracy estimates were discussed in this paper.