Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.Comment: To appear in ICRA 2017, Singapor

Autonomous Exploration of Large-Scale Natural Environments

This thesis addresses issues which arise when using robotic platforms to explore large-scale, natural environments. Two main problems are identified: the volume of data collected by autonomous platforms and the complexity of planning surveys in large environments. Autonomous platforms are able to rapidly accumulate large data sets. The volume of data that must be processed is often too large for human experts to analyse exhaustively in a practical amount of time or in a cost-effective manner. This burden can create a bottleneck in the process of converting observations into scientifically relevant data. Although autonomous platforms can collect precisely navigated, high-resolution data, they are typically limited by finite battery capacities, data storage and computational resources. Deployments are also limited by project budgets and time frames. These constraints make it impractical to sample large environments exhaustively. To use the limited resources effectively, trajectories which maximise the amount of information gathered from the environment must be designed. This thesis addresses these problems. Three primary contributions are presented: a new classifier designed to accept probabilistic training targets rather than discrete training targets; a semi-autonomous pipeline for creating models of the environment; and an offline method for autonomously planning surveys. These contributions allow large data sets to be processed with minimal human intervention and promote efficient allocation of resources. In this thesis environmental models are established by learning the correlation between data extracted from a digital elevation model (DEM) of the seafloor and habitat categories derived from in-situ images. The DEM of the seafloor is collected using ship-borne multibeam sonar and the in-situ images are collected using an autonomous underwater vehicle (AUV). While the thesis specifically focuses on mapping and exploring marine habitats with an AUV, the research applies equally to other applications such as aerial and terrestrial environmental monitoring and planetary exploration

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

The use of active sonar to study cetaceans

Cetacean species face serious challenges worldwide due to the increasing noise pollution brought to their environment by human activities such as seismic exploration. Regulation of these activities is vaguely defined and uncoordinated. Visual observations and passive listening devices, aimed at preventing conflicts between human wealth and cetaceans’ health have some fundamental limitations and may consequently fail their mitigation purposes. Active sonar technology could be the optimal solution to implement mitigation of such human activities. In my thesis, the proper sonar unit was used to test the feasibility to detect cetaceans in situ. Omnidirectional sonars could be the optimal solution to monitor the presence of cetaceans in the proximity of potential danger areas. To use this class of sonar in a quantitative manner, the first step was to develop a calibration method. This thesis links in situ measurements of target strength (TS) with variation trends linked to the behavior, morphology and physiology of cetacean. The butterfly effect of a cetacean’s body was described for a fin whale insonified from different angles. A relationship between whale respiration and TS energy peaks was tested through a simple prediction model which seems very promising for further implementation. The effect of lung compression on cetacean TS due to increasing depth was tested through a basic mathematical model. The model fit the in situ TS measurements. TS measurements at depth of a humpback whale, when post-processed, correspond to TS measurements recorded at the surface. Sonar technology is clearly capable of detecting whale foot prints around an operating vessel. Sonar frequency response shows that frequencies between 18 and 38 kHz should be employed. This work has established a baseline and raised new questions so that active sonar can be developed and employed in the best interest for the whales involved in potentially harmful conflicts with man

Communication-constrained multi-AUV cooperative SLAM

Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.United States. Office of Naval Research (Grant N00014-13-1-0588)National Science Foundation (U.S.) (Award IIS-1318392)United States. Office of Naval Research Globa

Hyperspectral Remote Sensing of Coastal Environment

Remotely sensed earth observation (EO) has revolutionized our understanding of our dynamic environment. Hyperspectral remote sensing is, in many ways, the ultimate optical remote sensing technology. Hyperspectral remote sensing is suited especially well for environmental studies due to it’s capability to discriminate between species and quantify the abundance of different materials and chemicals. In this thesis remote sensing methods for hyperspectral data, applicable in environmental monitoring of coastal environment are developed and tested.The planning of hyperspectral flight campaign HYPE08 in South-West Finland raised the need to validate and develop data processing methods for HYPE08 as well as for future hyperspectral flight campaigns. The research presented in this thesis can be largely considered as a response to this need. The study concentrates on four main topics: wetlands mapping, benthic mapping, water quality and atmospheric correction. The study was done using airborne hyperspectral data and field spectroscopy measurements.The results of this study emphasize the importance of local calibration and validation of methods used. Water quality retrieval algorithms developed in local environmental conditions outperformed those validated elsewhere. The results also show that hyperspectral remote sensing of benthic cover types is limited to rather shallow areas, indicating the need to use state of the art methodology in order to increase operational depth range. The proposed atmospheric correction algorithm produced very good results. The accuracy of model-based algorithm increases when Empirical-Line (EL) correction using spectral field measurements was applied to spectra generated by the model

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future