Search

Abstract Anthropogenic habitat alteration is leading to the reduction of global biodiversity. Consequently, there is an imminent need to understand the state and trend of habitat alteration across broad areas. In North America, habitat alteration has been linked to the decline of threatened woodland caribou. As such, habitat protection and restoration are critical measures to support recovery of self‐sustaining caribou populations. Broad estimates of habitat change through time have set the stage for understanding the status of caribou habitat. However, the lack of updated and detailed data on post‐disturbance vegetation recovery is an impediment to recovery planning and monitoring restoration effectiveness. Advances in remote sensing tools to collect high‐resolution data at large spatial scales are beginning to enable ecological studies in new ways to support ecosystem‐based and species‐based management. We used semi‐automated and manual methodologies to fuse photogrammetry point clouds (PPC) from high‐resolution aerial imagery with wide‐area light detection and ranging (LiDAR) data to quantify vegetation structure (height, density, class) on disturbances associated with caribou declines. We also compared vegetation heights estimated from the semi‐automated PPC‐LiDAR fusion to heights estimated in the field, using stereoscopic interpretation, and using multi‐channel TiTAN LiDAR. Vegetation regrowth was occurring on many of the disturbance types, though there was local variability in the type, height and density of vegetation. Heights estimated using PPC‐LiDAR fusion were highly correlated (r ≥ 0.87 in all cases) with heights estimated using stereomodels, TiTAN multi‐channel LiDAR and field measurements. We demonstrated that PPC‐LiDAR fusion can be operationalized over large areas to collect comprehensive and consistent vegetation data across landscape levels, providing opportunities to link fine‐resolution remote sensing to landscape‐scale ecological studies. Crucially, these data can be used to estimate rates of habitat recovery at resolutions that are not feasible using more commonly used satellite‐based sensors, bridging the gap between resolution and extent. Such data are needed to achieve effective and efficient habitat monitoring to support caribou recovery efforts, as well as a myriad of additional forest management needs.

Documenting and estimating species richness at regional or landscape scales has been a major emphasis for conservation efforts, as well as for the development and testing of evolutionary and ecological theory. Rarely, however, are sampling efforts assessed on how they affect detection and estimates of species richness and rarity. In this study, vascular plant richness was sampled in 356 quarter hectare time-unlimited survey plots in the boreal region of northeast Alberta. These surveys consisted of 15,856 observations of 499 vascular plant species (97 considered to be regionally rare) collected by 12 observers over a 2 year period. Average survey time for each quarter-hectare plot was 82 minutes, ranging from 20 to 194 minutes, with a positive relationship between total survey time and total plant richness. When survey time was limited to a 20-minute search, as in other Alberta biodiversity methods, 61 species were missed. Extending the survey time to 60 minutes, reduced the number of missed species to 20, while a 90-minute cut-off time resulted in the loss of 8 species. When surveys were separated by habitat type, 60 minutes of search effort sampled nearly 90% of total observed richness for all habitats. Relative to rare species, time-unlimited surveys had ∼ 65% higher rare plant detections post-20 minutes than during the first 20 minutes of the survey. Although exhaustive sampling was attempted, observer bias was noted among observers when a subsample of plots was re-surveyed by different observers. Our findings suggest that sampling time, combined with sample size and observer effects, should be considered in landscape-scale plant biodiversity surveys.

The article describes the design of the Community Seismic Network, which is a dense open seismic network based on low cost sensors. The inputs are from sensors hosted by volunteers from the community by direct connection to their personal computers, or through sensors built into mobile devices. The server is cloud-based for robustness and to dynamically handle the load of impulsive earthquake events. The main product of the network is a map of peak acceleration, delivered within seconds of the ground shaking. The lateral variations in the level of shaking will be valuable to first responders, and the waveform information from a dense network will allow detailed mapping of the rupture process. Sensors in buildings may be useful for monitoring the state-of-health of the structure after major shaking.

1) The UCLA Factor building is a testbed for predictive numerical modeling, wireless network, and smart event-detection developments related to structural health monitoring. Waveform data from the 72-node array in the 17-story moment-resisting steel frame Factor building are used in comparison with finite element calculations for predictive behavior. 2) We are mapping the subducted slab beneath Mexico and examining slow earthquakes which have been observed at this subduction zone, volcanic earthquakes and the propagation of seismic waves in Mexico City which was devastated in the 1985 Michoacan earthquake. We will compare our 50 instrument wireless array with the 50 instruments of a stand-alone array that has been installed by Caltech. The transect covered by all stations is 500 km long between Acapulco-Mexico City-Tampico.3) We are preparing to move our 50 station array to Peru to compare with Mexico results looking at the flat slab subduction.4) We are developing a low power high resolution wireless seismic array (GeoNet). It is going to be a high density self configuring network that would be possible to deploy in the aftershock zones.

A main thrust of the seismic application is the development of algorithms for performing high spatial resolution measurements and modeling of structures using wired and wireless arrays. The UCLA Factor building is a testbed for predictive numerical modeling, wireless network, and smart event-detection developments related to structural health monitoring. Waveform data from the 72-node array in the 17-story moment-resisting steel frame Factor building are used in comparison with finite element calculations for predictive behavior.

A testbed for structural health monitoring uses the embedded 72-sensor seismic network in the UCLA Factor building which records continuous data at 500 Hz. In addition, a 15-station prototype wireless seismic monitoring array has been developed and tested. It is based on the Crossbow mote technology and will be run side by side with digitzers in Factor for comparison.The Mica-2 mote network is controlled by Wisden software and is designed to monitor local areas such as buildings. Data from the wired building array have been used to compute time-varying displacements in 3D, to compute the finite impulse response of the building during the 9/28/04 Parkfield and 12/16/04 Santa Monica Bay earthquakes, and to apply time series analysis techniques to long time series and stacked spectra to identify the higher modes of vibration. Modes of building vibration are found to decrease in frequency at the times of large amplitude shaking by earthquakes or wind.

The Seismic Applications group is working to increase the practical limits of the density of seismic deployments and move to more rapid data gathering in temporary deployments. Temporary deployments in seismology typically consist of stand-alone sites in order to decrease installation time. Data must be collected either periodically from each site or at the end of the experiment and incorporated into a data base for analysis. Permanent seismic installations do transmit data immediately to central data bases through ethernet, point-to-point radio, and modem. We add the ’¡Èreal-time’¡É data collection of permanent installations to temporary networks with multi-hop networking. Off-the-shelf 802.11 wireless radio cards are incorporated with an Intel Stargate computer running Roofnet networking software. In-house software is developed to collect data and route it hop-by-hop through the network to a central data repository. Hop-by-hop data movement allows data to pass effectively through the radio network despite breaks or flaky links within the network.

Technological and scientific preparations are occurring for the development of a multi-hop radio-linked seismic array (MHRLSA) of 50 broadband stations (GURALP 3ts) and its first few deployments. A ruggedized data relay device (DRD) is being fabricated using Intel’¡Çs new low power, small form factor stargate motherboard. A DRD will be placed at each node of the array and configured as a local area network (LAN) with station spacing up to 10 km. The objective is to use protocols that have been developed for the world wide web to optimize the acquisition of real time seismic array data...