Nate Currit

Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

MORPHOLOGY-BASED BUILDING DETECTION FROM AIRBORNE LIDAR DATA

The advent of Light Detection and Ranging (lidar) technique provides a promising resource for three-dimensional building detection. Due to the difficulty of removing vegetation, most building detection methods fuse lidar data with multispectral images for vegetation indices and relatively few approaches use only lidar data. However, the fusing process may cause errors introduced by resolution and time difference, shadow and high-rise building displacement problems, and the geo-referencing process. This research presents a morphological building detecting method to identify buildings by gradually removing non-building pixels. First, a ground-filtering algorithm separates ground pixels with buildings, trees, and other objects. Then, an analytical approach removes the remaining non-building pixels using size, shape, height, building element structure, and the height difference between the first and last returns. The experimental results show that this method provides a comparative performance with an overall accuracy of 95.46 percent as in a study site in Austin urban area.

Detect Residential Buildings from Lidar and Aerial Photographs through Object-Oriented Land-Use Classification

Relating less directly to the physical reflectance from remote sensors, land-use analysis is comparably more challenging than land-cover studies, especially for residential land-uses. This research presents an object-oriented approach to detect residential land use of buildings directly from lidar data, aerial photography, and road maps to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then uses a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on seven land-use characteristics of buildings. Experiments based on the 8.25 km2 study site located in Austin, Texas proved the possibility and efficiency of directly detecting and identifying residential buildings from remote sensing images with 81.1 percent of residential buildings correctly classified.

Isometric scaling in home-range size of male and female bobcats (Lynx rufus)

For solitary carnivores a polygynous mating system should lead to predictable patterns in space-use dynamics. Females should be most influenced by resource distribution and abundance, whereas polygynous males should be strongly influenced by female spatial dynamics. We gathered mean annual home-range-size estimates for male and female bobcats (Lynx rufus (Schreber, 1777)) from previous studies to address variation in home-range size for this solitary, polygynous carnivore that ranges over much of North America. Mean annual home ranges for bobcats (171 males, 214 females) from 29 populations covering the entire north to south and east to west range demonstrated female home-range sizes varied more than an order of magnitude and that, on average, males maintained home ranges 1.65 times the size of females. Male home-range sizes scaled isometrically with female home-range sizes indicating that male bobcats increase their home-range size proportional to female home-range size. Using partial correlation analysis we also detected an inverse re- lationship between environmental productivity, estimated using the normalized difference vegetation index, and home- range size for females but not males. This study provides one of the few empirical assessments of how polygyny influen- ces home-range dynamics for a wide-ranging carnivore.

Assessing the impact of extreme climatic events on aspen defoliation using MODIS imagery

Recent studies document the decline of quaking aspen across large geographic areas of North America. Extreme climatic events are possible contributors to the decline, and drought is often cited as an important driver of aspen phenology. Little is known, however, about the effects of spring freeze events on aspen phenology, even though such events are projected to occur more frequently in future. This study uses moderate resolution imaging spectrometer (MODIS) imagery to assess the spatial pattern and magnitude of damage to aspen forests during spring freeze and summer drought events that occurred in Utah in 2007. The analysis finds above normal Normalized Difference Vegetation Index (NDVI) in early spring, and below normal NDVI following the freeze event and during the summer drought. Aspen damage is concentrated in certain terrain classes, depending on the type of extreme climatic event. These findings suggest there are predictable patterns of aspen defoliation that identify aspen stands vulnerable to extreme climatic events.

Extraction of water bodies from remotely sensed images

Extracting water bodies from remotely sensed imagery is an important procedure for many water related studies. It is considered as a challenging problem of automatically and effectively extracting water bodies from remotely sensed images, and many researchers rely on existing Geoinformation System (GIS) data or manual digitizing to obtain water body boundaries. This work firstly generalizes several water body characteristics in optical remotely sensed images and then proposes a segmentation-based water body extraction method. The proposed algorithm exploits several features and uses a perception machine (PM) of neural network (NN) to build a classifier. An overall classification accuracy of 96% indicates that this method holds promise for extracting water bodies from optical remotely sensed images.

Uncertainty with the scaling-up of remotely sensed evapotranspiration estimation

In this paper, the uncertainty issues in evapotranspiration estimation using remotely sensed data are discussed. The processes governing the mass, energy, and momentum exchange across the land-atmosphere interface are nonlinear, because of the interdependence of the dominant variables and parameters. Spatial resolution of remote sensing data also has an impact on the spatial pattern of evapotranspiration captured, due to the spatial variation of the surface. In order to study the nonlinear and spatial variation problems, evapotranspiration was first estimated at 30 meter and then aggregated to coarser scales using two approaches. One is to serve as true value of evapotranspiration, while the other one is assumed the linear resulted value. Results show that factors do have a nonlinear impact on evapotranspiration, however, this impact becomes very close to linear at 120m. The impact also varies with scale, as well as on different land cover types.