&EPA
United States
Environmental Protection
Agency
Industrial Environmental Research
Laboratory
Cincinnati OH 45268
EPA-600/7-79-194
August 1979
Research and Development
Characterization of
Vegetation and
Drainage in Strip
Mined Land Utilizing
Remote Sensing
Techniques
Interagency
Energy/Environment
R&D Program
Report
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology trans'fer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the INTERAGENCY ENERGY-ENVIRONMENT
RESEARCH AND DEVELOPMENT series. Reports in this series result from the
effort funded under the 17-agency Federal Energy/Environment Research and
Development Program. These studies relate to EPA's mission to protect the public
health and welfare from adverse effects of pollutants associated with energy sys-
tems. The goal of the Program is to assure the rapid development of domestic
energy supplies in an environmentally-compatible manner by providing the nec-
essary environmental data and control technology. Investigations include analy-
ses of the transport of energy-related pollutants and their health and ecological
effects; assessments of, and development of, control technologies for energy
systems; and integrated assessments of a wide range of energy-related environ-
mental issues.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/7-79-194
August 1979
CHARACTERIZATION OF VEGETATION AND DRAINAGE IN STRIP MINED
LAND UTILIZING REMOTE SENSING TECHNIQUES
by
Chris J. Johannsen, Agronomy
Terry W. Barney, Agronomy
A. Dale Coble, Agronomy
James E. Carrel, Biological Sciences
William McFarland, Bioengineering Programs
University of Missouri-Columbia
Columbia, Missouri 65211
and
David J. Barr, Mining, Petroleum and Geological Engineering
University of Missouri-Rolla
Rolla, Missouri 65401
SEA-CR IAG No. 684-15-20
Project Officer
Ronald D. Hill
Resource Extraction and Handling Division
Industrial Environmental Research Laboratory
Cincinnati, Ohio 45268
This study was conducted in cooperation with the Science and Education
Administration, Cooperative Research, USDA, Washington, DC 20250
INDUSTRIAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
Cincinnati, Ohio 45268
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DISCLAIMER
This report has been reviewed by the Industrial Environmental
Research Laboratory-Cincinnati, U. S. Environmental Protection Agency,
and approved for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the U. S. Environmental
Protection Agency, nor does mention of trade names or commercial products
constitute endorsement or recommedation for use.
The views and conclusions contained in this report are those of the
authors and should not be interpreted as representing the official
policies or recommendations of the Science and Education Administration-
Cooperative Research, U. S. Department of Agriculture.
11
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FOREWORD
When energy and material resources are extracted, processed, converted,
and used, the related pollutional impacts on our environment and even on
our health often require that new and increasingly more efficient pollution
control methods be used. The Industrial Environmental Research Laboratory-
Cincinnati (IERL-CI) assists in developing and demonstrating new and improved
methodologies that will meet these needs both efficiently and economically.
This report presents the results of surface vegetation and drainage
analyses of those sites and the methodologies used for accurate, timely and
useful applications of remotely gathered data for monitoring coal surface
mining reclamation activities.
Further information may be obtained from the Resource Extraction and
Handling Division.
David G. Stephan
Director
Industrial Environmental Research Laboratory
Cincinnati
iii
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ABSTRACT
Research conducted during this project has utilized remote sensing
images and data to study surface vegetation identification, vegetation
biomass and drainage patterns on coal strip mined land. Study sites were
located in Boone, Randolph and Macon counties in central Missouri. This
report presents the results of surface vegetation and drainage analyses of
those sites and the methodologies used for accurate, timely and useful
applications of remotely gathered data for monitoring coal surface mining
reclamation activities,,
A computer based technique was developed for assessing the extent of
reestablished vegetation on the study sites. Color infrared aerial photo-
graphs obtained over the sites were scanned and converted into digital
outputs. Classification accuracy was established by comparing the results
to known ground cover quantified by quadrat and area metering methods.
The methodology subsequently developed offers a reasonable compromise between
manual photo interpretation and automatic machine processing strategies for
assessment of vegetative ground cover on strip mined lands from remotely
sensed data.
Vegetative cover was verified in randomly located quadrats on the mined
sites. Reference standards were thus established for the analysis and
classification made by both area metering and digital image analysis methods,,
Although the computer system is more versatile, both methods are rapid and
highly (85-90%) accurate. Nine additional mine sites were studied for
further comparison of the effects of plant productivity, species diversity
and density to the remotely sensed classification.
Aerial photography used in the vegetation classification and the
computer ground cover analysis plus additional photography provided the
basis for analyzing pre- and post-mining drainage features. Pattern,
density and orientation of erosional features was analyzed and 1:24,000
scale maps were produced. The alteration from a pre-mined naturally dendritic
drainage pattern to a post-mined rectangular pattern has caused several
observed problems. The change in terrain surface creates a higher potential
for erosion0 The initial post-graded convex slopes have very low drainage
densities. This leads to a very high likelihood of erosion and often
results in excessive densities following heavy precipitation. Sediment
retention and plant establishment would profit greatly from a change to
concave slopes. Many sedimentation ponds could also be eliminated.
Constructing a random drainage pattern of the proper density would be a
substantial asset in reducing sediment outflow from the mined site.
IV
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This report was submitted in fulfillment of Contract No. 684-15-20 by
the University of Missouri in cooperation with the Science and Education
Administration, Cooperative Research Unit, USDA under the sponsorship of
the U.S. Environmental Protection Agency. This report covers the period
from July 1, 1976 to September 30, 1978 and was completed as of December 31,
1978=
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TABLE OF CONTENTS
Page
ABSTRACT iv
FIGURES viii
TABLES x
ACKNOWLEDGMENTS xi
I. INTRODUCTION 1
II. CONCLUSIONS 3
III. RECOMMENDATIONS 5
IV. SURFACE OBSERVATIONS 7
METHODS AND PROCEDURES 7
Vegetative Cover Determination 7
Vegetation Identification 11
Determination of Species Diversity 11
Importance Value Determination 12
Determination of Stability 12
RESULTS AND DISCUSSION 12
Vegetative Cover Determination 12
Vegetation Identification 18
Species Diversity 19
Productivity 23
V. COMPUTER ANALYSIS 27
METHODS AND PROCEDURES 27
Digital Image Generation 27
Methodology Development 28
Technique Overview 29
Technique Evaluation 29
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TABLE OF CONTENTS (continued)
Page
The Interactive Gray Level Slicing Technique 35
Analysis procedure 35
Scanning and display 35
Delineation of mine site boundary 41
Interactive gray level slicing 41
Classification and print-out 41
Accommodating Input Variables 41
RESULTS AND DISCUSSION 44
Precision of Analysis 44
Classification Accuracy 45
Seasonal Variations 47
VI. EROSION AND DRAINAGE ANALYSIS 49
METHODS AND PROCEDURES 49
Summary of Appropriate Theory and Literature 49
Drainage Map Preparation 51
Evaluation of Slope Geometry 52
ANALYSIS PROCEDURES 52
Drainage Density . 52
Drainageway Orientation 53
RESULTS AND DISCUSSION 60
Accuracy Standards 60
Mined Land Landscaping 60
Methods 60
Study Site Observations 62
Applications 63
VII. INFORMATION PROGRAM 67
VIII. REFERENCES 68
VT11
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FIGURES
Number Page
1. Aerial photograph composite showing study sites in Strip
Mine 1 located in Boone County, Missouri 8
2. Aerial photograph composite showing study sites in Strip
Mine 2 located in Randolph County, Missouri 9
3. Aerial photograph composite showing study sites in Strip
Mine 3 located in Macon County, Missouri 10
4. Percent cover verification employing aerial photography
and ground level techniqes 13
5. Number of species in vegetation types in pre-1 aw and post-
law mine sites 20
6. Importance of vegetation types in pre-1 aw and post-law
mine sites 20
7. Increase in woody plants with age of surface coal mines 21
8. Importance value curves for nine mine sites 21
9. Species-area curves for nine Missouri strip mine sites
reseeded to forage species 22
10. Plant species diversity in reseeded surface mines as a
function of time 22
11. Productivity of vegetation in nine strip mine sites 25
12. Annual change in plant productivity as a function of time .... 26
13. Relationship between diversity and stability in reseeded
strip mines 26
14. Digital image histogram showing general trends in gray
level response distribution for bare soil, vegetation
and water 30
IX
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FIGURES (continued)
Number Page
15. Technique evaluation results for automatic gray level
slicing routine 32
16. Technique evaluation results for supervised training
field routine 33
17. Technique evaluation results for interactive gray level
slicing routine 34
18. Interactive gray level slicing flow diagram 36
19. A digital image of post-law site 2 at Strip Mine 2 as
displayed on the black and white interactive digital display . . .37
20. Digital display of gray level slice representing water at
post-law site 2, Strip Mine 2 38
21. Digital display of gray level slice representing vegetation
at post-law site 2, Strip Mine 2 39
22. Line Printer output showing tabulated summary of picture
points for each class and acreage conversion 40
23. Line Printer output upon which the individual classes
have been delineated 42
24. Slope profile shape for four different types of slopes 50
25. Pre-mining drainage patterns of Strip Mine 1, Boone
County, Missouri 54
26. Post-mining drainage patters of Strip Mine 1, Boone
County, Missouri 55
27. Pre-mining drainage patterns of Strip Mine 2, Randolph
County, Missouri 56
28. Post-mining drainage patterns of Strip Mine 2, Randolph
County, Missouri 57
29. Drainage map showing early (pre-1950) mining plus natural
drainage patterns for Strip Mine 3, Macon County, Missouri ... .58
30. Post-mining drainage patterns of Strip Mine 3, Macon
County, Missouri 59
31. A random drainage pattern with A0 and Aj defined by
sub-unit drainage dividers 64
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TABLES
Number Page
1. Cover Characteristics of a Pre-1 aw and Post-law Site
at Strip Mine 1 13
2. Vascular Plants in Three Missouri Strip Mines 14
3. Vegetative Species Composition in Pre-law Site 2 at
Strip Mine 3 Seeded to Pasture 18
4. 1977 Vegetation Production by Vegetative Group of Three
Missouri Strip Mines in Different Stages of Reclamation 24
5. 1978 Vegetative Production by Vegetative Group of Three
Missouri Strip Mines in Different Stages of Reclamation 24
6. Preliminary Comparison of Overall Classification Accuracy
Between Interactive Gray Level Slicing and Training Field
Techniques 31
7. Precision Evaluation of Interactive Gray Level Thresholding,
Pre-law Site 1, Strip Mine 1 44
8. Accuracy of Interactive Gray Level Slicing for Multiple Scans
of Pre-law Site 1, Strip Mine 1 45
9. Classification Accuracy Evaluations for Additional Pre- and
Post-law Sites Utilizing the Interactive Gray Level Slicing
Technique 46
10. Seasonal Acreage Comparison for Post-law Site 3, Strip Mine 2 . . 47
11. Seasonal Acreage Comparison for Post-law Site 2, Strip Mine 1 . . 47
12. Seasonal Acreage Comparison for Post-law Site 2, Strip Mine 2 . . 48
13. A Summary of Pre-mined and Post-mined Drainage Density Shown
in Figures 25-30 52
14. Relationship of Oth Order Watershed Area and Drainage Density
To Watershed Parameters 61
xi
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ACKNOWLEDGMENTS
Work reported in this publication was financed by funds from the
Environmental Protection Agency as provided through the USDA Science and
Education Administration - Cooperative Research to the Missouri Agricultural
Experiment Station. The work was performed by faculty and staff from four
different University Departments within three Colleges located at two
campuses of the University of Missouri. The Department of Agronomy (College
of Agriculture) was the coordinating department with Mr. Terry Barney,
Research Specialist, providing major coordination in all phases of the work
during the first year. Responsibilities also included arrangement for
remote sensing data collection, analysis and interpretation. Mr. Dale Coble,
Research Specialist, performed the coordination role during the second year
with assistance from Mr. Barney.
Dr. James Carrel, Associate Professor of Biological Sciences (College
of Arts and Sciences) was responsible for the ground observations, vegetation
identification and analysis of ground cover at the mine sites. He was
assisted by a number of students, but especially by Mr. Steve Weems and
Ms. Leslie Bouchard, Graduate Research Assistants, with the field work and
laboratory analysis.
Dr. William McFarland, Assistant Professor of Electrical Engineering
(Bioengineering Programs, College of Engineering), was responsible for the
computer analysis phases. He was greatly assisted by Tachpong Hotrabhavananda,
Graduate Research Assistant, who performed computer programming and software
generation during the digitizing and analysis of the aerial photography.
Dr. David J. Barr, Professor of Geological Engineering (Department of
Mining, Petroleum and Geological Engineering, School of Mining, UMR) was
responsible for the hydrology and drainage research phase. He was primarily
assisted by C. Dale Eli frits and Shara McBee in the interpretation and
mapping work.
The cooperation of the coal companies operating in Missouri is greatly
appreciated. The Missouri Land Reclamation Commission and the Missouri Land
Reclamation staff of the Missouri Department of Natural Resources assisted
the investigators in numerous ways.
Many University administration staff needed to be consulted and informed
on the progress of the work reported herein. The secretarial and typing
skills of Ms. Pat Cook and Becky Manford are acknowledged. Samuel R. Aldrich,
University of Illinois, served as the Project Officer of this research and
xii
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Eilif V. Miller coordinated the effort for the Science and Education
Administration, Cooperative Research Unit, USDA. It was my pleasure to
work with all of the talent mentioned above as Principal Investigator.
Chris J. Johannsen
Professor of Agronomy
University of Missouri-Columbia
xiii
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INTRODUCTION
Current estimates of coal strip mined land in Missouri approach 76,000
acres (Young, 1978). This land is dispersed throughout thirty counties
in North Central and Western Missouri with four major and fifteen minor
companies presently engaged in surface coal mining operations. The Missouri
Land Reclamation Commission issues permits for over 2,000 acres each year.
This figure is steadily increasing and may take a sizable jump if plans for
establishing two coal gasification plants are realized.
Missouri ranks 9th among states in total coal reserves (Robertson, 1971).
There are thirty-one billion tons of coal that have been located and inven-
toried with-an additionally estimated 18 billion tons not verified by test
drilling.
The 67,000 acres of land mined prior to the 1972 Missouri Reclamation
Law are in varying stages of reclamation with most of the acreage needing
more vegetative cover. This project was planned to assist with developing
monitoring and inventorying techniques that could be used by the coal
companies and the Missouri Land Reclamation staff. A new mining law was
passed by the 1978 Missouri Legislature which incorporated the required
guidelines of the Federal Surface Mining Control and Reclamation Act of 1977.
The pre-law sites named in this study refer to sites mined prior to 1972
while the post-law mine sites were mined after that date. This study sought
to: (1) characterize the amount of vegetation that had been established on
both pre-law and post-law sites, (2) characterize the type of drainage
patterns resulting from the different mining conditions, (3) develop computer
analysis techniques that could extend the surface characterization of vege-
tation and soil and water over a larger area, and (4) relay the information
derived from the studies to individuals, groups and organizations that can
utilize them through an Extension Information Program.
Remote sensing data in the form of aerial photography (primarily color
infrared and black and white) were collected over the study sites at different
times of the growing season. The photography was used to locate the sites
for vegetation identification, to measure the areal extent of vegetation
areas, to classify the land cover categories in the computer analysis and to
develop drainage maps for each mine site.
The principal investigator and a research specialist coordinated the
work of the investigators and graduate students by holding project seminars,
arranging photographic missions and remote sensing analyses and communicating
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between the users and researchers.
The study sites in this project are identified by county location and
number which is a cooperative arrangement between the coal companies, the
University of Missouri and the Missouri Land Reclamation Commission. It was
felt that results, pro and con, to the interests of each group could be better
expressed if county and numerical locations were used. The study sites were
located in Boone County designated as Strip Mine 1 (Figure 1), in Randolph
County designated as Strip Mine 2 (Figure 2), and Macon County which was
designated as Strip Mine 3 (Figure 3). There are pre-law and post-law sites
in each study area. There were three main post-law sites with seven addi-
tional sites in the mine areas used for the surface observation studies.
Results from the work reported in this publication were made available
in preliminary form to the cooperating coal companies and the Missouri Land
Reclamation Commission. Meetings were held with professionals from many
organizations concerning the impacts of both strip mining and reclamation
efforts on local communities. A workshop was also held with the members of
the Missouri Mining Industry Council on applying the research results to
their work.
The most important result that researchers can report is the acceptance
and use of their efforts. Upon reviewing the results of this project, the
Missouri Land Reclamation Commission requested and received color infrared
aerial photography from the Environmental Protection Agency over all surface
mined lands of Missouri. Arrangements have been made with the University of
Missouri to provide computer output maps from the photography showing revege-
tation process and extent of the mined areas utilizing the techniques
developed under this contract.
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CONCLUSIONS
SURFACE OBSERVATIONS
1. All sites are dominated by forage species.
2. The topsoiled site has more weedy, volunteer species than sites covered
with spoil material.
3o Grading in post-law mines promotes plant species establishment, diversity,
productivity and stability.
4. Unless properly managed, strip mine succession tends toward a woody
condition.
5. Productivity of most mines increases with age to agronomically acceptable
levels.
6. Topsoiled mines are not more productive than ones of comparable age
covered with spoil material.
7. Topsoiling may offer some problems as well as solutions for establishment
of permanent pasture on surface mines in Missouri.
COMPUTER ANALYSIS
1. Digital image processing of color infrared aerial photography can be
effectively utilized in the classification of pre-law and post-law coal
strip mine sites into three basic categories: bare soil, vegetation
and water. Spatial estimates of each class can be compiled quickly and
accurately.
2. This methodology is not reliable or cost effective for measurements of
species composition or plant productivity. The methodology was designed
exclusively for measuring the area! extent of ground cover on surface
mines.
3. Varying scales and film formats of CIR imagery can be accommodated by
the computer based technique developed.
4. Periodic monitoring of coal strip mines for change detection with regard
to extent of revegetation is feasible.
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5. Digital image analysis of conventional CIR aerial photography offers a
reasonable trade-off between the high degree of classification accuracy
associated with manual interpretation methodologies and the temporal
efficiency of machine processing.
EROSION AND DRAINAGE ANALYSIS
1. Extensive erosion will occur in unreclaimed and reclaimed mine areas
until the necessary drainage pattern and density is constructed naturally
or artificially thus bringing the mine site more nearly into equillibrium
with the surrounding unmined area.
2. Mining and subsequent reclamation create terrain surfaces that require
greater drainage densities than exist prior to mining for maximum
erosion control.
3. Maximum erosion control dictates the need for use of concave slopes
wherever possible.
4. Observed reclamation practices create high potentials for erosion due
to initial low drainage densities and use of convex slopes.
5. Some disruption of natural flow with sedimentation ponds is advisable
to reduce sediment transport off the mine site. However, a designed
drainage system would reduce the number of ponds needed and extend
the usable life of those constructed.
6. A designed drainage system lends itself to the more uniform distribution
of water over the mine landscape thus enhancing revegetation efforts.
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RECOMMENDATIONS
SURFACE OBSERVATIONS
1. Pre-law surface mines that are undergoing rapid annual increases in
extent of vegetative cover should be left relatively undisturbed and
should be studied for several years until percent cover stabilizes.
2. Controlled cropping by mowing or grazing of pastures in reclaimed surface
mines should be performed every two years or so in order to retard
invasion by successional, woody plant species and to avoid replacement
of forage species. Pasture maintenance activities not only would
benefit vegetated areas, but if properly timed could enhance covering
of barren remnants, particularly steep slopes in pre-law mines, with
forage species.
3. Controlled cropping and selective reseeding of forages should also be
performed to promote and to maintain high levels of plant productivity
(>200 g/m ) in mineland pastures.
40 One or two additional years of study are needed to fully document annual
changes in vegetative cover, species composition, and particularly
productivity of pastures in selected Missouri surface mines.
COMPUTER ANALYSIS
1. Additional study is needed to document, in a statistically valid manner,
the accuracy of individual digital picture point classifications.
Although overall accuracies are good, further documentation will be
required for more detailed, site specific analyses and implementation
of routine monitoring schemes.
2. Techniques for interfacing digitized aerial photography data with other
mine site data in computerized data base format should be pursued.
3. Randomly selected mine sites from other areas of the United States
should be evaluated to test the flexibility and universality of this
assessment technique.
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EROSION AND DRAINAGE ANALYSIS
1. This study indicates that significant changes in drainage density, drain-
age orientation and slope shape occur during the reclamation process and
these changes increase the potential for severe initial erosion. A
detailed cost/benefit study should be implemented that would include:
a. Field experimentation with the mechanics of mine land "sculpturing"
to test drainage density, drainage orientation and slope shape
concepts.
b. Cost determinations for proper drainage channel design and construc-
tion.
c. Field measurements of drainage equation variables.
d. Determination of the optimum placement of detention ponds and their
effects on revegetation success within a designed drainage system.
e. Comparisons of sediment production and erosion control costs between
a study site and a standard practice site.
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SURFACE OBSERVATIONS
by
Dr. James E. Carrel and Steve Weems _!/
METHODS AND PROCEDURES
Surface observations are a verification of what is being observed by
remote sensing media. The methods vary depending upon the type of remote
sensing measurements, type of terrain, type and amount of surface cover,
personnel and equipment available, accessibility to the study site and other
related factors.
Study sites were located in pre-law and post-law minelands at three
different mining operations (Figures 1, 2 and 3). Measurements of species
identification and plant vigor throughout the growing season were obtained
on the pre-law minelands to provide needed information for the success of
species adaptation on different mined spoils which is useful in post-law
mining operations.
In this project, the surface observations were designed to provide:
(1) a detailed species composition in the different mine land conditions;
(2) a measure of the vegetative productivity of each study area; (3) a set
of reference standards for use in the computer image analysis. Further,
aerial photography techniques of collecting surface observations were also
used to compare with accuracies of ground level measurements.
Vegetative Cover Determination
Large scale (1:6,000) vertical aerial photographs of a surface mine
were printed either in black and white or color. The scale was verified in
two ways. In the laboratory, large (103m) distances between features indi-
cated on U.S.G.S. topographic maps (1:24,000) were compared with those visible
in the photographs. In the field, small (102m) distances measured with a tape
were compared with those visible in the photographs.
\J Associate Professor of Biological Sciences and Graduate Research Assistant
in Biological Sciences, University of Missouri-Columbia
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Post-law Site
#1
Post-law Site
#2
Pre-law Site
#1
Figure 1. Aerial photograph composite showing study sites in
Strip Mine 1 located in Boone County, Missouri.
:
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'
Figure 2. Aerial photograph composite showing study sites in Strip
Mine 2 located in Randolph County, Missouri.
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Pre-law Site
#1
Post-law Site
#2
Post-law Site
#3
Figure 3. Aerial photograph composite showing study sites in
Strip Mine 3 located in Macon County, Missouri.
10
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Cover classification in a surface mine proceeded in a step-wise fashion.
First, the perimeter of a mineland was marked on the photographic print.
A silhouette of the entire mine was made by inking a clear plastic overlay.
In a similar fashion transparent overlays of the total vegetation, woody
vegetation and water were prepared. Darkened areas (mm2) were measured
individually using a leaf area meter (Carrel jrt aK, 1977) and their real
areas were calculated by multiplication by the verified scale factor. The
areas occupied by bare soil or herbaceous vegetation were calculated by sub-
traction. Finally, relative abundance of the different cover classes was
determined.
Vegetation Identi fi cation
Vegetation growing in surface mines was separated into two categories:
woody and herbaceous. Trees and shrubs have a different texture and color in
aerial photographs than do grasses and legumes,so the two classes are readily
differentiated by a trained observer. In minelands artificially seeded with
forage species, woody plants are rare and individually identifiable.
Nine mine sites in Central Missouri reseeded with a standard mixture of
tall fescue and legumes were studied. Three pre-law sites had a ridge and
valley topography, whereas six post-law sites were graded to a gently rolling
terrain. Only one post-law site had been topsoiled before it was planted with
forages. Plant species composition and abundance in surface mines were
measured by quadrat sampling along transects. Quadrats (0.5m2) were estab-
lished at 30m intervals along parallel transects spaced 200m apart across
a mine. The above-ground plant biomass was clipped in late summer and segre-
gated by species for dry weight determination. Ground cover was quantified
by quadrat and area-metering methods. Samples were obtained in September-
October 1977 and 1978. Data for plant species composition, abundance, and
productivity were calculated using standard ecological formulas as follows:
Determination of Species Diversity—
a) Species richness = absolute # of species = N
b) Gleason's Index of Diversity
N
d = -j \ where:
N = total # of species
A = Area of study site
d = constant used to measure relative diversity
11
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Importance Value Determination--
.. , . r f • fquadrats the species occurs in
Absolute frequency of specks = H | quadrats sampled '~
Precent frequency = Absolute frequency -,00
Cummulative frequency of all species
Dry wt. of species = Cummulative wt. of quadrats by species X 2 g/m2
D „ n . Dn-nmacc _ Cummulative wt. (a species) v inn
Percent Bnomass - Cummulative wt! (all species) x 10°
Importance Value = Percent frequency + Percent Biomass (Scale of 200)
(Adapted from Curtis and Cottham, 1956)
Determination of Stability--
The flux in primary productivity (g/m2) from one growing season to the
next is one measure of stability for any given habitat. Plants were harvested
at the end of two successive growing seasons and species biomass determined.
Calculations were made as follows:
A p n+1 _ p p
/-A n n (n+1) where:
P n = productivity (g/m2 of a mine for the following year.
P (n+1) = productivity (g/m2) of a mine for the following year.
AP" = the change in productivity, either (+) or (-) from one year
to the next.
RESULTS AND DISCUSSION
Vegetative Cover Determination
Vegetative cover was determined for the different mine sites mined under
pre-law and post-law regulatory conditions. The findings of Strip Mine 1 are
typical of the conditions found at other locations (See Table 1). The pre-
law site contains 20.8% vegetative cover vs 48.4% for the post-law site.
The woody plants make up 0.4% and 0.1% of the vegetation area indicating that
woody species are not major invaders in Central Missouri mines under these
conditions.
12
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TABLE 1. COVER CHARACTERISTICS OF A PRE-LAW AND POST-LAW SITE AT STRIP MINE 1
Vegetation
Bare Soil
- Herbaceous
Woody
Subtotal
Pre-1 aw Site
Area (m2)
25,679
547
26,226
91,517
Cover (%)
Total
20.4
0.4
20.8
72.7
Land
21.8
0.5
2273
77.7
.and
Water
Total
117.743 93.5
8.173 6.5
125,916 100.0
Post-law Site
100.0
Vegetation
Bare Soil
Land
Water
Total
- Herbaceous
Woody
Subtotal
117,830
212
118,042
105,042
223,849
20,005
243,854
48.3
0.1
48.4
43.4
91,8
8.2
100.0
52.6
0.1
52.7
47.3
100.0
—
--
Two methods of verifying vegetative cover were used: ground level and
aerial photography. A comparison of the two methods (Figure 4) shows a
significant correlation (r = 0.95, p<.01) between the two methods (Carrel ejt
al_., 1978a). This indicates that the low altitude aerial photography approach
is very acceptable for obtaining surface observations over mined lands for
differentiating vegetation, soil, and water.
lOOr
CU
>
CU
* 50
O
o
Figure 4.
/
o
/o
o
Ideal 1:1 line
/
,o
o
0 50 100
Cover at Ground Level (%)
Percent cover verification employing aerial
photography and ground level techniques.
13
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TABLE 2. VASCULAR PLANTS IN THREE MISSOURI STRIP MINES
CLASS 7AMXLT SPECIES
Gytaosperaaa Cupressaceae Juniperos virginiana
?inacea« ?lnus echinata
Finus aylvestris
Angiospermae
Subclass
Monocotyledoneae Cyparaceae Carex brevoir
Carex vulpinoides
Scirpus iineatus
Gravineaa Agrostis hienalis
Alopecurus carolinianus
Andropoqon virgin! cus
Aristida lonispica
Aristida oligantha
Avena saciva
Srcsus ineraus
Bromus japonicua
Bromus racamosus
Pactylia qlomeratr'as
Echinoclca aiuricaca
Elynus riparus
Slymus virgin! cua
Fastuca elatior
Festuca octoflora
Fssruca oaradoxa
Eordeum pusiiion
Lolium perene
?anicua caoillaxe
Panicvm depauperat-om
Panicam dichotoniflorum
Panicua lanuainosun
Panicua lir.earif olium
Phletaa oratense
Poa comcressa
?oa pratensis
Set aria Jlauca
Setaria viridis
Sorgham halaper.se
Tridens flavus
Triticum a*stivalis
Juncaceae Juncus -enuis
Liliaceae Alliuai canadense
CCMMON NAME
Red Cedar
Short-leaf Pina
?ine
Sedge
Sedge
Bulrush
Hairgrasa
Foxtail
Broomsedge
3- awn Grass
3-awn Grass
Oats
Snooth Brome
Japanese Chess
Cheat/Chess
Orchard Grass
Barnyard Grass
Wild R?«
Eye
Meadow Fescue
Six Weeks fescue
Fescue
Little Barley
Perennial Ryegrass
Switch Grass
Fall Panic
Fall Panic
Fall Panic
Fall Panic
Timothy
Sluegrass
Blue grass
? oxtail
Yellow Foxtail
Johnson Grass
Purple top
Wheat
Rush
Wild Garlic
MEJE
A
A
A
A
ABC
A
AS
A
A
A
ABC
A
A
ABC
AB
ABC
ABC
A
A
ABC
A
B
AB
A
C
A
ABC
BC
C
ABC
A
A
ABC
BC
A
A
3C
.AB
A
(continued)
14
-------
TABLE 2. (continued)
CLASS FAMILY
Angiospermae
Subclass
Oictotyledoneae Acanthaceae
Acaraceae
Anacardaceae
Apocynaceae
Asc lepiadaceae
Setulaceae
*Caesalpiniaceae
Campanuiaceae
Caprifoiiaceae
Gary ophy 1 acsae
Chenopodiaceae
Compos itae
SPECIES
Ruellia humilis
Acer saccharinum
Phus glabra
Rhus radicans
Aoocynua cannabinum
Apocynum sibiricua
Ascleoias hirtella
Asclepias incarnata
Asclepiaa syriaca
Betula nigra
Cassia fasciculata
Gleditsia triacanthos
Eoecuiaria perfoliata
Symphori carpus oriiculanus
Cerastiuro vulgatum
Dianthus anneria
Silene antirrhina
Chenooodium ac.
Kochia sooparia
Achillea milafolivmi
Ambrosia artemisiifolia
Ambrosia bxdentata
Ambrosia trifida
Antsnnaria olantacinifolia
ArTeedsia annua
Aster cilosua
Eidens polyiecis
Cirsiun altissimus
Cirsitai vuioare
Srioeron canader.sis
Erigeron stri^osus
Erigaron sp.
Supatoriua altissimus
Supatorium sercunuza
Heli-amiius hirsutus
Lactuca canadensia
!^actuca Scaricla
Lacmca sp.
Rucebeckia hirta
Soiidaao aissouriensis
Solidaoo neaoralis
Soiidaco oeciolaris
Traoraacocon iubi'-is
Varnonia baidw.ini
COMMON NAME
Mild Petunia
Sugar Maple
Smooth Sumac
Poison Ivy
Indian Hemp
Indian Hemp
Milkweed
Swamp MiUcweed
Milkweed
River Birch
Partridge Pea
Honey Iiocust
Venus ' s Locking-
Glass
Coral Berry
Chickweed
Deptford Pink
Catchfly
Pigweed
Sunmer Cypress
Yarrow
Ragweed
Ragweed
Horse weed
Pussy ' s Tees
Srfeet Worawood
Aster
Tickseed Sunflower
Tall Thistla
Thistle
rleabane
Daisy Fleacane
Fleabane
Tall Thoroughwort
Late Boneset
Sunflower
Wild Lettuce
Prickly Lettuce
Lettuce
Slack-eyed Susan
Goldenrcd
Old- field Goldenrod
Broadieat 3oldanrod
Ooac's Heard
Iror.weed
MINE
A
A
AC
A
AB
A
B
C
AC
AC
AB
AC
A
ABC
A
A
A
3C
BC
A
BC
ABC
c
A
3C
ABC
ABC
C
ABC
ABC
\
A
C
ABC
C
ABC
ABC
ABC
A
A
A3C
A
A
AS
(continued)
15
-------
TABLE 2. (continued)
CLASS FAMILY
Angiosperaae
Subclass
Dicotyledoneae Crucifereae
Eb«naceae
Euphorbiacaaa
Fagaceae
Hyperacaceae
Labiatae
Horace ae
Cnagraceae
Ola ace ae
Oxalidaceae
*Papillionaeeae
Phytolacaceae
?lantaginaceae
Flantaceae
Polygonaceae
SPECIES
Barb area vulgaris
Brassica juncea
Lepidiura densiflorum
Lepidiua virgiriicum
Diospyros virginiana
Acalypha graciiis
Acalypha vircinica
Crcton capitatus
Huohorfaia macula ta
Ouercus alba
Quercua imbricaria
2uercua palustris
Cuercus velutina
Hypericun punctatus
Labiate sp.
iionarda fistuXoaa
Maclura pomifera
Oenothera biennis
?raxinus amaricana
?raxinus pennsylvanicans
Oxalis atricta
Cercis canadensia
Coronilla varia
Dearaod-Lum canes cens
Leapedeeza stipulaceae
Medicaqo saliva
Kelilotus alba
Melilotus officinalis
Robinia pseudoacacia
Trif oliuB pru tense
Wisteria frutescens
Phytolacca anericana
Plantago aristata
Plantaoo virginica
Plantanus occidentalis
Polygonum aviculare
?olvccnum lapathifolium
Polygonum pgrmsylvanicum
Runex acetoseila
Sxnaex altissinius
Runex Jri3pus
COtOlON NAME
Yellow Rocket
Mustard
Pepper Grass
Pepper Grass
Persimmon
3-seeded Mercury
3-seeded Mercury
Hogwort
Nodding spurge
White Oak
Shingle Oak
Pin Oak
Blade Oak
St. John ' s-wort
Mint
Wild Bargamot
Osage Orange
Evening Primrose
Vlhite Ash
Red Ash
Yellow wood Sorrel
Redbud
Crown Vetch
Beggar's Lice
Lespedeza
Alfalfa
White Sweet Clover
Yellow Sweet Clover
Black Locust
Red Clover
wisteria
Pokeweed
Plantair
Plantair
Sycamore
Kno tweed
Smartweed
Pinkweed
Sorrel
Pale Dock
Sour DocX
MINE
A
A
AC
ABC
A
B
A
BC
A
BC
AB
A
BC
B
C
C
A
A
AC
C
A
C
A
A
ABC
ABC
ABC
ABC
ABC
ABC
B
AC
A
A
A
AC
AC
ABC
ABC
C
3C
(continued)
16
-------
TABLE 2. (continued)
CLASS FAMILY
Anglos parmaa
Subclass
Dicotyledonaae Bhaonaceae
Rubiaceae
Rcseaceae
Salicaceae
Solanaceae
Typhacsae
'Jofcelliferae
Ulmaceae
Vale ri anaceae
Verbenaceae
Violaceae
Vitaceae
SPECIES
Caanoth'is anericanus
Galium apart ne
Fraqaria vesca
G£um lancinatum
Potantilla simplex
Primus serotinum
Rosa setiaeria
Rubus flagellaris
Rubus ostryfolixia
Rubus sp.
Populua deltcides
Salix interior
Salix nigra
Salix rigida
Solan um carolinenae
Solanioc rostratua
Tvpha latifolia
Oaucus carota
nonius americana
Ulous rubra
Valerianella radiata
Verbena stricta
Viola KitaibeUana
Parthenocissus quinquefolia
Vicis aestivalis
COMMON NAME
New Jersey Tea
Be da tr aw
Strawberry
Hough Avens
Cinquefoil
Black Cherry
Prairie Rose
Dewberry
Blackberry
Bramble
Popular
Sandbar Willow
Blade Willow
Willow
Horse Mettle
Buffalow Bur
Cattail
VJild Carrot
Elm
Slippery Elm
Corn Salad
Vervain
Johnny- j ump-up
Virginia Creeper
Suamer Grape
MINE
3
A
A
A
A
A
C
ABC
ABC
ABC
AC
AC
C
C
A
C
ABC
AC
C
A
A
A
A
A
ABC
*-Legumes
A-Abandoned Mines
B-Strip Mine 1
C-Strip Mine 2
17
-------
Vegetation Identification
The floral composition of three Missouri strip mines is listed in Table 2.
Principal invaders of surface mines studied in this project are representatives
from five major plant families: Gramineae, Papilionaceae, Caesalpiniaceae,
Compositae, and Polygonaceae. These colonizers are characterized by a high
degree of acid tolerance and drought resistance. Large seed sets and long
range dispersal (i.e., wind) are necessary for effective colonization.
It is of interest to look at the species composition in a pre-law mine
site (Table 3). This mine was aerially seeded with a mixture of Kentucky 31
fescue and alfalfa, making up 46.7 and 39.2% of the biomass, respectively.
Several other species such as cattail, sweet clover, smartweed, Indian Hemp,
and barnyard grass collectively made up an additional 12.2%. The importance
value which is the addition of percent frequency and percent biomass shows
that alfalfa is of equal importance to the fescue.
Species numbers and importance values were determined for all of the pre-
law and post-law sites with results showing that induced pasture species were
outnumbered by weedy herbs but had greater importance values (Figures 5 and 6).
The slight increase in the number and decrease in dominance of pasture
species in post-law sites is due to leveling to a rolling terrain. Less
erosion greatly enhances the probability of a new species being established.
Natural vegetation becomes established more quickly in post-law sites and
distribution of vegetation types is more even than in pre-law ones.
An increase in the number of woody species in pre-law sites is due to the
older condition of the mine (Figure 7). Advanced successional stages tend
toward a woody condition if not managed.
Fourteen species comprimise 90% of the total importance value in all mines.
The remaining species (51%) are relatively unimportant in terms of relative
frequency and contributing biomass but may be very important in terms of
stability (Figure 8).
TABLE 3. VEGETATIVE SPECIES COMPOSITION IN PRE-LAW SITE 2 AT STRIP MINE 3
SEEDED TO PASTURE
Frequency
Species
Medicago sativa
Festuca elatior
Mel i lotus officinal is
Ambrosia bidentata
Typha lati folia
Polygonum pennsylvanicum
Absolute
.813
.653
.120
.186
.080
.133
%
34.2
27.4
5.0
7.8
3.4
5.6
Biomass
g/m2
81.6
81.3
7.4
1.6
9.6
4.2
%
41.1
41.0
3.7
.8
4.8
2.1
Importance
Value*
75.3
68.3
8.7
8.6
8.2
7.7
(continued)
18
-------
Frequency
Species
Lactuca canadensis
Echinocloa muricata
Apocynum cannabinum
Rumex crispus
Agrostis hi email's
Salix interior
Tri folium hybridum
Setaria glauca
Gleditsia tricanthos
Poa pratensis
Eupatorium serotinum
Unidentified shrub
Asclepias incarnata
Lepidium sp.
Absolute
.146
.040
.013
.053
.013
.026
.013
.013
.013
.013
.013
.013
.013
.013
%
6.1
1.7
.5
2.2
.5
1.1
.6
.6
.6
.6
.6
.6
.6
.6
Biomass
g/mz
086
2.7
4.2
.69
1.9
.78
.51
.26
.26
.26
.24
.12
.05
.005
%
..4
1.4
2.1
.4
1.0
.4
.1
.1
.1
.1
.1
.1
.01
Importance
Value*
6.5
3.1
2.7
2.6
1.5
1.5
.9
.7
.7
.7
.7
.6
.6
.6
Total
2.380 100.4 198.5 100.0
200.4
importance Value = % Frequency + % Biomass for a given species.
Species Diversity
Plant species richness, a measure of diversity, is proportional to the
area sampled (Figure 9). Such species area relationships are typical of
terrestrial ecosystems. However, the curves for replanted strip mines do not
rise as quickly to an apparent plateau as they do for most natural communities.
This is because the few pasture species greatly dominate the areas sampled.
Comparison of pre-1 aw and post-law sites covered with spoil material shows
the same species richness. However, the single topsoiled site studied in this
project has a much greater number of species than the other sites. Most of
these additional plants are weedy forbs.
Although the absolute number of species is equitable between pre-1 aw and
post-law sites, post-law sites have greater diversity per unit area (Figure 10).
Management type rather than the age of the mine is the major factor contri-
buting to high plant diversity. A topsoiled site shows greatest diversity per
unit area.
19
-------
10
•« 9.
(U
o 8.
Q)
0.
o 6.
i.
Pasture
Weedy
Pre-law
Woody
Herbs
Pasture
Post-law
Weedy
Herbs
Woody
Figure 5. Number of species in vegetation types in pre-law and post-law mine
sites.
200-
150'
O)
OJ
£ 100'
»o
s-
o
Q.
E
i— »
(U
CD
2 50
OJ
-------
-a
o
o
3 (/>
CL)
O O
CD
S- CL
CD CO
.a
0
0 5
Age of Mine (years)
Figure 7. Increase in woody plants with age of surface
coal mines.
200
180
150
0)
"«
CD
O
G
(O
£ 100
Q-
£
i— i
CL)
(0
1 50
* x *».i_ K *
x - * x xi
X x * * * * x X
* x ** J5 x * ,
X x X 1
'//*«* !
* 5 * 1
X
,* x 1
X X
1
1
V i
X
1
1
i
X I
1
i
i
1 ' • 1 — . — I— — • —
10 15 20 25
Species Order
30
Figure 8. Importance value curves for nine mine sites.
21
-------
J_
(U
E
0)
O) T-
> o
•i- OJ
+J D-
»^-
o
«»-
o
X
-o
c
Pre-law Spoil (n=3)
Post-law Spoil (n=5)
Post-law Topsoil (n=l)
30
20
10
II
T3
10 20 30 40 50 60 70 80
Quadrat Number
Figure 9. Species-area curves for nine Missouri strip mine
sites reseeded to forage species.
10
Q Pre-law Spoil
A Post-law Spoil
O O Post-law Topsoil
0
Age of Mine (years)
Figure 10. Plant species diversity in reseeded surface
mines as a function of time.
22
-------
Productivity
Productivity of minelands appears to increase with age more rapidly in
gently rolling, post-law mines than in steeply sloping pre-law ones (Figure
11A and B). The difference between the sites is due largely to slope and
aspect related stresses on plant growth and survival, and does not appear
to be due to soil texture or chemistry (Carrel ejt al_., 1978b).
The topsoiled, post-law mine after three years is as productive as
spoil areas of comparable age (Table 4). There are no differences in growth
of forages at the topsoiled site as compared to post-law sites of spoil
material.
Wheat, a cultivator nurse crop, ceases to contribute biomass after 1 or
2 years (Tables 4 and 5). Its rapid growth and high initial productivity^
probably contributes some organic matter but more importantly provides soil
stability and increased water intake.
Forage species show a continuous increase in productivity at most sites
during the first 4-5 years of growth but legumes decline after 2-5 years
(Figure 11B and Tables 4 and 5). Differences among sites reflect time of
planting and sampling as well as edaphic variations.
A great deal of flux is observed in annual productivity among different
sites (Figure 12). This appears to be a direct reflection of the successional
state or age of mines. Change is great during the first 2 years as the nurse
crop dies and forages grow. After 2-4 years in pre-law and post-law sites,
a peak in productivity is reached. Thus, levels of stability first tend to
increase with age, then decrease.
Sites exhibiting low productive stability do not exhibit a low index
of diversity (Figure 13). This is contrary to ecological theory for plant
communities undergoing natural succession. But since a number of forage
and nurse crop species were planted in these mine sites, one would not expect
a typical diversity - stability relationship to be seen. Perhaps after a
decade, if these sites were not managed, there would be a positive correlation
between species diversity and stability of production.
23
-------
TABLE 4. 1977 VEGETATIVE PRODUCTION BY VEGETATIVE GROUP OF THREE MISSOURI
STRIP MINES IN DIFFERENT STAGES OF RECLAMATION
Standing Crop (g/m2)
Plant
Name
Wheat
Fescue
Legumes
Weeds
Total s
Post-law Sites
.25 yr.
26.2
0
30.4
64.7
121.3
.5 yr.
96.4
15.0
36.0
96.9
244.3
1 yr.
108.1
9.9
24.7
73.2
215.9
2 yr *
28.4
44.7
76.7
42.2
192
4 vr.
3.6
149.4
46.4
69.5
268.9
4 yr.
1.6
130.9
60.3
24.6
217.4
Pre-1 aw Sites
4 yr .
0
81.3
81.6
36.1
199
5 yr.
0
84.3
53.6
10.1
148
6 yr.
0
114
93
80
287
*Topsoiled site, all others covered with spoil material
TABLE 5. 1978 VEGETATIVE PRODUCTION BY VEGETATIVE GROUP OF THREE MISSOURI
STRIP MINES IN DIFFERENT STAGES OF RECLAMATION
Standing^ Crop (g/m2L
Plant
Name
Wheat
Fescue
Legumes
Weeds
Total s
Post-law Sites
1.25yr.
0
58
25
159.5
242.5
1.5 yr.
0
64.9
31.8
25.2
2 yr.
0
31.8
81.5
30.5
1
121.9J 143.8
3 yr *
0
85.9
37
63
185.9
5 yr.
0
100.1
23.7
118.7
242.5
5 yr.
0
186.3
14.1
28
228.4
Pre-1 aw Sites
5 yr.
0
91.2
60.2
32.7
183.9
6 yr.
0
54.3
38.7
30.5
123.5
7 yr.
0
88.9
37.6
36.9
i
163.4 '
*Topsoiled site, all others covered with spoil material.
24
-------
300
200
100
A /
-"£
/
A. All Species
10
200
100
B. Forage Species
10
Age of Mine (years)
1977 1978
A A Post-law Spoil
• o Post-law Topsail
• D Pre-1 aw Spoil
Figure 11. Productivity of vegetation in nine strip mine sites.
25
-------
CM
E
o>
$
>
•r-
u
-a
o
Q-
+150
+100
+ 50
- 50
-100
-150
H Pre-1 aw Spoil
A Post-law Spoil
O Post-law Topsoil
1 23 4 5 6
Years Since Seeding (n)
Figure 12. Annual change in plant productivity as a function
of time.
s_
Ol
Q
X
0)
T3
10
A
^ •
O Pre-1 aw Mines
A Post-law Mines
D
.10 .20
Index of Stability (1/Ap)
Figure 13. Relationship between diversity and stability in
reseeded strip mines.
26
-------
COMPUTER ANALYSIS
by
Dr. William McFarland and Terry BarneyI/
METHODS AND PROCEDURES
This section reports on the development of a computer based technique
for assessing the extent of revegetation on coal strip mine sites utilizing
digital image scanning of color-infrared (CIR) aeria! photography. The
primary goal was the development of a technique that would offer a reasonable
compromise, in terms of accuracy, time, expense and expertise, between manual
photo interpretation and automatic machine processing strategies for assess-
ment of vegetative ground cover on strip mine lands from remotely sensed data.
Digital Image Generation
The analysis techniques discussed in this report are based on the con-
version of standard Kodak Aerochrome Infrared Film 2443 aerial photographs
into digital images using an image scanner. The effective spatial resolution
of the digital images analyzed is determined by three factors: (1) the
spatial resolution of the scanner; (2) the size of the field of view scanned;
(3) the scale of the aerial photograph. A field of view (FOV) is established
for the image scanner that allows maximum presentation of a contiguous strip
mine site. The field of view is measured at the image scanner, converted to
the scale of the aerial photograph and then divided by the spatial resolution
of the scanner and transferred to one of two displays for evaluation and anal-
ysis. The digital image is also stored on disk file for immediate recall
and processing. The images can then be transferred from the disk file to
nine-track magnetic tape for long-term storage.
The first task in the analysis of photographic images by computer is the
construction of a digital image from the photograph. A digital image is a
two dimensional array of numbers that represent the visual two dimensional
image (photograph) to the computer. Because the computer can only operate on
digital values (numbers), the digitized photograph represents the data input
for all subsequent computer manipulations. All computer analysis of a photo-
graph is based upon the digitizing process and the characteristics of the
resultant digital image.
J/Associate Professor of Electrical Engineering and Bioengineering/Advanced
"Automation Program and Research Specialist, Extension Division, University
of Missouri-Columbia.
27
-------
A digital image has two primary components: spatial digital resolution
and gray level resolution. The spatial digital resolution is the matrix size
of the two dimensional array of numbers that constitutes the digital image.
The digital image is thus composed of N points on a line with M lines consti-
tuting a complete image. The spatial digital resolution is expressed as
N x M and represents a sampling over the field of view of the digitizing
camera or scanner. The spatial digital resolution determines the effective
spatial resolution of the digital image when the field of view (size of the
area scanned) is considered. For example, taking a digital scan of 256 points
(N) by 256 lines (M) over an area of photography 2 inches square (field of
view) scaled at 1:2000 feet results in an effective sampling spot size of
approximately 16 feet on a side. Therefore, each digital picture point (pixel)
at that scale represents a true area of 256 square feet.
Gray level resolution in conjunction with dynamic range is the second
primary component of a digital image. Dynamic range is defined as the range
of film densities that the scanner can accommodate. Low contrast images
present a small dynamic range while high contrast images present a large
dynamic range. The number of different levels into which the scanner can
divide its maximum dynamic range is defined as the gray level resolution.
Most image scanners operate on a dynamic range of approximately 0-1.5 D with
a resolution of 256 gray levels. These gray levels are called quantization
levels and represent minimum contrast that can be detected.
A spatial Data Model 108 Computer Eye was used to digitize all aerial
photographs at a spatial resolution of 256 x 240 with a gray resolution of
256 levels. A Ramtek GX 200 Color Display was used for display of the digital
image at a spatial resolution of 256 x 240 with 16 levels of color within
each of three primary colors giving a maximum color range of 4096 different
shades of color. A Ramtek 9051 Black and White Display was also used to attain
higher spatial resolution (up to 512 x 512) for display of the digital image
and interaction with the user in selecting image parameters. The Graph-Pen
Model GP-3 was used in conjunction with the displays for interactive data
manipulation and processing. Nine track 800 bpi tape drives and disk drives
were used for image storage and retrieval as well as program development and
storage. The PDP-11 RSX-11M operating system and programs are primarily
Fortran IV with some special purpose assembly language sub-routines.
Methodology Development
Three computer based techniques have been developed during this project
for assessing the extent of revegetation on strip mined land utilizing digital
image scanning of CIR aerial photography. Each technique attempts to separate
the digital image of a strip mine site into three classes: water, vegetation
and bare soil. The techniques differ in their method of determining gray
level thresholds for class separation. The three techniques have been labeled:
(1) training field method, (2) automatic gray level slicing froma histogram
(clustering method) and (3) interactive gray level slicing (thresholding method),
Results of repetitive scans were compared to evaluate the precision of each
technique. Classification accuracies were evaluated by comparing the results
to known ground cover quantified by quadrat and area metering methods of
28
-------
established accuracy. The interactive gray level thresholding approach
provided exceptionally consistent and accurate results within 3% of surface
observations.
Technique Overview—
The training field method consists of displaying the digitized strip
mine area on an interactive digital display and outlining areas in the image
using an X-Y sonic pen and tablet. As the analyst moves the pen a line is
generated on the display in direct relation to pen position. Any size or
shape outline can be generated. Using this technique, the analyst: (1)
identifies areas of water, vegetation and bare soil within the boundary of
the strip mine sites; (2) delineates and identifies representative samples
(training fields) for each class with the sonic pen; (3) evaluates the average
gray level value and variance of each training field within each class;
(4) calculates a representative mean value for each class and stores this
information for classification of the entire mine site.
The automatic gray level slicing routine (clustering method) provides
an image histogram, of the area to be classified, which displays frequency
of occurence of gray level values (See Figure 14). The program automatically
attempts to identify peaks and valleys in the histogram and slice it into
three gray level clusters. If three distinct gray level clusters exist in
the digital image, the histogram will reflect this with three distinct peaks
and corresponding valleys., the minimum valley points are picked and used as
separating points for the three classes. If the histogram does not reflect
three clusters, the technique is unacceptable unless the analyst can use a
two category classification.
The third technique, interactive gray level slicing, allows the analyst
to interactively select thresholds to separate classes within the digital
image. The digitized image is displayed on the interactive digital display
and through the use of a joystick the user can "call-up" any threshold. For
each threshold the display presents all points in the defined gray level
slice as black with all other points white. Changing the displayed points
takes less than two seconds, thus allowing for fast, accurate fine tuning
of the desired threshold level.
Technique Evaluation--
Each technique was evaluated for precision and overall classification
accuracy to determine which of the three would be best suited for routine and
operational use. The interactive gray level slicing approach provided the
best results. In developing the different techniques initial classification
results indicated the training field approach might be superior to the
interactive gray level slicing routine in terms of classification accuracy
(See Table 6).
29
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VEGETATION
BARE SOIL
I
tl
a
o
t-
»
1-4
X
UJ
WATER
-• V 'U ^ "V TJ 'U '
:;* ~* ai in -3 in a h- «.• a- c^ -<
;• -• ru fO -j tn x> r- x.
-•* f\i «i ^r in
FIGURE 14. Digital image histogram showing general trends in gray level response distribution
for bare soil, vegetation and water.
-------
TABLE 6. PRELIMINARY COMPARISON OF OVERALL CLASSIFICATION ACCURACY BETWEEN
INTERACTIVE GRAY LEVEL SLICING AND TRAINING FIELD TECHNIQUES
Interactive Gray Level Slicing Technique
Pre-1 aw Site 1, Strip Mine 1
Reference Standard Computer Results
Cover % Cover %
Vegetation 20.8 32
Bare soil 72.7 57
Water 6.5 11
Training Field Technique
Post-law Site 1, Strip Mine 1
Vegetation
Bare Soil
Water
48
43
9
56
33
11
Pre-law site 1 at Strip Mine 1 was selected as a control site for further
evaluation of each technique. Reference standard acreage figures were compiled
for each class. Each technique was used on three scans of the test site,
each scan representing a new independent trial. It is virtually impossible
to produce identical digital images from two scans of the same aerial photo-
graph. The three separate scans were performed at one week intervals. The
original site boundary used in the compilation of the reference acreages was
stored on disk and reused for each technique and scan to hold that variable
constant between computer and reference standard comparisons.
The results are shown in Figures 15, 16, and 17. Figure 15 indicates
that while the cluster analysis does quite well on scan 2, there was consider-
able variance among the other scans. When the scanned image does not contain
three peaks the classification accuracy of the clustering falls off dramati-
cally and results are difficult to repeat. This routine is easily frustrated
when contrasts between classes are not at a maximum, especially on pre-law
sites or active sites where dark shaley material is still exposed on the
surface and confused with vegetation. Figure 16 shows good consistency in the
results for the training field method but a substantial error in classification
between bare soil and vegetation occurred.
The precise results shown for the interactive gray level slicing (Figure
17) indicate that this approach is the most viable of the three in terms of
precision and classification accuracy. Classification results fell within 3%
of the reference measurements and repeated scanning produced nearly identical
acreage figures. Classification accuracy was higher than expected. The
scanning system alone is capable of introducing 3% noise error. This technique
also proved to be a good compromise between the other two methods in consider-
ing the amount of time required to perform an analysis and problems associated
with mine site type.
31
-------
24.2
23.8
20.8
20.Z
19.7
t8,9
17.7
17.3
16.8
163
13.7
12.9
4.5
3,6
WATM
VEGETATION
SAM SOU
CLASSIFICATION
CATEGOtlES
Figure 15.
Technique evaluation results for automatic
gray level slicing routine. Solid line is
the reference standard. Broken lines are
individual scans.
32
-------
24.2
23.8
2O.S
2O.2
19.7
18.9
18.2
17.7
17.3
18.8
16.3
13.7
12.9
4.5
3.S
WATEI VEGETATION BABE SOU
CLASSIFICATION CATEGORIES
Figure 16. Technique evaluation results for supervised
training field routine. Solid line is the
reference standard. Broken lines are indi-
vidual scans.
33
-------
24.2
WATEC VEGETATION JADE SOU
CLASSIFICATION CATEGORIES
Figure 17. Technique evaluation results for interactive
gray level slicing routine. Solid line is
reference standard. Broken lines are indi-
vidual scans.
34
-------
The interactive gray level technique is easily adapted to both pre- and
post-law mine site conditions and averages ten to twenty minutes for complete
analysis of a mine site. The automatic gray level slicing routine only takes
five to ten minutes but requires ideal contrast conditions to produce an
accurate three category classification. The training field technique requires
careful selection of training samples for the decision rule to accurately dis-
criminate water, vegetation and bare soil. To approach the accuracies of the
interactive technique would require an estimated two to three hours of
analysis per mine site. Supervised training procedures are much more efficient
when attempting classifications involving numerous classes where a high degree
of accuracy is desired.
The Interactive Gray Level Slicing Technique—
This section expands the description of the interactive gray level
slicing technique and sets forth the procedure followed in performing an
analysis using the technique. Figure 18 summarizes the technique in flow
chart form. The thresholding routine includes man-machine interaction to
quickly assign a range of gray level values to areas of water, vegetation and
bare soil. Working from the digitized image displayed on the interactive
display (Figure 19), the analyst begins the process by selecting an initial
threshold value to produce a gray level slice at the lower end of the gray
level value scale. This corresponds to the class that appears the darkest on
the displayed digital image. All picture points from 0 to the threshold cut
off are set black and all others white. The analyst then visually evaluates
the acceptability of a threshold for a particular class by comparing the
displayed binary image with the original aerial photograph and the digitized
image of the photo. Using the joystick the analyst can almost instantaneously
change the threshold value until an acceptable proportion is obtained for a
given class. Figures 20 and 21 show the digital display of gray level slices
representing water and vegetation for post-law site 2 at Strip Mine 2. When
the gray level slices are established for each class, the site boundary is
delineated with the sonic pen and a point by point classification is performed
on the entire site. A line printer printout (Figure 22) is made to present
the results along with a tabulated summary of the number of picture points
in each class. A scale factor is applied to translate these totals into acres.
Analysis procedure—An initial set up positioned the aerial photograph
containing the mine site under the digitizing camera. Adjustments were made
in camera height and focus to establish a field of view that allows the mine
site to fill the digitizing area without any overflow. This eliminated the
need for multiple scans to cover the entire site thus enhancing cost effective-
ness. The actual size of the area to be scanned was measured for calculating
pixel size and determining final acreage figures. With present optics, fields
of view from 1 inch square to approximately 24 inches square can be accommodated.
Regardless of the size of the field of view, the camera records a 256 x 240
matrix over the entire field of view.
Scanning and display—Once the field of view and scale factor were
established, the aerial photograph was digitized at 256 x 240 points at a
gray level resolution of 8 bits (256 levels). Prior to the actual digitization
the scanning parameters were adjusted to insure a high quality digital image.
35
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INTERACTIVE GRAY LEVEL SLICING
INPUT -
PHOTOGRAPH
lONTIAST ADJUSTMENT
SCALE ADJUSTMENT
FOV MEASWEMENT
REGISTRATION
flLTEKING
ClASS
INTERPRETATION
JOYSTICK
SELECTED GMT LEVEL
SLICES
ACREAGE LISTINGS
PRINTOUTS
Figure 188 Interactive gray level slicing routine
flow diagram.
36
-------
Figure 19t
A digital image
as displayed on
digital display
of post-law site 2 at Strip Mine
the black and white interactive
37
-------
Figure 20. Digital display of gray level slice representing
water (Black areas) within mine boundary (White)
at post-law site 2, Strip Mine 2.
38
-------
Figure 21. Digital display of gray level slice representing
vegetation (Black areas) at post-law site 2,
Strip Mine 2.
39
-------
/////////..../////////////// ..... ///.,./.
i .1 ii 1 1 1 ...... /////.////...//////. 1 1 1 1 1 1 1 1 1 1 1 u 1 1 1 1 1 1 n
./. ..... .//////
/////////,././///..
..///////,' ..... //
,.//
///./
»•**» l»V// I ft ..t 1 1
..,/ 1 1 I I .1 I % , ,i I
....//..//' .......
//../•/,./// ...... ,
PCI ;
TI,»TS ;N :L*?
/ .-. C I. »' * .C K k •:-; 1 -
Figure 22. Line printer output showing tabulated suirenary of picture points
for each class, acreage conversion and the upper bound limits
for the gray level slices. Classes shown are bare soil (•)»
water (W) and vegetation (/).
40
-------
Adjustments included f-stop, digitizer range and zero adjustment. As the
digital image was obtained from the scanner it was simultaneously stored on
disk and transferred to the interactive black and white display memory. This
task was accomplished in approximately one minute., The digital image'was kept
on display and the original aerial photo was moved to a viewing box adjacent
to the display monitor.
Delineation of the mine site boundary—The mine site boundary was
delineated on the digital image using the sonic pen and tablet. The analyst
positioned a starting cursor on the mine site boundary by moving the pen to
fix the cursor point at a starting location. The mine boundary was then out-
lined by the analyst locating the pen to move the cursor on the display to
fall on the boundary of the mine site. The mine outline was stored on disk
for later recall.
Interactive gray level slicing—A joystick was used to quickly vary the
upper boundary of the gray level slice being displayed,, The lower boundary
was set, initially at zero, and the upper boundary was translated from the
joystick position in its X direction. The analyst focused on the display and
varied the joystick position to alter the size of the gray level slice. The
display has a video table look-up option in its video generation that allows
the entire 256 x 240 image to be sliced in l/30th of a second. Ease of mani-
pulation at this stage allows a desired gray level slice to be obtained and
evaluated visually within minutes depending upon the geometric complexity
of the class patterns. The threshold values selected for each gray level
slice were recorded for subsequent classification.
Classification and print-out—The classification of the strip mine site
is accomplished on a point by point basis for all points within the boundary
delineated with the sonic pen. Each point was classified as water, vegetation
or bare soil depending on its gray level value. The classification output was
stored on disk and spooled out to the line printer which assigned selected
characters to each point as a pseudo map output. Each character space repre-
sents a picture point on the image but the printout presents a scale and aspect
ratio based on the mechanics of the printer. Figure 23 is a sample of the
printer output. The printouts are useful for evaluation of spatial trends and
overall technique performance in showing where there has been obvious misclas-
sification. The printouts also summarize the number of picture points in each
class from which acreage figures are calculated.
Accommodating Input Variables--
The input data for the computer based analytical process is photographic
film. There are four major factors that influence the behavior of film when
exposed and the character of the resultant image. These factors are: (1) film
composition; (2) film handling; (3) film format; and (4) image acquisition.
There are no absolute relationships between exposure of a film and the densi-
ties recorded. The ability of the analyst to group the resultant film
densities (gray levels) into meaningful classes will be influenced to some
degree by variables within each of the factors stated above. Only individual
users can evaluate the tradeoffs between costs and classification accuracy in
determining the extent to which individual variables will be accommodated.
In this study the priorities were placed on absolute minimizing of costs while
41
-------
';. t* A tf * * * i'J h -( «i
It Wrffc'JHr *"'•< -
..I lv « i.« W *M V I-; < A .» i-. hTl/L . .^/ / /
:•• v i-. !•' '•, M H K VI i-. w •< »• .t v t •' • i
..;> -f •**+vi;.'.!«W.MJ •.S'.-i-fcTT/l- .*\/11II
M ta «i * W Iv W i-: W ' x t" ^ H .1 '1 •••< it" V " "
\, v* u * *< w w H w;<( ••; t'1" W1-.
i'l i^ 1H a .. W ;
*• U
i. u -, i- v: n V * n W •• »i n ui ti tr »• W W M w M w W
* M >• ^ '' H -J H * 'f 4 w N U * W W >• '*• H W
I. '•> >' '•<» '- U * f' W » W * 1 * ta W W I*
w i • w « »t' fc n W > I- v« >• (r» W * w k " Ui I" * W *
V W W M W W » * w I
. hv>UI>. ^^'/MWMMi^tavl^lMrfMW *V71.
»i,-H»-'.V»l> ul'K»i»>w»»i'*«k'ht.Vik' k V- In »! r V! K I" H W w i! X »i W
Vih j«lls 4K«W
" ^ w •»«"'•' •! "^ W * W W m 'i<
/////////////
i i in n i it i n ti it u n n n n in Hi / / / /
/ / I / 1 1 i > 1 1 1
1 1 1 / ;t i / 1 1 1 1 ii 1 1
i lun/ii/iinn /
/// 1 / 1 1
i fin 1 1 mi
« w m » * M
Figure 23. Line printer output upon which the individual classes
have been delineated. Bare soil (•)» water (W) and
vegetation (/).
42
-------
generating an acceptable degree of overall accuracy in classification results.
Within this context, some variables were accommodated to varying degrees
while others were left totally uncontrolled and accepted as recognized limita-
tions on the technique.
In considering film composition, a major variable is the wavelength
composition of the exposing light. This variable was accommodated through
film type selection and minimal amounts of filtering during digitization.
The emphasis on low cost ruled out the use of special multi-band film and
camera packages and dictated the use of one standard readily available aerial
film. Kodak Aerochrome Infrared Film Type 2443 was selected because it is a
readily available standard product. Its sensitometric qualities provide
excellent bare soil/vegetation enhancement as well as good discrimination
between land and water interfaces. Also, the film is less susceptible to the
influence of poor quality atmospheric conditions.
In addition to enhancing gray level separation through film type selec-
tion, better class separation can be obtained on some images using a combina-
tion of filtering and multiple scans. Specifically, a Kodak No. 25 red filter
commonly used in color print processing, was used for better discrimination
between water and vegetation. Computer programs were developed which allow
two images to be classified and then combined to give a composite classifica-
tion. The image is scanned without filtering. Vegetation and bare soil are
classified allowing water to fall into either of the two other classes. A
subsequent scan is made using the red filter. This image is classified into
water and other. The two classifications are then combined to produce a
composite three category classification of water, vegetation and bare soil.
Variables introduced through film handling procedures remained basically
uncontrolled. Standard storage procedures were followed prior to and after
exposure. Fluctuations in recorded film densities due to variations in pro-
cessing were, to some extent, handled by adjustments of scanner parameters
as the film was digitized. All 70mm film utilized in the study was developed
using standard processing techniques for the 2443 film type. A majority of
the classification work performed with the nine inch film utilized film nega-
tives with altered color balance to further enhance contrasts on the mine
sites. This can be accomplished at no extra cost over standard development
to film positive format.
The principle variables in film format are image scale, actual image size
and image base. Variations in film size and scale are accommodated by magni-
fication. By utilizing a bellows system and various lenses, the field of view
can be reduced to one square inch or enlarged to a 24 square inch size. This
flexibility allows the analyst to use various scales of photography as well
as film sizes. This study utilized both 70mm and 9 inch x 9 inch film sizes
and contact scales of 1:6",000; 1:10,000; 1:24,000; and 1:46,000.
Registration becomes a serious requirement when comparisons want to be
made of the same area with two different photographs (quite often of differing
scales and format) on a point by point basis. While digital translation and
rotation techniques can be used, they are very time consuming. Therefore, it
43
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was elected to register images at the scanning input stage. This is accomplished
by digitizing one image as a reference. This image is stored on disk and also
displayed on the black and white display. Three points are located on this
image and their x-y coordinates are read and displayed on a real time TV scan-
ning monitor. The second image to be digitized is displayed simultaneously on
the TV monitor. The scanning camera and photograph are repositioned such
that the reference points overlay the three points displayed from the first
image. Site boundaries delineated on the first image can then be accurately
overlayed on the second image.
With regard to image base, film transparencies in positive and negative
form and paper prints are available. Transparencies produce the best contrasts
thus minimizing data degradation due to influences of image base material.
Variables introduced into the input data through image acquisition pro-
cedure were not accommodated. Calibrated RC-8 cameras were used for acquisi-
tion of 9 inch x 9 inch film. The 70mm camera was not calibrated. Scale
fluctuations and distortions due to aircraft tilt and altitude variations went
uncontrolled.
RESULTS AND DISCUSSION
Precision of Analysis
Precision can be evaluated independently of accuracy and should be used
to denote the consistency of a measuring system. The ability to reproduce
results on the same input data is the measure of precision. A digital computer
will provide the same numbers whenever it is given exactly the same input data.
The techniques developed have certain inherent process variables which make it
impractical to expect exactly the same numbers upon repetition. The process
variables include: (1) image contrast due to scanning parameters; (2) gray
level slices due to analyst interpretation; (3) noise in the image scanner;
and (4) field of view measurement. Table 7 shows the evaluation of precision
based on analysis performed on pre-1 aw site 1 at Strip Mine 1.
TABLE 7. PRECISION EVALUATION OF INTERACTIVE GRAY LEVEL THRESHOLDING,
PRE-LAW SITE 1. STRIP MINE 1
Bare SoilVegetationWater
Acres % Acres % Acres %
Reference Standard
#1 18.9 46 17.6 43 4.5 11
#2 16.8 41 19.7 48 4.5 11
Computer Analysis
Scan 1 20.3 49 16.3 40 4.4 11
Scan 2 20.2 49 16.5 40 4.3 11
Scan 3 19.7 48 17.4 43 3.9 9
*Scan 4 20.9 51 16.0 39 4.1 10
*Scan 5 20.5 50 16.0 39 4.5 11
Total Area = 41.0 Acres ^
*Analyst for #4 and #5 scans was the interpreter for reference standards #1 & #2
44
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The first two rows of the table show the precision of the reference
standard. This data was used as ground truth for comparison of computer results.
It is interesting to note the variation between the two results. In each case
the same photo interpreter using the same technique described in surface obser-
vation analyzed the same mine site at two different times. There was 5%
variation in the classification of bare soil and vegetation. Water was consis-
tent. Under computer analysis #4 and #5 are the results of this same photo
interpreter using the computer and the interactive gray level slicing method.
There is much better consistency between these two results (!%} than between
the two reference standards. While the area classified water is practically
the same in all four cases the areas called bare soil and vegetation vary 4%-
9%, depending on which of the reference standards we choose to call correct.
The other three runs are from three different scans at three different times
employing the same photo interpreter-analyst responsible for the additional
computer results in this report. Note that while these numbers are not
exactly the same as in analysis scans 4 and 5 there is less variation between
all five of these examples than between the two reference standards. This
experiment shows the possibility that the interactive gray level slicing method
is more precise, even between different analysts, than the reference standard
interpretation.
Classification Accuracy
The primary goal of computer based approach to vegetative cover assessment
was to combine the accuracy of conventional photo interpretation methodologies
with the speed and efficiency of machine processing. Accuracy evaluation
was based on comparing the total acreage figures compiled by the computer for
each class to reference standard acreages compiled by the surface observations
team with techniques of established accuracy. This approach evaluates the
overall accuracy of each class. It does not reflect the accuracy of the
classification on a pixel by pixel basis. At the time this study was performed,
true-to-scale plotter output for the classifications was not available to check
point by point accuracy.
Table 8 shows the overall accuracy figures for multiple scans of pre-1 aw
site 1 at Strip Mine 1 using the interactive gray level slicing technique.
Computer error does not exceed 3% on any of the scans.
TABLE 8. ACCURACY OF INTERACTIVE GRAY LEVEL SLICING FOR MULTIPLE SCANS OF
PRE-LAW SITE 1, STRIP MINE 1
Scan 1
Bare Soil
Vegetation
Water
Computer Interpretations
Area Cover
(acres) (%)
20.32
16.32
4.44
49
40
11
Reference Standards
Area Cover
(acres)
18.90
17.66
4.52
46
43
11
Computer Error
Area Cover
(acres) (%)
+1.42 +3
-1.32 -3
- .08 0
(continued)
45
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Scan 2
Bare Soil
Vegetation
Water
Scan 3
Bare Soil
Vegetation
Water
Computer Interpretations
Area Cover
(acres)
20.22
16.58
4.28
19.71
17.48
3.88
Reference Standards
Area Cover
(acres) (%)
49
40
11
48
43
9
18.90
17.66
4.52
18.90
17.66
4.52
46
43
11
46
42
11
Computer Error
Area Cover
(acres) (%)
+1.32 +3
-1.08 -3
- .24 0
+ .81 +2
- .18 0
- .64 -2
Total area = 41.08 acres
It is important to note that the identical aerial photographs and mine
site boundaries were used in all of the direct comparisons made between the
computer results and the reference standard results. Table 9 lists the results
for the additional evaluations of the interactive gray level slicing technique
on both pre- and post-law sites at Strip Mines 1 and 2.
TABLE 9. CLASSIFICATION ACCURACY EVALUATIONS FOR ADDITIONAL PRE-AND POST-LAW
SITES UTILIZING THE INTERACTIVE GRAY LEVEL SLICING TECHNIQUE
Mine Site II Procedure 2/ Bare Soil
Area Cover
(acres)
Vegetation
Area Cover
(acres) (%}
Water Total
Area Cover Area
(acres) (%) (acres)
2-1
2-2
2+2
2+3
1+2
(May)
1+2
(July)
Reference
Computer
Reference
Computer
Reference
Computer
Reference
Computer
Reference
Computer
Reference
Computer
27.0
29.0
49.3
42.4
49.5
51.5
36.2
31.6
9.6
11.1
4.7
4.9
38
41
60
51
80
83
59
51
23
27
11
12
27.5
25.4
17.4
19.1
6.7
5.2
17.2
23.0
22.8
23.2
28.0
28.6
39
36
21
23
11
9
28
37
56
56
69
70
16.7
16.8
16.0
21.2
5.2
4.7
8.3
7.1
8.8
6.8
8.4
7.6
23
23
19
26
9
8
13
12
21
17
20
18
71.2
71.2
82.7
82.7
61.4
61.4
61.7
61.7
41.1
41.1
41.1
41.1
I/ Strip Mine 1 or 2, site 1 or 2, pre-law (-), post-law (+).
?/ Reference acreages compiled by conventional photo interpretation and
computer analysis by interactive gray level slicing.
The evaluation results indicate that the interactive gray level slicing
technique is very competitive with the area metering manual photo interpretive
method despite the fact that the computer is working from a disadvantage in
46
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terms of effective spatial resolution. In general, the computer based approach
cannot accurately measure objects less than two times the effective resolution
and the preferred ratio is 5:1. The reference standard maps were compiled at
a resolution (minimum mapping size) of ,5m2. In contrast, the effective
spatial resolutions of the digitized images were always five to ten times
the area metering resolution. Yet the computer technique produced comparable
results.
Seasonal Variations
The aerial photography used in this study was collected May, July and
October. As Tables 10, 11, and 12 indicated, there were sizable shifts in
the overall acreages for the vegetation and bare soil categories at the mine
sites where multi-temporal analysis was done. Factors influencing the amount
and densities of vegetative cover which in turn affect total acreages when
classification is carried out include: (1) strip mine type (pre- or post-law)
and final landscape geometry; (2) special vegetation composition; (3) the age
of the mine; (4) fertility treatments; and (5) time of year.
A primary component in assessing the cost effectiveness of a procedure is
the ratio of the length of time required to perform the analysis to the size
of the area analyzed. The interactive gray level slicing technique can
accomplish a single scan evaluation in 10 to 20 minutes. Of course, the actual
acreage classified for any given scan will depend on the size of the field of
view and contact scale of the aerial photograph being digitized. Each of the
seasonal evaluations shown in Table 12 took approximately 20 minutes. In
contrast, the reference standard technique took 4% hours. A traditional ground
survey might have taken up to 20 hours had it been performed. Also note in
Table 12 the wide range of scales that were accommodated with single scans.
TABLE 10. SEASONAL ACREAGE COMPARISONS FOR POST-LAW SITE 3, STRIP MINE 2.
Bare Soil
Vegetation
Water
Total Acres
May, 1978
(acres)
23
31.6
7.1
61.7
October, 1978
(acres)
7.6
45.0
9.1
TABLE 11. SEASONAL ACREAGE COMPARISON FOR POST-LAW SITE 2, STRIP MINE 1
Bare Soil
Vegetation
Water
Total Acres
May, 1978
(acres)
11.1
23.2
6.8
41.1
July, 1978
(acres)
4.9
28.6
7.6
47
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TABLE 12. SEASONAL ACREAGE COMPARISON FOR POST-LAW SITE 2, STRIP MINE 2
Date:
Air Photo Scale:
Bare Soil
Vegetation
Water
Total Area
May, 1977
1:6,000
Area Cover
(acres) (%)
October, 1977
1:24,000
Area Cover
(acres) (%)
July, 1978
1:46,000
Area Cover
(acres) (%}
51.9
5.2
4.7
61.8
84
8
8
26.2
31.2
4.4
42
51
7
21.2
34.9
5.7
34
57
9
48
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EROSION AND DRAINAGE ANALYSIS
by
Dr. D.J. Barr, C. Dale Elifrits and Shara McBee I/
METHODS AND PROCEDURES
The prime objective of this phase of the project was to determine the
effect of surface mining and reclamation practice on drainage pattern and
slope shape and to estmate how their change might contribute to the erosion
potential of a site. Investigators working in the Remote Sensing Laboratory
at the University of Missouri-Rolla, interpreted and analyzed pre-mine and
post-mine aerial photography to yield base maps of drainage pattern orienta-
tion. Although many photographs were interpreted, only those of suitable
scale, quality and season of exposure were used in the preparation of the
maps.
Summary of Appropriate Theory and Literature
During the initial stages of this study, a report entitled "Some Factors
Affecting Erosion" (Brickner, 1977) was prepared so as to establish the
accepted theories of erosion and drainage pattern development. In terms of
how different slope profiles as illustrated in Figure 24 are affected by
precipitation runoff and erosion, the following trends are generally accepted:
1. Concave slopes are least affected by erosion, yield the least
amount sediment, and change shape slower than other profiles.
2. Convex slopes erode the fastest, yield the most sediment, and
change shape faster than other profiles.
3. Uniform and complex slopes are affected to an intermediate degree
although long uniform slopes can be severely eroded in a single
rain storm.
4. All slope profiles will tend to erode to a concave shape, given
sufficient time, and the steepness of the toe of the slope is
most significant in overall erosion.
J/Professor, Graduate Instructor and Graduate Assistant, respectively. Depart-
ment of Mining, Petroleum and Geological Engineering, University of Missouri-
Rolla.
49
-------
Convex
•M
O)
CD
CU
1C
Uniform
Slope Length (Feet)
Figure 24. Slope profile shape for four different types of slopes.
Source: Brickner, 1977
-------
Drainage patterns are created as a result of erosion. In a natural ter-
rain without geologic or man-made controls, a totally random pattern defined
as dendritic will result. The frequency of channel occurrence or density of
pattern will be a function of rainfall, slope, geology, surficial soil type
and vegetative cover. In general, the less permeable the soil, the greater
will be drainage density. Any natural or man-made control that would tend
to disrupt the hydrologic regime could increase the erosion potential.
Drainage Map Preparation
Panchromatic and color infrared aerial photography was used to delineate
drainage patterns at the three strip mine sites. Color infrared photography
was obtained at a scale of 1:6,000 in a photographic mission flown for this
project and was also used in the Surface Observations and Computer Analysis
studies. This photography provided post-mine drainage information. Sources
for pre-mine panchromatic photography included the Missouri Historical Soci-
ety, the Boone County Planning Department and the USDA Agricultural Stabili-
zation and Conservation Service. This photography was of various scales and
was obtained in missions flown in different years.
A base map was prepared for pre-mine and post-mine drainage at each site
by tracing roads and cultural features on a base sheet. Whenever possible,
only that portion of the photograph with the least amount of distortion,
approximately the center two-thirds, was used. Clear and lightly frosted
acetate were used as a base sheet. Lightly frosted acetate was originally
used, but even with additional lighting, it was difficult to see the detail
on the underlying photograph. Clear acetate proved to be much better as it
allowed the interpreter to see details more clearly.
After the base maps were prepared, they were taped to a table and one
photograph of a stereo pair was aligned under the base map. The second
photograph was placed on top of the base map in such a way that the photo-
graphs could be viewed stereoscopically. While viewing the photographs
stereoscopically, the interpreters traced the drainage paths with either
colored pencils or ink onto the base map. The process was repeated until
the drainage maps at each mine site were completed.
Both mirror and pocket stereoscopes were used to view the photography.
Mirror stereoscopes were found to be superior in that they reduced eye
fatigue and provided a wider field of view. As both mental and physical
fatigue were encountered by interpreters while tracing drainage patterns,
interpreters found that they were more efficient and precise if they spent
approximately one hour tracing drainage and one-quarter of an hour involved
with another aspect of the project before mapping drainage again.
After the drainage maps were interpreted and delineated, a Map-0-Graph
Enlarger-Reducer was used to adjust the maps to a common scale of 1:24,000.
The drainage maps were placed in the Map-0-Graph which projected an image of
the map onto the topographic base map of that area. The image scale was
then adjusted until the roads on the image matched those on the topographic
map. The topographic map was then replaced by a piece of acetate so that the
image of the drainage pattern could be traced.
51
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Figures 25 and 26 represent pre-nrine and post-mine drainage patterns for
Strip Mine #1, Boone Co., Missouri. Pre- and Post-Mine drainage patterns
for Strip Mine #2, Randolph County, Missouri are presented in Figures 27 and
28. Strip Mine site #3 contained pre-1950 mine areas. Thus, the pre-mine
drainage indicated in Figure 29 contains older workings. Post-mine drainage
patterns are presented in Figure 30.
Evaluation of Slope Geometry
In conjunction with drainage delineation, photo interpreters estimated
the shape of pre-mine and post-mine ridges and hills from the three-dimen-
sional view created by the stereoscopic photography. Slope profile shapes
were defined as indicated in Figure 24.
Interpretations of pre-mine topography indicated that stream channel
profiles and hillside slopes both exhibited a concave shape. This was in
agreement with what one should expect for natural, undisturbed terrain.
The shape of hills and ridges was much more variable in those same areas
after mining and after reclamation. Pre-law sites exhibited higher relief
with convex to uniform slope profiles. Post-law sites exhibited less relief
but longer uninterrupted convex to uniform slope profiles. In all cases,
post mine conditions deviated significantly from the natural slope profile
shapes as they existed prior to mining.
ANALYSIS PROCEDURES
Drainage Density
One way in which to estimate the degree of integration and completeness
of drainage development is to calculate the drainage density of a given area.
Drainage density is defined as the ratio of the total length of stream
channel within a basin to the area of that basin (Drf= Z L ). In order to
compare post-mine drainage density with pre-mine drainag§ density, representa-
tive areas larger than 0.2 sq. miles were selected from various portions of
the pre-and post-mine drainage pattern maps of each of the three mine sites.
A mechanical map measure was used to measure the total length of stream where-
as a polar planimeter was used to measure area. Drainage densities as calcu-
lated by formula are summarized in Table 13.
TABLE 13. A SUMMARY OF PRE-MINED AND POST-MINED DRAINAGE DENSITY CALCULATED
FROM DRAINAGE PATTERNS AS SHOWN IN FIGURES 25-30
Figure
Number
25
26
Strip Mine
Site
1
1
Pre-Mined Natural
Drainage Density
11.13 miles/mi2
Post-Mined (Mined Area)
Drainage Density
?8.fl milp<;/milA2 fnro-law^
13.9 miles/mile2 (post-law)
__ „ 17.9 miles/mile2 (Composite)
27 2 12.5 miles/mi2
52
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Figure Strip Mine Pre-Mined Natural Post Mined (Mined Area)
Number Site Drainage Density Drainage Density
28 2 10.44 miles/mile2(post-law)
29 3 9.45 miles/mi2
(including older mine sites)
30 3 ' 11.37 miles/mile2
Strip Mine 1 drainage density values must be associated with their
respective terrain conditions. The 28.0 miles of stream per square mile is
a value measured from a section of land abandoned after mining was completed
prior to the reclamation laws' effective date. This area is highly eroded
and shows much gulley development in the spoil piles which were not regraded
nor covered with soil. The rock materials of the spoil piles were not
revegetated at the time of the photography from which this data was taken.
The area where 13.9 miles of stream per square mile were measured is an
area where reclamation practices were applied some three to four years before
the photography was taken. During the time since reclamation was completed,
a drainage system has developed. The relative stream density developed on
the terrain created by the application of reclamation practices can be seen
as slightly higher than that of a natural terrain.
Strip Mine 2 drainage data values were measured from an area of recent
(1-2 years old at the time of photography) reclaimed terrain. It can be
seen that this relatively young terrain shows a lower than natural drainage
density.
"pre
Strip Mine 3 drainage data is affected by the fact that part of the
-mined" mapped area is an area of previously disturbed land. This land
was disturbed by mining prior to the date of the historical photography used
to construct the drainage map. Thus the relative variation of stream density
from "pre-mined" to "post-mined" tine at this location is of low magnitude.
Drainageway Orientation
Two types of reorientation of the drainage system can be observed. One
type is that of the elimination of random location of drainageways by the
placement of designed terraces, diversions and waterways as the spoil piles
are reshaped, thus leaving large segments of terrain not integrated into a
drainage system. Examples of such areas can be seen on Figure 28 where large
areas exist with no surface drainage pattern. Also, many of these designed
drainageways are linear and in a parallel to rectangular pattern.
A second type of reorientation is the displacement of main channelways
through the mine area. Frequently, this changes the length of channel (most
often shortening it) and modifies the floodway of the channel. In the case
of Strip Mine 2, the main channel of the Chariton River was moved to enhance
coal recovery and production within the mine boundaries.
53
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SCALE
1:24000
1/2
MILES
LEGEND
NATURAL DRAINAGE
WATER IMPOUNDMENTS
ROADS
Fiqure 25. Pre-mininn drainage patterns of Strip Mine 1, Boone County, Missouri,
-------
en
en
LEGEND
NATURAL DRAINAGE
MINING CREATED DRAINAGE
WATER IMPOUNDMENTS
ROADS
Figure 26. Post-mininq drainage patterns of Strip Mine 1, Boone County, Missouri.
-------
LEGEND
NATURAL DRAINAGE
I:24000
WATER IMPOUNDMENTS
ROADS
Figure 27. Pre-mining drainage patterns of Strip Mine 2, Randolph County,
Missouri.
56
-------
36|3I
T iTe"
SCALE
I:24000
fe
^2
i i i I
MILES
LEGEND
NATURAL DRAINAGE . t ,
MINING CREATED DRAINAGE-^1'
WATER IMPOUNDMENTS
ROADS ———
Figure 28. Post-mining drainage patterns of Strip Mine 2, Randolph County,
Missouri.
57
-------
LEGEND
NATURAL DRAINAGE
1:24000
WATER IMPOUNDMENTS
ROADS
Figure 29. Drainage map showing early (pre-1950) mining plus natural drainage
patterns for Strip Mine 3, Macon County, Missouri.
58
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0
b
SCALE
I:24000
1/2
MILES
LEGEND
NATURAL DRAINAGE
MINING CREATED DRAIN AGE
WATER IMPOUNDMENTS
ROADS _——
Figure 30» Post-mining drainage patterns of Strip Mine 3, Macon County,
Missouri,,
59
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RESULTS AND DISCUSSION
Accuracy Standards
The study of drainage, erosion and sedimentation is certainly not exact.
Most relationships for predicting runoff and sediment yield are empirical
and subject to variation by orders of magnitude. The study of drainage
patterns has traditionally been even more qualitative. The results and
discussions presented in this report are mostly qualitative with some sugges-
tions based upon promising theory.
Mined Land Landscaping
When a natural terrain surface is strip mined, an entirely new landscape
is exposed to the hydrologic cycle. As can be observed on any natural or
man-made slope, subaerial erosion constantly works to level the landscape
to a base level. Theory and observation suggest that precipitation runoff
will concentrate in drainage channels and that these channels will erode
headward until the entire surface slope contains an integrated random pattern
of channels. After the pattern has been established, the channels will
down-cut and tend to erode all of the material lying above elevation of the
bed of the trunk stream toward which runoff and sediment is concentrated.
When natural terrain is disturbed by strip mining, the entire surface
will be out of equilibrium with respect to the surrounding natural drainage.
The disturbed area will utimately reach equilibrium through erosion and sedi-
mentation whether or not the land is reclaimed. The central question should
be how to accelerate the state of equilibrium with practical and economically
justifiable reclamation practices. Current reclamation practice seems to
dictate that the mined area be made as smooth as possible with vegetative
cover as soon as possible. Unfortunately, natural terrain which might be
considered to be in erosional equilibrium is seldom smooth. Natural terrains
are sculptured with concave drainage profiles and concave side slopes and
contain numerous branching tributaries. Such surface features provide for
efficient collection of runoff and provide soil moisture for vegetative
growth for minimum erosion.
It is proposed that disturbed mined land ought to be regraded so as to
approximate the sculptured surface of a natural terrain with numerous branch-
ing, randomly oriented channels and concave drainage profiles and side slopes.
Resulting efficient drainage would benefit revegetation in that surface water
would be disturbed more evenly over the reclaimed surface.
Methods
In order to create a sculptured surface with drainage positioned in a
natural way, three characteristics of the reclaimed terrain must be calculated
or estimated. First, some measure of the largest sized surface area allowed
to contribute runoff to a single drainage channel segment must be made.
Theoretical investigations of watershed properties* indicate that it is possi-
ble to calculate the least size area from which runoff will erode a drainage
channel.
Unpublished Research, Melvin Schaefer, Graduate Instructor, Univ. of Mo.-Rolla
1977. 60
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The equation relating the smallest surface area requiring a drainageway
(Oth order watershed area) to properties of the terrain is as follows:
Equation 1 1 _ ,2748300, n ,. , -• 5/3
AQ l Cj ; TS 13/6 u " 9) bB
2
where: A = Oth order watershed (mi) (Least Size Area)
C, = Constant of a parabolic stream X-sect
n = Manning's n
T = Erosional resistance of soil/—,,>
S \£J.<-)
i-$ = Runoff rate
Sg = Average hillside slope in basin
An analysis of the parameters in the given equation yields the information
found in Table 14.
TABLE 14. RELATIONSHIP OF Oth ORDER WATERSHED AREA AND DRAINAGE DENSITY
TO WATERSHED PARAMETERS
Parameter Trend AQ Trend Drainage Density
T decreases decreases increases
S"D increases decreases increases
D
n increases decreases increases
A second characteristic of terrain necessary for designing efficient
drainage is that of drainage density (length of channel per unit area).
Shaefer established a relationship between the previously defined least size
area and drainage density using the Law of Stream Areas as indicated in
Equation 2:
Equation 2 I = D . (^) where
A_ Q I
o LJ
A = Previously defined
„ . ... /e stream lengths x
D. = Drainage density ( a )
61
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Rfl = ratio of A^ (area contributing runoff to 1st order streams)
toAQ
L- = Average length of first order stream segments (receiving
runoff from A., areas)
When Equation 2 is substituted in Equation 1, the resulting Equation 3
provides a way in which to calculate a theoretical drainage density for a
given set of terrain properties.
FnnaMnn in- 12748300 l^ , T\ \ ,• ^ T^3
Ec»uatl0n 3 Dd - b^TT !) (rT^ (l-$) SB
K 1 S
The third set of terrain characteristics needed for drainage design
includes drainage orientation and channel and side-hill slopes. As stated
previously, natural drainage systems free of external control are randomly
oriented in a branching, dendritic pattern. Such a pattern could be layed
out by observation or by using statistical procedures called a "random walk."
However, the random pattern naturally facilitates the movement of sediment
through the drainage system and often is modified by man so as to cause sedi-
mentation to occur within a predictable location. Optimum drainage channel
and hill-side slopes are not easily predicted. Slopes formed of loose,
coarse grained material may lie at the material angle of repose (30°- 35°)
whereas slopes formed of fine grained clayey material may range from l°to 15°.
Drainage design might be facilitated by using slope values exhibited by
natural terrains in the local area.
Study Site Observations
An evaluation of drainage patterns, drainage density, drainage orientation
and slope geometry was made by comparing pre-mined and post-mined conditions
with hydrologic conditions hypothesized to be the most efficient to carry
runoff with a minimum of erosion.
The drainage density values indicated in Table 14 show that the youngest
site (Strip Mine 2) exhibited a lower drainage density upon reclamation than
the site had prior to mining. Theory would indicate, however, that mining
and reclamation should decrease soil shear strength, increase surface rough-
ness (Manning's n) and possibly increase average side slopes. All of these
trends require a great increase in drainage density for efficient runoff as
seen in Equation 1 and Table 15. The conclusion reached is that erosion will
occur until the necessary drainage pattern and density has been constructed.
Strip Mine 1 contains sub-units which tend to confirm that erosion will
continue until an efficient drainage system has developed. The post-law
reclaimed site, older than Strip Mine 2, exhibits a slightly greater drainage
density than the site exhibited prior to mining. The much older pre-1 aw site
exhibits a much greater drainage density. As a dense drainage pattern is
created, the sediment yield due to erosion will be great.
62
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In addition to the potential for erosion created by unnaturally low
drainage densities left as a result of reclamation, the shape of reclaimed
slopes also creates the potential for erosion. The shape of hills and ridges
was more variable in those areas after mining with terrain shape dependent
upon the method of reclamation. Pre-law sites had higher relief with convex
to uniform slopes. Post-law sites exhibited less relief with complex to
uniform long uninterrupted slopes. In all cases, post-mined conditions
deviated significantly from natural slope shapes as they existed prior to
mining.
The slope geometry could be an important aspect of controlling erosion on
mined lands. Hydrologic studies consistently indicate that a "graded stream"
having a concave profile is the most efficient for transport of runoff with
minimum erosion. The same is true for hill and ridge side slopes. In the
process of reclamation, the terrain is usually left with convex sloping
surfaces. The soil that exists between the final grade and some lower
concave sloping surface is bound to be eroded away during the process of
drainage development.
Reorientation of drainage channels transverse to the dendritic pattern
tends to trap sediment resulting from the previously described phenomena.
In terms of erosion, therefore, lakes created by highwalls transverse to the
natural flow of drainage act as sedimentation ponds. Such situations probably
tend to impede erosion and at least ensure that sedimentation will occur
within the mine site.
Applications
Although none of the concepts presented herein have been evaluated for
economic praticality or for that matter by field testing, there is strong
evidence that reclamation could be improved by making reclaimed surfaces more
nearly the shape of natural surfaces. Initial grading for creation of
concave slope profiles and well-defined randomly oriented channels could
reduce the potential for natural erosion to carve these shapes. Also, the
drainage on previously reclaimed mine areas could be modified so as to induce
more randomness in drainage as well as concave slopes and thus reduce natural
erosion. Further study might lead to tested values for desired terrain
characteristics, but at present a sculptured terrain as previously described
would have to be designed using a combination of theoretical, and estimated
numbers as well as a great deal of artistic interpretation and inference.
The following outlines a series of steps which, if undertaken, would tend
to cause reclaimed land to be more natural in shape and consequently less
prone to severe initial erosion:
A. Runoff Area Design - Some estimate of A0(least size area or Oth order
watershed) must be made. AQ defines the smallest area high in the
drainage network, the runoff from which supports the creation of a
drainage channel. Figure 31 illustrates the concept of A0. In
nature A0 would tend to decrease until equilibrium is reached. A
reasonable first approximation for A0 would be values measured from
63
-------
Figure 31. A random drainage pattern with AQ and
by sub-unit drainage divides.
defined
64
-------
maps or airphotos of the pre-mine site. Values for some Central
Missouri watersheds range from 0.4 to 0.6 acres.
Although mining and subsequent reclamation would tend to reduce optimum
A0 values as indicated in Table 15, the pre-mine values would at least provide
a starting point. A reclamation specialist could implement this factor fay
making sure that no surface area larger than A0 exists without being drained
by a well-defined channel.
It will be noted that new erosion channels formed on reclaimed land tend
to reduce the magnitude of the A0 parameter. A reclamation specialist could
modify channeling after inspection of such an area to reduce the size of
A0 sub-units and thus alleviate erosion.
Another approach would be that of estimating AI from maps or airphotos.
Aj, illustrated in Figure 31, represents the area contributing runoff to the
first order tributaries (the smallest channels highest in the drainage
hierarchy). Values of A^ in Randolph County Missouri as measured from
several watersheds varied from 2.0 to 3.85 acres.
A third approach toward determining minimum sized surface areas allowed
to contribute runoff to a channel would involve calculation using Equation 1.
The dependent variables in Equation 1 could be measured or estimated from
similar terrain or measured in a field test for use in similar reclaimed
areas. In natural Mid-Missouri watersheds Cx = 12-13, N = 1.5-2, TS = 0.4-
0.6, ( i-4>) = 1.2 ± and SB = 2° - 16°. Of all the variables, TS is most sig-
nificant in causing change in Ao. Equation 1 would be most helpful when
used in conjunction with a field test aimed at collecting values for extrapo-
lation to similar areas.
B. Drainage Density - Design of drainage density should include a
drainage plan with enough channel length to yield the appropriate
drainage density for the site. The drainage layout could best
be sketched in a natural appearing random (dendritic) branching
pattern. A first approximation to the required drainage density
could be measured from pre-mine maps and photos and then increased
somewhat for effects of disruption. A second approach would be
that of drainage density calculation using Equation 3. As with
Equation 1, dependent variable values could be estimated or measured
from field tests. In the case of natural Mid-Missouri watersheds
Ra may typically vary from 3.5 - 4.0.
Drainage channels should be constructed to resemble natural
channels. All channels should have a parabolic cross-section.
However, the depth of channel should grade from shallow (0.5 feet±)
at the head-end to several feet depth for trunk collector channels.
Top bank widths should range from perhaps 12 feet wide for small
tributaries to broader channels in the trunk streams. The gradient
of the channels should be steepest at the head-end with flattening
toward the mouth of the primary trunk stream.
65
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C. Slope Profiles - In the process of creating drainage channels some
effort should be taken to sculpture the land into concave shapes
where possible. Slopes in natural terrains in Central Missouri
may vary from 2° to perhaps 16° with shapes approximating concave
or complex cross-sections. Similar shapes within watershed sub-unit
would tend to reduce severe initial erosion. In some measured
sites in Central Missouri, the largest slope length contributing
runoff to a channel in natural terrain was about 150 feet. A value
somewhat less than this should be used in a reclaimed site.
66
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INFORMATION PROGRAM
by
Dr. Chris J. Johannsen ]_/
The investigators of this project have presented the research results to
the Missouri Land Reclamation Commission on a regular basis. The Commission
members were especially encouraged by the computer analysis techniques for
inventorying and estimating the area of vegetation, bare soil and water on
mined Iands0 The Land Reclamation staff was encouraged to obtain color
infrared photography over all mined areas in Missouri. A cooperative agree-
ment was signed with the Environmental Protection Agency to acquire high
altitude color infrared photography (1:48,000). The photography was flown
in July, 1978. Arrangements have been made with the Bio-Engineering Programs,
University of Missouri-Columbia to analyze the photography by computer and
provide maps and data of vegetation, soil and water areas by mining location.
Meetings have been held with Area Agricultural and Area Community Develop-
ment Extension Specialists located near mined lands. Concerns were expressed
by the specialists for information to provide land owners regarding lease
arrangements, reclamation procedures and total community impact. A conference
on "Coal and You" was held in the Green Hills Area of North Central Missouri
which addressed some of these concerns. Numerous requests have been
received from land owners for additional information.
A conference on "Remote Sensing in the Future of Missouri Mining" was
held in March, 1977, for members of the Missouri Mining Industry Council.
This conference was the result of discussions with industry members and
the Land Reclamation Commission. A workshop has been requested by industry
on prime farmland. This has become a major concern since the passage of the
Surface Mining Control and Reclamation Act of 1977. Coal companies are
currently sending their preliminary reclamation plans to the investigators
for comment before submitting them to the Land Reclamation Staff.
The requests for copies of papers, reports and results from individuals
has been steadily increasing since the initiation of this grant. Land owners
and coal companies have requested assistance and advice on such topics as
vegetation establishment, erosion and drainage problems, and alternative
uses of mined lands. Approximately three newspaper articles, six radio tapes
and one television presentation have been made during the past two years
which involved results from this project.
I/Professor of Agronomy, University of Missouri-Columbia
67
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REFERENCES
Brickner, T., 1977, Some Factors Affecting Erosion. Unpublished research
report. University of Missouri-Roll a, Roll a, Missouri 65401
Carrel, J.E., J.E. DeMott, and D.M. Zight, 1977, Surface Mine Revegetation:
Area Metering of Ground Cover. Fifth Symposium on Surface Mining and
Reclamation, Louisville, Kentucky.
Carrel, J.E., C.J. Johannsen, T.W. Barney, and W. McFarland, 1978a, Measure-
ments of Vegetative Cover In Surface Mines. Twelfth Symposium of Remote
Sensing of Environment, Manila Phillipines.
Carrel, J.E., K. Wieder, V. Leftwich, S. Weems, C.L. Kucera, L. Bouchard and
M. Game, 1978b, Strip Mine Reclamation: Production and Decomposition of
Plant Litter. Proceedings International Congress for Energy and Eco
Systems. University of North Dakota, Grand Forks, North Dakota.
Curtis, J.T. and G. Cottham, 1956, Plant Ecology Workbook, Laboratory, Field
and Reference Manual. Burgess Publishing Company, Minneapolis, Minnesota.
Robertson, C.E., 1971, Evaluation of Missouri's Coal Resources. Missouri
Geological Survey and Water Resources, Rolla, Missouri 65401, RI No. 48.
Young, J.A., 1978, Missouri's Coal Mining and the Environment. 208 Water
Quality Management Guide (WQG-5), University of Missouri, Columbia.
68
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/7-79-194
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
Characterization of Vegetation and Drainage in Strip
Mined Land Utilizing Remote Sensing Techniques
5. REPORT DATE
Align Si" 1Q7Q -j
6. PERFORMING ORGANIZATION CODE
7, AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
C. Johannsen, T. Barney, A. Coble, J. Carrel,
and P. Barr
***•• • P -!•!•• -r~ •• -
ORGANIZATION NAME AND ADDRESS
University of Missouri-Rolla
Rolla, Missouri 65401
10. PROGRAM ELEMENT NO.
1NE623
11. CONTRACT/GRANT NO.
SEA-CR IAG 68U-15-20
12. SPONSORING AGENCY NAME AND ADDRESS
Industrial Environmental Research Laboratory
Office of Research and Development
U. S. Environmental Protection Agency
Cincinnati, Ohio 1+5268
13. TYPE OF REPORT AND PERIOD COVERED
final
14. SPONSORING AGENCY CODE
600/12
15. SUPPLEMENTARY NOTES
1 fi ABSTRACT
Research conducted during this project has utilized remote sensing images and
data to study surface vegetation identification, vegetation biomass and drainage
patterns on coal strip mined land. Study sites vere located in Boone, Randolph and
Macon counties in central Missouri. This report presents the results of surface
vegetation and drainage analyses of those sites and methodologies used for accurate,
timely and useful applications of remotely gathered data for monitoring coal surface
mining reclamation activities. A computer based technique was developed for assessing
the extent of reestablished vegetation on the study sites. Color infrared aerial
photographs obtained over the sites were scanned and converted into digital outputs.
Classification accuracy was established by comparing the results to known ground
cover quantified by quadrat and area metering methods. The methodology subsequently
developed offers a reasonable compromise between manual photo interpretation and
automatic machine processing strategies for assessment of vegetative ground cover on
strip mined lands from remotely sensed data. Vegetative cover was verified in randomly
located quadrats on the mined sites. Reference standards were thus established for
the analysis and classification made by both area metering and digital image analysis
methods. Although computer system is more versatile, both methods are rapid and highly
^-90%) accurate. Nine additional mine sites were studied for further comparison of
the effects of plant productivity, species diversity and density to the remotely sensed
17.
lea Lion.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
COSATI Field/Group
Coal
Surface Mining
Remote Sensing
Drainage
Vegetation
Missouri
Reclamation
Color infrared
Computer
Aerial "photo
18. DISTRIBUTION STATEMENT
Release to the Public
19. SECURITY CLASS (This Report/
Unclassified
1. NO. OF PAGES
83
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
69
4 U.S. GOVERNMENT PRINTING OFFICE: 1979 -657-060/5388
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