Detecting and Tracking the Positions of Wild Ungulates Using Sound Recordings.

Monitoring wild ungulates such as deer is a highly challenging issue faced by wildlife managers. Wild ungulates are increasing in number worldwide, causing damage to ecosystems. For effective management, the precise estimation of their population size and habitat is essential. Conventional methods used to estimate the population density of wild ungulates, such as the light census survey, are time-consuming with low accuracy and difficult to implement in harsh environments like muddy wetlands. On the other hand, unmanned aerial vehicles are difficult to use in areas with dense tree cover. Although the passive acoustic monitoring of animal sounds is commonly used to evaluate their diversity, the potential for detecting animal positions from their sound has not been sufficiently investigated. This study introduces a new technique for detecting and tracking deer position in the wild using sound recordings. The technique relies on the time lag among three recorders to estimate the position. A sound recording system was also developed to overcome the time drift problem in the internal clock of recorders, by receiving time information from GPS satellites. Determining deer position enables the elimination of repetitive calls from the same deer, thus providing a promising tool to track deer movement. The validation results revealed that the proposed technique can provide reasonable accuracy for the experimental and natural environment. The identification of deer calls in Oze National Park over a period of two hours emphasizes the great potential of the proposed technique to detect repetitive deer calls, and track deer movement. Hence, the technique is the first step toward designing an automated system for estimating the population of deer or other vocal animals using sound recordings.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m-3, 16.25 mg·m-3, and 19.05 mg·m-3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m-3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m-3).