Binaural sound source localization is an important technique in friendly Human-Robot Interaction (HRI) for its easy-implementation with only two microphones. This paper develops a robust method based on a hierarchical probabilistic model. Reliable frequency sub-bands are used to PHAT — ργ method in the first layer to obtain a priori crude Interaural Time-Delay (ITD) and the probabilistic distribution of candidate azimuths. The second layer utilizes Interaural Intensity Difference (IID) to reduce matching time and refine candidate azimuths as well as elevations. A novel feature named Interaural Matching Filter (IMF), which can eliminate the difference between ITDs and IIDs, is proposed in the third layer. The probability of sound source location is acquired by computing the similarity between the IMF of received binaural signal and the IMFs in templates. Finally, combined with the former ITDs and IIDs, the similarity matrix is used to make a decision of sound source location based on a Bayes rule. Our innovation lies in adding selecting reliable frequency components into time-delay estimation and foremost taking IMF as a feature of sound source. Compared with several state-of-the-art algorithms, experimental results show our approach has a better performance even in noisy environments without increasing storages, and also has less time complexity.

Interaural Intensity Difference (IID) and Interaural Time Difference (ITD) are two important cues for robot acoustic localization both in Artificial Intelligence (AI) and Human-Robot Interaction (HRI) areas. However, it is a challenge job to localize a sound source accurately and swiftly only by two acoustic sensors. In this paper, a time-delay compensation based two-layer probabilistic model is presented for binaural sound source localization. In the first layer, a weighting function of Generalized Cross Correlation (GCC) named PHAT-ργ is used in low-frequency to obtain the prior time-delay. And in this layer a crude estimate of azimuth can also be acquired. At the same time, the probability of all possible time-delay lags can be achieved from the training data. In the Second layer, a new improved algorithm of IID based on time-delay compensation(named IIDδτ) is introduced to refine the probability of the azimuth and the elevation. Lastly, localization result is obtained by Bayes-Rule method. Comparing with three state-of-art algorithms, experimental results show that the proposed method has higher accuracy and costs less time for sound source localization.

The new data assimilation algorithm is developed to estimate LAI from time-series MODIS reflectance data (MOD09A1). The canopy radiative transfer model (ACRM) is coupled with an empirical LAI dynamic model, and the ensemble Kalman smoother (EnKS) is used to estimate the parameters of the coupled model from MOD09A1 data. The preliminary analysis using MODIS surface reflectance data at some AmeriFlux network sites was performed to validate this method. The results show that the algorithm is helpful to produce the temporally continuous LAI estimation of cropland efficiently. By comparing with the field measured LAI, the retrieved LAI has been significantly improved and shown more smooth in time series than the MODIS LAI product.

The Effective Plant Area Index (PAIe) of forest canopy is an important parameter in the canopy reflectance modeling and validation. PAIe can be transformed to leaf area index (LAI) with clumping index. But it is still very difficult to obtain its ground truth value by in situ measurement. In this study, we measured PAIe of 3 typical wood land sites in China by means of three indirect optical techniques: plant canopy analyzer LAI-2000, TRAC, and Digital Fisheye Camera. The 5 measured stands include Qinghai spruce, peach, poplar, willow and silver chain. In this paper, the forest canopy PAIe measured by those three instruments is compared firstly. Then our new approach is how to use the three measured data to get the better PAIe estimation with less overall error. The method of minimizing overall error is adopted to produce PAIe of the sample plots in our study sites, which has less overall error comparing with the PAIe measured by each individual instrument. This approach is also validated by using computer simulated wide-angle viewing pictures when true LAI/PAIe values are given.

Adsorptive non-point source pollution associated with soil erosion has brought big problems to water environment and land productivity. As the most important water source area of Hainan, the Songtao Reservoir Basin (SRB) has faced with a crisis of water contamination caused by land exploitation. The objectives of this study are: (1) to assess the soil erosion risk and adsorptive non-point source pollution load; (2) to put forward a scheme for land management and planning to control soil erosion and nonpoint source pollution. Taking GIS and remote sensing as main techniques, an analysis was conducted to assess the non-point source pollution load caused by eucalyptus and rubber cultivation. Research findings indicate that soil erosion in the eucalyptus and rubber plantations is much more serious than the natural forest, and causes heavier adsorptive non-point source pollution. Adopting soil and water conservation facilities can effectively control the soil erosion and non-point source pollution which can bring a lasting ecological and economic benefit.