Developing a large-scale forest monitoring system able to take advantage of the complementary nature of optical and radar remote sensing data presents a number of technical and conceptual challenges. This paper investigates the issue of sensor interoperability in a time series of Landsat and ALOS-PALSAR data for purposes related to forest mapping and monitoring. The proposed approach relies on the processing methods developed in the frame of an existing and operational Landsat-based forest monitoring system. These methods are here applied to a PALSAR dataset within a bioregion of north-eastern Tasmania, Australia. Particular attention is given to the selection of training data in an attempt to generate results comparable to those obtained with the original Landsat-only time series, thereby allowing for a relevant assessment of interoperability. Results are presented in the form of forest maps and areal forest estimates. Despite similar gross amounts of forest extents, these results highlight differences in the forest (and change) classifications produced using different sensors. Combinations of sensors should therefore be carefully considered in light of what is required of the monitoring system.

Recent technological advances in the field of radar remote sensing have allowed the deployment of an increasing number of new satellite sensors. These provide an important source of Earth observation data, which add to the currently existing optical data sets. In parallel, the development of robust methods for global forest monitoring and mapping is becoming increasingly important. As a consequence, there is significant interest in the development of global monitoring systems that are able to take advantage of the potential synergies and complementary nature of optical and radar data. This paper proposes an approach for the combined processing of Landsat and ALOS-PALSAR data for the purpose of forest mapping and monitoring. This is achieved by incorporating the PALSAR data into an existing operational Landsat-based processing system. Using a directed discriminant technique, a probability map of forest presence/absence is first generated from the PALSAR imagery. This SAR classification data is then combined with a time series of similar Landsat-based maps within a Bayesian multitemporal processing framework, leading to the production of a time series of joint radar-optical maps of forest extents. This approach is applied and evaluated over a pilot study area in northeastern Tasmania, Australia. Experimental outcomes of the proposed joint processing framework are provided, demonstrating its potential for the integration of different types of remote sensing data for forest monitoring purposes.

The contribution of the world's forested ecosystems to the global carbon budget is well recognized. Increasingly however, we see the ongoing depletion of forest stocks, biomass burning and land cover change; key contributors to greenhouse gas emissions and ultimately climate change. Through international agreements such as UNFCCC REDD, individual country's are charged with a greater responsibility to account for carbon gains and losses, a move which may spur improved management and more sustainable use of forest and natural resources. With such variability in forest and land cover dynamics, individual country's Measurement, Reporting and Verification (MRV) systems must adapt to detect extensive clearing or more subtle change at the landscape level. The acquisition of both optical and radar remotely sensed data at moderate spatial resolution has, and will continue to support regional assessment of forest and land cover change. In this paper we investigate the interoperability of optical and radar data acquired over Tasmania, Australia, for forest information monitoring. The fundamental requirement of an initial baseline from which subsequent change can be measured is outlined. Classification methodologies and forest cover estimates over the timeframe of image acquisition are presented. The independent and combined use of optical and radar data for forest extent mapping, with the emphasis on processing time-series data, is discussed. The methodologies presented in this paper are robust, consistent over a time-series and issues surrounding interoperability are globally applicable.

In this paper we propose an approach implementing the dual polarised Entropy/alpha decomposition [1] and coherence optimisation [8] for improved forest mapping. We explore the dual polarised Entropy/alpha decomposition for better forest/non-forest discrimination and the multiple partial polarimetric coherence optimisation for subsequent forest canopy/height estimation. The integrated forest discrimination and coherence-forest height estimation for the task of producing forest extent, change and trend information are examined with in situ LiDAR data.

Consistent estimation of carbon stocks at national level requires the integration of wall-to-wall, time-series satellite and in situ data of forest area, type and change. In this paper we demonstrate a consistent approach to the generation of wall-to-wall time-series mosaics using ALOS PALSAR data acquired over 2007 to 2009 for Tasmania, Australia. The project is part of a series of National Demonstrators initiated by the Group on Earth Observations (GEO) Forest Carbon Tracking (FCT) task that emphasize the contribution and operational use of satellite measurements for forest monitoring and national carbon accounting. Interoperability between optical and Synthetic Aperture Radar (SAR) derived forest measurements will also be demonstrated. The project will deliver a series of forest monitoring products and technical documentation, which will be made available as a guide to GEO member countries with a desire to develop their own national carbon accounting systems.

In many research fields of engineering and acoustics, the image-source model represents one of the most popular tools for the simulation of sound fields in virtual reverberant environments. This can be seen as a result from the relative simplicity and flexibility of this particular method. However, the associated computational costs constitute a well known drawback of image-source implementations, as the required simulation times grow exponentially with the considered reflection order. This paper proposes a method that allows for a fast synthesis of room impulse responses according to the image-source technique. This is achieved by modeling the diffuse reverberation tail as decaying random noise, where the decay envelope for the considered acoustic environment is determined according to a recently proposed method for the prediction of energy decay curves in image-source simulations. The diffuse reverberation model presented in this paper thus produces impulse responses that are representative of the specific virtual environment under consideration (within the general assumptions of geometrical room acoustics), in contrast to other artificial reverberation techniques developed on the basis of perceptual measures or assuming a purely exponential energy decay. Furthermore, since image-source simulations are only used for the computation of the early reflections, the proposed approach achieves a reduction of the computational requirements by up to two orders of magnitude for the simulation of full-length room impulse responses, compared to a standard image-source implementation.

Australia's National Carbon Accounting System Land Cover Change Program (NCAS-LCCP) has been developed over the past decade to provide fine-scale monitoring of forest land cover changes for the continent, and to estimate greenhouse gas (GHG) emissions from land use and land use changes. Time series Landsat coverages of the continent are processed to provide the monitoring component of the system. Forest extent and change products were the initial priority and have been routinely produced since 2002. Since then, a number of other monitoring products have been trialled and developed based on the NCAS-LCCP image archives, including the Woody Vegetation Trends described here. This product has been produced nationally within forested areas, providing indicators of changing forest density and degradation relevant to GHG accounting, and also to conservation and natural resource management.

This paper describes a new method for the online parameter optimization of various models used to represent the target dynamics in particle filters. The optimization is performed with an evolutionary strategy algorithm, by using the performance of the particle filter as a basis for the objective function. Two different approaches to forming the objective function are presented: the first assumes knowledge of the true source position during the optimization, and the second uses the position estimates from the particle filter to form an estimate of the current ground-truth data. The new algorithm has low computational complexity and is suitable for real-time implementation. A simple and intuitive real-world application of acoustic source localization and tracking is used to highlight the performance of the algorithm. Results show that the algorithm converges to an optimum tracker for any type of dynamics model that is capable of representing the target dynamics.

This paper addresses the coordinated use of video and audio cues to capture and index surveillance events with multimodal labels. The focus of this paper is the development of a joint-sensor calibration technique that uses audio-visual observations to improve the calibration process. One significant feature of this approach is the ability to continuously check and update the calibration status of the sensor suite, making it resilient to independent drift in the individual sensors. We present scenarios in which this system is used to enhance surveillance.

A particle filter algorithm is developed for the problem of online subsample time-delay estimation between noisy signals received at two spatially separated sensors. The delay is modeled as an adaptive FIR filter whose coefficients are determined by the tracker's particles, and updated on a sample-by-sample basis. Efficient tracking of the delay parameter over time is ensured with the derivation of a global system model integrating the target dynamics for both near-field and far-field operation. Experimental simulations are carried out to assess the algorithm's convergence and tracking performance, and demonstrate that the proposed method is able to efficiently track time delays with stationary signals as well as speech

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. In this paper we present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.