There are no author-identified significant results in this report

Bartlett, D.

Klemas, V.

Reed, L.

Rogers, R.

English

NASA Technical Reports Server

'Te aa1101 oub?6r R sporrsrv E 7.4 -1 0.5 3 1
In the interest of early and wide dis-
semination of Earth Resources Survey
Program information and wiiho .t iiability
for any use made thereot."
INVENTORIES OF DELAWARE'S COASTAL VEGETATION
AND LAND-USE UTILIZING DIGITAL PROCESSING OF ERTS-1 IMAGERY
V. Klemas and D. Bartlett
College oarine Studies
University of Delaware
Newark, DE 19711
R. Rogers and L. Reed
Bendix Aerospace Systems Division
3300 Plymouth Road
Ann Arbor, MI 48107
(E74-105 3 1) INVENTORIES OF DELAWARE'S N74-25849
.COASTAL VEGETATION AND LAND-USE UTILIZING
!DIGITAL PROCESSING OF ERTS-1 .IMAGERY
,Report on Significant Results (Delaware Unclas
Univ.) 2 p HC $4.00 CSCL 08A G3/13 00531 9
May 24, 1974
Report on Significant Results
NASA ERTS-1 Contract NAS 5-21837
UN 362 SR 9654
Prepared for
GODDARD SPACE FLIGHT CENTER
GREENBELT, MD 20771
- I-
https://ntrs.nasa.gov/search.jsp?R=19740017736 2020-03-23T08:10:11+00:00Z
Significant Results
The primary objective of the analysis was to map and inventory the
significant ecological communities of Delaware's coastal zone. To this
end, eight vegetation and land use discrimination classes were selected
as follows:
1. Phragmites communis (Giant Reed grass)
2. Spartina alterniflora (Salt marsh cord grass)
3. Spartina patens (Salt marsh hay)
4. Shallow water and exposed mud
5. Deep water ( 2 meters)
6. Forest
7. Agriculture
8. Exposed sand and concrete
Canonical analysis showed that classification accuracy was quite good
with Spartina alterniflora, exposed sand - concrete, and forested land -
all discriminated with between 94% and 100% accuracy.' The shallow
water-mud and deep water categories were classified with accuracies of
88% and 93% respectively with all errors in classification occurring as
one water category being classed as the other, a condition which is
neither surprising nor bothersome with the overlap which these two
classes exhibit no matter what the measurement technique used. Fnrag-
mites communis showed a classification accuracy of 83% with all confusion
occurring with Spartina natens which may be due to use of mixed stands
of these species as training sets. Both species occupy similar environ-
ments within the coastal zone. Discrimination of Spartina patens was
very poor (accuracy =-52%) due, almost certainly, to difficulties in
locating large, pure stands of S. patens for use as training sets.
Classification accuracy for agriculture was also very poor (51%).
Limitations of time and available class-memory space resulted in limiting
the analysis of agriculture to very gross identification of a class which
actually consists of many, varied signature classes. There is no doubt
in the investigators' mind that if crop inventory had been the primary
objective of the study, substantially better results could have been
achieved in discriminating agricultural land categories.
Abundant ground truth was available in the form of vegetation maps
compiled from NASA-RB-57 photography and in the photos themselves - both
color and color-IR. Blow ups of portions of the thematic maps digitally
derived from ERTS data showed very good correlation with known sites.
Cal-comp plots of thematic data at scales up to 1:24,000 showed excellent
cartographic precision \jien! overlayed onto existing maps.
It is believed that .with further refinement of training set selec-
tion, sufficiently accurate results can be obtained for all categories
producing a useful planning and management tool.


Inventories of Delaware's coastal vegetation and land-use utilizing digital processing of ERTS-1 imagery

The author has identified the following significant results. Analysis of ERTS-1 color composite images using analogy processing equipment confirmed that all the major wetlands plant species were distinguishable at ERTS-1 scale. Furthermore, human alterations of the coastal zone were easily recognized since such alterations typically involve removal of vegetative cover resulting in a change of spectral signature. The superior spectral resolution of the CCTs as compared with single band or composite imagery has indeed provided good discrimination through digital analysis of the CCTs with the added advantage of rapid production of thematic maps and data

OQe lfljlabfne e4 AW s E 7. 4 - 1 0.5 9.0i0 the inferest of early and wide dis- Q 35 q
£sOmination of Earth Resources Survey
Program information and without liabilityfor any use made thereof."
INVENTORIES OF DELAWARE'S COASTAL VEGETATION
ND LAND-USE UTILIZING DIGITAL PROCESSING OF ERTS-1 IMAGERY
V. Klemas and D. Bartlett am t LnCollege of Marine Studies 4 c
University of Delaware,
Newark, DE 19711 o a
R. Rogers and L. Reed 
Bendix Aerospace Systems Division , t-
3300 Plymouth Road
Ann Arbor, MI 48107
LO
presented at the
Ninth International Symposium on C
Remote Sensing of Environment
University of Michigan
Ann Arbor, Michigan
April 15-19, 1974
https://ntrs.nasa.gov/search.jsp?R=19740020719 2020-03-23T05:33:53+00:00Z
INVENTORIES OF DELAWARE'S COASTAL VEGETATION
AND LAND-USE UTILIZING DIGITAL PROCESSING OF ERTS-1 IMAGERY
V. Klemas and D. Bartlett
College of Marine Studies
University of Delaware
Newark, DE 19711
R. Rogers and L. Reed
Bendix Aerospace Systems Division
3300 Plymouth Road
Ann Arbor, MI 48107
SUMMARY
Work carried out over the past two years at the University of
Delaware indicated that the coastal zone of Delaware andi in particular,
its extensive tidal wetlands offer a unique opportunity to evaluate ERTS-1
sensing capabilities and the feasability of using digital processing
techniques to map and inventory coastal vegetation and land-use. Major
vegetation species found in the tidal wetlands have beenishown to be
easily distinquished on the basis of reflectance characteristics in
aerial photographyl,2,3 and it was thought that the same characteristics
could be used to map the wetlands using ERTS-1 data. Analysis of ERTS
color-composite images using analogue processing equipment confirmed
that all the major wetlands plant species were distinguishable at ERTS
scale.4 Furthermore, human alterations of the coastal zone were easily
recognized since such alterations typically involve removal of vegetative
cover resulting in a change of spectral signature.4 The superior spectral
resolution of the CCTs as compared with single band or composite imagery
has indeed provided good discrimination through digital analysis of the
CCTs with the added advantage of rapid production of thematic maps and
data. 5
Digital Techniques
Computer software, techniques and procedures used to transform
ERTS CCT's into the land use maps and data described here were developed
in the Bendix Earth Resources Center.
Transforming ERTS CCT's into thematic data is a four step process,
performed as follows:
1. Locate Training Areas
In defining the location of the training areas, the CCT containing
the areas of interest is screened on the color-coded TV monitor.
Utilizing available ground truth, the training areas are identified
on a grey scale printout and their locations conveyed to the computer
by the investigator.
22. Develop Target Characteristics
Inputting the training area coordinates (boundaries) to the computer
permits the ERTS spectral measurements within these boundaries to be
extracted (edited) from the CCTs and placed into computer disk files.
3. Evaluate Target Characteristics and Classification Techniques
Once the numerical descriptions which define the spectral charac-
teristics of each land use category of interest are determined a set
of canonical codfficients are derived for each land use category-of
interest.
Before producing decision data on a complete ERTS CCT, a number of
tests are applied to evaluate the computer's capability of performing the
desired land use classification. One test is to compute and display the
canonical variables in the form of "scatter diagrams." These plots.aid
the investigator in determining the degree of separability being obtained
between the target and background categories.
Another test of the canonical coefficients is to develop a class-
ification accuracy table which provides the investigator with a quanti-
tative measure of the classification accuracy achieved by the canonical
coefficients in the decision processing. The percentage of each target
group which would be classified as another target group is shown thus
identifying the areas where signature overlap may introduce errors into
the decision processing.
A final test of the canonical coefficient is to view the computer
decisions on areas where ground truth is available, thus rapidly determining
the effectiveness of the decision processing.
4. Produce Decision Data Products
The decision analysis may be presented in a number of ways, including:
a. The area covered by each category in terms of percent of
total area processed, acres and square kilometers.
b. Color-coded decision imagery showing any or all of the
categories at any convenient scale.
c. Cal-Comp-or Gerber plots of any category, geometrically
corrected and plotted at a map scale specified by the
investigator.
c2z
Results
The primary objective of the analysis was to imap and inventory the
significant ecological communities of Delaware's (oastal zone. To this
end, eight vegetation and land use discrimination classes were selected
as follows:
1. Phragmites communis (Giant Reed grads)
2. Spartina alterniflora (Salt marsh cdrd grass)
3. Spartina patens (Salt marsh hay)
4. Shallow water and exposed mud
5. Deep water ( 2 meters)
6. Forest
7. Agriculture
8. Exposed sand and concrete
Canonical analysis showed that classification accuracy was quite good
with Spartina alterniflora, exposed sand - concrete, and forested land -
all discriminated with between 94% and 100% accuracy. The shallow
water-mud and deep water categories were classified with accuracies of
88% and 93% respectively with all errors in classification occurring as
one water category being classed as the other, a condition which is
neither surprising nor bothersome with the overlap which these two
classes exhibit no matter what the measurement teqhnique used. hrag-
mites communis showed a classification accuracy of 83% with all confusion
occurring with Spartina patens which may be due to use of mixed stands
of these species as training sets. Both species qccupy similar environ-
ments within the coastal zone. Discrimination of Spartina patens was
very poor (accuracy = 52%) due, almost certainly, to difficulties in
locating large, pure stands of S. patens for use as training sets.
Classification accuracy for agriculture was also very poor (51%).
Limitations of time and available class-memory space resulted in limiting
the analysis of agriculture to very gross identification of a class which
actually consists of many, varied signature classes. There is no doubt
in the investigators' mind that if crop inventory had been the primary
objective of the study, substantially better results could have been
achieved in discriminating agricultural land cate ories.
Abundant ground truth was available in the fdrm of vegetation maps
compiled from NASA-RB-57 photography and in the photos themselves - both
color and color-IR. Blow ups of portions of the thematic maps digitally
derived from ERTS data showed very good correlation with knoxwn sites.
Cal-comp plots of thematic data at scales up to 1:j24,000 showed excellent
cartographic precision when overlayed onto existing maps.
It is believed that with further refinement df training set selec-
tion, sufficiently accurate results can be obtained for all categories
producing a useful planning and management tool.
&
References
1. Klemas, V., Daiber, F., Bartlett, D., Crichton, 0., Fornes, A.,
Application of Automated Multispectral Analysis to Delaware's
Coastal Vegetation Mapping, American Society of Photogrammetry
Annual Meeting, Washington, D. C., March 11-16, 1972..
2. Re'imold, R. J., Gallagher, J. L., and .Thompson, D. E., "Remote
Sensing of Tidal Marsh"'in Photogrammetric Engineering, Am. Soc.
of Photogrammetry, 1973.
3. Wobber, F. T. and Anderson, R. R., "Operational Wetlands Mapping
Using Multiband Aerial Photography" in Coastal Mapping Symposium,
Am. Soc. of Photogrammetry, 1972.
4. Klemas, V., Bartlett, D., Daiber, F., Mapping Delaware's Coastal
Vegetation and Land Use from Aircraft and Satellites, A.S.P.,
Symposium on Remote Sensing in Oceanography, Orlando, Fla.,
October 2-5, 1973.
5. Rogers, R. H., and Reed, L. E. (1973) "Automated Land Use Mapping
from Spacecraft Data" - Bendix Special Report #4097.


Inventories of Delaware's coastal vegetation and land-use utilizing digital processing of ERTS-1 imagery

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