1. 1
Mapping Reclaimed Strip Mined Areas in the Mill Creek Watershed through Change Detection
Using NDVI
Kelsey M. Slayton
Clarion University of Pennsylvania
This paper is submitted to the Honors Program of Clarion University of Pennsylvania in
fulfillment of the requirement of Senior Honors Program Thesis.
May 2015
Dr. Yasser Ayad, Advisor
Mr. Mitch McAdoo, Advisor
Dr. Christopher Hughes, Advisor
Dr. Rod Raeshler, Honors Program Director

2. 2
Mapping Reclaimed Strip Mined Areas in
the Mill Creek Watershed through
Change Detection Using NDVI
Kelsey M. Slayton
Abstract
For much of the mid 20th century, Western Pennsylvania has undergone extensive strip mining.
These practices have been drastically reduced in recent years, but the repercussions of past
mining efforts remain in full force. Acid Mine Drainage (AMD) has been a major polluter of
Western PA’s environment, increasing the acidity of streams and rivers and devastating their
ecology. The Mill Creek Watershed is no exception to the effects of AMD. Much of this
watershed has been polluted, proof of which is evident in the orange streams and creeks that
abound in the area. Efforts to reduce the effects of AMD can be made. However, it can be
difficult to locate point sources of AMD while out in the field. These point sources are the old
mines that have undergone reclamation and are very difficult to distinguish from naturally
vegetated locations. Therefor the purpose of this project is to create a reference map to be used
in the field to easily locate old strip mines and determine areas most likely to be point sources
for AMD. Using ArcMap GIS technology, historic strip mine locations were mapped within the
Mill Creek Watershed, showing extensive mining to the south of Mill Creek. A change detection
of vegetation within the watershed was then performed by employing NDVI and Image
Differencing techniques (using ENVI remote sensing software) in order to locate which areas
have undergone successful and most significant reclamation efforts. This study found that over
2,000 acres of the watershed were mined in the past, and that at least 27% of the historically
mined areas have seen extensive vegetation increase. Most notably, the area near Jones Run to
the south of Mill Creek displays the greatest increase in vegetative health of the entire
watershed. The final product of this project is a field map detailing areas of reclaimed mines,
historically mined areas, and all areas displaying an increase in vegetation.
Introduction
Mining in Pennsylvania has been a part of the state’s history and culture as far back as the late
1700s (“Pennsylvania Mining History” n.d.). However, coal mining did not become widespread
until the early 1900s, when it was used extensively to fuel the steel and iron industries prevalent
in western Pennsylvania. It was during this time that underground coal mining efforts began to

3. 3
make a change to surface mining, with strip mining being especially prevalent in the Mill Creek
watershed - the study area of this project. Unfortunately, this widespread mining effort has had
lasting effects on the ecological system of the area long after many of these mines were
reclaimed.
Mining efforts in Clarion and Jefferson counties, and more specifically in the study area
of the Mill Creek watershed, were limited before the 1920s. It was at this time that interest in the
area’s resources began to take hold. After the first and second World Wars the industry was
flourishing in this area, and from 1945 to the late 1960s, much of the Mill Creek watershed had
been stripped. By the late 1960s, many of these mines were then abandoned, with about 55 of
them draining into Little Mill Creek (Linton, n.d.). By the 1970s investigations were being made
into the health of the watershed and what reclamation efforts could be made.
In the mid-1970s an investigation was undertaken that studied the abandoned mines in
the Mill Creek watershed and the ecological effect that runoff through these mines was having
on the streams and creeks in the watershed. The findings were reported and suggested
reclamation efforts were proposed in an article published by the Engineering & Associated
Design Services on August 17th, 1977 as part of Operation Scarlift. (“Scarlift Reports”, n.d.).
These reports were undertaken with the purpose of remediating the ravages of land and water
from historic mining practices. For the Mill Creek watershed, the biggest problem that needed to
be addressed was Acid Mine Drainage (AMD). AMD is caused when fracturing of the
overburden through coal mining processes allows groundwater to infiltrate bedrock that was
formerly impermeable. These bedrock strata often contain high concentrations of iron pyrite,
which is leached from the bedrock when rain and groundwater percolate through the strata. This

4. 4
causes the water to become very acidic, and when the water is brought to the surface the leached
metals react with the oxygen to form a bright orange, highly acidic precipitate (Myers, 2011).
Even with efforts being made to install passive treatment systems within the watershed since the
1990s, there are still areas affected by AMD. Therefore, the purpose of this project is to delineate
the areas within the watershed that were historic mine sites and areas that could be reclaimed
strip mines, and to provide this information in an informative map that could help distinguish
areas within the watershed that are most likely point sources of AMD.
Study Area
The Mill Creek watershed is located in western Pennsylvania, and centers around Mill Creek, a
stream that is about 20 miles long (Fig. 1). This creek flows through portions of Clarion and
Millcreek townships in Clarion County, and Eldred and Union Townships in Jefferson County. It
lies about 2 miles east of the borough of Clarion and two miles northwest of the borough of
Brookville. The drainage area of the basin is about 56 square miles, with the creek flowing
westerly to its confluence with the Clarion River in Millcreek Township, Clarion County
(Linton, n.d.). The watershed includes about 6162 acres of State Game Lands No. 74 and is
bordered by the town of Sigel to the northeast and Fisher to the northwest. Based upon the
findings of the Operation Scarlift Mill Creek report, the area north of Mill Creek has been left
largely untouched by historic mining activity. There were about 100 acres of strip mining
activity found in the northerly section, which is in stark contrast to the area found south of Mill
Creek. This area had a significant portion of its land mined, with about 2000 acres having been
mined (according to reports made through Operation Scarlift). The mining in this area was
limited strictly to coals, leaving plenty of opportunity for AMD. The area most severely affected

5. 5
in the Mill Creek watershed is the sub watershed of Jones Run (Commonwealth of Pennsylvania
Department of Environmental Resources, 1977). This areas is easily discernable through aerial
photography and historical topographic maps. As such, though this project seeks to create a
comprehensive study and map of the entire Mill Creek watershed, special attention will be paid
to the areas surrounding Jones Run (Fig. 2).
Methods
Data Acquisition and Pre-Processing
The Landsat 5 satellite, was launched into orbit on March 1st 1984. The platform contained two
sensors: the Multispectral Scanner System (MSS) and the Thematic Mapper (TM). The Thematic
Mapper is a multispectral scanning sensor that is advantageous over the MSS sensor due to its
“Higher resolution, sharper spectral separation, improved geometric fidelity, and greater
radiometric accuracy and resolution” (NASA 2015). Landsat 5 data was used for this study,
rather than data from any of the other 3 Landsat satellites, due to the undisturbed series of
available satellite imagery from 1984 to 2012. Consistently using Landsat 5 data over the 27-
year period allowed for change analyses over the study site without having to control for spectral
variability associated with using multiple platforms.
Two dates from Landsat 5 TM (June 1984, August 2011) from the USGS Global
Visualization Viewer (GloVis) path 17, row 31 were acquired (Fig. 3). To reduce scene-to-scene
variation due to sun angle, soil moisture, atmospheric condition, and vegetation phenology
differences, both scenes were collected between the months of June and August. These dates
allowed for the time of peak biomass to be studied, allowing better results for both the study of

6. 6
vegetation health and the study of possible increases or decreases in biomass. The starting date
(June 1984) represented past health of areas that had been strip mined. Hypothetically speaking,
areas that had been mined in the past would either not have any active reclamation at this time,
or would have only been under reclamation for 10 years at the most. Current conditions were
represented by the August 2011 date, which, hypothetically, could possibly show a significant
increase in any areas that had been reclaimed. This would allow almost 30 years of vegetation
growth to take place on any areas that had been reclaimed in the study area. Ideally images from
the same month should have been acquired, but due to availability of images that had less than
10% cloud cover over the study area the closest anniversary dates that could be found were from
June 1984 and August 2011. The _MLT document from both image downloads were opened in
ENVI, which automatically displays bands 1-5 and 7 layer stacked, excluding the thermal band.
Region of Interest (ROI) subsets were made of each area, focusing on the Mill Creek Watershed
(Fig. 4). Once the data was correctly loaded into ENVI and spatial subsets were made, data
analysis could take place.
In addition to the satellite imagery acquired for remote sensing analysis, historical
topographic maps were downloaded from the NationalMap.Gov to be used to create polygons of
historically mined areas. These areas would then be used in later analysis. Six USGS 1:24000-
scale Quadrangle topographic maps dating from 1967 to 1969 were downloaded as PDFs
corresponding to the areas of Stranttonville, Brookville, Sigel, Lucinda, Cooksburg, and Corsica.
These six maps together covered the extent of the Mill Creek Watershed. In order to use these
PDF files for analysis in ArcMap, they were converted to JPEGs and the white borders were
cropped off.

7. 7
Radiometric Normalization
Radiometric correction on remotely sensed data is important and essential for ensuring that high-
quality information is retrieved from remote sensors. It ensures that terrestrial variables retrieved
from optical satellite sensor systems are calibrated to a common physical scale. These
corrections are applied to image data prior to the retrieval of land, atmosphere, or ocean
information so that any measurements and methods used in analysis yield self-consistent and
accurate geophysical and biophysical data (Teillet and Coburn, 2010).
In the past, models have been used to convert Landsat DN to reflectance using the CosT
approach (Chavez, 1996), converting the Landsat 5 imagery to reflectance using values
published for that purpose (Chander et. Al, 2009). These models have been applied when using
ERDAS Imagine software; however, the ENVI software used for analysis in this project comes
with tools that function as part of the software and can run this process automatically when used
properly and within the right context. For this project, the Radiometric Calibration tool was used
in ENVI, which can be found in the Radiometric Correction toolset folder. By radiometrically
calibrating the two images (1984 and 2010), radiometric errors from sensor defects, variations in
scan angle, and system noise were all compensated for to produce an image that represents true
spectral radiance at the sensor.
Change DetectionMethods
Normalized Difference Vegetation Index (NDVI)
As stated earlier, the study area of this project has been strip mined in the past – some areas extensively
so. However,all strip mining operations in the Mill Creek watershed have come to an end and today it
can sometimes be hard to discern which areas in the field could have been strip mined. This is because

8. 8
most of the areas in the watershed are now overgrown with vegetation. However,this vegetation growth
is both helpful and crucial to the making of this project. If the areas where an increase in vegetation vigor
were able to be deduced, then these areas could be studied as areas most likely to have undergone
reclamation. This project functions under the assumption that any area exhibiting a significant increase in
vegetation health from the past to the present could potentially be a reclaimed strip mined area.This is
where the NDVI comes into play.
NDVI stands for Normalized Difference Vegetation Index, which is an equation that takes into
account the amount of infrared energy from the electromagnetic spectrum reflected by vegetation.
NDVIs are important because healthy vegetation reflects very strongly in the near-infrared portion of the
electromagnetic spectrum, while unhealthy vegetation will reflect poorly or not at all (Fig. 5). Using the
following equation, healthy vegetation can be identified in an output image:
𝑁𝐼𝑅 − 𝑅𝑒𝑑
𝑁𝐼𝑅 + 𝑅𝑒𝑑
i.e.
𝐵𝑎𝑛𝑑 4 − 𝐵𝑎𝑛𝑑 3
𝐵𝑎𝑛𝑑 4 + 𝐵𝑎𝑛𝑑 3
This transform produces a single band of data with values ranging -1 to +1, where higher values
indicate more, or healthier, vegetation within a pixel (Bonneau et. al, 1999). This image ratio
was applied first to the 1984 image (Fig. 6), and then to the 2011 image (Fig 7). The resulting
images each identified healthy vegetation in the area as white pixels with a +1 value. Black
pixels have a value of – 1, representing areas of unhealthy vegetation as well as roads, water, and
built up environments.

9. 9
Image Differencing
Once an NDVI was applied to each image, image differencing was selected to detect changes in
vegetation vigor between the two images. Image differencing is based on the idea that when the
pixel values of two images are subtracted from each other, values lying at or near the tails of the
histogram represent a significant change in radiance (Deer, 1995). When performing image
differencing in ENVI, the initial state image (Time 1) is subtracted from the final state image
(Time 2) – i.e. (Final-Initial). In the case of NDVI image differencing, positive values
correspond to an increase in vegetation, while negative values represent a decrease in vegetation.
Using ENVI remote sensing software, the Image Change Workflow was selected from the
Change Detection toolbox. Image registration was skipped since the two images were already
coregistered. The Time 1 file was chosen as the 1984 image, and the Time 2 file was chosen as
the 2011 image. Under the Change Method Choice panel, Image Difference was selected. Under
the Image Difference panel, the parameters to use for the difference analysis were set. The
Difference of Input Band and Band 1 selection were accepted – there being only one band of
data ranging from -1 to +1. After the difference analysis was complete, the default setting of
Apply Thresholding was accepted. This option allows the user to set parameters that help the
algorithm determine which areas have a big change (Image Change Tutorial, n.d). In the case of
this project, big increases in vegetation health were of interest, so Increase Only was selected
from the dropdown menu under the Auto-Thresholding tab. In the Select Auto-Thresholding
Method dropdown list, Otsu’s method was chosen. Otsu’s is a histogram shape-based method. It
is based on discriminate analysis and uses zeroth- and the first order cumulative moments of the
histogram for calculating the value of the thresholding level (Image Change Tutorial, n.d).

10. 10
Under the Cleanup – Refine Results tab, Enable Smoothing and Enable Aggregation were both
selected and their default settings were accepted. The enable Smoothing option removes
speckling noise, and the Enable Aggregation option removes small regions from the image. This
produces an output that is cleaner and more simplified for quicker and easier analysis. The data
was then exported as a Change Class Image (Fig. 8) and as Change Class Vectors (Fig. 9), to be
used for analysis in ArcMap. The Change Class Statistics (Fig. 10) and the Difference Image
(saved as a raster file) (Fig. 11) were both exported as well.
Digitizing Polygons
To better be able to determine what areas of increased vegetation from the Change Detection
methods above would correspond to reclaimed mines, areas that were mined in the past had to be
determined. This was done by referencing historic topographic maps and digitizing polygons that
covered areas corresponding to strip mines marked on the maps.
First, the six topographic JPEG files were uploaded into an ArcMap document along with
a basemap covering the study area. However, since the topographic maps acquired had no spatial
reference they had to be georeferenced. This was done using the Georeferencing toolbar in
ArcMap. Once all six maps were georeferenced, the Editor toolbar was used to digitize polygons
corresponding to strip mined areas on the maps (Fig. 12). These digitized polygons were then
saved as a feature class in the project’s geodatabase, to be used for further analysis.
Analysis
The data from the Image Change analysis was exported as a shapefile into ArcMap, where it
could be displayed on a map and total acreage of increased vegetation could be discerned. Total
acreage of increased vegetation was calculated creating a new field in the attribute table and

11. 11
calculating the geometry for that field. By using the coordinate system of the data source and
selecting the units to be US acres within the Calculate Geometry dialogue box, total acres for
each area could be calculated. However, all the areas of increased vegetation do not necessarily
correlate to areas that have been strip mined in the past. To determine the acreage of increased
vegetation that can be considered the product of reclamation efforts, the relationship between
historically mined areas and areas of increased vegetation was investigated. It was decided that
areas which showed an increase in vegetation that corresponded to historically mined areas
would be considered reclaimed strip mines. This relationship was further investigated using the
following process:
1. The Class Change shapefile (increased vegetation) was first intersected with the Historic
Strip Mine shapefile, which was digitized from historical georeferenced topographic
maps.
2. The areas where these two polygons overlapped were saved as a new polygon shapefile
and named Intersected. This allowed the areas of increased vegetation that corresponded
to historically mined sites to be delineated.
3. From there, a new field was added to the Intersected shapefile and was named
ACREAGE.
4. This field was then calculated by right-clicking on the ACREAGE field and choosing the
Calculate Geometry option. Within this dialogue box the Property was set as Area, the
coordinate system was selected as ‘Use coordinate system of the data source’, and the
Units were set as Acres US [ac]. Once these parameters were set and run, the acreage for
each parcel of land could be determined.

12. 12
Results
The resulting data from the NDVI Image Differencing process showed areas within the image
that exhibited a significant increase in vegetation from 1984 to 2011. This data then had to be
analyzed and compared to the areas that had been mined in the past. By looking at the
SHAPE_AREA field of the Historically Mined shapefile and converting it to acreage, it was
determined that the total area of land cover that was mined in the past was 2,021.93 acres. This
same analysis was applied to the Increased Vegetation shapefile, as stated above, and the
resulting area was 4,414.09 acres. Then the statistics of the Reclaimed Mines shapefile, which
resulted from the intersection of the Historic Mines and Increased Vegetation shapefiles, was
analyzed. Looking at the statistics (Fig. 13) of the acreage field within the Reclaimed Mines
shapefile it was determined that a total of 519.263 acres of land showed an increase in vegetation
since 1984 where strip mines have existed in the past. These statistics show that 27% of land that
was mined in the past has now undergone significant recovery from 1984.
In addition to an assessment of the entire watershed, the sub-watershed of Jones run was
given special attention. This area has witnessed the most extensive strip mining efforts within the
entire watershed. By looking at the statistics of the historic strip mines that lie within the sub
watershed, it was determined that this area had 484.58 acres of mined land in the past. There is
one mine of particular note in this area, reaching from the northern section of the watershed
down into the southwestern section (Fig. 14). This mine is notable because it has seen a 78%
increase in vegetative health from 1984 to 2011. According to the statistics of the shapefile, the
historic mine covered 150.29 acres of land, 115.65 of which have seen a significant increase in
vegetation. This area bears the most significance since it lies in an area that was extensively

13. 13
mined in the past, which leaves the potential for extensive AMD pollution. Also, since it has
undergone such a significant reclamation effort, it is likely difficult to discern from any other
naturally vegetated field or land in the area. This area has now been located and marked on a
map so that it can be easily identifiable in the field.
Overall, the final result of this study is a detailed map showing reclaimed strip mines in the
Mill Creek Watershed, as well as historically mined areas and all areas showing an increase in
vegetative health from 1984 to 2011 (Fig 15).
Conclusion
By applying an NDVI image ratio on two anniversary date satellite images the change in
vegetation health could be both visualized and analyzed. This data was further investigated by
performing an Image Difference using the two NDVIs, which allowed the change in health
between the two dates to be identified. By comparing the change in the images (as it pertains to
increased vegetative health) with the areas that have been mined in the past (as referenced by
historical topographic maps), areas that have undergone significant reclamation efforts were
delineated. However, this data is not conclusive. There are some important factors that need to be
taken into account to understand the study as a whole, as well as the meaning of the results. First,
the prudent observer should note that Landsat data is only available as early as the 1980s.
However, Mill Creek was finished being actively mined by the early 70s. That means that there
was a 5 to 10 year period where reclamation efforts could have already begun. If satellite data
were available from the very date these mines became abandoned, then there would most likely
be a much more significant change in vegetative health, and this would have yielded higher
results in acreage totals of reclaimed mines. Also, Image Differencing only takes into account
areas of significant change. There are certainly areas that show slight increases in health, but

14. 14
these are not included in the Image Difference output. If this study is to be repeated, a more
detailed look at image thresholding could be applied to the Image Difference workflow.
Overall, this study provided valid and accurate results as it pertains to the change detection
of vegetative health from 1984 to 2011. Areas of increased health corresponding to historic strip
mines can be stated with confidence as having undergone significant reclamation efforts and
resulted in substantial recovery of vegetation cover. These areas are now easily and readily
identifiable on a map, which can be used in the field for future exploration of the Millcreek
watershed AMD sites. By referencing this map, researchers will be able to concentrate their
efforts in locating point sources of AMD, and hopefully will be able to use this information to
implement remediation plans for the polluted streams within the watershed.

15. 15
Figure 1. Reference map of the study area – shows the boundary of the Mill Creek Watershed in
black, and boundary of Jones Run sub watershed in red.
Figure 2. Reference map of the Jones Run sub watershed. Boundary of the sub watershed is
highlighted with a red outline.

16. 16
Figure 3. Landsat 5 Imagery used for this project, downloaded from the USGS GloVis website.
Figure 4. Spatial subset ROIs of the Mill Creek Watershed, used in all remote sensing analysis in
this project

17. 17
Figure 5. Graph showing the amount of reflectance within a certain wavelength for healthy
(green) vegetation, unhealthy (dry) vegetation, and bare soil. Near Infrared (NIR) reflects in the
small portion of the much larger region called infrared (IR), located between the visible and
microwave portions of the electromagnetic spectrum. NIR makes up the part of IR closest in
wavelength to visible light and occupies the wavelengths between about 700 nanometers and
1500 nanometers (0.7 µm - 1.5 µm).
Figure 6. NDVI Image output for June 1984. -1 values correspond to black pixels, showing
unhealthy or barren land. +1 values would be white pixels showing healthy vegetation.
Near Infrared

18. 18
Figure 7. NDVI Image output for August 2011. -1 values correspond to black pixels, showing
unhealthy or barren land. +1 values would be white pixels showing healthy vegetation.
Figure 8. Change Class Image file. The blue pixels show areas of significant increase in
vegetation health.

19. 19
Figure 9. This is the Class Change vector file displayed in ArcMap over a layer showing the area
of the Mill Creek watershed. The green polygons correspond to areas that showed a big increase
in vegetative health.
Figure 10. This is the Class Change statistic histogram, which is an output of the Image Change
workflow used to perform the Image Differencing technique. It shows that any values lying at or
near the tails of this histogram display a significant change in radiance. An image threshold value
of .173 was automatically chosen by the program, and all pixel with values lying at or above that
threshold value were outputted as areas of big increase in vegetative health.

20. 20
Figure 11. This is the complete Difference Image outputted from the Image Change workflow
process used to complete the change detection for this project. Areas of white pixels have values
near +1 and correspond to an increase in vegetative health from 1984 – 2011. Areas of black
pixels have values of -1 and correspond to a decrease in vegetative health during that time period
(as well as the non-vegetated land cover classes). Areas with gray pixels have values indicating
little of no change.
Figure 12. Shows digitized polygons (in red) that correspond to strip mined areas, which were
determined by analyzing the georeferenced topographic map lying beneath the polygons.

21. 21
Figure 13. Statistics of the Intersected shapefile, which corresponds to Reclaimed Strip Mines.
The statistics were viewed from the calculated ACREAGE field, and by looking at the sum for
that field it could be determined that a total of 519.26 acres of land underwent significant
reclamation from 1984 – 2011.

22. 22
Figure 14. Map of the Jones Run Sub Watershed within the Mill Creek Watershed

23. 23
Figure 15. Final Map of the Mill Creek Watershed, identifying areas of historical mines, areas of increased vegetation, and likely reclaimed areas

24. 24
Work Cited
Antwi, E., Krawczynski, R., & Wiegleb, G. (n.d.). Detecting the effect of disturbance on habitat
diversity and land cover change in a post-mining area using GIS. Landscape and Urban
Planning, 22-32.
Bonneau, L. R., Shields, K. S., & Civco, D. L. (1999). Using satellite images to classify and
analyze the health of hemlock forests infested by the hemlock woolly adelgid. Biological
Invasions, 1(2-3), 255-267.
Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved.
Photogrammetric engineering and remote sensing, 62(9), 1025-1035.
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric
calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote
sensing of environment, 113(5), 893-903
Commonwealth of Pennsylvania Department of Environmental Resources. (1977). Mine
Drainage Abatement Project for the Mill Creek Watershed (Engineering Report Project
No. SL. 133-5). Altoona, PA: Stidinger, Roman, Over, Over, Reilly, Tomlison, Young.
Deer, P. (1995). Digital change detection techniques in remote sensing.
Demirel, N., Düzgün, Ş, & Emil, M. (n.d.). Landuse change detection in a surface coal mine area
using multi-temporal high-resolution satellite images. International Journal of Mining,
Reclamation and Environment, 342-349.
Gondalia, Rahul B. Remote Sensing Vegetation Reclamation on Surface Mines in Appalachia: A
Case Study on Hobet Mine in West Virginia. Thesis. Michigan University, 2010. N.p.:
n.p., n.d. Print.
Hu, Z., Li, H., & Du, P. (n.d.). Case study on the extraction of land cover information from the
SAR image of a coal mining area. Mining Science and Technology (China), 829-834.

25. 25
Image Change Tutorial (Using ENVI) | Exelis VIS Docs Center. (n.d.). Retrieved April 3, 2015,
from http://www.exelisvis.com/docs/ImageChangeTutorial.html
Landsat TM imagery and NDVI differencing to detect vegetation change: Assessing natural
forest expansion in Basilicata, southern Italy. (n.d.). Retrieved April 3, 2015, from
http://www.sisef.it/iforest/contents/?id=ifor0909-007
Linton, Kenneth J. 'Some Aspects of the Ecology of Mill Creek: Report to the Pennsylvania
Department of Mines and Mineral Industries'. Clarion State College Print.
Martin, L., & Howarth, P. (n.d.). Change-detection accuracy assessment using SPOT
multispectral imagery of the rural-urban fringe. Remote Sensing of Environment, 55-66.
Mudrák, O., Frouz, J., & Velichová, V. (n.d.). Understory vegetation in reclaimed and
unreclaimed post-mining forest stands. Ecological Engineering, 783-790.
Myers, Amy Beth. 'Analysis of Changes in Macroinvertebrate Communities on The Mill Creek
Watershed Resulting From Passive Treatment Of Acid Mine Drainage'. B.S. Clarion
University of Pennsylvania, 2011. Print.
Overview of Radiometric Calibration. (n.d.). Retrieved April 3, 2015, from
http://isis.astrogeology.usgs.gov/IsisWorkshop/index.php/Overview_of_Radiometric_Ca
libration
Pennsylvania Mining History. (n.d.). Retrieved March 28, 2015, from
http://www.dep.state.pa.us/msi/mininghistory.html
Petropoulos, G., Partsinevelos, P., & Mitraka, Z. (n.d.). Change detection of surface mining
activity and reclamation based on a machine learning approach of multi-temporal
Landsat TM imagery. Geocarto International, 323-342.
Radiometric Calibration (Using ENVI) | Exelis VIS Docs Center. (n.d.). Retrieved April 3, 2015,
from http://www.exelisvis.com/docs/RadiometricCalibration.html

26. 26
Teillet, P., & Coburn, C. (2010). Radiometric Correction. In Encyclopedia of Geography. SAGE
Publications.
The Thematic Mapper. (2015, April 2). Retrieved April 3, 2015, from
http://landsat.gsfc.nasa.gov/?p=3229
Townsend, P., Helmers, D., Kingdon, C., Mcneil, B., Beurs, K., & Eshleman, K. (n.d.). Changes
in the extent of surface mining and reclamation in the Central Appalachians detected
using a 1976–2006 Landsat time series. Remote Sensing of Environment, 62-72.