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Home PE&RS Journals In Press Peer Reviewed Articles

PE&RS Journals

In Press Peer Reviewed Articles

As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.


May 2015 Issue

Identifying Urban Watershed Boundaries and Area, Fairfax County, Virginia

Tammy E. Parece and James B. Campbell

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Urban hydrology differs from that of natural environments, and thus urban watersheds require innovative evaluation techniques. Typical geospatial evaluation of urban hydrology begins with identification of water flow and watershed boundaries. This study identifies steps to delineate a highly urbanized watershed in Fairfax County, Virginia. Using standard techniques for natural watersheds and one-meter2 resolution lidar, watershed and flow accumulation raster datasets were derived. Then, modifications encountered within urban landscapes i.e., impervious surfaces, stormwater inlets, pipes, and retention ponds along with high-resolution aerial photos and lidar-derived contour lines were integrated into the analysis. Regions redirecting water flow from stream channels and areas redirecting water flow into the stream channels were identified. These areas were removed or added, reducing the area by almost 17 percent, and the watershed boundary was significantly altered. This analysis illustrates the significance of the distinctive characteristics of the urban landscape in accurate delineations of urban watersheds.

 


Parallel Performance of Typical Algorithms in Remote Sensing-based Mapping on a Multi-Core Computer

Jinghui Yang and Jixian Zhang

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Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed..

 


Accuracy Analysis of a Dual Camera System with an Asymmetric Photogrammetric Configuration

Bo Wu, Lei Ye, and Yuansheng Yang

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This paper presents a dual camera system combining a wide field-of-view (FOV) surveillance camera and a pan-tilt-zoom (PTZ) camera with an asymmetric photogrammetric configuration, and focuses on the analysis of its attainable measurement accuracy. First, we discuss the geometric modeling of the asymmetric photogrammetric configuration and analyze the accuracy of measurement based on error propagation for different baseline lengths, different focal lengths, and different pan angles of the PTZ camera. Second, we performed a comprehensive accuracy analysis based on Monte Carlo simulation, which incorporated artificial noise into the input data. Third, we conducted actual experiments in indoor and outdoor environments to verify the theoretical and simulation results. We found that the baseline length between the dual cameras was the main factor influencing measurement accuracy. Increase of the PTZ camera focal length could improve the measurement accuracy, but this trend was not significant when its focal length was relatively long. The pan angle of the PTZ camera also influenced the measurement accuracy, but this influence was not significant at short ranges. From these discoveries, we present an optimum configuration of the dual camera system for better than 1 percent measurement accuracy of the range within a normal observation range (e.g., 60 m). This proposed dual camera system provides enhanced machine vision capabilities that can be used in various applications.

 


Evaluation of Lidar-derived DEMs through Terrain Analysis and Field Comparison

Cody P. Gillin, Scott W. Bailey, Kevin J. McGuire, and Stephen P. Prisley

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Topographic analysis of watershed-scale soil and hydrological processes using digital elevation models (DEMs) is commonplace, but most studies have used DEMs of 10 m resolution or coarser. Availability of higher-resolution DEMs created from light detection and ranging (lidar) data is increasing but their suitability for such applications has received little critical evaluation. Two different 1 m DEMs were re-sampled to 3, 5, and 10 m resolutions and used with and without a low-pass smoothing filter to delineate catchment boundaries and calculate topographic metrics. Accuracy was assessed through comparison with field slope measurements and total station surveys. DEMs provided a good estimate of slope values when grid resolution reflected the field measurement scale. Intermediate scale DEMs were most consistent with land survey techniques in delineating catchment boundaries. Upslope accumulated area was most sensitive to grid resolution, with intermediate resolutions producing a range of UAA values useful in soil and groundwater analysis..

 


Refining High Spatial Resolution Remote Sensing Image Segmentation for Man-made Objects through aCollinear and Ipsilateral Neighborhood Model

Min Wang, Yanxia Sun, and Guanyi Chen

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Man-made objects, such as buildings and roads, which are important targets for information extraction from high spatial resolution (HSR) remote sensing images, often feature straight boundaries. This study employs this knowledge on HSR image segmentation by embedding a straight-line constraint in region-based image segmentation. A new concept called collinear and ipsilateral neighborhood is proposed and applied to hard-boundary constraint-based image segmentation for accuracy improvement. In the experimental areas, the method accuracy measured by recall ratio r increases from 0.036 to 0.048 (on the average) after the refinement, with significantly smaller decreases in precision p that are all less than 0.006. In sum, the proposed technique effectively reduces over-segmentation errors and maintains the same level of under-segmentation error ratio, particularly in man-made areas. It facilitates subsequent object-based image analyses, including feature extraction, object recognition, and classification.

 


Lidar Detection of the Ten Tallest Trees in theTennessee Portion of the Great Smoky Mountains National Park

Chris W. Strother, Marguerite Madden, Thomas R. Jordan and Andrea Presotto

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This paper describes a method for predicting the locations and heights of the ten tallest trees in the Tennessee portion of the Great Smoky Mountains National Park. Iterative computation tools were utilized to process the data along with the lidar-derived bare earth digital elevation models and digital surface models to create canopy height models for the Tennessee portion of the park. A height threshold of 51.8 meters was chosen as the minimum value for a tree of extraordinary height. Ten potential sites containing tall trees were identified using this methodology, and seven of the top ten ranking trees’ heights were field measured using accepted forestry methodology. The trees detected using these methods are potentially the tallest trees ever measured on the East Coast of the United States. These methods show that unique tall trees can be successfully detected in a large, heterogeneous forest area using lidar data.

 

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