Plant biogeography and conservation on a tropical island: Isla del Coco, Costa Rica

Isla del Coco (Cocos Island) is a small volcanic island located in the Pacific 500 km west of Costa Rica. Three collecting trips to Isla del Coco, in addition to herbarium research, were completed in order to assess the floristic diversity of the island. The current flora of Isla del Coco contains 262 plant species of which 37 (19.4%) are endemic. This study reports 58 species as new to the island. Seventy-one species (27.1%) were identified as introduced by humans. In addition, five potentially invasive plant species are identified. Seven vegetation types are identified on the island: bayshore, coastal cliff, riparian, low elevation humid forest, high elevation cloud forest, landslide and islet. ^ The biogeographic affinities of the native and endemic species are with Central America/northern South America and to a lesser extent, the Caribbean. Endemic species in the genus Epidendrum were investigated to determine whether an insular radiation event had produced two species found on Isla del Coco. Phylogenetic analysis of the internal transcribed spacer (ITS) of nuclear ribosomal DNA was not able to disprove that the endemic species in this genus are not sister species. Molecular biogeographic analyses of ITS sequence data determined that the Isla del Coco endemic species in the genera Epidendrum, Pilea and Psychotria are most closely related to Central American/northern South American taxa. No biogeographical links were found between the floras of Isla del Coco and the Galápagos Islands. ^ The native and endemic plant diversity of Isla del Coco is threatened with habitat degradation by introduced pigs and deer, and to a lesser extent, by exotic plant species. The IUCN Red List and RAREplants criteria were used to assess the extinction threat for the 37 endemic plant taxa found on the island. All of the endemic species are considered threatened with extinction at the Critically Endangered (CR) by the IUCN criteria or either CR or Endangered (EN) using RAREplants methodology. ^

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. ^