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G

eographic information systems (GIS) are becoming

a daily tool for professionals in natural resources as

interest in spatial relationships rise (Gumbricht, 1996).

Geographic information systems analyze spatial informa-

tion by incorporating the location, properties, and any

significant attributes of the object being studied (Bolstad,

2003). With the popularity of GIS growing, it is becoming

increasingly important for students in the natural resources

field to understand the structure and possible application

of new spatial data types. This article details an advanced

student exercise that uses Light Detection and Ranging

(LIDAR) data to estimate the change of earthen material

on Folly Beach, SC, between 1996 and 2000. Other studies

have used LIDAR to estimate sea cliff change (Rosser et al.,

2005; Young and Ashford, 2006), riverbank erosion (Thoma

et al., 2005), and the impact of hurricanes on beach erosion

(Zhang et al., 2005).

Light detection and ranging is one method of collecting

high resolution elevation data and is collected by aircraft.

As the plane flies over a preset path, thousands of laser

pulses are sent to the ground and the amount of time it

takes for the echo to reach the aircraft is measured (Thoma

et al., 2005). The laser travel time, when combined with

altitude, allows for precise estimates in ground elevation

to be computed (Thoma et al., 2005). As LIDAR becomes

a more common tool in GIS, it will become increasingly

important for new professionals in the field of natural

resources to be comfortable using data in this format. A

current limitation to both students and professionals alike is

the availability of free LIDAR data. At this time, only small

portions of North America have LIDAR data available to the

public. These areas consist mainly of coastal areas, and

extend only a limited amount inland.

James Island, located on the coast of South Carolina

near Charleston, is estimated to have had eight tropi-

cal storms or hurricanes that passed over or near the

island between 1996 and 2000 (National Oceanographic

and Atmospheric Administration, 2006), possibly causing

precipitation and/or temporary changes in the tide and sea

level. Hurricanes and tropical storms are known to affect

beach and dune erosion (Coch, 1994). It was the goal of

this study to determine the net gain or loss of materials

on Folly Beach during this period. The final product was an

estimated figure of beach erosion, in cubic meters (m3).

The educational objectives of this exercise were to

introduce the concept of LIDAR, teach the students how

to create a DEM from a LIDAR point shapefile, and also to

become familiar with the Cut\Fill tool.

Course Information

Forestry 816, Remote Sensing and GIS in Natural

Resources, is a course designed specifically to meet the

needs of natural resources students. Forestry 816 is the

second of two GIS courses offered at Clemson University,

Clemson, SC. The first GIS course in the sequence is an

introductory class that teaches entry level analysis, data

development and management, and is a prerequisite for

Forestry 816. Forestry 816 meets once per week during a

4-hour block of time that combines both lecture and labora-

tory. The course focuses on advanced tools in GIS, such as

Advanced GIS Exercise: Estimating Beach and Dune Erosionin Coastal South Carolina

Steven T. Hall* and Christopher J. Post

Department of Forestry and Natural Resources, 261 Lehotsky Hall,

Clemson Univ., Clemson, SC 29634-0359. Received 7 June 2006.

*Corresponding author (sthall@clemson.edu).

J. Nat. Resour. Life Sci. Educ. 37:4952 (2008).

http://www.JNRLSE.org

American Society of Agronomy

677 S. Segoe Rd., Madison, WI 53711 USA

ABSTRACT Many natural resources graduate students across the nation are being required to learn proper use of

geographic information systems (GIS) to include not only in their graduate research, but to also prepare for a career as

a professional in natural resources. This demand creates a need for graduate students to be properly instructed in GIS.Advanced GIS exercises can be useful in teaching common techniques and methodologies in GIS. We have developed

an advanced GIS exercise that uses the spatial analyst extension in GIS and light detection and ranging (LIDAR) data.

The goal of this laboratory exercise was to determine the amount of erosion that has occurred over a 4-year period on

the coast of South Carolina. All students, individually, had the opportunity to use the Cut/Fill tool and create their own

digital elevation model (DEM). Students stated that this exercise helped them to understand how to work with LIDAR

data and also how to estimate erosion. Independently, students estimated that there has been a loss of more than 50,000

m3of material from Folly Beach during the 4-year period included in this study.

Copyright 2008 by the American Society of Agronomy. All rights reserved. No part of this periodical may be reproduced or transmitted in

any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system,

without permission in writing from the publisher.

Abbreviations: DEM, digital elevation model; GIS, geographic

information system; LIDAR, light detection and ranging; NAD, North

American Datum; UTM, universal transverse mercator.

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Spatial Analyst, and is considered intermediate in terms of

difficulty. Much time is spent during the semester teaching

new tools and ideas to the students.

The course is taught by the Department of Forestry

and Natural Resources at Clemson University. Forestry

816 offers students a problem based learning environment

that allows them the opportunity to work on real-world

problems, including their own research. During the spring

2006 semester, seven graduate students were enrolled in

the course.

Before beginning the laboratory exercise, students were

given a presentation on what LIDAR is and how the data

can be collected. Additionally, students were explained that

LIDAR data can be useful in other types of analysis, such

as forest biomass (Nelson et al., 2004), surface classifica-

tion (Filin, 2004), canopy height (Lovell et al., 2005), and

fire behavior (Riano et al., 2003). The exercise itself was

explained in detail and a guide was provided to the stu-

dents to aid them during the exercise.

Materials and Methods

The study area for this exercise was Folly Beach, located

on James Island on the coast of South Carolina. LIDAR was

flown during the fall of 1996 and 2000. The tidal conditionsat the time of data collection are unknown to the authors.

The area of concern for this exercise was 27.2 ha in size

and was confined to the shoreline immediately adjacent to

the Atlantic Ocean. ArcGIS Desktop software version 9.1

(ESRI, Redlands, CA) was used for the completion of this

exercise. Additionally, the Spatial Analyst extension and the

LIDAR Data Handler extension (National Oceanographic and

Atmospheric Administration Coastal Services Center, 2006b)

were required. This exercise was broken down into two

subparts: (i) collecting and preprocessing data and creating

an analysis mask, and (ii) creating a model to perform the

analysis.

Collecting and Preprocessing DataTwo sources of public data were utilized for this exercise

(Table 1). LIDAR ASCII files were downloaded from the

NOAA Coastal Services Center (National Oceanographic and

Atmospheric Administration Coastal Services Center, 2006a)

with a horizontal resolution of 5 m, and vertical accuracy of

15 cm, using the LDART (LIDAR Data Retrieval Tool) avail-

able from the NOAA (National Oceanographic and Atmo-

spheric Administration Coastal Services Center, 2006a).

Each dataset was projected in 1983 NAD UTM Zone 17N.

Having selected a small area for this exercise, the LIDAR

datasets were easily manageable with regard to file size.

In addition to the LIDAR data, a LIDAR Data Han-

dler extension (National Oceanographic and Atmospheric

Administration Coastal Services Center, 2006b) was also

downloaded to ensure proper importing of the data. The

LIDAR Data Handler was used to convert the raw data (x, y,

and z) to point shapefiles and is available from NOAA at no

cost. Digital orthophotographs (1999) were attained from

the South Carolina Department of Natural Resources GIS

Data Clearinghouse (South Carolina Department of Natural

Resources, 2006). The digital orthophotographs down-

loaded were 1 m in resolution and also projected in NAD

1983 UTM Zone 17N. The SCDNR GIS Data Clearinghouse

requires a username and password to access all GIS data;

however, usernames are available to the public at no cost,

as well as the data itself.

With the data collected and imported to ArcMap, it was

necessary to create an analysis mask such that only a small

section of James IslandFolly Beachwould be considered

in the analysis. To do this, the students individually cre-

ated a new polygon shapefile in ArcCatalog, and using the

digital orthophotographs traced and outline of the beach

in ArcMap. The new beach polygon was then set as the

mask and output extent in the ArcMap Environments. This

was done such that analysis occurred only in the area of

interest, and surrounding objects, such as houses and large

vegetation, did not skew the final result.

Model Development and Data Analysis

With all of the preprocessing complete, students began

working on their model using Model Builder. A model is a

graphic representation of the processes used during the

analysis, that when viewed is similar to a data flow dia-

gram (McCoy, 2004). The analysis tools can be executed

simultaneously from within Model Builder, allowing the user

to design, store and execute their model within the same

interface. Models are built by dragging the necessary

tools from ArcToolbox into the Model Builder window. Once

the tools are available, the specific settings for each tool

can be customized for the current project.

A new toolbox was created, and within the toolbox a new

model (Fig. 1). In the model, two tools were used: Feature

to Raster and Cut/Fill. The Feature to Raster tool converted

the LIDAR data to a raster from its original format, a shape-

file. This step was necessary given the raster prerequisites

of the Cut\Fill tool. Additionally, the procedure resulted in

a high resolution Digital Elevation Model (DEM). A DEM is a

graphical representation of elevation in grid format (Bol-

stad, 2003). For this exercise, the grid cell size was 5 m.

With the DEM created, the Cut/Fill tool was used to

determine the difference in elevation between the two data-

sets (Fig. 2). The volumetric difference between 1996 and

2000 on Folly Beach was 50,044

m3. This value indicated that,

during the 4-year period, more than

50,000 m3of material was lost from

Folly Beach.

With the amount of material

theoretically lost determined, a

class discussion was held to estab-

lish any error that may be included

in the figure. The students deter-

Table 1. Data descriptions and sources for this laboratory exercise

Feature Format Projection Source

1996 LIDAR ASCII NAD 1983 UTM Zone 17N National Oceanographic and

Atmospheric Administration

2000 LIDAR ASCII NAD 1983 UTM Zone 17N National Oceanographic and

Atmospheric Administration

Digital Orthophotos MrSID NAD 1983 UTM Zone 17N South Carolina Department of

Natural Resources

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mined that at least two factors may have contributed error,

assuming error existed at all. The first, and most obvious,

was the tidal conditions when the LIDAR was flown. Had

the tide been in during the first flight and out during the

second, the total materials lost may have been overesti-

mated (or underestimated, were the opposite true). Addi-tionally, the students decided that changes in vegetation,

which was inevitably included to some degree, may have

added a minor amount of error to the analysis.

Student Response

All of the students were able to complete the laboratory

exercise in 2 to 4 hours and found the project to be of mod-

erate difficulty. Although converting features to raster and

creating shapefiles was covered in the previous introductory

GIS course, this project exposed students to LIDAR for the

first time and the Cut/Fill tool. Students were also asked to

answer several questions related to the exercise (Table 2).

Fig. 1. Flow model developed within the ArcGIS Model Builder application.

Fig. 2. A portion of Folly Beach that was included in this study. This map includes DEMs from 1996 and 2000and also the difference in beach volume over the 4-year period.

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Most students were able to answer the questions with little

difficulty.

Student Feedback

Student feedback indicated that students enjoyed the

real-world application of GIS and that it helped them to

understand the basic concepts of LIDAR (Table 3). One stu-

dent was unaware that LIDAR could be used to determine

the amount of material lost over a period of time. Many

other students did not realize that a single dataset, such as

LIDAR, could have so many different applications.

Students noted that the exercise was clear, effective,

and easy to understand. They felt comfortable with thebasic concepts that is LIDAR and were interested in explor-

ing other applications of the data. When asked how the

exercise could be improved, students suggested that more

information on where to find public LIDAR data be avail-

able, and also a more in-depth explanation of how raw

LIDAR coordinates are converted to point shapefiles.

Conclusion

Overall, this exercise was effective in introducing stu-

dents to LIDAR and how it can be used in natural resources,

specifically when estimating the amount of erosion over a

period of time; although the exercise does not cover all of

the potential uses of LIDAR. Students were able to use raw

data points and convert them to point shapefiles, and then

convert the shapefiles to a high resolution raster, creating

a DEM, which was the first DEM many of them had ever

created. The students results indicated that there had been

a large loss of earthen materials on Folly Beach. However,

after the class discussion, they came to realize that a signif-

icant amount of error could be associated with the material

lost. Based upon student comments, the laboratory exercise

was useful when introducing students to LIDAR data.

Acknowledgments

We wish to extend our thanks to the students in Forestry

816, Remote Sensing and GIS in Natural Resources, 2006

spring semester, for their participation in this class exercise.

References

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Gumbricht, T. 1996. Application of GIS in training for environmental

management. J. Environ. Manage. 46:1730.

Filin, S. 2004. Surface classification from airborne laser scanning

data. Comput. Geosci. 30(910):10331041.

Lovell, J.L., D.L.B. Jupp, G.J. Newnham, N.C. Coops, and D.S. Culve-

nor. 2005. Simulation study for finding optimal lidar acquisi-

tion parameters for forest height retrieval. For. Ecol. Manage.

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McCoy, J. 2004. ArcGIS 9. Geoprocessing in ArcGIS. ESRI Press,

Redlands, CA.

National Oceanographic and Atmospheric Administration. 2006.

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pastall.shtml (accessed 18 Mar. 2006; verified 3 Mar. 2008).

National Hurricane Center, Miami, FL.

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maps.csc.noaa.gov/TCM/ (accessed 18 May 2006; verified 3

Mar. 2008). NOAA Coastal Services Center, Charleston, SC.

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www.csc.noaa.gov/crs/tcm/lidar_handler.html (accessed

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carbon in delaware using an airborne profiling LIDAR. (vol 19,

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Table 2. Questions assigned to students for thislaboratory exercise.

1. How is LIDAR data useful?

2.

Why did you make a shapefile to outline the beach area of

James Island? If you hadnt made the shapefile, how would

your results have been affected?

3.

How much material was lost or gained between 1996 and

2000? What is the likely cause of this change?

Table 3. Student survey questions and results

Survey question Avg. SD

1. Did this exercise help you understand the

basic concepts of LIDAR? (1 = no, not at all

to 5 = yes, I understand the basic concepts

of LIDAR)

4.33 0.81

2. How would you rate this laboratory exercise?

(1 = easy to 5 = very difficult)

2.5 0.54

3. How could this exercise be improved? N/A

Seven students surveyed.