Indicator Bats Program: A System for the Global Acoustic Monitoring of Bats

Bats are an important component of mammalian biodiversity and fill such a wide array of ecological niches that they may offer an important multisensory bioindicator role in assessing ecosystem health. There is a need to monitor population trends of bats for their own sake because many populations face numerous environmental threats related to climate change, habitat loss, fragmentation, hunting, and emerging diseases. To be able to establish bat ultrasonic biodiversity trends as a reliable indicator, it is important to standardize monitoring protocols, data management, and analyses. This chapter discusses the main issues to be considered in developing a bat ultrasonic indicator. It focuses on the results from indicator bats program (iBats), a system for the global acoustic monitoring of bats, in Eastern Europe. Finally, the chapter reviews the strengths and weaknesses of the Program and considers the opportunities and threats that it may face in the future.

Productivity is a poor predictor of plant species richness

For more than 30 years, the relationship between net primary productivity and species richness has generated intense debate in ecology about the processes regulating local diversity. The original view, which is still widely accepted, holds that the relationship is hump-shaped, with richness first rising and then declining with increasing productivity. Although recent meta-analyses questioned the generality of hump-shaped patterns, these syntheses have been criticized for failing to account for methodological differences among studies. We addressed such concerns by conducting standardized sampling in 48 herbaceous-dominated plant communities on five continents. We found no clear relationship between productivity and fine-scale (meters−2) richness within sites, within regions, or across the globe. Ecologists should focus on fresh, mechanistic approaches to understanding the multivariate links between productivity and richness.

Automated sensory data alignment for environmental and epidermal change monitoring

In this paper we present research adapting a state of the art condition-invariant robotic place recognition algorithm to the role of automated inter- and intra-image alignment of sensor observations of environmental and skin change over time. The approach involves inverting the typical criteria placed upon navigation algorithms in robotics; we exploit rather than attempt to fix the limited camera viewpoint invariance of such algorithms, showing that approximate viewpoint repetition is realistic in a wide range of environments and medical applications. We demonstrate the algorithms automatically aligning challenging visual data from a range of real-world applications: ecological monitoring of environmental change, aerial observation of natural disasters including flooding, tsunamis and bushfires and tracking wound recovery and sun damage over time and present a prototype active guidance system for enforcing viewpoint repetition. We hope to provide an interesting case study for how traditional research criteria in robotics can be inverted to provide useful outcomes in applied situations.

Remote monitoring of underwater objects

A system for monitoring conditions in a remote environment. The system comprising a data transmission network including a plurality of data sensing nodes. Each data sensing node includes an environment sensing means for periodically sensing the environment around node, a transmission means for periodic wireless transmission of sensed data to adjacent data sensing nodes. These adjacent data sensing nodes combining their sensed data with the received data from other data sensing nodes and on transmit the combined data.

Challenges in field monitoring of energy performance of air conditioners

Experimental work could be conducted in either laboratory or at field site. Generally, the laboratory experiments are carried out in an artificial setting and with a highly controlled environment. By contrast, the field experiments often take place in a natural setting, subject to the influences of many uncontrolled factors. Therefore, it is necessary to carefully assess the possible limitations and appropriateness of an experiment before embarking on it. In this paper, a case study of field monitoring of the energy performance of air conditioners is presented. Significant challenges facing the experimental work are described. Lessons learnt from this case study are also discussed. In particular, it was found that on-going analysis of the monitoring data and the correction of abnormal issues are two of the keys for a successful field test program. It was also shown that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. Before monitoring system was set up to collect monitoring data, it is recommended that an initial analysis of sample monitored data should be conducted to make sure that the monitoring data can achieve the expected precision. In the case where inevitable inherent errors were induced from the installation of field monitoring systems, appropriate remediation may need to be developed and implemented for the improved accuracy of the estimation of results. On-going analysis of monitoring data and correction of any abnormal issues would be the key to a successful field test program.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.