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Rice lodging causes a loss of yield and leads to lower-quality rice. In Japan, Koshihikari is the most popular rice variety, and it has been widely cultivated for many years despite its susceptibility to lodging. Reducing basal fertilizer is recommended when the available nitrogen in soil (SAN) exceeds the optimum level (80–200 mg N kg−1). However, many commercial farmers prefer to simultaneously apply one-shot basal fertilizer at transplant time. This study investigated the relationship between the rice lodging and SAN content by assessing their spatial distributions from unmanned aircraft system (UAS) images in a Koshihikari paddy field where one-shot basal fertilizer was applied. We analyzed the severity of lodging using the canopy height model and spatially clarified a heavily lodged area and a non-lodged area. For the SAN assessment, we selected green and red band pixel digital numbers from multispectral images and developed a SAN estimating equation by regression analysis. The estimated SAN values were rasterized and compiled into a 1 m mesh to create a soil fertility map. The heavily lodged area roughly coincided with the higher SAN area. A negative correlation was observed between the rice inclination angle and the estimated SAN, and rice lodging occurred even within the optimum SAN level. These results show that the amount of one-shot basal fertilizer applied to Koshihikari should be reduced when absorbable nitrogen (SAN + fertilizer nitrogen) exceeds 200 mg N kg−1. [ABSTRACT FROM AUTHOR]

Detecting field diversities via remote sensing is becoming important to monitor vegetation dynamics at large scale. The characteristics of the remotely sensed image, depending on the study organism and habitat, affect the efficiency of measuring α-and β-diversities. Therefore, we examined the impact of image resolutions and spectral plot extents on the accuracy of estimating forest α-diversities and compositional variances on the active volcano Mount Usu, northern Japan. Low- (3.2 m) and high-resolution (0.8 m) IKONOS multispectral images were used to create spectral indicators from pixels covering the field plots (narrow extent) and from pixels including neighbouring area (wide extent). Six forest diversity indices were obtained for canopy and for canopy-herb layer (total diversity): species richness (S), Shannon (H'), evenness (J'), Gini-Simpson (D), and true diversity of order 1 (N1 = expH') and order 2 (N2 = 1/D). Changes in species composition were assessed by dissimilarity matrices. The spectral diversity indicators were calculated from the combination of image resolutions and spectral plot extents, and then compared with field diversities. The low-resolution–narrow extent based spectral indicators showed the highest correlations with canopy and total diversities. The best spectral indicators were derived from the scores of the first axis of principal component analysis and from the near infrared band, reaching high correlations with both canopy and total field diversity indices. Of the six field diversities, J' showed the highest correlations with single spectral indicators, and N1, 2 showed the highest correlations with pairs of spectral indicators. The correlations between spectral and field dissimilarities were lower than the correlations between α-diversities and spectral indicators, and were unaffected by the resolution and extent. In conclusion, the best spectral indicators were obtained from the low-resolution–narrow extent combination, and the indicators estimated canopy and total field diversity indices of temperate forests equally. [ABSTRACT FROM AUTHOR]

In this study, an approach is proposed for determining the depths of shallow waters from multispectral satellite images. This approach is a hybrid system that uses a Random Forest Ensemble (RFE) technique for Neural Network (NN) and Support Vector Regression (SVR) algorithms to improve accuracy of bathymetry estimations by reducing both errors and prediction variance. The study was performed over two different coastal areas: the first is the port of Alexandria, Egypt, which has a low turbidity silt-sand bottom area with a depth range of 10.5 m; the second area is Shiraho, Japan, which is a heterogeneous bottom composition coral reef area with a varied depths range up to 12.5 m. Landsat-8 and Quickbird satellite images were used for these two study areas, respectively, as examples of low- and high-resolution images. These images were corrected for both atmospheric and sun-glint errors. The results were validated using echosounder field points. Four reflectance values of green, red, blue divided by red, and green divided by red bands were assigned to reference point locations. These values were used for algorithm training and testing processes. The SVR and NN output values were combined using RFE. In order to evaluate the accuracy of the BE approach, we compared its outputs to that of the NN and SVR approaches. AU of the results were evaluated using RMSE and R2 values. The RFE approach was found to yield a RMSE of 0.64 and 0.90 m for the two areas, resulting in an almost 20 and 10 cm improvement. These results suggest that the proposed RFE approach outperforms both the NN and SVR algorithms, when they are used individually. [ABSTRACT FROM AUTHOR]

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the landcover pattern in a region is essential for landuse planning and resources management. In this study ALOS multispectral images were used to compare landcover patterns in three study areas, namely Tokyo, Kyoto, and Taipei, of different degrees of urbanization. From the results of landuse/landcover classification, Shannon diversity index at cell level was used for landcover pattern analysis. Existing landcover pattern of the three study areas were also compared by investigating cell distribution in a landcover coverage-ratio space. Both the landcover type richness and evenness are low in the Tokyo study area and built-up is the single dominant landcover type in almost all cells. In comparison, landcover patterns of the Kyoto and Taipei study areas are more diversified, with significant amount of cells having mixed and non-dominant landcover types. Kyoto is least urbanized and enjoys a good mixture of different landcover types. It was found that cell-average NDVI alone can be used for delineating areas of certain dominant landcover types. Implementation of such method does not require an a priori LULC classification, and thus is particularly useful when good training data for LULC classification are not available. An urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI was proposed and used to explore the spatial variation of degree of urbanization. Area-average urbanization indices of the Tokyo, Kyoto, and Taipei study areas were calculated to be 0.91, 0.55, and 0.72, respectively. Such results are consistent with the results of qualitative evaluation using different landscape metrics. [Copyright &y& Elsevier]

An unmixing method was used to estimate the area of alder trees from multitemporal Thematic Mapper (TM) imagery.of the Kushiro mire, an important wetland area in Japan. Because the spectral radiance (endmember) of alder coverage within a pixel must be precisely known before the unmixing method is applied, we first collected ground truth data for the canopy area of alder using field survey methods, and aerial photographs. The alder endmember was calculated by linking TM imagery and the ground truth data. Alder coverage was estimated more precisely than it was with classification by the maximum-likelihood method. Furthermore, the alder area could be evaluated within a pixel, which cannot be classified as alder by the maximumlikelihood method. [ABSTRACT FROM AUTHOR]

Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes. [ABSTRACT FROM AUTHOR]