A Serological and Disc Electrophoretic Study of North American Typha

Pollen and seed proteins of seven selected North American and Puerto Rican Typha populations were compared using two serological methods and disc electrophoresis. These methods were capable of discriminating among all taxa studied: Typha latifolia, T. angustifolia, T. X glauca, and T. domingensis. The two hybrid populations were found to contain proteins not found in either parent. Typha domingensis was serologically the most distinct of the four taxa. The diagnostic morphological characteristics for Typha species were studied in all populations, and statistical comparisons are presented. Data from the morphological observations agreed with the information obtained from the chemosystematic research. All data indicate that the three taxa should be maintained as separate species. The hybrid nature of the putative T. X glauca is verified by both the biochemical and morphological data. Observed morphological and biochemical differences support taxonomic treatments in which T. domingensis is designated as a separate species.

Taxonomy and distribution of taxa in the genus Gomphonema from the Florida Everglades, U.S.A.

Located at a subtropical latitude, the expansive Florida Everglades contains a mixture of tropical and temperate diatom taxa, as well as a unique flora adapted to the calcareous, often excessively hot, seasonally flooded wetland conditions. This flora has been poorly documented taxonomically, although diatoms are recognized as important indicators of environmental change in this threatened ecosystem. Gomphonema is a dominant genus in the freshwater marsh, and is represented by highly variable species complexes, including Gomphonema gracile Ehrenberg, Gomphonema intricatum var. vibrio Ehrenberg sensu Fricke, Gomphonema vibrioides Reichardt & Lange-Bertalot and Gomphonema parvulum (Kützing) Grunow. These taxa have been shown to exhibit wide morphological variation in other regions, resulting in considerable nomenclatural confusion. We collected Gomphonema from 237 sites distributed throughout the freshwater Everglades and used qualitative and quantitative morphological data to identify 20 distinguishable populations. Taxonomie assignments were based on descriptions and/or observations of type material of relevant taxa when possible, but deviations from original morphological range descriptions were common. We then compared morphological variation in Everglades Gomphonema taxa to that reported for the same taxa in other regions and suggest revisions of taxonomie concepts when necessary.

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^