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.

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 However, these sources are, for the most part, outdated, incomplete, incompatible, or/and inaccurate.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.

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. ^

Aerial view of Shark River Slough with SRS-2 in the background

http://digitalcommons.fiu.edu/fce_lter_photos/1271/thumbnail.jpg

Understanding the Challenges of Expanding Community Forestry in Northwest Cameroon

The tropical afro-montane forest of the Northwest region is unique and under direct threat from the high population density of the region. Community-based forestry management is an opportunity to sustainably manage the remaining forest fragments. Community forestry was introduced to Cameroon with the legislation of the 1994 Forestry Law. Over two decades later little research has been conducted in the Northwest region of Cameroon. Twenty-four semi-structured interviews were conducted, and samples of forestry records were analyzed as exploratory research that would act as a base for further research. This research found that the tenure of the community over the community forest needed to be strengthened, marginalized populations needed to be empowered to participate, and governance needed to be improved both nationally, and locally. Further research will strengthen these conclusions and help Cameroon, and community forests around the world, be effectively established and managed.