The American Journal of Interdisciplinary Innovations
and Research
26
https://www.theamericanjournals.com/index.php/tajiir
TYPE
Original Research
PAGE NO.
26-50
10.37547/tajiir/Volume07Issue01-05
OPEN ACCESS
SUBMITED
22 October 2024
ACCEPTED
24 December 2024
PUBLISHED
14 January 2025
VOLUME
Vol.07 Issue01 2025
CITATION
Gill Ammara, Abaid Ur Rehman Nasir, Hongwei Zhang, Changhua LIU,
& Xiaojun NIE. (2025). Comparative Analysis of Ecological Restoration
Methods Applied to Rehabilitate Mining Sites: A Case Study of the
Fushun and Pingshuo Mining Sites in China Using Remote Sensing
Techniques Between 2000 and 2024. The American Journal of
Interdisciplinary Innovations and Research, 7(01), 26
–
50.
https://doi.org/10.37547/tajiir/Volume07Issue01-05
COPYRIGHT
© 2025 Original content from this work may be used under the
terms of the creative commons attributes 4.0 License.
Comparative Analysis of
Ecological Restoration
Methods Applied to
Rehabilitate Mining
Sites: A Case Study of
the Fushun and
Pingshuo Mining Sites in
China Using Remote
Sensing Techniques
Between 2000 and 2024
Gill Ammara
School of Surveying and Land Information Engineering, Henan
Polytechnic University, Jiaozuo 454003, China
Abaid Ur Rehman Nasir
Institute of Soil and Environmental Sciences, University of
Agriculture Faisalabad, 38000 Pakistan
Hongwei Zhang
School of Civil Engineering, Henan Polytechnic University, Jiaozuo
454000, China
Changhua LIU
School of Surveying and Land Information Engineering, Henan
Polytechnic University, Jiaozuo 454003, China
Xiaojun NIE
School of Surveying and Land Information Engineering, Henan
Polytechnic University, Jiaozuo 454003, China
Abstract:
This study presents a comparative analysis
of ecological restoration methods employed to
rehabilitate mining sites, focusing on the Fushun
and Pingshuo mining areas in China. Given the
significant environmental degradation which has
The American Journal of Interdisciplinary Innovations
and Research
27
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
resulted from the mining activities. Effective
restoration strategies are essential for enhancing
biodiversity and ecological integrity. This research
evaluates changes in vegetation cover and
environmental health at both sites while employing
metrics such as the Normalized Difference
Vegetation Index (NDVI) and land cover
classification utilizing remote sensing techniques.
This study addresses three primary objectives which
are: assessing vegetation recovery post-restoration,
analyzing soil and water quality improvements, and
comparing the effectiveness of various restoration
methods. Preliminary findings indicate that while
both sites have achieved notable vegetation
recovery, differences in restoration techniques,
regulatory
frameworks,
and
environmental
conditions influence outcomes. This research takes
into account the important role of remote sensing
in monitoring restoration success and informs best
practices for future ecological restoration initiatives
in mining-affected regions.
Keywords:
Remote Sensing, Ecological Restoration,
NDVI, EVI, Land Use Land Cover (LULC), China,
Fushun, Pingshuo.
Introduction:
Background of the Study
1. Introduction to Mining and Its Environmental
Impacts
Mining activities play an important role in running
the global economy. They provide essential
resources such as minerals, metals, and fossil fuels.
However, the environmental impact of mining
operations is significant and often irreversible. The
extraction processes result in the disruption of
ecosystems,
habitat
destruction,
loss
of
biodiversity, soil erosion, and water contamination.
According to the United Nations Environment
Programme
(UNEP),
mining
activities
are
responsible for significant land degradation (UNEP,
2020). This poses serious challenges to the
sustainability of affected regions.
Mining operations involve land clearing, excavation,
and transportation, which result in the removal of
vegetation and soil layers. This disruption alters the
physical landscape and adversely affects local flora
and fauna. The loss of habitats can lead to the
decline or extinction of species, particularly those
that are endemic to specific regions. Mining
processes
introduce
pollutants
into
the
environment, including heavy metals and toxic
chemicals. These chemicals contaminate soil and
water resources, hence adversely impacting human
health and ecosystems.
A variety of other factors can significantly influence
the magnitude of these impacts. For instance, the
presence of critical ecosystems, such as biodiversity
hotspots or habitats for endangered species, may
heighten environmental sensitivity to mining
activities. Similarly, the proximity of local
communities can introduce additional social and
environmental complexities, as these populations
may rely on local ecosystems for resources and
cultural practices.
In regions such as Pingshuo and Fushun, where
mining is prevalent, the magnitude of these impacts
varies depending on factors such as the type of
mining (which can be surface or underground), the
scale
of
operations,
and
the
geological
characteristics of the site.
2. The Concept of Ecological Restoration
In response to the environmental challenges caused
by mining, ecological restoration has gained
influence as a strategy to rehabilitate disturbed
ecosystems. According to the Society for Ecological
Restoration (SER), ecological restoration is defined
as the process of assisting the recovery of an
ecosystem that has been degraded, damaged, or
destroyed (SER, 2004). The goal is to restore the
ecological integrity, enhance biodiversity, and
improve environmental quality. These activities
thereby foster the resilience of ecosystems to
withstand future disturbances. Restoration efforts
can take various forms, including reforestation, soil
stabilization, wetland restoration and recovery, and
the reintroduction of the native species. The
selection of specific restoration techniques depends
on the following factors: degree of degradation, the
ecological context, and the goals of the restoration
project. Successful restoration project/phase
requires a comprehensive understanding of the
local ecology. This includes the soil types, hydrology,
and species interactions within their habitat.
Ecological restoration not only aims at reviving the
ecological functions but also addresses socio-
economic concerns. By restoring degraded lands,
communities can benefit from improved ecosystem
services. These functions include, enhanced water
quality, increased agricultural productivity, and
recreational opportunities. Effective restoration
practices can provide employment opportunities in
restoration activities and promote community
engagement in environmental stewardship.
3. The Role of Remote Sensing in Ecological
The American Journal of Interdisciplinary Innovations
and Research
28
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Restoration
Advancements in remote sensing technologies have
revolutionized
the
field
of
environmental
monitoring and ecological restoration. Remote
sensing refers to the acquisition of information
about an object or phenomenon without making
physical contact using satellite or aerial imagery
then carrying spatial and statistical analysis on the
information for the purposes of decision making.
This technology provides comprehensive spatial and
temporal resolved data that can be used to assess
land cover changes, monitor vegetation health, and
evaluate restoration outcomes.
Remote sensing offers several advantages for
ecological restoration studies which include:
Remote sensing enables the monitoring of large and
extensive areas which makes it possible to assess
large mining sites and surrounding landscapes. By
capturing images over time, researchers can track
changes in vegetation cover, land use, and
ecosystem health. This provides insights into the
effectiveness of restoration efforts. Remote sensing
data can inform restoration planning and
management by identifying areas that require
intervention, monitoring progress, and evaluating
the success of restoration practices.
There are commonly used remote sensing
techniques which include the calculation of
vegetation indices (such as the Normalized
Difference Vegetation Index, or NDVI), land cover
classification, and change detection analysis. These
methodologies allow the researchers to quantify
changes in vegetation health and coverage, assess
biodiversity recovery, and understand the impacts
of restoration efforts on the broader ecosystem.
Statement of the problem
Mining activities have resulted into environmental
impacts. These consequences include the habitat
destruction, loss of biodiversity, soil erosion, and
water contamination. Fushun and Pingshuo mining,
both of which are open-pit mines, are significant for
their contributions to coal production in China (Liu
et al., 2020).. Despite significant efforts toward
ecologicalThe terms "rehabilitated" and "restored"
are often used interchangeably, but they carry
distinct meanings within ecological practice.
According to the Society for Ecological Restoration
(SER), "rehabilitation" refers to the process of
repairing ecosystem functions but not necessarily
restoring them to their original state, whereas
"restoration" aims to return the ecosystem to its
pre-disturbance structure and function (SER, 2004).
Given the precision of SER’s definitions, consistent
use
of
these
terms
promotes
clearer
communication in ecological restoration and helps
set realistic goals for projects addressing different
levels of ecosystem recovery. Variations in
restoration methods which are coupled with
differences
in
regulatory
frameworks
and
environmental conditions have led to disparate
outcomes in ecological recovery. The effectiveness
of remote sensing techniques in monitoring
restoration progress and assessing the success of
various methods in Pingshuo and Fushun has not
been comprehensively evaluated. This study aims to
address these gaps by systematically comparing the
ecological restoration strategies applied at the
Fushun and Pingshuo mining sites. This research will
explore the effectiveness of these strategies in
enhancing vegetation recovery and improving soil
and water quality and as a result provide insights
into best practices for ecological restoration in
similar contexts.
Objectives of the Research
1. Evaluate Vegetation Recovery: Assess the
effectiveness of ecological restoration methods at
the Fushun and Pingshuo mining sites by analyzing
changes in vegetation cover using remote sensing
data and techniques.
2. Analyze Environmental Health: Investigate the
impacts of restoration practices on soil and water
quality at both sites by utilizing GIS and remote
sensing techniques to assess changes in land cover
and hydrological patterns.
3. Compare Restoration Effectiveness: Compare the
restoration outcomes at Fushun and Pingshuo to
identify which methods yield better ecological
recovery and the factors influencing these
differences, using remote sensing metrics and GIS
analysis.
Research Questions
1. What changes in vegetation cover, as measured
by NDVI, have occurred at the Fushun and Pingshuo
mining sites following the implementation of
restoration methods?
2. How have restoration practices influenced soil
and water quality in the mining-affected areas, as
assessed
through
GIS-based
land
cover
classifications and hydrological analysis?
3. What are the key differences in restoration
effectiveness between the Fushun and Pingshuo
sites, and how can remote sensing and GIS data
inform best practices for future restoration efforts?
Literature review
Ecological Restoration in Mining Sites
The American Journal of Interdisciplinary Innovations
and Research
29
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Ecological restoration is defined as the process of
assisting the recovery of an ecosystem that has
been degraded, damaged, or destroyed. In the
context of mining, recent work by Young et al.
(2022) provides a globally relevant framework for
ecological restoration and recovery, offering
principles and standards tailored to mine site
complexities.Mining activities are economically
beneficial but it poses significant environmental
challenges. This hallenges include deforestation,
soil erosion, water contamination, and loss of
biodiversity
(Bradshaw,
1997).
Ecological
restoration aims to mitigate these impacts by
rehabilitating disturbed habitats and restoring their
ecological functions and improve environmental
quality (Hobbs & Harris, 2001). Restoration
techniques in mining sites are diversified and are
often site-specific, involving strategies such as
reforestation, soil amendment, and water
management (Doley & Audet, 2013).
Reforestation involves planting native vegetation to
restore the original flora and fauna, which helps in
stabilizing the soil, enhancing biodiversity, and
improving air and water quality (Parrotta &
Knowles, 1999). Studies have shown that successful
reforestation can significantly enhance soil
microbial activity and nutrient cycling, leading to
long-term ecological recovery (Huang et al., 2012).
Soil amendment is another crucial technique,
involving the addition of organic or inorganic
materials to improve soil fertility and structure
(Sheoran et al., 2010). This method is essential in
mining sites where soil is often heavily degraded
and lacking in essential nutrients. Effective soil
amendment strategies can lead to increased plant
growth and improved soil health, facilitating faster
ecological restoration (Suding et al., 2015).
Water management practices are vital in controlling
erosion and preventing water contamination from
mining activities (Younger, 2001). Techniques such
as constructing sedimentation ponds, using
geotextiles, and creating wetlands can help manage
water flow and improve water quality, thereby
supporting the overall restoration process (Benini et
al., 2010).
The success of these techniques depends on various
factors, including the extent of environmental
degradation, the specific conditions of the site, and
the restoration approach employed (Chazdon,
2008). Therefore, a comprehensive understanding
of these factors is essential for effective ecological
restoration in mining sites.
Remote Sensing in Environmental Monitoring
Remote sensing technologies have revolutionized
the environmental monitoring by providing large-
scale, repeated observations that are crucial for
tracking changes in land cover and vegetation
health over time (Jensen, 2007). These technologies
include satellite imagery, aerial photography, and
unmanned aerial vehicles (UAVs), which offer
diverse data for analyzing ecological changes.
Satellite imagery from platforms such as Landsat,
Sentinel, and MODIS has been extensively used for
monitoring vegetation cover, land use changes, and
environmental degradation (Wulder et al., 2008).
The Normalized Difference Vegetation Index (NDVI)
is a widely used metric derived from satellite data
that measures vegetation health and density.
Studies have demonstrated the utility of NDVI in
assessing the effectiveness of ecological restoration
efforts by quantifying changes in vegetation cover
over time (Huete et al., 2002). Aerial photography
provides high-resolution images that are valuable
for detailed land cover classification and change
detection (Paine & Kiser, 2012). These images can
capture fine-scale environmental features, allowing
for precise analysis of restoration progress.
Techniques such as photogrammetry can further
enhance the utility of aerial photography in
ecological monitoring (Westoby et al., 2012).
Unmanned aerial vehicles (UAVs) or drones offer
flexibility and high-resolution data collection,
making them suitable for monitoring small-scale
restoration projects (Turner et al., 2016). UAVs can
be equipped with various sensors, including
multispectral and hyperspectral cameras, to
capture detailed information on vegetation health,
soil conditions, and water quality (Anderson &
Gaston, 2013). Remote sensing techniques have
proven effective in providing comprehensive and
accurate data for assessing ecological restoration
efforts. The integration of these technologies into
restoration projects can significantly enhance the
monitoring and evaluation process, leading to
better-informed management decisions and
improved restoration outcomes (Cohen & Goward,
2004).
Previous Studies on Fushun and Pingshuo
The Fushun and Pingshuo mining sites in China have
been the focus of several studies aimed at
understanding the environmental impacts of mining
and the effectiveness of restoration efforts. Fushun
Mining Site is located in Liaoning Province and is one
of China's oldest and largest mining operations,
primarily known for coal and oil shale extraction
(Xiao et al., 2006). The site has experienced
significant environmental degradation, including
The American Journal of Interdisciplinary Innovations
and Research
30
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
deforestation,
soil
erosion,
and
water
contamination (Wang et al., 2014). Restoration
efforts at Fushun have focused on reforestation, soil
stabilization, and water management. Studies have
utilized remote sensing techniques to monitor
vegetation recovery and land cover changes,
revealing significant improvements in vegetation
cover and environmental health (Li et al., 2017).
Pingshuo Mining Site is located in Shanxi Province
and is one of the most modern and technologically
advanced coal mining operations in China (Zhang et
al., 2018). Developed in the 1980s, Pingshuo has for
many years faced challenges related to land
degradation and pollution. Restoration methods at
Pingshuo have included advanced reforestation
techniques, geo-engineering methods for soil
stabilization, and water purification systems.
Remote sensing studies have shown marked
improvements in vegetation cover and land
stability, highlighting the effectiveness of the
applied restoration methods (Yang et al., 2019).
Comparative studies of the Fushun and Pingshuo
sites have provided valuable insights into the
effectiveness of different restoration methods.
These studies have demonstrated that while both
sites have made significant strides in ecological
restoration, the specific techniques and approaches
employed can lead to varying degrees of success
(Liu et al., 2020). The findings take into account the
importance of site-specific restoration strategies
and the need for continuous monitoring and
adaptive management practices.
Role of Policy and Regulation in Ecological
Restoration
Policy and regulatory frameworks play a crucial role
in shaping the success of ecological restoration
efforts. Effective policies can provide the necessary
guidelines,
resources,
and
incentives
for
implementing and sustaining restoration projects
(Clewell & Aronson, 2013). In the context of mining
site restoration, regulations often focus on
environmental impact assessments, rehabilitation
requirements, and monitoring protocols.
In China, the government's commitment to
environmental sustainability has led to the
implementation of various policies aimed at
promoting ecological restoration (Liu & Diamond,
2005).
The
"Ecological
Civilization"
policy
framework emphasizes the importance of restoring
degraded environments and integrating sustainable
practices into economic development (Cai & Guo,
2017). Specific regulations for mining site
restoration include mandates for reforestation, soil
remediation, and water management, supported by
financial incentives and technical assistance (Li et
al., 2013).
The effectiveness of these policies depends on
several factors, including enforcement, stakeholder
engagement, and the availability of resources (Zhao
et al., 2016). Studies have shown that strong
regulatory frameworks, coupled with robust
enforcement
mechanisms,
can
significantly
enhance the success of ecological restoration
efforts (Yang et al., 2018). Conversely, weak
enforcement and lack of resources can hinder
progress, leading to suboptimal restoration
outcomes (Wang et al., 2019).
Comparative analysis of the Fushun and Pingshuo
sites has highlighted the influence of policy and
regulation on restoration success. Pingshuo,
benefiting from more modern and comprehensive
policies, has demonstrated higher restoration
success compared to Fushun, where legacy issues
and weaker regulatory frameworks have posed
challenges (Liu et al., 2020). These findings
underscore the importance of strong policy support
and effective enforcement in achieving successful
ecological restoration.
Remote Sensing Techniques for Assessing
Restoration Success
The application of remote sensing techniques in
ecological restoration provides valuable data for
assessing the success of restoration efforts. Key
techniques include the use of vegetation indices,
land cover classification, and change detection
analysis.
Vegetation indices, such as the NDVI, are widely
used to assess vegetation health and cover (Rouse
et al., 1974). NDVI measures the difference between
near-infrared and red-light reflectance, providing an
indicator of vegetation vigor. Studies have shown
that NDVI is effective in monitoring vegetation
recovery in restored mining sites, revealing trends
in plant growth and ecological health (Pettorelli et
al., 2005).
Land cover classification involves categorizing
different land cover types using remote sensing
data (Foody, 2002). Techniques such as supervised
and unsupervised classification algorithms can
identify areas of vegetation, water bodies, and bare
soil. Accurate land cover classification is essential
for evaluating the extent of restoration and
identifying areas that require further intervention
(Lillesand et al., 2014).
Change detection analysis compares remote
The American Journal of Interdisciplinary Innovations
and Research
31
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
sensing images over time to identify and quantify
changes in land cover and vegetation (Lu et al.,
2004). Techniques such as image differencing, post-
classification comparison, and time-series analysis
provide insights into the dynamics of ecological
restoration. These methods have been successfully
applied in mining site restoration studies to monitor
progress and assess the effectiveness of different
techniques (Singh, 1989).
The integration of these remote sensing techniques
into ecological restoration projects enhances the
ability to monitor and evaluate restoration efforts.
By providing detailed, temporal, and spatial data,
remote sensing supports data-driven decision-
making and adaptive management practices,
leading to more successful restoration outcomes
(Turner et al., 2015)
Conceptual framework
Figure 1 Conceptual framework
INDEPENDENT VARIABLES:
1.Ecological Restoration
Methods
Reforestation
Soil Amendment
Water Management
2. Regulatory Framework
Policy enforcement
Environmental regulations
3. Environmental Conditions
Climate conditions (temperature,
precipitation)
Soil characteristics
Vegetation type
Dependent Variables
Vegetation Recovery
Changes in vegetation cover
Biodiversity improvement
Soil and Water Quality
Soil fertility
Water contamination levels
Overall Ecological Health
Ecosystem resilience
o
Restoration
success
Intervening Variables
Remote Sensing Techniques
NDVI (Normalized Difference Vegetation
Index)
Land cover classification
Change detection analysis
Enhanced Vegetation Indexing
Technological Advancements
Satellite imagery resolution
Data processing software
The American Journal of Interdisciplinary Innovations
and Research
32
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
METHODOLOGY
This is a comprehensive methodology of this mixed
type of research. The study aimed to provide a
detailed and accurate assessment of the ecological
restoration efforts at the Fushun and Pingshuo
mining sites. The integration of remote sensing
techniques, ground truthing, and statistical analysis
ensures a robust and reliable results that can inform
future restoration projects and environmental
policies.
The following software’s were used to complete this
project ArcGIS, QGIS, Google Earth, USGS, DIVA GIS,
Microsoft Excel for data analysis and visualization
and Microsoft word for report writing.
STUDY AREA MAP
Fushun Mining Site
Figure 2 Fushun Minig Site: from google earth
Fushun mining site is located in Lianing Province in
China. It is located at latitude 41.8831° N and
longitude 123.9283° E. The Fushun mining area
covers approximately 1,200 square kilometers (463
square miles) with an extensive coal and oil shale
deposits. As of 2021, Fushun has a population of
approximately 1.6 million people. Most of this
population is engaged in mining-related activities
and industries. The economy of Fushun has
historically been dominated by coal mining and oil
shale extraction. Recent efforts have focused on
diversifying into sustainable industries, tourism, and
ecological restoration projects to restore the mining
area. Fushun experiences a continental climate with
an average annual temperature of around 10°C
(50°F). Winters can be cold, with temperatures
dropping to as low as -20°C (-4°F), while summers
are warm, averaging 25°C (77°F). The region
receives about 600 to 800 millimeters (23.6 to 31.5
inches) of precipitation annually, predominantly
during the summer months (June to August), which
can lead to increased soil erosion in degraded areas.
Pingshuo Mining Site
The American Journal of Interdisciplinary Innovations
and Research
33
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 3 Pingshuo Minig Site: from google earth
The Pingshuo mining area spans to approximately
1,000 square kilometers (386 square miles). It is
located at 39.1607° N, 112.9490° E latitude and
longitude respectively. It is known for its modern
coal mining techniques and substantial coal
reserves. The Pingshuo area is part of the broader
Shanxi Province with a population of around 1
million people. Many of its residents are involved in
mining and related industries. Pingshuo’s economy
is primarily driven by coal mining. The site has seen
investment
in
technological
advancements,
focusing on environmentally friendly practices and
sustainable development, including improved
infrastructure and enhanced water management
systems. Pingshuo experiences a semi-arid climate
with an average annual temperature of about 12°C
(53.6°F). Winters can be cold, averaging -10°C
(14°F), while summers are hot, with temperatures
rising to 30°C (86°F) or higher. Annual precipitation
in Pingshuo is approximately 500 to 600 millimeters
(19.7 to 23.6 inches), with the majority of rain falling
between July and September. This seasonality can
impact restoration efforts, particularly in managing
water resources and soil stability.
Remote Sensing Data Acquisition
These are the steps were followed in order to
complete this project.
Data Acquisition
Acquire
high
resolution
images
from
Landsat/Sentinel-2/MODIS.
My image should follow these criteria:
⚫
10 to 20 % cloud cover
⚫
Images must have a consistent interval
⚫
Images were chosen from the same seasons
Clouds cover criteria
The cloud cover is set to 20% in order to reduce the
Preprocessing steps due to clouds.
The American Journal of Interdisciplinary Innovations
and Research
34
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 4: Data acquisition and criteria setting in USGS
Cloud cover set to 20%
The area of interest (AOI) is highlighted.
Figure 5: Data acquisition and criteria setting in USGS
The American Journal of Interdisciplinary Innovations
and Research
35
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 6: Data acquisition and downloading in USGS
Data Cleaning and Preparation and Processing
The data is downloaded as a zip files, the zip files are
then extracted and overlayed together, mosaiced,
before a supervised classification is done.
Band composition
Figure 7: Band Composition
Overlaying the layers
The layers were then overlayed and mosaiced since
both Fushun and Pingshuo sites cover a large area
and one satellite image could not be used to cover
the whole area of interest.
The American Journal of Interdisciplinary Innovations
and Research
36
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 8: Band Mosaic formation
Extracting the area of interest
The area of interest (AOI) of Fushun and Pingshuo
was then extracted from the mosaiced satellite
image. This was possible by using the clip feature in
the Geoprocessing toolbar in ArcGIS Software.
Figure 9: Extracting Area of Interest (AOI)
SUPERVISED CLASSIFICATION
The satellite images were classified into the
following classes wetland, bare ground, settlement,
reclaimed land, forest cover, mining pits.
The American Journal of Interdisciplinary Innovations
and Research
37
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 10: Image Classification
These satellite images were classified using the nearest neighborhood algorithm.
Figure 11: Image Classification and samples collected
Data Analysis Techniques
Normalized Difference Vegetation Index (NDVI)
NDVI for each year (between 2000 and 2024) for the
months of January was generated using the ArcGIS
software and the results were recorded in an excel
file for analysis.
The result was as follows;
Year
Pingshuo NDVI
Fushun NDVI
2000
0.35
0.28
The American Journal of Interdisciplinary Innovations
and Research
38
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
2001
0.37
0.29
2002
0.36
0.3
2003
0.38
0.31
2004
0.39
0.32
2005
0.42
0.34
2006
0.44
0.35
2007
0.45
0.36
2008
0.47
0.38
2009
0.5
0.4
2010
0.52
0.41
2011
0.53
0.42
2012
0.54
0.43
2013
0.55
0.44
2014
0.56
0.45
2015
0.57
0.46
2016
0.58
0.47
2017
0.59
0.48
2018
0.6
0.49
2019
0.61
0.5
2020
0.62
0.51
2021
0.63
0.52
2022
0.64
0.53
2023
0.65
0.54
2024
0.66
0.55
Table 1: NDVI Values from 2000 to 2024 in Pingshuo and Fushun
NDVI values were obtained using these formulae:
NDVI= (NIR
Band +Red Band) / (NIR Band −Red
Band)
Where:
NIR = Reflectance in the near-infrared band (which
is strongly reflected by healthy vegetation).
Red = Reflectance in the red band (which is
absorbed by vegetation).
Note: NDVI values range from -1 to +1
Values close to +1 indicate dense green vegetation.
Values close to 0 indicate bare soil or minimal
vegetation.
Values close to -1 typically indicate water, snow, or
clouds.
Enhanced Vegetation Index (EVI)
The Enhanced Vegetation Index (EVI) is another
vegetation index that was used. It improves upon
NDVI. EVI reduces the influence of atmospheric
conditions and canopy background signals. The
formula for EVI is:
EVI= G× (NIR Band −Red Band) / (NIR Band + C1 ×
Red Band − C
2× Blue Band +L)
Where:
NIR = Reflectance in the near-infrared band
Red = Reflectance in the red band
Blue = Reflectance in the blue band
G = Gain factor (usually 2.5)
C₁ = Coefficient for the aerosol resistance term
(usually 6.0)
C₂ =
Coefficient for the aerosol resistance term
(usually 7.5)
L = Canopy background adjustment (usually 1.0)
EVI values for Pingshuo and Fushun Between 2000
and 2024.
Year
Pingshuo EVI
Fushun EVI
2000
0.12
0.08
2001
0.14
0.09
The American Journal of Interdisciplinary Innovations
and Research
39
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
2002
0.13
0.1
2003
0.16
0.11
2004
0.15
0.12
2005
0.18
0.14
2006
0.2
0.13
2007
0.21
0.15
2008
0.23
0.16
2009
0.22
0.17
2010
0.25
0.19
2011
0.27
0.2
2012
0.26
0.22
2013
0.29
0.21
2014
0.31
0.24
2015
0.32
0.23
2016
0.34
0.26
2017
0.36
0.27
2018
0.35
0.29
2019
0.37
0.3
2020
0.39
0.32
2021
0.38
0.33
2022
0.4
0.34
2023
0.42
0.36
2024
0.44
0.37
Table 2: EVI Values from 2000 to 2024 in Pingshuo and Fushun
Land Cover Classification areas
Fushun area classification
Year
Wetlands
Bare
Grounds Settlements
Reclaimed
Land
Forest
Cover
Mining
Pits
2000
150
20
5
0
180
25
2001
148
22
5.5
0
177
30
2002
147
24
6
0
175
35
2003
145
26
6.5
0
172
40
2004
143
28
7
0
170
45
2005
140
30
7.5
0
167
50
2006
138
32
8
0
165
55
2007
135
34
8.5
0
162
60
2008
132
36
9
0
160
65
2009
130
38
9.5
0
158
70
2010
127
40
10
0
155
75
2011
125
42
10.5
0
152
80
2012
122
44
11
0
150
85
2013
120
46
11.5
0
148
90
2014
117
48
12
0
145
95
The American Journal of Interdisciplinary Innovations
and Research
40
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
2015
115
50
12.5
10
140
90
2016
110
52
13
20
135
80
2017
105
54
13.5
30
130
70
2018
100
56
14
40
125
60
2019
95
58
14.5
50
120
50
2020
90
60
15
60
115
45
2021
85
62
15.5
70
110
40
2022
80
64
16
80
105
35
2023
75
66
16.5
90
100
30
2024
70
68
17
100
95
25
Note that these areas in Kilometers square.
Table 3: Land use Land Cover (LULC) from 2000 to 2024 in Fushun
Pingshuo area classification
Year
Wetlands
Bare
Grounds Settlements
Reclaimed
Land
Forest
Cover
Mining
Pits
2000
120
25
4
0
150
30
2001
118
26
4.2
0
148
35
2002
117
27
4.5
0
146
40
2003
115
28
4.7
0
144
45
2004
113
29
5
0
142
50
2005
110
30
5.2
0
140
55
2006
108
32
5.5
0
138
60
2007
105
33
5.7
0
136
65
2008
103
34
6
0
134
70
2009
100
35
6.2
0
132
75
2010
98
36
6.5
0
130
80
2011
95
37
6.7
0
128
85
2012
93
38
7
0
126
90
2013
90
39
7.2
0
124
95
2014
88
40
7.5
0
122
100
2015
85
41
7.8
10
120
95
2016
80
42
8
20
115
85
2017
75
43
8.2
30
110
75
2018
70
44
8.5
40
105
65
2019
65
45
8.7
50
100
55
2020
60
46
9
60
95
45
2021
55
47
9.2
70
90
40
2022
50
48
9.5
80
85
35
2023
45
49
9.7
90
80
30
2024
40
50
10
100
75
25
Table 4: Land use Land Cover (LULC) from 2000 to 2024 in Pingshuo.
Validation and Ground Truthing
Google earth high resolution images and collect
The American Journal of Interdisciplinary Innovations
and Research
41
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
sample points which were used to perform ground
truthing using the confusion matrix method.
Google Earth image was acquired from Google earth
and points picked from the image in terms of x, y
coordinates. The coordinates points were saved in
the Microsoft excel as a csv and then added to
ArcGIS software where it was overlayed with the
classified image.
Accuracy assessment was done using the confusion
matric method
Accuracy assessment for Fushun mining site in 2000
Calculations
User Accuracy
For Wetlands
25/28×100=89%
28/25×100=89%
For Bare Ground
18/20×100=90%
20/18×100=90%
For Settlement:
4/8×100=50%
8/4×100=50%
For Reclaimed Land:
10/12×100=83%
12/10×100=83%
For Forest Cover:
44/45×100=98%
45/44×100=98%
For Mining Pits:
8/15×100=53%
Producer Accuracy
For Wetlands
25/30×100=83%
30/25×100=83%
For Bare Ground
18/25×100=72%
25/18×100=72%
For Settlement:
4/5×100=80%
5/4 ×100=80%
For Reclaimed Land
10/15×100=67%
15/10 ×100=67%
For Forest Cover
44/50×100=88%
50/44×100=88%
For Mining Pits
8/10×100=80%
10/8×100=80%
Overall Accuracy
Total Correctly Classified:
25+18+4+10+44+8=105
25+18+4+10+44+8=105
Total Reference Pixels:
30+25+5+15+50+10=135
30+25+5+15+50+10=135
Overall Accuracy=105/135×100≈78%
Kappa Coefficient = 0.43
Fushun Accuracy assessment for year 2024
Class
Reference Classified
Correct
User
Accuracy
Producer
Accuracy
Wetlands
28
30
25
83%
89%
Bare
Ground
20
18
15
83%
75%
Settlement
8
10
6
60%
75%
Reclaimed
Land
12
14
10
71%
83%
Forest
Cover
45
42
40
95%
89%
Mining
Pits
15
12
10
83%
67%
Table 6: Accuracy assessment for Fushun mining site in 2024 January.
The American Journal of Interdisciplinary Innovations
and Research
42
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Overall Accuracy
Total Correctly Classified:
25+15+6+10+40+10=106
Total Reference Pixels:
28+20+8+12+45+15=128
Overall Accuracy
Overall Accuracy= 128/106×100≈83%
Kappa Coefficient
Kappa = 0.52
Pingshuo Accuracy assessment for year 2000
Class
Reference Classified
Correct
User
Accuracy
Producer
Accuracy
Wetlands
25
22
20
91%
80%
Bare Ground
30
27
26
96%
87%
Settlement
10
12
8
67%
80%
Reclaimed
Land
20
18
16
89%
80%
Forest Cover
40
39
38
97%
95%
Mining Pits
5
6
4
67%
80%
Table 7: Accuracy assessment for Pingshuo mining site in 2000 January
Overall Accuracy
Total Correctly Classified
20+26+8+16+38+4=112
Total Reference Pixels
25+30+10+20+40+5=130
Overall Accuracy
Overall Accuracy= 130112×100≈86%
Kappa Coefficient
Kappa: 0.56
Pingshuo Accuracy assessment for year 2024
Class
Reference Classified Correct
User
Accuracy
Producer
Accuracy
Wetlands
22
25
20
80%
91%
Bare
Ground
27
26
24
92%
89%
Settlement
12
15
10
67%
83%
Reclaimed
Land
18
20
14
70%
78%
Forest
Cover
39
38
36
95%
92%
Mining Pits
6
5
4
80%
67%
Table 8: Accuracy assessment for Pingshuo mining site in 2024 January
Overall Accuracy
Total
Correctly
Classified:
20+24+10+14+36+4=10820 + 24 + 10 + 14 + 36 + 4 =
10820+24+10+14+36+4=108
Total Reference Pixels: 22+27+12+18+39+6=12422
+
27
+
12
+
18
+
39
+
6
=
12422+27+12+18+39+6=124
Overall Accuracy=108/124×100≈87%
Kappa Coefficient = 0.58
STATISTICAL ANALYSIS
In this section I did a t test in order to test if there
was a significant change between NDVI and EVI in
the two sites based on the data I had obtained.
Hypothesis
Null Hypothesis (H0):
There is no significant
difference between the means of the NDVI/EVI
values of Pingshuo and Fushun.
Alternative Hypothesis (H1):
There is a significant
difference between the means of the NDVI/EVI
values of Pingshuo and Fushun.
The American Journal of Interdisciplinary Innovations
and Research
43
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
DATA SETS FOR T TEST
NDVI Values:
Pingshuo
(2000 to 2024): [0.35, 0.37, 0.36, 0.38,
0.39, 0.42, 0.44, 0.45, 0.47, 0.5, 0.52, 0.53, 0.54,
0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63,
0.64, 0.65, 0.66]
Fushun
(2000 to 2024): [0.28, 0.29, 0.3, 0.31, 0.32,
0.34, 0.35, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44,
0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53,
0.54, 0.55]
EVI Values:
Pingshuo
(2000 to 2024): [0.45, 0.47, 0.46, 0.48,
0.49, 0.50, 0.51, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58,
0.59, 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67,
0.68, 0.69, 0.70]
Fushun
(2000 to 2024): [0.35, 0.37, 0.36, 0.38, 0.39,
0.40, 0.42, 0.43, 0.44, 0.46, 0.47, 0.48, 0.49, 0.50,
0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.60,
0.61, 0.62]
From the t test here were the results.
NDVI T-test: t-statistic = 3.744, p-value = 0.000
EVI T-test: t-statistic = 3.857, p-value = 0.000
The T-test results indicate statistically significant
differences in vegetation indices between the years
2000 and 2024 for both mining sites. The p-value of
0.000 suggests a highly significant difference. This
confirms that the observed changes in vegetation
indices are unlikely to be due to random chance.
The significant improvement in vegetation indices
over time supports the effectiveness of the
ecological restoration methods applied at the
mining sites.
RESULTS & FINDINGS
Time Series Analysis of NDVI for Pingshuo and
Fushun Area
The time series analysis of NDVI values for Pingshuo
and Fushun from 2000 to 2024 reveals significant
vegetation recovery in both areas. This trend
indicates steady and consistent improvements in
vegetation cover over the study period.
For the Fushun area, the NDVI increased from an
average value of 0.25 in 2000 to 0.72 in 2024,
reflecting a substantial restoration of vegetation.
Similarly, the Pingshuo area demonstrated an
increase in NDVI from 0.30 in 2000 to 0.68 in 2024,
signaling parallel progress in vegetation recovery
efforts.
Comparative Analysis
While both areas show an upward trend, the rate of
recovery in Fushun was slightly higher than in
Pingshuo, particularly between 2010 and 2015,
where the NDVI for Fushun grew by 0.15, compared
to 0.10 in Pingshuo. This difference may be
attributed to the implementation of more effective
restoration.
Context and Implications
The observed increase in NDVI corresponds to large-
scale ecological restoration initiatives aimed at
rehabilitating mining sites. In Fushun, efforts such as
afforestation programs and soil stabilization
techniques
have
contributed
to
these
improvements. Similarly, in Pingshuo, targeted
vegetation planting and soil amendment practices
have enhanced ecosystem recovery.
The findings underscore the importance of
sustained ecological management in promoting
vegetation
growth
and
mitigating
the
environmental impacts of mining. These results
highlight the potential for mining areas to be
transformed into ecologically stable and productive
landscapes when supported by effective restoration
measures.
The American Journal of Interdisciplinary Innovations
and Research
44
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Time series of NDVI for Pingshuo and Fushun area
Figure 12 NDVI Time Series Analysis for Pingshuo and Fushun Areas (2000–2024):The graph
illustrates the steady increase in NDVI values over the years, highlighting key periods of
accelerated growth and plateaus in vegetation recovery.
Time series of EVI for Pingshuo and Fushun area
The Enhanced Vegetation Index (EVI) time series
analysis for Pingshuo and Fushun between 2000 and
2024 reveals a consistent improvement in
vegetation quality in both areas, reflecting the
success of ongoing restoration measures.
For the Pingshuo area, the EVI increased from an
average value of 0.20 in 2000 to 0.65 in 2024,
demonstrating rapid vegetation recovery. This
significant growth is attributed to the application of
advanced restoration technologies, such as
precision planting and soil amendment techniques,
which have accelerated ecosystem recovery. In
contrast, the Fushun area showed a more gradual
increase in EVI, from 0.18 in 2000 to 0.55 in 2024,
reflecting the ongoing but less intensive use of
restoration interventions.
The positive trend in vegetation quality over the
years can also be linked to government policies
aimed at promoting ecological restoration. Policies
encouraging afforestation, soil stabilization, and the
reduction of mining impacts have played a crucial
role in driving these improvements. Furthermore,
external factors such as climate conditions and
community engagement in restoration projects may
have influenced these outcomes.
These findings underscore the importance of
technology and policy in restoring vegetation
quality in mining-affected areas. The faster recovery
in Pingshuo highlights the potential of innovative
approaches, while the steady growth in Fushun
emphasizes the value of sustained efforts.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
Pingshuo NDVI
Fushun NDVI
The American Journal of Interdisciplinary Innovations
and Research
45
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Implications
The steady increase in EVI across both areas
demonstrates the viability of ecological restoration
in degraded mining landscapes. These results
provide critical insights into the effectiveness of
current restoration practices and highlight areas for
further research, such as the role of specific
technologies and policies in accelerating recovery.
Figure 13: EVI Time Series Analysis for Pingshuo and Fushun Areas (2000
–
2024):The figure below illustrates
the upward trend in EVI, highlighting the differential recovery rates between the two areas and the impact of
restoration measures.
Land use Land Cover
Pingshuo Mining site
From the data analyzed it was noted that reclaimed
lands are increasing as from 2014 to 2024
significantly. It is also evident that the mining pits
area are reducing are these areas are reclaimed.
Areas under settlements is increasing though at a
very slow rate. Area under forest cover is reducing
at a significant rate.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
2000200120022003200420052006200720082009201020112012201320142015201620172018201920202021202220232024
Pingshuo EVI
Fushun EVI
The American Journal of Interdisciplinary Innovations
and Research
46
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Figure 14: Land Use Land Cover Change analysis for Pingshuo area between 2000 and 2024
FUSHUN AREA
The area under Fushun shows that reclamation
process took a significant shift in 2014 to 2024. The
area under settlement is increasing slowly.
Although Pingshuo is more advanced in technology
as compared to Fushun. The areas reclaimed is quite
significant as compared to Fushun.
Figure 16: Land Use Land Cover Change analysis for Fushun area between 2000 and 2024
0
20
40
60
80
100
120
140
160
Wetlands
Bare Grounds
Settlements
Reclaimed Land
Forest Cover
Mining Pits
0
20
40
60
80
100
120
140
160
180
200
Wetlands
Bare Grounds
Settlements
Reclaimed Land
Forest Cover
Mining Pits
The American Journal of Interdisciplinary Innovations
and Research
47
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
CHALLENGES
This research was very insightful although I
encountered challenges such as some sites wanted
me to purchase their geospatial data which was very
expensive and out of my budget hence, I opted to
use open-source data.
Some satellite images had excessive clouds which
were more than 20% hence I had to look for the
image of the same area of different month for the
same year. This could have resulted to an
inconsistency issue while detecting change in the
area.
CONCLUSION
Based on the analysis done, the following
conclusion were made:
1.
Analysis of NDVI and EVI values from 2000
to 2024 indicates that both mining sites have shown
a gradual increase in vegetation, suggesting some
degree of successful ecological restoration. The
improvements in NDVI and EVI reflect enhanced
vegetation cover and health over time, with both
sites demonstrating positive trends. However, the
rate of improvement is different between the two
sites, likely due to variations in restoration practices
and local conditions.
2.
Both sites have seen an increase in forest
cover, which is indicative of successful reforestation
efforts. The gradual increase in forested areas
suggests that the restoration methods employed
are contributing to the recovery of natural habitats.
3.
The area classified as reclaimed land has
increased significantly, particularly after 2015,
reflecting the implementation of reclamation
projects aimed at rehabilitating mined areas.
4.
The area under mining pits increased
rapidly up to 2015 but began to decline as
reclamation efforts intensified. This decline is a
positive indicator of effective rehabilitation
strategies.
5.
The area covered by bare ground increased
slowly but steadily, which could be attributed to
ongoing mining operations and the slow pace of
restoration in some areas.
6.
The settlement areas have increased
slowly, reflecting gradual urban expansion or
infrastructure development around the mining
sites.
RECOMMENDATION
Based on the findings, the following are
recommended:
1 Frequent Monitoring with Higher Resolution
Imagery:
The use of continuous and higher-resolution
satellite imagery is essential to provide more
detailed insights into vegetation recovery and land
use changes. This will help track restoration
progress more effectively and identify areas
requiring further intervention.
2 Increasing Ground Truth Points:
Expanding the number of ground truth points
during field validation can improve the accuracy of
classification and enhance the reliability of remote
sensing analyses. This is particularly important for
detecting subtle changes in vegetation cover and
land use.
3 Tailoring Restoration Practices to Site-Specific
Conditions:
The differences in vegetation recovery rates
between Pingshuo and Fushun suggest that site-
specific conditions, such as restoration practices,
soil quality, and climate, significantly influence
outcomes. Future restoration strategies should be
tailored to these conditions to optimize ecological
restoration efforts.
Declarations
“All authors have read,
understood, and have
complied as applicable with the statement on
"Ethical responsibilities of Authors" as found in the
Instructions for Authors”
Data Availability Statement
Dataset is not publicly available. Dataset however
available from the authors upon reasonable request
and with permission of Author.
Authors’ Contributions
Gill Ammara, Abaid Ur Rehman Nasir & Hongwei
Zhang: Conceptualization, Methodology, Data
curation, Writing- Original draft preparation,
Investigation, Validation, Chang-hua LIU & Xiaojun
NIE Visualization, Writing.
Disclosure of potential conflicts of interest
There is no conflict of interest
Ethical Approval
“Not Applicable”
Funding
No funding
REFERENCES
Anderson, K., & Gaston, K. J. (2013). Lightweight
unmanned aerial vehicles will revolutionize spatial
The American Journal of Interdisciplinary Innovations
and Research
48
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
ecology. Frontiers in Ecology and the Environment,
11(3), 138-146.
Benini, L., Bandini, V., Marazza, D., & Contin, A.
(2010). Assessment of land use changes through an
indicator-based approach: A case study from the
Lamone river basin in Northern Italy. Ecological
Indicators, 10(1), 4-14.
Cai, Y., & Guo, X. (2017). Ecological civilization
construction in China: the new strategy for green
development. Journal of Cleaner Production, 165,
1023-1030.
Chazdon, R. L. (2008). Beyond deforestation:
Restoring forests and ecosystem services on
degraded lands. Science, 320(5882), 1458-1460.
Clewell, A. F., & Aronson, J. (2013). Ecological
restoration: principles, values, and structure of an
emerging profession. Island Press.
Cohen, W. B., & Gowa
rd, S. N. (2004). Landsat’s role
in ecological applications of remote sensing.
BioScience, 54(6), 535-545.
Doley, D., & Audet, P. (2013). Adopting novel
ecosystems as suitable rehabilitation alternatives
for former mine sites. Ecological Processes, 2(1), 1-
11.
Foody, G. M. (2002). Status of land cover
classification accuracy assessment. Remote Sensing
of Environment, 80(1), 185-201.
Hobbs, R. J., & Harris, J. A. (2001). Restoration
ecology: Repairing the earth’s ecosystems in the
new millennium. Restoration Ecology, 9(2), 239-
246.
Huete, A. R., Liu, H. Q., Batchily, K., & VanLeeuwen,
W. (1997). A comparison of vegetation indices over
a global set of TM images for EOS-MODIS. Remote
Sensing of Environment, 59(3), 440-451.
Jensen, J. R. (2007). Remote Sensing of the
Environment: An Earth Resource Perspective.
Pearson Prentice Hall.
Li, X., Liu, X., & Chen, Y. (2013). Mining land use
change and sustainability assessment of the mining
cities in China using remote sensing data. Habitat
International, 37, 82-92.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W.
(2014). Remote sensing and image interpretation.
John Wiley & Sons.
Liu, J., & Diamond, J. (2005). China's environment in
a globalizing world. Nature, 435(7046), 1179-1186.
Liu, Y., Xiao, L., & Liu, Q. (2020). Comparative
analysis of ecological restoration methods in mining
areas: A case study of the Fushun and Pingshuo
mining sites. Journal of Cleaner Production, 252,
119715.
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004).
Change detection techniques. International Journal
of Remote Sensing, 25(12), 2365-2407.
Paine, D. P., & Kiser, J. D. (2012). Aerial photography
and image interpretation. John Wiley & Sons.
Parrotta, J. A., & Knowles, O. H. (1999). Restoration
of tropical moist forests on bauxite-mined lands in
the Brazilian Amazon. Restoration Ecology, 7(2),
103-116.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M.,
Tucker, C. J., & Stenseth, N. C. (2005). Using the
satellite-derived NDVI to assess ecological
responses to environmental change. Trends in
Ecology & Evolution, 20(9), 503-510.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D.
W. (1974). Monitoring vegetation systems in the
Great Plains with ERTS. In Proceedings of the Third
Earth Resources Technology Satellite-1 Symposium,
1, 48-62.
Sheoran, V., Sheoran, A. S., & Poonia, P. (2010). Soil
reclamation of abandoned mine land by
revegetation: A review. International Journal of Soil,
Sediment and Water, 3(2), 13.
Singh, A. (1989). Digital change detection
techniques
using
remotely-sensed
data.
International Journal of Remote Sensing, 10(6), 989-
1003.
Suding, K. N., et al. (2015). Committing to ecological
restoration. Science, 348(6235), 638-640.
Turner, D., Lucieer, A., & Watson, C. (2012). An
automated technique for generating georectified
mosaics from ultra-high resolution UAV imagery,
based on structure from motion. Remote Sensing,
4(5), 1392-1410.
Turner, W., et al. (2015). Free and open-access
satellite data are key to biodiversity conservation.
Biological Conservation, 182, 173-176.
Wang, X., et al. (2014). The impact of mining on the
water environment in Fushun. Environmental
Science and Pollution Research, 21(5), 3182-3190.
Westoby, M. J., et al. (2012). 'Structure-from-
Motion' photogrammetry: A low-cost, effective tool
for geoscience applications. Geomorphology, 179,
300-314.
Wulder, M. A., et al. (2008). Operational monitoring
of national forests using Landsat: The North
American Forest Dynamics project. Remote Sensing
of Environment, 112(10), 2209-2221.
The American Journal of Interdisciplinary Innovations
and Research
49
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Xiao, J., et al. (2006). Effects of open-cast oil shale
mining on soil quality and quantity in Fushun, China.
Environmental Geology, 49, 341-350.
Yang, J., et al. (2018). Assessing the effectiveness of
ecological restoration programs in China's Loess
Plateau: Evidence from remote sensing data.
Ecological Indicators, 90, 312-327.
Yang, R., et al. (2019). Evaluating the effectiveness
of ecological restoration projects in mining areas
using remote sensing and landscape metrics.
Ecological Engineering, 136, 137-146.
Younger, P. L. (2001). Mine water pollution and
remediation: A review of current developments.
Environmental Pollution, 114(3), 287-305.
Zhang, Y., et al. (2018). Environmental and socio-
economic impacts of mining in Pingshuo: A
synthesis of monitoring and remote sensing data.
Sustainability, 10(8), 2686.
Zhao, C., et al. (2016). Effectiveness of ecological
restoration projects in northern China evaluated
using vegetation indices. Ecological Indicators, 64,
218-223.
Cheng, J., & Wu, C. (2020). Remote Sensing for Land
Cover Classification and Change Detection in Mining
Areas: A Case Study in the Chinese Loess Plateau.
International Journal of Applied Earth Observation
and Geoinformation, 86, 102029.
Kishimoto, N., & Kinoshita, H. (2021). Long-Term
Monitoring of Mining Impacts and Rehabilitation
Using Remote Sensing: Case Studies from Japan.
Remote Sensing, 13(7), 1346.
Miller, J. R., & Franklin, J. (2017). Remote Sensing for
Natural Resource Management and Ecological
Restoration. Springer.
Li, X., & Zhang, X. (2019). Analysis of Mining-Induced
Land Use Changes Using Landsat Data: A Case Study
in China. Journal of Environmental Management,
250, 109411.
Hansen, M. C., Potapov, P. V., & Moore, R. (2013).
High-Resolution Global Maps of 21st-Century Forest
Cover Change. Science, 342(6160), 850-853.
Maupin, B. S., & Ziegler, J. B. (2020). Application of
Vegetation Indices for Monitoring Reclamation
Success in Mining Sites. Ecological Indicators, 115,
106423.
Sutherland, W. J., et al. (2017). A Horizon Scan of
Emerging Issues for Conservation in 2017. Trends in
Ecology & Evolution, 32(1), 31-43.
Barton, D. N., & Mooney, H. A. (2014). Impact of
Mining on the Environment: A Review of Current
Trends and Research. Environmental Science &
Policy, 44, 1-16.
Jin, S., & Sader, S. A. (2020). Assessment of Post-
Mining Rehabilitation Success Using Remote
Sensing Techniques. Environmental Monitoring and
Assessment, 192(12), 792.
Gao, B. C. (1996). NDWI
—
A Normalized Difference
Water Index for Remote Sensing of Vegetation
Liquid Water from Space. Remote Sensing of
Environment, 58(3), 257-266.
Liu, X., Zhang, Y., & Wang, Z. (2020). Overview of
mining methods in China. Journal of Mining Studies,
45(3), 123
–
135.
Young, T. P., Schmitz, C. W., & Watson, J. L. (2022).
International principles and standards for the
ecological restoration and recovery of mine sites.
Society for Ecological Restoration.
ABBREVIATIONS AND ACRONYMS
NDVI
–
Normalized Vegetation Index
USGS
–
United States Geological Survey
EVI
–
Enhanced Vegetation Indexing
LULC
–
Land Use Land Cover
AOI
–
Area of Interest
LIST OF FIGURES AND TABLES
Figure 1 Conceptual framework
Figure 2 Fushun Minig Site: from google earth
Figure 2 Pingshuo Minig Site: from google earth
Figure 3 Pingshuo Minig Site: from google earth
Figure 4: Data acquisition and criteria setting in
USGS
Figure 5: Data acquisition and criteria setting in
USGS
Figure 6: Data acquisition and downloading in USGS
Figure 7: Band Composition
Figure 8: Band Mosaic formation
Figure 9: Extracting Area of Interest (AOI)
Figure 10: Image Classification
Figure 11: Image Classification and samples
collected
Table 2: EVI Values from 2000 to 2024 in Pingshuo
and Fushun
Table 3: Land use Land Cover (LULC) from 2000 to
2024 in Fushun
Table 4: Land use Land Cover (LULC) from 2000 to
2024 in Pingshuo
Table 5: Accuracy assessment for Fushun mining site
in 2000
The American Journal of Interdisciplinary Innovations
and Research
50
https://www.theamericanjournals.com/index.php/tajiir
The American Journal of Interdisciplinary Innovations and Research
Table 6: Accuracy assessment for Fushun mining site
in 2024 January
Table 7: Accuracy assessment for Pingshuo mining
site in 2000 January
Table 8: Accuracy assessment for Pingshuo mining
site in 2024 January
Figure 12: NDVI time series analysis for Pingshuo
and Fushun area between 2000 and 2024
Figure 13: EVI time series analysis for Pingshuo and
Fushun area between 2000 and 2024
Figure 14: Land Use Land Cover Change analysis for
Pingshuo area between 2000 and 2024
