Dissertations and Theses @ UNI
Award Winner
Recipient of the 2008 Outstanding Master's Thesis Award - First Place.
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Availability
Open Access Thesis
Keywords
Trees--Iowa--Cedar Falls--Identification; Optical radar--Iowa--Cedar Falls; Remote sensing--Iowa--Cedar Falls;
Abstract
This thesis explores a data fusion approach combining hyperspectral, LiDAR, and multispectral data to classify tree species in an urban environment. The study area is the campus of the University of Northern Iowa.
In order to use the data fusion approach, a wide variety of data was incorporated into the classification. These data include: a four-band Quickbird image from April 2003 with 0.6m spatial resolution, a 24-band AISA hyperspectral image from July 2004 with 2m spatial resolution, a 63-band AISA Eagle hyperspectral image from October 2006 with lm spatial resolution, a high resolution, multiple return LiDAR data set from April 2006 with sub-meter posting density, spectrometer data gathered in the field, and a database containing the location and type of every tree in the study area.
The elevation data provided by the LiDAR was fused with the imagery in eCognition Professional. The LiDAR data was used to refine class rules by defining trees as objects with elevation greater than 3 meters. Classes included honey locust, white pine, crab apple, sugar maple, white spruce, American basswood, pin oak and ash.
Results indicate fusing LiDAR data with these imageries showed an increase in overall classification accuracy for all datasets. Overall classification accuracy with the October 2006 hyperspectral data and LiDAR was 93%. Increases in overall accuracy ranged from 12 to 24% over classifications based on spectral imagery alone. Further, in this study, hyperspectral data with higher spatial resolution provided increased classification accuracy.
The limitations of the study included a LiDAR data set that was acquired slightly before the leaves had matured. This affected the shape and extent of these trees based on their LiDAR returns. The July 2004 hyperspectral data set was difficult to georectify with its 2m resolution. This may have resulted in some minor issues of alignment between the LiDAR and the July 2004 hyperspectral data.
Future directions of the study include developing a classification scheme using a Classification And Regression Tree, utilizing all of the LiDAR returns in a classification instead of just the first and fourth returns, and examining an additional LiDAR-derived data set with estimated tree locations.
Year of Submission
2007
Year of Award
2008 Award
Degree Name
Master of Arts
Department
Department of Geography
First Advisor
Ramanathan Sugumaran, Chair, Thesis Committee
Date Original
5-2007
Object Description
1 PDF file (viii, 98 pages)
Copyright
©2007 Matthew A. Voss
Language
en
File Format
application/pdf
Recommended Citation
Voss, Matthew A., "Tree species identification in an urban environment using a data fusion approach" (2007). Dissertations and Theses @ UNI. 562.
https://scholarworks.uni.edu/etd/562
Comments
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