Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13-0.38. Compared with the other three methods it improves about 0.06-0.12. The average gradient of BEMD is 4%-6% greater than for other methods. BEMD maintains spatial frequency 3.2-4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%.

No abstract

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015, and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.The characteristics that affect TC intensity are nonlinear and thus difficult to predict [2]. In recent years, many researchers have studied TC intensity prediction primarily using numerical forecasting and statistical methods [6][7][8][9][10]. Numerical forecasting is the main tool used to forecast TCs around the world, and systems such as the European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System (ECMWF-IFS) [11], the Japan Meteorological Agency's global spectral model (JMA-GSM) [12], and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) [13] have been developed as operational techniques. Statistical intensity forecast methods such as Climatology and Persistence (CLIPER) [14] and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) [15] have also been developed as operational techniques. However, advancements in TC intensity prediction have been relatively slow, although notable work has been done in the past 20 years on predicting TC paths [16]. One of the reasons for this is that the internal structure of storms is not yet sufficiently understood. TCs have an asymmetric structure, which may be caused by thermal and dynamic factors such as uneven distribution of sea surface temperature and humidity, horizontal or vertical shear, and asymmetric distribution of convection [2,17]. Changes in TC intensity are controlled by many e...