There are many reasons to want to know the true surface area of the landscape, especially in landscape analysis and studies of wildlife habitat. Surface area provides a better esti- mate of the land area available to an animal than planimetric area, and the ratio of this surface area to planimetric area provides a useful measure of topographic roughness of the landscape. This paper describes a straightforward method of calculating surface-area grids directly from digital elevation models (DEMs), by generating 8 3-dimensional trian- gles connecting each cell centerpoint with the centerpoints of the 8 surrounding cells, then calculating and summing the area of the portions of each triangle that lay within the cell boundary. This method tended to be slightly less accurate than using Triangulated Irregular Networks (TINs) to generate surface-area statistics, especially when trying to analyze areas enclosed by vector-based polygons (i.e., management units or study areas) when there were few cells within the polygon. Accuracy and precision increased rapid- ly with increasing cell counts, however, and the calculated surface-area value was con- sistently close to the TIN-based area value at cell counts above 250. Raster-based analy- ses offer several advantages that are difficult or impossible to achieve with TINs, includ- ing neighborhood analysis, faster processing speed, and more consistent output. Useful derivative products such as surface-ratio grids are simple to calculate from surface-area grids. Finally, raster-formatted digital elevation data are widely and often freely available, whereas TINs must generally be generated by the user.

/pdf/calculating-landscape-surface-area-from-digital-elevation-2eo0klnay0.pdf

Calculating landscape surface area from digital elevation models

Reef fish assemblage relationships with in situ and lidar topographic measurements across the seascape were analyzed to evaluate the possibility of using lidar metrics as a proxy for prediction models. In situ topographic complexity (i.e., linear rugosity) was measured from 346 point-count fish surveys spanning the reef seascape. Lidar topographic measurements (i.e., surface rugosity, elevation, and volume) were obtained from a high-resolution lidar bathymetric dataset of each survey’s footprint. The survey sites were characterized by an independently derived benthic habitat map. Reef fish abundance and species richness appeared to increase with increasing topographic complexity. Although significant, the relationship was weak. Habitat characterization showed that these relationships changed across the seascape. The relationship between topographic complexity and species richness was more pronounced in shallow habitats, whereas, topographic complexity related more closely to abundance in offshore habitats. In situ rugosity measurement yielded the best explanation of fish assemblage structure parameters, but the weaker lidar metric correlations followed similar trends. Accordingly, lidar-measured topographic complexity may be a useful metric for reef fish distributional models. Such predictive models could have many scientific and management applications including: estimating fish stocks, viewing data trends across the seascape, and designing marine protected areas. However, better understanding of the appropriate spatial scale, measurement scale, and fish operational scale is needed, as well as more research on the dynamics of how reef fishes relate to topographic complexity and other ecological factors influencing distributions across the seascape. ADDITIONAL INDEX WORDS: Seascape ecology, habitat complexity, rugosity, coral reef fish, lidar, GIS Walker, B.K.; Jordan, L.K.B., and Spieler, R.E., 2009. Relationship of reef fish assemblages and topographic complexity on southeastern Florida coral reef habitats. Journal of Coastal Research, SI(53), 39–48.

/pdf/relationship-of-reef-fish-assemblages-and-topographic-3166ssue6c.pdf

Relationship of Reef Fish Assemblages and Topographic Complexity on Southeastern Florida Coral Reef Habitats

The concept of a digital elevation model (DEM) can be used for a digital representation of any single‐valued surface such as a terrain relief model (digital terrain model, DTM). DEMs are widely used in remote sensing, geographical information systems (GIS), and virtual reality. Estimating the accuracy of a DEM is an essential issue in the acquisition of spatial data, particularly for applications that require a highly accurate DEM, such as engineering applications. The accuracy of a DEM is subject to many factors such as the number of sampling points, the spatial distributions of the sampling points, the methods used for interpolating surface elevations, the propagated error from the source data, and other factors. Of these factors, this study will focus on estimating the mean elevation error in a DEM surface that is caused by errors of component nodes in a triangulated irregular network (TIN). This paper will present a newly derived mathematical formula, with the details of the procedure used to derive t...

Estimation of average DEM accuracy under linear interpolation considering random error at the nodes of TIN model

Ice-radar is a very useful tool for the study of glaciers and ice sheets. Whereas the radar applications discussed in other chapters, like satellite-borne SAR (Chapter 9), mostly focus on the surface of the ice, applications of ground-penetrating radar (GPR) try to peer into and through the ice. This is achieved by investigating not just a single pulse reflected from the medium, but a whole time series of reflections. This chapter is not intended to present a complete overview of GPR, but just to provide the reader with the necessary background to the subject in order to clearly understand the main focus of the chapter, which is the measurement of ice thickness as a tool to determine the ice volume stored by glaciers and their temporal variations. We will start by presenting the fundamentals of radio-wave propagation in ice, followed by a description of the main radar systems used in glaciology and the key elements of a typical radar system. We will then explain how such systems are used in fieldwork and outline the main steps of radar data processing. Next we will give an overview of the applications of GPR in glaciology, and finally, we will discuss in more detail how ice volume and bed topography are determined from radar-measured ice thickness, paying particular attention to the error estimates. We will split the applications of radar in glaciology into two main types: those that are principally concerned with the internal properties of the ice body, i.e. the full snow/firn/ice column from glacier surface to its base, and those more directed at basal properties, for which one focuses on the interface between the ice body and the underlying bed. We will pay more attention to internal layering and density/water content. The former, because of its use as a tool to determine accumulation rates, and the latter because of their influence on radio-wave velocity and hence on ice thickness and volume estimates, and also for the role of density in the volume to mass conversion.

Terresrial photogrammetry in glacier studies

Dense Acropora cervicornis aggregations, or patches, have been documented within nearshore habitats in Southeast Florida (SE FL) despite close proximity to numerous anthropogenic stressors and subjection to frequent natural disturbance events. Limited information has been published concerning the distribution and abundance of A. cervicornis outside of these known dense patches. The first goal of this study was to conduct a spatially extensive and inclusive survey (9.78 km2) to determine whether A. cervicornis distribution in the nearshore habitat of SE FL was spatially uniform or clustered. The second goal was to investigate potential relationships between broad-scale seafloor topography and A. cervicornis abundance using high resolution bathymetric data. Acropora cervicornis was distributed throughout the study area, and the Getis-Ord Gi* statistic and Anselin Local Moran's I spatial cluster analysis showed significant clustering along topographic features termed ridge crests. Significant clustering was further supported by the inverse distance weighted surface model. Ordinal logistic regression indicated 1) as distance from a ridge increases, odds of reduced A. cervicornis abundance increases; 2) as topographic elevation increases, odds of increased abundance increases; and 3) as mean depth increases, odds of increased abundance increases. This study provides detailed information on A. cervicornis distribution and abundance at a regional scale and supports modeling its distributions in similar habitats elsewhere throughout the western Atlantic and Caribbean. Acropora cervicornis is frequently observed and in areas an abundant species within the nearshore habitat along the SE FL portion of the Florida Reef Tract (FRT). This study provides a better understanding of local habitat associations thus facilitating appropriate management of the nearshore environment and species conservation. The portion of the FRT between Hillsboro and Port Everglades inlets should be considered for increased management and protection to reduce local stressors.

/pdf/investigating-the-spatial-distribution-and-effects-of-3lzilro1pm.pdf

Investigating the spatial distribution and effects of nearshore topography on Acropora cervicornis abundance in Southeast Florida

Over the last two decades, the Delaunay triangulation has been the only choice for most geographical information system (GIS) users and researchers to build triangulated irregular networks (TINs). The classical Delaunay triangulation for creating TINs only considers the 2D distribution of data points. Recent research efforts have been devoted to generating data-dependent triangulation which incorporate information on both distribution and values of input data in the triangulation process. This paper compares the traditional Delaunay triangulations with several variant data-dependent triangulations based on Lawson's local optimization procedure (LOP). Two USGS digital elevation models (DEMs) are used in the comparison. It is clear from the experiments that the quality of TINs not only depends on the vertex placement but also on the vertex connection. Traditonal two step processes for TIN construction, which separate point selection from the triangulation, generate far worse results than the methods which i...

Comparison of existing triangulation methods for regularly and irregularly spaced height fields

This paper evaluates the accuracy of triangulated irregular networks (TINs) and lattices in ARC/INFO. The results of an empirical comparison of the two models over two selected topographic sites are presented. Both vector and raster data were used to build the models. Three pairs of models were constructed based on 1,600, 4,000, and 9,000 sample points for the study area of the State Botanical Garden of Athens, Georgia, and 400, 800, and 1,600 sample points for the study area of Lake Lucerne of Wisconsin. The two models were assessed based on the same number of input sample points. Overall, TINs performed better than lattices. The quality of lattices decreased more dramatically than that of TINs when the number of sample points used for the construction of the models decreased. With an increase in the number of sample points used, the difference in performance between the two models decreased. The results of the evaluation directly depend on the comparison criteria and modeling algorithms. The evaluation is slightly sensitive to test indices used and the distribution of test points. The spatial pattern of residuals on spot heights was quite different from that on randomly selected test points. Users should choose models carefully based on the purpose of their application, the accuracy required, and the computer resources that are available.

An Assessment of the Accuracy of Triangulated Irregular Networks (TINs) and Lattices in ARC/INFO

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Comparison of existing triangulation methods for regularly and irregularly spaced height fields

An Assessment of the Accuracy of Triangulated Irregular Networks (TINs) and Lattices in ARC/INFO