Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis

Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis

AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

Spatial decision support system for coastal flood management in Victoria, Australia

Coastal climate impact can affect coastal areas in a variety of ways, such as flooding, storm surges, reduction in beach sands and increased beach erosion. While each of these can have major impacts on the operation of coastal drainage systems, this thesis focuses on coastal and riverine flooding in coastal areas. Coastal flood risk varies within Australia, with the northern parts in the cyclone belt most affected and high levels of risk similar to other Asian countries. However, in Australia, the responsibility for managing coastal areas is shared between the Commonwealth government, Australian states and territories, and local governments. Strategies for floodplain management to reduce and control flooding are best implemented at the land use planning stage. Local governments make local decisions about coastal flood risk management through the assessment and approval of planning permit applications. Statutory planning by local government is informed by policies related to coastal flooding and coastal erosion, advice from government departments, agencies, experts and local community experts. The West Gippsland Catchment Management Authority (WGCMA) works with local communities, Victorian State Emergency Services (VCSES), local government authorities (LGAs), and other local organizations to prepare the West Gippsland Flood Management Strategy (WGFMS). The strategy aims at identifying significant flood risks, mitigating those risks, and establishing a set of priorities for implementation of the strategy over a ten-year period. The Bass Coast Shire Council (BCSC) region has experienced significant flooding over the last few decades, causing the closure of roads, landslides and erosion. Wonthaggi was particularly affected during this period with roads were flooded causing the northern part of the city of Wonthaggi to be closed in the worst cases. Climate change and increased exposure through the growth of urban population have dramatically increased the frequency and the severity of flood events on human populations. Traditionally, while GIS has provided spatial data management, it has had limitations in modelling capability to solve complex hydrology problems such as flood events. Therefore, it has not been relied upon by decision-makers in the coastal management sector. Functionality improvements are therefore required to improve the processing or analytical capabilities of GIS in hydrology to provide more certainty for decision-makers. This research shows how the spatial data (LiDAR, Road, building, aerial photo) can be primarily processed by GIS and how by adopting the spatial analysis routines associated with hydrology these problems can be overcome. The aim of this research is to refine GIS-embedded hydrological modelling so they can be used to help communities better understand their exposure to flood risk and give them more control about how to adapt and respond. The research develops a new Spatial Decision Support System (SDSS) to improve the implementation of coastal flooding risk assessment and management in Victoria, Australia. It is a solution integrating a range of approaches including, Light Detection and Ranging (Rata et al., 2014), GIS (Petroselli and sensing, 2012), hydrological models, numerical models, flood risk modelling, and multi-criteria techniques. Bass Coast Shire Council is an interesting study region for coastal flooding as it involves (i) a high rainfall area, (ii) and a major river meeting coastal area affected by storm surges, with frequent flooding of urban areas. Also, very high-quality Digital Elevation Model (DEM) data is available from the Victorian Government to support first-pass screening of coastal risks from flooding. The methods include using advanced GIS hydrology modelling and LiDAR digital elevation data to determine surface runoff to evaluate the flood risk for BCSC. This methodology addresses the limitations in flood hazard modelling mentioned above and gives a logical basis to estimate tidal impacts on flooding, and the impact and changes in atmospheric conditions, including precipitation and sea levels. This study examines how GIS hydrological modelling and LiDAR digital elevation data can be used to map and visualise flood risk in coastal built-up areas in BCSC. While this kind of visualisation is often used for the assessment of flood impacts to infrastructure risk, it has not been utilized in the BCSC. Previous research identified terrestrial areas at risk of flooding using a conceptual hydrological model (Pourali et al., 2014b) that models the flood-risk regions and provides flooding extent maps for the BCSC. It examined the consequences of various components influencing flooding for use in creating a framework to manage flood risk. The BCSC has recognised the benefits of combining these techniques that allow them to analyse data, deal with the problems, create intuitive visualization methods, and make decisions about addressing flood risk. The SDSS involves a GIS-embedded hydrological model that interlinks data integration and processing systems that interact through a linear cascade. Each stage of the cascade produces results which are input into the next model in a modelling chain hierarchy. The output involves GIS-based hydrological modelling to improve the implementation of coastal flood risk management plans developed by local governments. The SDSS also derives a set of Coastal Climate Change (CCC) flood risk assessment parameters (performance indicators), such as land use, settlement, infrastructure and other relevant indicators for coastal and bayside ecosystems. By adopting the SDSS, coastal managers will be able to systematically compare alternative coastal flood-risk management plans and make decisions about the most appropriate option. By integrating relevant models within a structured framework, the system will promote transparency of policy development and flood risk management. This thesis focuses on extending the spatial data handling capability of GIS to integrate climatic and other spatial data to help local governments with coastal exposure develop programs to adapt to climate change. The SDSS will assist planners to prepare for changing climate conditions. BCSC is a municipal government body with a coastal boundary and has assisted in the development and testing of the SDSS and derived many benefits from using the SDSS developed as a result of this research. Local governments at risk of coastal flooding that use the SDSS can use the Google Earth data sharing tool to determine appropriate land use controls to manage long-term flood risk to human settlement. The present research describes an attempt to develop a Spatial Decision Support System (SDSS) to aid decision makers to identify the proper location of new settlements where additional land development could be located based on decision rules. Also presented is an online decision-support tool that all stakeholders can use to share the results

Building models from multiple point sets with kernel density estimation

One of the fundamental problems in computer vision is point set registration. Point set registration finds use in many important applications and in particular can be considered one of the crucial stages involved in the reconstruction of models of physical objects and environments from depth sensor data. The problem of globally aligning multiple point sets, representing spatial shape measurements from varying sensor viewpoints, into a common frame of reference is a complex task that is imperative due to the large number of critical functions that accurate and reliable model reconstructions contribute to. In this thesis we focus on improving the quality and feasibility of model and environment reconstruction through the enhancement of multi-view point set registration techniques. The thesis makes the following contributions: First, we demonstrate that employing kernel density estimation to reason about the unknown generating surfaces that range sensors measure allows us to express measurement variability, uncertainty and also to separate the problems of model design and viewpoint alignment optimisation. Our surface estimates define novel view alignment objective functions that inform the registration process. Our surfaces can be estimated from point clouds in a datadriven fashion. Through experiments on a variety of datasets we demonstrate that we have developed a novel and effective solution to the simultaneous multi-view registration problem. We then focus on constructing a distributed computation framework capable of solving generic high-throughput computational problems. We present a novel task-farming model that we call Semi-Synchronised Task Farming (SSTF), capable of modelling and subsequently solving computationally distributable problems that benefit from both independent and dependent distributed components and a level of communication between process elements. We demonstrate that this framework is a novel schema for parallel computer vision algorithms and evaluate the performance to establish computational gains over serial implementations. We couple this framework with an accurate computation-time prediction model to contribute a novel structure appropriate for addressing expensive real-world algorithms with substantial parallel performance and predictable time savings. Finally, we focus on a timely instance of the multi-view registration problem: modern range sensors provide large numbers of viewpoint samples that result in an abundance of depth data information. The ability to utilise this abundance of depth data in a feasible and principled fashion is of importance to many emerging application areas making use of spatial information. We develop novel methodology for the registration of depth measurements acquired from many viewpoints capturing physical object surfaces. By defining registration and alignment quality metrics based on our density estimation framework we construct an optimisation methodology that implicitly considers all viewpoints simultaneously. We use a non-parametric data-driven approach to consider varying object complexity and guide large view-set spatial transform optimisations. By aligning large numbers of partial, arbitrary-pose views we evaluate this strategy quantitatively on large view-set range sensor data where we find that we can improve registration accuracy over existing methods and contribute increased registration robustness to the magnitude of coarse seed alignment. This allows large-scale registration on problem instances exhibiting varying object complexity with the added advantage of massive parallel efficiency