Aerial Videography & Land-Cover Mapping
Personnel: David Williams, Tracy Onega, Ernie Buford, David Capen, Chris Boget
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Aerial Videography & Land-Cover Mapping Personnel: David Williams, Tracy Onega, Ernie Buford, David Capen, Chris Boget |
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As part of New England's
Gap Analysis program,
new methods for integrating aerial-videography with the process of
land use classification are being developed here at the
Spatial Analysis Lab (SAL),
located in the Aiken Natural Resource Center at the
University of Vermont.
Here is a brief overview taken from Joel Schlagel's report.
Aerial videographic imagery, with frame location identified using global positioning system receivers, has been demonstrated to be an efficient and cost effective method for gathering ground-truth data for satellite image interpretation and post-classification accuracy assessment (Graham, 1993; Slaymaker, et. al., 1995).
A more recent study was conducted by Eric L. Lambert (former SAL staff):
In order to better develop the methods by which aerial-videography
and image processing can be integrated for the purpose of developing
very large sets of training samples, a pilot study was carried out.
This study was done using a subset of a full Landsat TM scene from
northwest Vermont, dated October 6, 1992. The area covered by this
subseted scene is approximately 260,000 Hectares (1 Hectare = 10,000
square meters). In addition, ancilliary data such as stream networks,
major roadways and town and county boundaries were clipped to conform
with this study area.
Within Imagine (Version 8.2), an unsupervised classification was
first performed using all 6 bands (1-5 and 7) of the TM subset. The
result of this classification was a thematic map of 50 classes. This
map served as a starting point in our initial attempts at defining a set
of spectrally unique class signatures for the approximately 10-15 unique
landuse and forest types.
GPS data from two flight paths (June 4, 1994 & May 15, 1995) were
differentially corrected using data recorded at the GPS base station
located in Aiken Center (UVM). These two series of points were then
overlaid on the TM scene (Figures 1 and 2). We currently have air video
taken during three primary time periods:
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Figure 1. ERDAS Imagine viewer showing a section of the October, 1992
TM scene (bands 3,2,1) with GPS points from the June 4, 1994 (red) and
May 15, 1995 (blue) overlaid. The white "inquire cursor box" is showing
the area magnified in Figure 2.
Click on image for full view (265K) |
Figure 2. ERDAS Imagine viewer depicting a magnified section of
the Landsat TM scene with GPS points from the June 4, 1994 (red) overlaid.
Note the single highlighted point which corresponds to the selected
record shown in Figure 4.
Click on image for full view (251K) |
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Identifying the land use types which were associated with each of the
50 class signatures from the unsupervised (isodata) classification
involved several steps. First, both the wide angle and zoomed videos
were viewed on two monitors. The videos were then stopped at frames
where the landuse within the zoomed frame was considered homogeneous.
This is related to the fact that the area of the zoomed video frame is
approximately equal to the pixel size within the TM image (30 meters).
The time code reference for the video frame was then read off the video
monitor (Figure 3).
Using the time code from the video frame of interest, the vector
attribute file associated with the series of GPS points taken during the
videography flight was searched to find the matching point (Figure 4).
Figure 3. A sample wide-angle video frame showing time code reference.  Click on image for full view (171K)
Figure 4. A part of the ERDAS Imagine table which lists the point
attributes from the Arc/Info file associated with the series of GPS
points from the June 4th flight. Note the single record highlighted in
yellow. This record corresponds to the point which is highlighted in
Figures 1 and 2.
 Click on image for full view (17K)
When the matching point is located and selected in the attribute
table, the GPS point is similarly highlighted on the TM scene (see Figures
1 & 2). Using another Imagine viewer containing an image of
the isodata thematic map, the isodata class number which underlies the
selected GPS point is determined. This isodata class is then labeled
with an appropriate land use type. In addition, more specific notes are
taken which describe, in more detail, the species composition for a
forested frame.
This process is then iterated many (hundreds to thousands) times in
order to accumulate an appropriate number of samples for each unique
land use type and isodata class.
Summary
While this process is still ongoing, the initial results are very
pleasing. It is quite possible to "visit" almost 100 sites in a day
using these integrated videography techniques. The number of training
signatures which can then be applied to perform any type of supervised
classification is significantly (10-20 times) larger than could be
anticipated if one was using traditional groundtruthing methods. As
such, the accuracy of the final landuse classification can be vastly
improved while using substantially less time (and money) than would be
needed to acquire the same number of training sites without the use of
the integrated videography.
References Graham, L.E. 1993. Airborne Video for Near Real-Time Resource Applications. Journal of Forestry. 91(August):28-32 Slaymaker, D.M, K.M.L. Jones, C.R. Griffin, and J.T. Finn, 1995. Mapping Deciduous Forests in New England Using Aerial Videography and Multi-Temporal Landsat TM Imagery. In Review. |
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| Updated: 30 June 2000 |
