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. ^

The 2008 Terrestrial Vegetation of Biscayne National Parko FL, USA Derived From Aerial Photography, NDVI, and LiDAR

Established as a National Park in 1980, Biscayne National Park (BISC) comprises an area of nearly 700 km2 , of which most is under water. The terrestrial portions of BISC include a coastal strip on the south Florida mainland and a set of Key Largo limestone barrier islands which parallel the mainland several kilometers offshore and define the eastern rim of Biscayne Bay. The upland vegetation component of BISC is embedded within an extensive coastal wetland network, including an archipelago of 42 mangrove-dominated islands with extensive areas of tropical hardwood forests or hammocks. Several databases and vegetation maps describe these terrestrial communities. However, these sources are, for the most part, outdated, incomplete, incompatible, or/and inaccurate. For example, the current, Welch et al. (1999), vegetation map of BISC is nearly 10 years old and represents the conditions of Biscayne National Park shortly after Hurricane Andrew (August 24, 1992). As a result, a new terrestrial vegetation map was commissioned by The National Park Service Inventory and Monitoring Program South Florida / Caribbean Network.

Evaluating High-Resolution Aerial Photography Acquired by Unmanned Aerial Systems for Use in Mapping Everglades Wetland Plant Associations

Mapping of vegetation patterns over large extents using remote sensing methods requires field sample collections for two different purposes: (1) the establishment of plant association classification systems from samples of relative abundance estimates; and (2) training for supervised image classification and accuracy assessment of satellite data derived maps. One challenge for both procedures is the establishment of confidence in results and the analysis across multiple spatial scales. Continuous data sets that enable cross-scale studies are very time consuming and expensive to acquire and such extensive field sampling can be invasive. The use of high resolution aerial photography (hrAP) offers an alternative to extensive, invasive, field sampling and can provide large volume, spatially continuous, reference information that can meet the challenges of confidence building and multi-scale analysis.

A comparative analysis of school satisfaction and dropout risk of middle school students attending programs for students with emotional handicaps, severe emotional disturbance and general education students attending center schools and satellite schools

This study evaluated school satisfaction as an indicator of dropout risk of students with Emotional Handicaps (EH) and students with Severe Emotional Disturbance (SED). The students attended two different kinds of middle schools in a largely urban school district in South Florida. One hundred eight students in grade 8 (ages 13-16) participated in this study. Participants were administered the National Dropout Prevention Assessment (NDPA). Forty participants with EH and SED attended a special center school. Thirty-one participants with EH and SED attended satellite programs in a regular middle school. Thirty-seven general education participants attended the same regular middle school. Overall school satisfaction scores were generated, as well as three primary factors (school, environment and personal) and 16 subscales (school atmosphere, future income, difficulty level of classwork, teacher relationships, peer relationships, intrinsic interest in classwork, school hours, classwork stress, general attitude towards school, family influence, perceived opportunity for career, future goals, travel distance, leisure time, self-appraisal of performance, and self-esteem).^ Comparison of students with EH and SED revealed that both groups of students were rated at "low risk" of becoming dropouts on the Environmental factor and the Difficulty of Schoolwork subscale. Students with EH were rated at "caution risk" risk on the Travel Distance subscale. Students with SED were rated at "high risk" on this subscale.^ There were no significant differences in school satisfaction and dropout risk between different program delivery models. There were also no significant differences for category of students (EH, SED) by school type (center school, satellite program). All students were rated at "low risk" of dropping out of school.^ There were significant differences between general education students and students with EH and SED attending satellite programs. Students with EH and SED were rated at "caution risk" for dropping out on the Travel Distance and the Leisure Time subscales. Discussion of results, implications for practice and recommendations for further research are included. ^

MUIS News: Maps and User Imagery Services @ FIU Green Library Vol. 2, Issue 1 2008

Florida International University's Fall 2008 Map and User Imagery Services Newsletter.

Integration of Satellite and Financial Data to Model Future Economic Impact of Citrus Crops (Final Project Report)

This study analyzed the health and overall landcover of citrus crops in Florida. The analysis was completed using Landsat satellite imagery available free of charge from the University of Maryland Global Landcover Change Facility. The project hypothesized that combining citrus production (economic) data with citrus area per county derived from spectral signatures would yield correlations between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. A positive correlation between these two data types would allow us to predict the economic impact of citrus using spectral data analysis to determine final crop harvests.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 2, Issue 2 Spring 2009

Florida International University's Spring 2009 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 1 Fall 2009

Florida International University's Fall 2009 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 2 Fall 2009

Florida International University's Fall 2009 Map and User Imagery Services Newsletter; Vol. 3, issue 2.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 2, Spring 2010

Florida International University's Spring 2010 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 4, Issue 1 Fall 2012

Florida International University's Fall 2012 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 4, Issue 2 Spring/Summer 2013

Florida International University's Spring/Summer 2013 Map and User Imagery Services Newsletter.

MIUS News : Maps and Imagery User Services @ FIU Green Library: Vol. 5 Issue 1 Fall 2013

Florida International University's Fall 2013 Map and User Imagery Services Newsletter