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Environ Monit Assess (2015) 187: 557 DOI 10.1007/s10661-015-4766-1

Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery Jing Li & Carl E. Zipper & Patricia F. Donovan & Randolph H. Wynne & Adam J. Oliphant

Received: 23 February 2015 / Accepted: 20 July 2015 / Published online: 7 August 2015 # Springer International Publishing Switzerland 2015

Abstract Surface mining disturbances have attracted attention globally due to extensive influence on topography, land use, ecosystems, and human populations in mineral-rich regions. We analyzed a time series of Landsat satellite imagery to produce a 28-year disturbance history for surface coal mining in a segment of eastern USA’s central Appalachian coalfield, southwestern Virginia. The method was developed and applied as a three-step sequence: vegetation index selection, persistent vegetation identification, and mined-land delineation by year of disturbance. The overall classification accuracy and kappa coefficient were 0.9350 and 0.9252, respectively. Most surface coal mines were identified correctly by location and by time of initial disturbance. More than 8 % of southwestern Virginia’s >4000-km2 coalfield area was disturbed by surface coal mining over the 28-year period. Approximately 19.5 % of the Appalachian coalfield surface within the most intensively mined county (Wise County) has been disturbed by

J. Li China University of Mining and Technology, D11 Xueyuan Road, Beijing 100083, People’s Republic of China C. E. Zipper (*) : P. F. Donovan Department of Crop and Soil and Environmental Sciences, Virginia Polytechnic Institute and State University, Smyth Hall, Blacksburg, VA 24061, USA e-mail: [email protected] R. H. Wynne : A. J. Oliphant Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

mining. Mining disturbances expanded steadily and progressively over the study period. Information generated can be applied to gain further insight concerning mining influences on ecosystems and other essential environmental features. Keywords Remote sensing . Appalachian coalfield . Mining . Change detection . Trajectory analysis

Introduction Mining operations produce materials that are essential to human activities, but they also disturb the environment (Bell et al. 2001). Surface mining involves removal of plant cover, soil, and geologic materials as a means of exposing economically valued minerals, affecting the land surface, topography, terrestrial ecosystems, and, by extension, water resources. When mining occurs close to populated areas, it can also impact the quality of life experienced by local residents. In some areas, mining operations may even cause social conflicts in affected areas due to differing perspectives among miners, residents, and local officials concerning tradeoffs among landscape disturbance effects and economic benefits (Bridge 2004; Kemp et al. 2010). In the USA, the Surface Mining Control and Reclamation Act (SMCRA) was established as a national law in 1977. The SMCRA applies specifically to coal mines due to the prevalence of this type of mining in the USA and the widespread problems that were being experienced in mining areas prior to its passage. The

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SMCRA was intended to Bstrike a balance between protection of the environment…and the Nation’s need for coal as an essential source of energy…^ (Section 102), and it requires that mined areas should be reclaimed to a land use capability that is equal to or higher than that which preceded mining. Canada, Australia, China, India, and other nations with abundant coal resources have also established national laws, regulations, or other forms of government policy to mandate reclamation standards. Although the extent of mining disturbances and restoration methods varies among world regions, issues concerning coal mining and reclamation are universal. These concerns are especially acute in the world’s two largest economies, the People’s Republic of China and the USA, due to extensive use of coal to satisfy the energy demands of both nations, currently and historically. Evaluation of past mining disturbances, environmental impacts, and restoration success can aid development of policies intended to guide future mining and environmental restoration activity. Knowledge of where and when mining has occurred is essential information when evaluating the success of environmental restoration practices at regional or societal level. Remote sensing has been used widely and effectively to monitor land use type and pattern change (Campbell and Wynne 2011), generally, and to detect and characterize mining disturbances (Akram and Imran 2012; Areendran et al. 2013; Brom et al. 2012; Chatterjee et al. 1996; Latifovic et al. 2005; Malaviya et al. 2010; Prakash and Gupta 1998; Schmidt and Glaesser 1998; Xiao and Wei 2007). In recent years, time series analysis of remote sensing data (a method for analyzing images as temporal sequences) has come into increasing use for monitoring land cover change (Huang et al. 2010; Kennedy et al. 2007; Schroeder et al. 2007; Song et al. 2007) and ecosystem processes (Lawrence and Ripple 1999; Kennedy et al. 2014; Pickell et al. 2014). Sen et al. (2012) developed a time-series analysis technique for detecting coal mines in Appalachia, USA, that is based on the premise that patterns of vegetation index change through time (trajectories) can differentiate mines from undisturbed natural areas and that the trajectories for reclaimed mines can differentiate the mines from urban disturbances. However, the Sen et al. (2012) method requires >7 years of post-mining multispectral data to identify the disturbance-and-recovery trajectory that they found to be characteristic of surface coal mines. This requirement is problematic for analyses that

Environ Monit Assess (2015) 187: 557

concern recent issues because it prevents identification of active and recent mining and fails to identify areas that have not been fully revegetated. We analyzed remote sensing data from Landsat satellites to characterize landscape change caused by surface coal mining over a 28-year period in southwestern Virginia, a segment of eastern USA’s Appalachian coalfield. Our objectives were to develop a method for preparing an inclusive inventory of mined land by mining date, to apply that method, and to characterize the progressive nature of landscape change due to coal surface mining in the study area. It is our intent that the method should have potential for application over broad areas in a time-efficient manner with high accuracy.

Data and methodology Study area The study area is the southwestern Virginia coalfield (Fig. 1). This area was selected for study due to its recent history of intensive coal surface mining while considering the authors’ history of working in the area and consequent familiarity with on-the-ground conditions. Coal has been mined commercially within the study area since the 1880s (Hibbard 1990). Virginia’s southwestern coalfield occurs within the Appalachian Plateaus physiographic province (Fenneman 1938), the Central Appalachians Level III ecoregion (Omernik 1987), and the USA’s Appalachian coalfield (Milici et al. 2013). Landscapes have formed from sandstones, shales, and other sedimentary strata of Mississippian and Pennsylvanian age (Seaber et al. 1988). Within this region, geologic strata are generally flat-lying and interbedded with coal seams. The region is elevated relative to adjacent terrain, causing it to serve as a headwater source area. The dominant process of topography formation has been dissection by surface-water streams (Fenneman 1938). The Appalachian coalfield was the USA’s predominant source of coal historically and was responsible for 70 % of US coal production through the 1960s (Milici 2005). Virginia’s southwestern coalfield includes all or parts of seven counties and occupies >4000 km2. Approximately three fourths of study area, 3019 km2, is classified as forest land cover, most as deciduous, by the National Land Cover Database (NLCD) 2011 (Jin et al. 2013). The region also contains lands that are maintained in grass cover for livestock


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Fig. 1 Location of the study area in the Appalachian coalfield of eastern US and within Landsat scene 18–34. The study area includes all or portions of seven Southwest Virginia counties, which are named in the graphic

grazing and developed areas, but little cultivated land, open waters, and wetlands. Much of the livestock grazing land is on former mine sites. Extensive surface coal mining has been conducted in the southwestern coalfield since the 1960s (Hibbard 1990). All of Virginia’s current coal production occurs in the southwestern coalfield. Through the year 2011, approximately 20 % of the study area (>820 km2) had been included in mining permit areas (Virginia DMME 2014; see Fig. 1), but not all permitted areas have been mined. Data acquisition and preprocessing Landsat images for Path 18 Row 34 (WRS II grid system) were obtained from the US Geological Survey (USGS) Earth Explorer Landsat data archive (http://earthexplorer.usgs.gov/). Images selected for use were level 1T (terrain corrected) products co-

registered by the provider, Earth Resources Observation and Science Center; the co-registration was visually verified by our team prior to analysis. Images were also processed by the provider with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) processor (Masek et al. 2006); such processing included surface reflectance correction for atmospheric effects with a cloud mask, a cloud shadows mask, and a water and snow mask. Images were obtained as the Bbest available,^ considering our criteria as described below, for each 1-year interval over the period extending from 1984 and 2011. It was our goal to obtain images for dates within the peak growing season, which we defined as extending from June 1 through September 30. When no images of suitable quality were available from that period for a given year, the image selection window was extended to May 15 through

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October 31. Images were selected with no clouds obscuring the study area when possible; otherwise, images were selected to minimize cloud cover over the study area. In order to avoid dealing with data gaps caused by a scan line corrector (SLC) problem, no post-2003 Enhanced Thematic Mapper (ETM+) images were used. Extensive cloud cover was present in all available LEDAPS leaf-on images for 1991, 1996, 2006, and 2009; hence, no images from those 4 years were used. The image dataset consisted of 24 images, each representing one growing season (Table 1). Polygonal shapefiles defining mining permit areas were acquired from Virginia Department of Mines, Minerals and Energy (DMME), Division of Mined Land Reclamation, in year 2013, and an updated layer for active permits was obtained in 2014. These

Table 1 List of Landsat image dates and their sensor types

shapefiles include most but not all mining permits issued by Virginia’s mine regulatory agency and include the following categories of mine permit: state-issued permits (1966–1977), interim permits issued during the period when that state regulatory program was transitioning to SMCRA-mandated federal program (1977–1981), SMCRA permits issued (1981 and later) that are no longer active (permits have been released), and currently active mining permits. Collectively, these areas are described in our text as Bmining permits.^ These polygonal boundaries were merged together as auxiliary data. High-resolution aerial imagery was obtained from the National Agricultural Imagery Program (NAIP; USDA 2014) for the years 2003, 2004, 2005, 2008, 2009, 2011, and 2012. These images are acquired during the agricultural growing seasons in the continental USA.

Acquisition Date

Sensor type

Scene identifier

Fraction masked as clouds and cloud shadows (% of study area)

9/17/1984

TM

LT50180341984261XXX01

0.22

9/20/1985

TM

LT50180341985263XXX04

0.84

6/19/1986

TM

LT50180341986170XXX05

0.00

6/6/1987

TM

LT50180341987157XXX02

0.02

8/27/1988

TM

LT50180341988240XXX03

0.02

6/17/1989

TM

LT50180341989162XXX02

1.61

10/20/1990

TM

LT50180341990293XXX03

2.47

10/25/1992

TM

LT50180341992299XXX02

0.00

6/6/1993

TM

LT50180341993157XXX02

6.14

10/15/1994

TM

LT50180341994288XXX03

0.10

8/31/1995

TM

LT50180341995243AAA02

0.44

9/5/1997

TM

LT50180341997248AAA02

0.01

8/23/1998

TM

LT50180341998235XXX01

0.01

9/3/1999

TM

LE70180341999246EDC00

7.31

6/9/2000

TM

LT50180342000161AAA02

8.83

9/8/2001

ETM+

LE70180342001251EDC00

0.01

5/22/2002

ETM+

LE70180342002142EDC00

0.01

6/2/2003

TM

LT50180342003153LGS01

0.00

9/24/2004

TM

LT50180342004268GNC01

8.27

5/22/2005

TM

LT50180342005142GNC01

6.77

9/17/2007

TM

LT50180342007260GNC02

0.84

9/3/2008

TM

LT50180342008247GNC01

1.14

Date format is month/date/year

9/9/2010

TM

LT50180342010252EDC00

3.06

TM thematic mapper, ETM+ Enhanced Thematic Mapper

10/30/2011

TM

LT50180342011303GNC01

0.38


The Southwest Virginia portion of the Appalachian coalfield was defined as a polygon using digital geologic data obtained from Virginia DMME, Division of Geology and Mineral Resources. The NLCD 2011 land cover data were also obtained and used to aid the classification process. After stacking the six bands of each image, the coalfields polygon was used to mask out the study area. Cloud, cloud shadow, and water were delineated in individual images by masking and excluded from analysis. For training data, 509 random points were generated within the merged mining permit boundaries, and an additional 50 random points outside mining permits in highly developed areas (residential, commercial, and industrial, as classified by NLCD) were also generated. The status of each training point was defined as either vegetated or bare (non-vegetated) for each Landsat image. This classification was performed by visually inspecting each individual Landsat image displayed as a combination of bands 2, 3, and 4 while referencing all available NAIP images as a means of verifying classifications for years when NAIP images were available. Once this classification was complete, each point was given a second classification by evaluating its time series trajectory: PV for those points with persisting vegetation, as determined by the presence of vegetated cover in all Landsat images; EM, meaning that the point site was disturbed by mining at one or more times over the observation period (with mining disturbances identified as described below); and OD, meaning that vegetation disturbances other than mining were present. Other disturbances (OD) were further sub-classified to DP, meaning developed sites; Non-MD, meaning all other disturbances except for mining (EM); and developed (DP). Detailed methods Overview of the research approach Land classification Our study objectives are to develop a method to discriminate mined lands from unmined lands and to identify the approximate time of mining for the mined lands. We classified the land type into three categories: PV, OD, and EM. Lands classified as PV were covered with vegetation, such as natural forest, herbaceous vegetation, or unmanaged non-forest vegetation, on each image date. Lands classified as OD were developed (DP) or

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otherwise affected in a manner that removed vegetation (Non-MD), such as by forest harvest, but not mined. Some areas that are classified in OD category appeared to have been mined prior to the study period but were not fully revegetated at the beginning of observation period. Mining disturbance definition Figure 2 shows a mined pixel trajectory of vegetation index (VI) over a time when the pixel is mined and defined as extending from time T0 to time T4. Time T0 is prior to mining; T1 is the end of vegetation removal; T3 is initial revegetation after mining; the period between T1 and T3 is the active excavation and backfilling activity, when no vegetation or vegetation residue is present. The minimum VI occurs at T2, which is during the excavation backfilling process, and T4 is when the planted vegetation has developed to a more mature stage. In theory, the mining disturbance date should be T1. However, the vegetation removal date T1 is difficult to detect in practice. Generally, overburden excavation begins shortly after vegetation removal; the temporal resolution of our image sequence is not sufficiently high to identify a distinct T1 date. Hence, we define the mining disturbance date as the first detection of mining stage where the vegetation index (VI) is ≤ the bare-ground threshold, namely, the first detected date following T1 and prior to T3. Such definition enables an automated detection. Work flow Our methodology includes three primary steps (Fig. 3). First, using training data, we compared several VIs for their capability to distinguish bare ground from vegetated land. The best-performing VI was determined and computed for each scene, and VI images were stacked. Second, VI values were extracted for each training point on each image. Then, CART (v7.0; Salford Systems, Inc.) classification was conducted to identify the VI threshold that best separates bare ground from vegetated land on each image. Those thresholds were applied across the time series to separate and exclude PV land from other lands. Third, minimum VI and VI standard deviations across time were calculated, combined with mining permit boundary and NLCD dataset, and applied to distinguish EM from OD. The EM distribution and its disturbed date finally delineated. Each of these three steps is described with further detail below.

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Pixel’s trajectory VI Vegetated/Bare ground threshold

T0

T1 T2

T3

T4

Date

Fig. 2 An example of a mined pixel’s vegetation index (VI) trajectory, showing mining stage division and definition of the disturbance year. T1 is the end of vegetation removal; the period from T1 to T3 is bare ground (active excavation and backfilling); T3 is post-mining revegetation that has developed to a point where VI

is ≥ the bare-ground threshold; and T4 represents the post-mining vegetation at a more mature stage. The pixel shown is located at 82° 27′ 56.911″ W, 37° 8′ 15.068″ N. The trajectory period is from 1986 (T0) to 2007 (T4)

Step 1. Vegetation index selection. Using data from the image acquired on September 3, 2008, a trial was executed to select the VI best suited to the study purpose. Several multispectral VIs were tested: These included normalized difference vegetation index (NDVI; Rouse et al. 1973), normalized burning ratio (NBR; Key and Benson 1999), and normalized difference moisture index (NDMI; Hardisky et al. 1983). In addition, tasseled cap (Kauth and Thomas 1976) greenness-brightness difference index (TC G-B) was computed by subtracting greenness reflectance values from the brightness. Landsat band 3 (B3) and band 4 (B4) were also tested (Bi and Bai 2007).

To conduct the test, all VI values of each training point were extracted. The VI values for vegetated training pixels and for bare-ground training pixels were plotted and compared using JMP software (Fig. 4). Of the VIs tested, NDVI gave the best separation of pixel types. NDVI is effective for detecting green vegetation and is widely used in land cover studies (Defries and Townshend 1994; Tucker et al. 2005). NDVI was also found by Sen et al. (2012), after similar testing, to be the VI best suited for mining disturbance detection. Hence, NDVI was selected as the VI for use in delineating mining disturbance trajectories. Step 2. Persisting vegetation identification. Varying phenology and other characteristics of our

Fig. 3 A flowchart of the overall approach for reconstructing mining history by Landsat time series analysis


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Fig. 4 Comparison of vegetation indices’ (VIs’) capabilities to distinguish bare (b) and vegetated (v) training pixels. Box plots represent the 25th and 75th percentiles, and the whiskers extend to the 5th to the 95th percentile

images prevented definition of single threshold for use in separating bare ground from vegetation that could be applied across all images. Hence, we identified a separate bareground threshold for each image. With the 509 training points, CART classification was conducted to identify the bare-ground threshold on each date that could best separate vegetated land from bare ground (Fig. 5). If a pixel’s NDVI is always higher than the bare-ground threshold, it is classified as PV. Otherwise, it is classified as disturbed and then further subclassified as disturbed either by mining (EM) or by other activities (OD). We reprocessed each NDVI image by assigning 0 or 1 to those pixels whose values are higher or lower, respectively, than each date’s corresponding bareground threshold. By using the cell statistics tool in ArcGIS, maximal (or total) assigned value of 24 dates of each pixel was computed. Those pixels whose maximal assigned value is equal to 0 were classified as PV. Step 3. Mined land and disturbance date delineation. Mined (EM) lands have properties that can be used to distinguish them from other disturbances. First, due to SMCRA, they are expected to be located within mining permit boundary. However, we found that the digitized permit boundary data files covered majority but not all mining permit areas for reasons that include issues concerning manual digitization of older mining permits; we do not consider this finding as evidence for illegal mining.

Second, Non-EM disturbances such as forest harvest cause plant removal. For mining activities, removing top soil and stripping large amounts of rocks are the essential and key steps that follow plant clearing. Hence, we hypothesized that EM points will have a lower minimum NDVI values in the trajectory than will Non-MD disturbances. Third, compared with EM land after revegetation, highly developed areas (DP) such as roads, commercial/ industrial areas, and apartment complexes with high-percentage impervious surface cover may also have lower NDVI values. Once developed, such lands can be treated as permanently disturbed. Correspondingly, we expected that the NDVI values of DP pixels would be relatively stable subsequent to the disturbance, while the NDVI trajectory of EM pixels shows greater variability following the primary disturbance due to the nature of post-mining revegetation. Fourth, previously developed areas are rarely, if ever, disturbed by mining. However, some mined areas are converted into developed lands after mining is complete. As a result of these observations, we developed two hypotheses: (1) The minimum NDVI for (EM) mined lands will generally be lower than the minimum NDVI for Non-MD lands (other disturbances), and (2) the standard deviation of NDVI after disturbance will be greater for EM (mined lands) than for DP (developed) lands. As a means of testing the above hypothesis, we calculated the minimum NDVI value and NDVI

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Fig. 5 Mean NDVI and vegetated/bare ground threshold for each image in the time series

standard deviation for each training point for the study period. The result showed that these two parameters are effective indicators to distinguish EM land from NonMD and a large majority of DP, respectively. In order to reduce influence by seasonal differences on these tests, we conducted normalization processing for those four images acquired during the month of October (Fig. 5), when leaf coloration in the study area is typically altered by the physiological process of senescence resulting in generally lower NDVIs across the images and for the bare-ground threshold. NDVI values for each of these four images were normalized by adding the difference between its average NDVI value and the average NDVI for the 24 scenes. Following normalization, the minimum NDVI threshold and NDVI standard deviation threshold, 0.4205 and 0.1101, were determined by using CART classification method. Those pixels located inside the mining permit boundary whose minimum NDVIs were lower than 0.4205, or located outside the mining permit boundary whose minimum NDVIs were lower than 0.4205 and NDVI standard deviations were higher than 0.1101, were classified as EM. Otherwise, they were classified as OD. Visual inspection of those results revealed that EM lands which remained unvegetated for extended periods produced NDVI standard deviations similar to certain DP lands. Hence, these land types were not discriminated effectively by the procedure, and some highly developed lands were incorrectly classified by the procedure as EM. Therefore, we applied an additional procedure which used the NLCD 2011 dataset to improve the classification result. Lands initially classified as EM located outside of mining permit boundaries but classified by NLCD 2011 as Developed (Low Intensity, Medium Intensity, and High Intensity) were adjusted by changing classification from EM to OD.

Once these operations were complete, assignment of dates for initial mining disturbances was relatively straightforward. Each’s pixel’s NDVI spectral trajectory was analyzed. The first date on which an EM pixel’s NDVI was found to be less than the bareground threshold was defined as the year of mining disturbance. A map for the study area was generated which identified all EM pixels by mining disturbance year. Accuracy assessment Using the disturbance year map derived through the above method, a validation dataset consisting of 1180 points was created by ArcGIS. Dataset creation was initiated by placing points randomly within classified categories: 400 points within PV, 60 within OD, and 30 points within each mining date category. From this initial dataset, we manually deleted points if >1 were located in a homogenously classified pixel group such that only one remained. We also moved points within pixels located adjacent to pixels of a differing classification toward the interior of homogenously classified pixel groups, such that at least one pixel of identical classification separated it from the nearest pixel of differing classification. After completing this operation, a dataset comprised of 1123 validation samples was used for the accuracy assessment. We visually inspected all 24 Landsat images and all available NAIP images to determine, to the best of our ability, if each validation point belonged to the PV, OD, or EM. If coal mining disturbance (EM) was found, we recorded the mining date of that validation point correspondingly. The disturbance date defined in that way was treated as reliable


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(Healey et al. 2005; Huang et al. 2009). By comparing the derived reference datasets and the corresponding classification result on the map, the confusion matrix was constructed. Overall accuracy, kappa coefficient, and user’s and producer’s accuracies of each disturbed date class were calculated (Campbell and Wynne 2011). The overall accuracy was calculated by using diagonal entries divided by the total number of pixels examined, and the kappa coefficient was calculated as Eq. (1): N ^¼ κ

r X

xii −

r X

i¼1

N − 2

ðxiþ xþi Þ

i¼1 r X

ð1Þ

ðxiþ xþi Þ

i¼1

^ is the kappa coefficient, r is the classified where κ category number in the confusion matrix, N is the total number of samples, xi + and x+ i are the total number of row i and column i, respectively, and xii is the diagonal cell number. The resulting error matrix was utilized to produce corrected area estimates from the category-specific map marginals (proportion of total area mapped in a given category) following established methods elucidated in Card (1982), Wynne et al. (2000), and Musy et al. (2006). The 95 % confidence intervals for the true marginal proportions were calculated as plus or minus two times the square root of the variance.

Results and discussion Classification accuracy The confusion matrix, Table 2, provides classification accuracy which was calculated by various accuracy assessment measures. The overall accuracy is 0.9350. The kappa coefficient value is 0.9252. The user’s and producer’s accuracies of all classes ranged from 0.71 to 1.00, and 0.75 to 1.00, respectively. The user’s and producer’s accuracies of EM classes ranged from 0.71 to 1.00, and 0.79 to 1.00, respectively. Many errors occurred at the edges of mining polygons. The assignment of some pixels belonging to one disturbed year, such as 1992, to another disturbed year, such as 1984, normally occurred at the juncture area or adjacent EM area of

two disturbed dates. Some mined pixels located at linear mining sites, which may be even one or two pixels in width, were incorrectly assigned to OD. The long and narrow linear mining features typically occur along the contours of the region’s mountains at coal outcrops where coal is being accessed by means such as contour mining (the excavation of a narrow strip of land on a slope along a coal outcrop) or highwall mining (the excavation of a prior contour mine for the purpose of extracting additional coal with a penetrating device such as an auger). Cloud contamination during mining periods also contributed to classification errors, in some cases by increasing the time interval between the interpretable areas of the images. The lowest accuracies were obtained for 1994 (user’s accuracy), which was one of the October images where NDVIs were affected by leaf senescence; for 2005 (producer’s accuracy), which had earliest seasonal data of all the images used; for 1993 (producer’s accuracy), also a relatively early seasonal image; and for OD lands (other disturbances). All other calculated accuracies were ≥88 % (Table 2). Many of the EM lands incorrectly classified as OD within the mining permit polygons are in a condition, as evidenced by the NAIP aerial imagery, that suggests they were mined prior to 1984. It is possible that some mined lands may not have been classified as such due to a rapid mining and revegetation process; the highest likelihood of such occurrence would be during the 2-year gaps in the multitemporal image sequence. However, mined lands in this region often remain open without vegetation for >1-year periods as a complex mining process proceeds, often by accessing multiple coal seams that overlie one another but with intervening non-coal geologic materials that must be excavated sequentially. During our study, we did not become aware of any mining disturbances that our methods failed to detect for this reason.

Mined land area estimation and spatial distribution The area estimation of different classified types is one well-known application of remote sensing (Canters 1997). Mined (EM) land area estimation has significant implications for mining and reclamation monitoring and policy making, which makes it

User’s acc.

PV OD 1984 1985 1986 1987 1988 1989 1990 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2010 2011 Column total Produc. acc.

75.3

98.9

1998

1999

Reference data

1 93

1

1

1 1 3

1 1

13 70

OD

362

358 4

PV

Reference data

2000

96.3

27

1 26

1984

2001

92.0

25

23

1 1

1985

2002

86.4

22

19

1 1 1

1986

2003

95.8

24

23

1

1987

2004

96.7

30

29

1

1988

2005

2007

92.9

28

1

26 1

1989

2008

100

26

26

1990

2010

92.6

27

25

1 1

1992

2011

80.0

30

1 24

1

1 3

1993

100

20

20

1994

100

25

25

1995

Row total

90.3

31

28

1

1 1

1997

Table 2 Confusion matrix derived using the design-based accuracy assessment method for persistent vegetation (PV) and non-mining disturbances (OD) across the full study period, and for mining disturbances by year of initial disturbance

557 Page 10 of 17 Environ Monit Assess (2015) 187: 557

28

1

1

2003

2

2004

1

1

2

2005

1

1

2007

29

1

1

2008

1

2010

2011 383 29

88

29

2 29

31

28

31

100

88.2

91.7

User’s accuracy (acc.) and producer’s accuracy are expressed as percents

93.9

93.3

93.9

93.5

78.6

93.5

93.5

31

96.3

100

23

33

23 24

27

33

30

2011

Column total Produc. acc.

34

22

26 28

30

2010

2008

2007

2005

2004

2003

2002

2001

2000

Overall acc.

93.5

95.8

24 1123

100

96.7

100

95.7

93.5

100

88.0

96.8

96.6

100

100

100

92.6

71.4

100

96.2

96.3

96.3

96.7

95.8

90.5

100

89.7

79.5

93.5

User’s acc.

26

30

29

23

31

31

25

31

29

31

28

1998

1999

28

1997

31

1994

31

24 28

1993

28

26

1992

27

27

1990

1

27

1989

1995

30

1988

1

24

1987

22

1

1

2002

21

1

1

2001

1986

1

1

2000

23

1

1

1999

Row total

1985

1984

OD

PV

1998

Reference data

Table 2 (continued)

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necessary to pay attention to quality of area estimation and to assess the estimates’ reliability. Proportions (±95 % confidence intervals) for the mapped categories in Virginia’s southwestern coalfield are as follows: PV, 84.20±1.11 %; OD, 7.62±1.20 %; and EM, 8.18±0.57 %. The area of surface mining detected by the analysis (EM) is 328.60±22.90 km2, which includes the unreclaimed mined land disturbed prior to the observation period; PV and OD areas are 3383.51±44.60 and 306.21±48.22 km2, respectively. Although the overall fraction of land within the Southwest Virginia coalfield disturbed by surface mining, as detected over the 1984–2011 study period, is less than 10 % of total land area, certain portions of the area have been disturbed by mining more intensively (Fig. 6). Most of the EM lands are located in Virginia’s three major coal mining counties, Wise, Buchanan, and Dickenson. These three counties, together, accounted for 88 % of total 1984–2011 mining disturbance. Of these three counties, Wise County was, by far, the most intensively affected by surface mining with 183 km2 of mining disturbance over the study period (Figs. 7 and 8; Li et al. 2015). Surface mining in Wise County accounted for 55 % of the total mining disturbance detected in the entire Virginia coalfield over the study period, and nearly 20 % of the county area located within the Appalachian coalfield was disturbed by mining activities over the study period (Table 3). Mining disturbances through time A total of 77.47 km2 was classified EM with a mining date of 1984, far greater than the annual average for all analysis years extending from 1984 to 2011, 13.69 km2 year−1 (σ=14.12; Fig. 6). The large quantity EM land estimated by our methods for 1984 was primarily due to lands which were mined prior to 1984 and had not been revegetated by 1984 and, hence, were identified as EM lands with a mining date of 1984. Some mining areas, especially within complex mining operations, can remain in a disturbed and non-vegetated condition for several years. The EM lands with nominal mining dates of 1997 and 2007, 18.49 and 23.07 km2, respectively, include unclaimed EM land disturbed during the prior year when a leaf-on Landsat image with low cloud contamination was not accessible. In


addition, clouds and cloud shadows also affect the inter-annual analyses of results. Taking years 1991, 1996, 2006, and 2009 into account but excluding 1984, average EM land increase is 9.30 km2 year−1. Average EM land per year (1985 through 2011, 27 years), in Buchanan, Dickenson, Wise, and other counties, was 1.77, 1.35, 5.00, and 1.18 km2, respectively. Virginia mined land disturbances in a broader temporal context The disturbances detected by our study can be viewed in a broader temporal context. Since the mid-2000s, Virginia surface coal mine production has fallen precipitously. Hence, it is likely that the rate of additional land disturbance by mining disturbance has slowed as well. From an average of ~10 million tons per year produced over the 2000–2009 decade and a peak production of 11.4 million tons in 2005, Virginia surface mine production has been declining steadily; 8.2 million tons was produced in 2010, but surface production declined to 4.4 million tons in 2013 (US EIA 2015a), while total coal production, undifferentiated by mine type, declined by another 10 % in 2014 relative to 2013 levels (US EIA 2015c). Factors causing Appalachian coal production declines include increasing displacement of coal by natural gas as an electric generation fuel in the eastern USA (US EIA 2015c), and increasing regulatory controls on air emissions by electric generators (US EPA 2012) which have also depressed coal demand by providing regulatory incentives for switching fuels from coal to gas (US EIA 2015c). Electric generation demand is the primary market for US coal production (US EIA 2015a). Looking to the future, further declines of Appalachian coal production are expected (US EIA 2015b). Increased environmental controls on water resource effects by surface mining (e.g., Copeland 2015; US EPA 2015) have also contributed to Appalachian surface mine production declines. Looking to the past, however, makes it clear that the mining disturbances documented by our study extend an earlier legacy of mining disturbance in the study area. A report prepared in 1980 documented the existence of ~280 km2 of mining disturbance in the Virginia coalfield as of that time (D’Appolonia Inc 1980). Pre-1980 disturbances cannot be added to our totals for the purpose of estimating cumulative


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Fig. 6 Cumulative land disturbance in Virginia’s southwestern coalfield (as detected in 1984–2011) by year of initial detection for the disturbance for the three major mining counties (Buchanan,

Dickenson, and Wise) and the other four coalfield counties combined (left axis), and annual incremental disturbance for all counties combined (bars, right axis)

disturbance because much of the mining that we documented took place on previously mined areas. It is clear, however, that pre-1984 disturbances remain in evidence in the southwestern Virginia coalfield today, and, hence, our estimates are not a complete inventory of the area’s mining disturbance. As a means of extending our estimates, we identified lands classified as OD by our study methods that occur within the mine-permit boundaries. Our own experience and a careful review of aerial photography indicate that the vast majority of these areas appear as pre-1984 mining disturbances. Hence, we can extend our estimate of mining disturbances in the Virginia coalfield by considering these areas as well as quantities determined by our study methods (Table 3) to approach more closely the full extent of disturbance. We do not expect the sum of withinpermit OD lands and our estimate of 1984–2011 mining disturbances to constitute a complete record of southwestern Virginia’s mining disturbance,

however, because we are aware of pre-1984 mined lands that have become well vegetated and, hence, were not classified as OD. It is also possible that some of the OD lands within the mining permits were disturbed by activities other than mining. However, our experience and review of aerial imagery indicate that a large majority of OD lands within the mining permit boundaries were disturbed by pre1984 mining or are located adjacent to areas defined as EM by our procedures, suggesting that such disturbances were mining-related. Although we see our study as having been executed successfully, it leaves further questions unanswered. The Southwest Virginia coalfield constitutes a small fraction of the Appalachian coalfield, which occurs within an ecosystem that is among the most diverse non-tropical terrestrial ecosystems on planet Earth (Ricketts et al. 1999; Riitters et al. 2000). Many authors have raised questions concerning cumulative effects of surface coal mining over the past

Fig. 7 PV, OD, and EM land distribution in study area. The yellow rectangle shows location of Fig. 8

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Fig. 8 Mining disturbance year map for the portion of the study area that is represented by the yellow rectangle in Fig. 7

half-century on ecosystems across the Appalachian coalfield (Wickham et al. 2007; Drummond and Loveland 2010; Wickham et al. 2013). The method described, or some variation thereof, has potential for application across larger sections of the Appalachian coalfield as a means of beginning to

Table 3 Summary of cumulative Appalachian coalfield disturbance, by county

a

Data for Wise County includes the City of Norton, which is bounded on all sides by the lands of Wise County

County

answer such questions by clearly defining the extent of mining disturbance. Similarly, questions concerning restoration and recovery of mining-affected ecosystems at landscape scale in the Appalachian coalfield remain unanswered. It is possible that processes of natural succession will,

Total Appalachian coalfield area

Total mining disturbance, all detection years (1984–2011)

(km2)

(km2)

Buchanan

1305.29

62.45

Dickenson

862.27

Lee

130.32

Russell

Fraction of coalfield area disturbed, all detection years (1984–2011) (% of area)

Additional lands classified as OD within mine permit boundaries (km2)

4.78

12.35

44.92

5.21

11.33

13.27

10.18

2.17

292.82

16.42

5.61

4.38

Scott

183.47

0.88

0.48

0.12

Tazewell

310.44

7.42

2.39

1.61

941.34

183.23

19.46

26.31

4025.94

328.60

8.16

58.26

a

Wise

Total coalfield (all counties)


eventually, restore Appalachian forest ecosystems on mine sites (Brenner et al. 1984). Today, new reclamation methods are being applied on active mines with the goal of accelerating those processes (Zipper et al. 2011b). However, recent studies of mine sites reclaimed using conventional reclamation practices reveal little evidence of terrestrial ecosystem recovery by natural processes (Simmons et al. 2008; Zipper et al. 2011a). Successful development of methods for interpreting Landsat data to characterize terrestrial ecosystem status on natural areas (Kennedy et al. 2014) indicates that there is potential for similar applications on mined lands. However, clear definition of the extent and timing of disturbance, as we have done here, is a prerequisite for efforts to answer such questions.

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Our study revealed that mining disturbance has been intensive in a segment of the central Appalachian coalfield. From 1984 to 2011, a 329km2 land, about 8 % of the study area, was disturbed by mining. Mining disturbance was progressive, with annual areas affected varying from year to year but averaging 9.30 km2 (about 0.2 % of the study area) year−1 from 1985 to 2011. The mining, however, was not evenly distributed over the study area, as more than half of the total disturbance occurred in a single county, and approximately 20 % of that county’s Appalachian coalfield area was disturbed by mining within the study period. Our methods were able to provide an indication of the spatial extent by mining disturbances that occurred prior to the study period but are unable to provide a full accounting of those disturbances.

Conclusion Time series analysis of Landsat TM/ETM+ imagery was well suited for the task of identifying surface coal mining locations and quantifying mining disturbances over a broad area by approximate date of initial disturbance. Hence, the data generated also demonstrate cumulative effects of mining in the study area, as overall landscape disturbance has progressed with time. The method was enabled by the Landsat’s temporally rich archive, spatial and spectral resolution, and easy availability of images. The NDVI was found well suited for identifying earth disturbance by mining. In our study, a three-step approach based on temporal NDVI trajectory analysis was proposed and verified as effective for reconstructing mining history in an intensively mined landscape. Our method offers potential for application over larger areas of the Appalachian coalfield. Data generated through application of our method, and of more advanced techniques for characterizing terrestrial ecosystem disturbance and recovery that are being applied to forested ecosystems, have potential to illuminate the true nature of environmental restoration and management challenges being left in the wake of the intensive coal surface mining that has occurred over the past half-century in the eastern USA’s Appalachian coalfield. Although clouds occur frequently in the study area due to its humid climate, the data-rich Landsat archive enabled the study’s successful execution despite those obstacles.

Acknowledgments We are grateful for support provided by China Scholarship Council. We appreciate Dr. Jie Ren’s help on post-classification process and Dr. Yang Shao’s recommendation on this paper. We also thank the US Geological Survey, USDA Farm Service Agency, and Virginia Department of Mines Minerals and Energy (DMME) for open access to the data. We offer sincere thanks to Daniel Kestner, Virginia DMME, for his advice and assistance to our study efforts.

Conflict of interest The authors declare that they have no conflict of interest.

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