Modeling vegetation fires and fire emissions

Fire is the most important ecological and forest disturbance agent worldwide, is a major way by which carbon is transferred from the land to the atmosphere, and is globally a significant source of greenhouse gases and aerosols. Wildfires across all major biome types globally consume about 5% of net annual terrestrial primary production per annum, and release about 2-4 Pg C per annum, of which approximately 0.6 Pg C comes from tropical deforestation and below-ground peat fires. The global figure is equivalent to about 20-30% of global emissions from fossil fuels. Tropical savannas comprise the largest areas burned and greatest emissions sources from vegetation wildfires. Fires in Mediterranean forests and shrublands, tropical forests and boreal forests are also significant sources of emissions because they are generally characterised by much higher fuel loads per unit area compared with grasslands. Improved satellite data and sophisticated biogeochemical modeling enables emis-sions assessments on a global scale with fine spatial and temporal resolution. Emissions estimates are still comparable to those based on older inventory-based techniques, but uncertainties remain large. Fires increase during El Niño periods because parts of the tropics where humans use fire as a tool for deforestation experience drought conditions. These spikes contribute to the inter-annual variability of CO2 and CH4 observed in the atmosphere. Recently developed dynamic fire-vegetation models are capable of simulating the extent of wildfires as well as their emissions of CO2 and other greenhouse gases for ambient as well as for projected climatic conditions. The performance of fire-vegetation models however needs to be strongly improved and validated

Coupled atmosphere-wildland fire modeling with WRF-Fire

We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various ignition modes. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.Comment: Version 3.3, 41 pages, 2 tables, 12 figures. As published in Discussions, under review for Geoscientific Model Developmen

Multi-modal video analysis for early fire detection

In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages

NEW, MULTI-SCALE APPROACHES TO CHARACTERIZE PATTERNS IN VEGETATION, FUELS, AND WILDFIRE

Pattern and scale are key to understanding ecological processes. My dissertation research aims for novel quantification of vegetation, fuel, and wildfire patterns at multiple scales and to leverage these data for insights into fire processes. Core to this motivation is the 3-dimensional (3-D) characterization of forest properties from light detection and ranging (LiDAR) and structure-from-motion (SfM) photogrammetry. Analytical methods for extracting useable information currently lag the ability to collect such 3-D data. The chapters that follow focus on this limitation blending interests in machine learning and data science, remote sensing, wildland fuels (vegetation), and wildfire. In Chapter 2, forest canopy structure is characterized from multiple landscapes using LiDAR data and a novel data-driven framework to identify and compare structural classes. Motivations for this chapter include the desire to systematically assess forest structure from landscape to global scales and increase the utility of data collected by government agencies for landscape restoration planning. Chapter 3 endeavors to link 3-D canopy fuels attributes to conventional optical remote sensing data with the goal of extending the reach of laser measurements to the entire western US while exploring geographic differences in LiDAR-Landsat relationships. Development of predictive models and resulting datasets increase accuracy and spatial variation over currently used canopy fuel datasets. Chapters 4 and 5 characterize fire and fuel variability using unmanned aerial systems (UAS) and quantify trends in the influence of fuel patterns on fire processes

Applications in Low-Power Phased Array Weather Radars

Low-cost X-band radars are an emerging technology that offer significant advantages over traditional systems for weather remote sensing applications. X-band radars provide enhanced angular resolution at a fraction of the aperture size compared to larger, lower frequency systems. Because of their low cost and small form factor, these radars can now be integrated into more research and commercial applications. This work presents research and development activities using a low-cost, X-band (9410 MHz) Phase-Tilt Radar. The phase-tilt design is a novel phased array architecture that allows for rapid electronic scanning in azimuth and mechanical tilting in elevation, as a compromise between cost and performance. This work focuses on field studies and experiments in three meteorological applications. The first stage of research focuses on the real-world application of phased array radars in forest fire monitoring and observation. From April to May 2013, a phase-tilt radar was deployed to South Australia and underwent a field campaign to make polarimetric observations of prescribed burns within and around the Adelaide Hills region. Measurements show the real-time evolution of the smoke plume dynamics at a spatial and temporal resolution that has never before been observed with an X-band radar. This dissertation will perform data analysis on results from this field campaign. Results are compared against existing work, theories, and approaches. In the second stage of research, field experiments are performed to assess the data quality of X-band phased array radars. Specifically, this research focuses on the measurement of and techniques to improve the variance of weather product estimators for dual-polarized systems. Variability in the radar products is a complicated relationship between the radar system specifications, scanning strategy, and the physics governing precipitation. Here, the variance of the radar product estimators is measured using standard radar scanning strategies employed in traditional mechanical antenna systems. Results are compared against adaptive scan strategies such as beam multiplexing and frequency diversity. This work investigates the improvement that complex scanning strategies offer in dual-polarized, X-band phased array radar systems. In the third stage of research, simulations and field experiments are conducted to investigate the performance benefits of adaptive scanning to optimize the data quality of radar returns. This research focuses on the development and implementation of a waveform agile and adaptive scanning strategy to improve the quality of weather product estimators. Active phased array radars allow radar systems to quickly vary both scan pointing angles and waveform parameters in response to real-time observations of the atmosphere. As an evolution of the previous research effort, this work develops techniques to adaptively change the scan pointing angles, transmit and matched filter waveform parameters to achieve a desired level of data quality. Strategies and techniques are developed to minimize the error between observed and desired data quality measures. Simulation and field experiments are performed to assess the quality of the developed strategies

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

The occurrence and origin of salinity in non-coastal groundwater in the Waikato region

Aims The aims of this project are to describe the occurrence, and determine the origin of non-coastal saline groundwater in the Waikato region. High salinity limits the use of the water for supply and agricultural use. Understanding the origin and distribution of non-coastal salinity will assist with development and management of groundwater resources in the Waikato. Method The occurrence of non-coastal groundwater salinity was investigated by examining driller’s records and regional council groundwater quality information. Selected wells were sampled for water quality analyses and temperatures were profiled where possible. Water quality analyses include halogens such as chloride, fluoride, iodide and bromide. Ratios of these ions are useful to differentiate between geothermal and seawater origins of salinity (Hem, 1992). Other ionic ratio approaches for differentiating sources and influences on salinity such as those developed by Alcala and Emilio (2008) and Sanchez-Martos et al., (2002), may also be applied. Potential sources of salinity include seawater, connate water, geothermal and anthropogenic influences. The hydrogeologic settings of saline occurrence were also investigated, to explore the potential to predict further occurrence. Results Numerous occurrences of non-coastal saline groundwater have been observed in the Waikato region. Where possible, wells with relatively high total dissolved solids (TDS) were selected for further investigation. Several groundwater samples are moderately saline and exceed the TDS drinking water aesthetic guideline of 1,000 g m-3 (Ministry of Health, 2008). Selected ion ratios (predominantly halogens) were used to assist in differentiating between influences on salinity such as seawater and geothermal. Bromide to iodide ratios, in particular, infer a greater geothermal influence on salinity, although other ratios are not definitive. The anomalously elevated salinity observed appears natural but nevertheless has constrained localised groundwater resource development for dairy factory, industrial and prison water supply use. Further work may show some relationship with geology or tectonics, which could assist prediction of inland saline groundwater occurrence