The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

The utility of LiDAR for landscape biodiversity assessment

The potential of LiDAR to inform landscape biodiversity assessments is investigated. The objectives of this research are to examine how LiDAR discrete return and full waveform systems can be used to recover forest structure information, how LiDAR intensity can be used for biodiversity assessment and whether the utility of LiDAR can compliment traditional survey methods. Experiments using LiDAR discrete return and full waveform systems were conducted. An eight category forest characterisation scheme (FCS) derived from a LiDAR full waveform system was proposed and validated using field derived variables. Intensity variables derived from LiDAR full waveform were explored to determine its utility. The applicability of the proposed scheme was also examined by comparing two independent LiDAR full waveform datasets of the same area and by comparing to commonly used field-based biodiversity metrics. From these surveys, it was concluded that conventional discrete return systems can be used to recover forest structure information for forests with an ecologically simple structure. Vertically stratified LiDAR intensity, using range information, has potential to recover canopy cover, grass cover and the amount of fallen trees. The combination of LiDAR intensity mean and standard deviation can be used to differentiate forest types; sparse canopy with few fallen trees or dense canopy with many fallen trees. The LiDAR full waveform system experiment demonstrated that the FCS allows for quantification of gaps (above bare ground, low vegetation and medium vegetation), canopy cover and its vertical density as well as the presence of various canopy strata (low, medium and high). Regression analysis showed LiDAR derived variables were good predictors of field recorded variables. The FCS clearly showed the potential of full waveform LiDAR to provide information on the complexity of habitat structure. The exploratory analysis of intensity derived from LiDAR full waveform system displayed the potential of intensity variables to recover forest structure variables, however further study is required to account for the utility of intensity variables. In terms of the applicability of the FCS, a multiple dataset comparison showed that the FCS was resilient when recovering canopy cover, openings above the ground and medium vegetation, and presence of mid-storey vegetation and high trees, however it was less so when recovering openings above low vegetation, the presence of understorey vegetation and vertical canopy density of high trees. These last categories were considered to be affected by the difference in the pulse repetition frequency. Obtaining sufficient multiple returns by setting an appropriate pulse repetition frequency is the key to maintaining good performance of the scheme. The FCS was also found to be incompatible with commonly used field-based biodiversity metrics due to the qualitative and subjective measurements used in field-based metrics. Refinement in field methodology would be necessary for measuring structural variables to maximise the utility of FCS in their metrics. This study demonstrated how LiDAR technology can be used to derive forest structure information for landscape biodiversity assessment. The method proposed in this study is versatile, repeatable and quantitative, which can provide useful information to inform decisions and conservation strategies

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Journal of Applied Hydrography

Fokusthema: Fernerkundung und Laserbathymetri

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Principled methods for mixtures processing

This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences

Development and Application of Tools for Avalanche Forecasting, Avalanche Detection, and Snowpack Characterization

Avalanche formation is a complex interaction between the snowpack, weather, and terrain. However, detailed observations typically can only be made at a single point and must be extrapolated over the slope or regional scale. This study aims to provide avalanche forecasters with tools to evaluate the snowpack, avalanche hazard, and avalanche occurrence when manual observations are not feasible. Avalanches that occur within the new storm snow are a prevalent problem for the avalanche forecasters with the Idaho Transportation Department (ITD) along Highway 21. We have implemented a real time SNOw Slope Stability (SNOSS) model that provides an index to the stability of that layer. SNOSS has been run real time starting during the winter of 2011/2012 with model results outputted to a webpage for easy viewing by avalanche forecasters. To further improve the accuracy of SNOSS, the model was evaluated with a large database of avalanches from the Utah Department of Transportation (UDOT). Using weather data and SNOSS results, the probability of an avalanche day producing a natural direct action avalanche was calculated using a Balanced Random Forest (BRF). In the future, we hope that the BRF can provide a probability of an avalanche occurrence given the current weather and snowpack conditions that can be utilized by avalanche forecasters in their normal operations. The concern for avalanche forecasters with highway operations is the threat of an avalanche releasing and hitting a highway. Infrasound generated by an avalanche moving downhill can be detected and tracked using array processing techniques. This will allow avalanche forecasters to evaluate the avalanche hazard more effectively by determining when and where avalanches have occurred. An avalanche detection system has been developed to detect avalanches in near real time using infrasound arrays. The system processes the infrasound data on-site, automatically detects events, and classifies the events using multiple neural networks. If an avalanche has been detected, the system will transmit the necessary information over satellite to be viewed by avalanche forecasters on a webpage