ASPRS

PE&RS March 2004

VOLUME 70, NUMBER 3
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
JOURNAL OF THE AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING

Peer-Reviewed Articles

297 A Basis for Estimating Digital Camera Parameters
Don Light

Abstract  Download Full Article
Matching the diffraction-limited optical resolution with the appropriate detector size is a fundamental design requirement for digital imaging systems. A useful Design Function based on the Airy disk is λF/p = 0.82. Where λ is the average wavelength, F is the camera F-number, and p is the detector sampling pitch (pixel size). A second metric, attributed to Schade and reported by Holst (1999), produces an often used Design Function: λF/p = 1. Examples demonstrate the use of the Design Functions to determine basic parameters, F-number, focal length, aperture diameter, and pixel size for an imaging system. Pixel size is selected from commercially available arrays and the other parameters are estimated given the required ground sampled distance (GSD) with either of the two Design Functions. One of the primary uses for the Design Functions is to provide optical systems engineers with a simple, fast, and proven means of arriving at first-order estimates for electro-optical camera designs. It follows that estimating costs for building large space camera systems should be less complicated. Mobile Digital Cameras for As-Built Surveys of Roadside Features

301 Mobile Digital Cameras for As-Built Surveys of Roadside Features
Kandiah Jeyapalan

Abstract Download Full Article
A method for determining the three-dimensional locations of roadside features appearing on multiple sequential images captured using a mobile video-logging system without any ground control is described. The digital camera was calibrated using a special three-dimensional calibration range and Calib software to simultaneously determine the interior and exterior orientation elements on a local system. The software was then used to determine the local three-dimensional coordinates of roadside features using the sequential imagery from the mobile video-logging system captured along a highway at highway speed. The relative locations were transformed to the absolute locations using the locations of the camera's exposure stations from the Global Positioning System. The relative accuracy of the locations obtained was 5 cm, and absolute accuracy using the code phase kinematic Global Positioning System was 2 m. This paper also shows the two-dimensional geographic information system, three-dimensional geographic information system, and virtual reality created for three test sites using the imagery from the mobile video-logging system. NASA's Global Orthorectified Landsat Data Set

313 NASA’s Global Orthorectified Landsat Data Set
Compton J. Tucker, Denelle M. Grant, and Jon D. Dykstra

Abstract Download Full Article
NASA has sponsored the creation of an orthorectified and geodetically accurate global land data set of Landsat Multi-spectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper data, from the 1970s, circa 1990, and circa 2000, respectively, to support a variety of scientific studies and educational purposes. This is the first time a geodetically accurate global compendium of orthorectified multi-epoch digital satellite data at the 30- to 80-m spatial scale spanning 30 years has been produced for use by the international scientific and educational communities. We describe data selection, orthorectification, accuracy, access, and other aspects of these data.

323 Mapping Snowpack Depth Beneath Forest Canopies Using Airborne Lidar
Chris Hopkinson, Mike Sitar, Laura Chasmer, and Paul Treitz

Abstract Download Full Article
An evaluation of airborne lidar (Light Detection And Ranging) technology for snow depth mapping beneath different forest canopy covers (deciduous, coniferous, and mixed) is presented. Airborne lidar data were collected for a forested study site both prior to and during peak snowpack accumulation. Manual field measurements of snow depth were collected coincident with the peak snowpack lidar survey, and a comparison between field and lidar depth estimates was made. It was found that (1) snow depth distribution patterns can be mapped by subtracting a "bare-earth" DEM from a "peak snowpack" DEM, (2) snow depth estimates derived from lidar data are strongly related to manual field measures of snow depth, and (3) snow depth estimates are most accurate in areas of minimal understory. It has been demonstrated that airborne lidar data provide accurate snow depth data for the purpose of mapping spatial snowpack distribution for volume estimations, even under forest canopy conditions.

331 Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget
Michael E. Hodgson and Patrick Bresnahan

Abstract Download Full Article
As part of a countywide large-scale mapping effort for Richland County, South Carolina, an accuracy assessment of a recently acquired lidar-derived data set was conducted. Airborne lidar (2-m nominal posting) was collected at a flying height of 1207 meters above ground level (AGL) using an Optech ALTM (Airborne Laser Terrain Mapper) 1210 system. Unique to this study are the reference point elevations. Rather than using an interpolation approach for gathering observed elevations at reference points, the x-y coordinates of lidar points were located in the field and these elevations were surveyed. Using both total-station-based and rapid-static GPS techniques, observed vertical heights were measured at each reference lidar posting. The variability of vertical accuracy was evaluated for six land-cover categories. Root-mean-squared error (RMSE) values ranged from a low of 17 to 19 cm (pavement, low grass, and evergreen forests) to a high of 26 cm (deciduous forests). The unique error assessment of lidar postings also allowed for the creation of an error budget model. The observed lidar elevation error was decomposed into errors from lidar system measurements, horizontal displacement, interpolation error, and surveyor error. A cross-validation approach was used to assess the observed interpolated lidar elevation error for each field-verified reference point. In order of decreasing importance, the lidar system measurements were the dominant source of error followed by interpolation error, horizontal displacement error, and surveyor error. Observed elevation error in steeper slopes (e.g., 25°) was estimated to be twice as large as those on low slopes (e.g., 1.5°).

341 Automating the Analysis of Remotely Sensed Data
Chris Skelsey, A.N.R. Law, Mark Winter, and J.R. Lishman

Abstract Download Full Article
Land cover is a complex phenomenon, its appearance and transition influenced by the soil, topography, climate, politics, ecology, and other aspects of land use. An operational study of land-cover change must consider such factors, and, if automation is to be employed, a new approach to software architecture and reasoning is required. The Macaulay Institute has developed the concept of task orientation as a means of supporting such work, and is currently investigating its utility in the update of the Land Cover of Scotland (1988) dataset. This paper describes the motivation behind the development of ETORA-II, an operational toolkit providing a task-orientated capability, and SYMOLAC-II, a proof-of-concept application. The final aim of this work is to produce an environmental information system for Scotland's land cover.

351 Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery
Le Wang, Peng Gong, and Gregory S. Biging

Abstract Download Full Article
The cost of forest sampling can be reduced substantially by the ability to estimate forest and tree parameters directly from aerial photographs. However, in order to do so it is necessary to be able to accurately identify individual treetops and then to define the region in the vicinity of the treetop that encompasses the crown extent. These two steps commonly have been treated independently. In this paper, we derive individual tree-crown boundaries and treetop locations under a unified framework. We applied a two-stage approach with edge detection followed by marker controlled watershed segmentation. A Laplacian of Gaussian edge detection method at the smallest effective scale was employed to mask out the background. An eight-connectivity scheme was used to label the remaining tree objects in the edge map. Subsequently, treetops are modeled based on both radiometry and geometry. More specifically, treetops are assumed to be represented by local radiation maxima and also to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate touching and clumping trees and to produce a segmented image comprised of individual tree crowns. Our methods were developed on a 256- by 256-pixel CASI image of a commercially thinned trial forest. A promising agreement between our automatic methods and manual delineation results was achieved in counting the number of trees as well as in delineating tree crowns. Landscape Dynamics and Risk Modeling of Human Alveolar Echinococcosis

359 Landscape Dynamics and Risk Modeling of Human Alveolar Echinococcosis
F. Mark Danson, Philip S. Craig, Wai Man, Dazhong Shi, and Patrick Giraudoux

Abstract Download Full Article
Human alveolar echinococcosis (AE) is a rare but fatal liver disease caused by a parasitic tapeworm. Between 1994 and 1997 a medical survey in a rural area in central China revealed the highest incidence rate of the disease recorded in the world to date, with 15.8 percent of the population infected in one village. Hypotheses on the nature of the transmission mechanisms from the natural to human environment focused on the effects of recent landscape change from forest to agricultural land. Archived Landsat MSS and TM data were used to examine relationships between landscape and human AE prevalence in 31 villages. The results showed a significant positive correlation between AE and the proximity of villages to forest, grassland, and shrubland vegetation, and a negative correlation with the area of cultivated land. A predictive model, based on spatial characteristics of the landscape, is now being developed with the aim of designing management tools for disease control.

Top Home