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).

Estimating Soil Moisture Distributions across Small Farm Fields with ALOS/PALSAR.

The ALOS (advanced land observing satellite) has an active microwave sensor, PALSAR (phased array L-band synthetic aperture radar), which has a fine resolution of 6.5 m. Because of the fine resolution, PALSAR provides the possibility of estimating soil moisture distributions in small farmlands. Making such small-scale estimates has not been available with traditional satellite remote sensing techniques. In this study, the relationship between microwave backscattering coefficient (σ) measured with PALSAR and ground-based soil moisture was determined to investigate the performance of PALSAR for estimating soil moisture distribution in a small-scale farmland. On the ground at a cabbage field in Japan in 2008, the soil moisture distribution of multiple soil layers was measured using time domain reflectometry when the ALOS flew over the field. Soil moisture in the 0-20 cm soil layer showed the largest correlation coefficient with σ (r = 0.403). The σ values also showed a strong correlation with the ground surface coverage ratio by cabbage plants. Our results suggested that PALSAR could estimate soil moisture distribution of the 0-20 cm soil layer across a bare field and a crop coverage ratio when crops were planted.

Using remotely sensed imagery to estimate potential annual pollutant loads in river basins.

Land cover changes around river basins have caused serious environmental degradation in global surface water areas, in which the direct monitoring and numerical modeling is inherently difficult. Prediction of pollutant loads is therefore crucial to river environmental management under the impact of climate change and intensified human activities. This research analyzed the relationship between land cover types estimated from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery and the potential annual pollutant loads of river basins in Japan. Then an empirical approach, which estimates annual pollutant loads directly from satellite imagery and hydrological data, was investigated. Six water quality indicators were examined, including total nitrogen (TN), total phosphorus (TP), suspended sediment (SS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO). The pollutant loads of TN, TP, SS, BOD, COD, and DO were then estimated for 30 river basins in Japan. Results show that the proposed simulation technique can be used to predict the pollutant loads of river basins in Japan. These results may be useful in establishing total maximum annual pollutant loads and developing best management strategies for surface water pollution at river basin scale.

Spatial assessment of the alder tree in Kushiro Mire, Japan using remotely sensed imagery--effects of the surrounding land use on Kushiro Mire.

The relationship between alder (Alnus japonica) distribution and surrounding land use in Kushiro Mire was spatially assessed using remotely sensed imagery. From the result, it was found out that the expanding area of alder trees in Kushiro Mire was affected by the agricultural land area in the upper course of the river basin and flooding in the lower course of the river. The soil sediments flowing into the Kushiro Mire from the agricultural land resulted in heavy sedimentation that favors the growth of alder trees. Consequently, the number and density of alder trees has increased. The future distribution of alder trees was predicted based on the mechanism of expansion of the alder-tree area in Kushiro Mire, and it was found that large vegetation areas in Kushiro Mire will be changed to areas with alder trees.

Estimation of chlorophyll concentration in lakes and inland seas with a field spectroradiometer above the water surface.

The estimation of chlorophyll concentration in the water by use of a field spectroradiometer above the water surface is necessary for the removal of the effect of specular reflection at the water surface. The amount of specular reflection from the water surface was assessed on the basis of the spectral signature data that was measured above and below the water surface. Furthermore, a method to remove the effect of specular reflection from spectral signature data that was measured above water surface was proposed. Finally, chlorophyll-a concentration was estimated accurately from the spectral signature measured by field spectroradiometer above water surface with the proposed surface reflection model.