6,887 research outputs found
Estimating exploitable stock biomass for the Maine green sea urchin (Strongylocentrotus droebachiensis) fishery using a spatial statistics approach
The objective of this study was to investigate the spatial patterns in green sea urchin (Strongylocentrotus
droebachiensis) density off the coast of Maine, using data from a fishery-independent survey program, to estimate the exploitable biomass of this species. The dependence of sea
urchin variables on the environment, the lack of stationarity, and the presence of discontinuities in the study area made intrinsic geostatistics inappropriate for the study; therefore, we used triangulated irregular
networks (TINs) to characterize the large-scale patterns in sea urchin density. The resulting density surfaces were modified to include only areas of the appropriate substrate
type and depth zone, and were used to calculate total biomass. Exploitable biomass was estimated by using two
different sea urchin density threshold values, which made different assumptions about the fishing industry. We
observed considerable spatial variability on both small and large scales, including large-scale patterns in sea urchin density related to depth and fishing pressure. We conclude that the TIN method provides a reasonable spatial approach for generating biomass estimates for a fishery unsuited
to geostatistics, but we suggest further studies into uncertainty estimation and the selection of threshold
density values
Geodesics in Heat
We introduce the heat method for computing the shortest geodesic distance to
a specified subset (e.g., point or curve) of a given domain. The heat method is
robust, efficient, and simple to implement since it is based on solving a pair
of standard linear elliptic problems. The method represents a significant
breakthrough in the practical computation of distance on a wide variety of
geometric domains, since the resulting linear systems can be prefactored once
and subsequently solved in near-linear time. In practice, distance can be
updated via the heat method an order of magnitude faster than with
state-of-the-art methods while maintaining a comparable level of accuracy. We
provide numerical evidence that the method converges to the exact geodesic
distance in the limit of refinement; we also explore smoothed approximations of
distance suitable for applications where more regularity is required
Surface networks
© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou
Can building footprint extraction from LiDAR be used productively in a topographic mapping context?
Chapter 3Light Detection and Ranging (LiDAR) is a quick and economical method for obtaining
cloud-point data that can be used in various disciplines and a diversity of applications.
LiDAR is a technique that is based on laser technology. The process looks at the two-way
travel time of laser beams and measures the time and distance travelled between the laser
sensor and the ground (Shan & Sampath, 2005). National Mapping Agencies (NMAs)
have traditionally relied on manual methods, such as photogrammetric capture, to collect
topographic detail. These methods are laborious, work-intensive, lengthy and hence,
costly. In addition because photogrammetric capture methods are often time-consuming,
by the time the capture has been carried out, the information source, that is the aerial
photography, is out of date (Jenson and Cowen, 1999). Hence NMAs aspire to exploit
methods of data capture that are efficient, quick, and cost-effective while producing high
quality outputs, which is why the application of LiDAR within NMAs has been increasing.
One application that has seen significant advances in the last decade is building
footprint extraction (Shirowzhan and Lim, 2013). The buildings layer is a key reference
dataset and having up-to-date, current and complete building information is of paramount
importance, as can be witnessed with government agencies and the private sectors
spending millions each year on aerial photography as a source for collecting building
footprint information (Jenson and Cowen, 1999). In the last decade automatic extraction
of building footprints from LiDAR data has improved sufficiently to be of an acceptable
accuracy for urban planning (Shirowzhan and Lim, 2013).peer-reviewe
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