boundary demarcation of the damaged cultivated land caused by coal mining subsidence
TRANSCRIPT
ORIGINAL PAPER
Boundary demarcation of the damaged cultivated land causedby coal mining subsidence
Xianlei Xu • Yanling Zhao • Zhenqi Hu •
Yang Yu • Fang Shao
Received: 31 January 2013 / Accepted: 4 July 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract Due to the subsidence caused by coal mining
activities, the cultivated land in the eastern plain of China
has suffered serious damage. Currently, there is no criterion
for demarcating damaged cultivated land for crop compen-
sation, land requisition, and/or land reclamation. This paper
describes the development of a new method and assessment
criterion for determining the boundaries of damaged culti-
vated land caused by coal mining subsidence. A damage
evaluation indicator system is proposed, based on the ana-
lysis of the subsidence influence and land damage types. The
establishment of demarcation criterion is achieved by
comprehensively analyzing the influence of the groundwater
depth, surface inclination, chemical pollution, and the visual
cultivated land sensitivity (VCLS). To assist the practicality
of using this system, a digital elevation model (DEM) and
additional information about the surface slopes of the
Yanzhou coal mining area are generated; this enables the
damaged cultivated land to be classified into three grades.
The results reveal that the most important mild-damage
boundary for Yanzhou coal mining area can be determined
by the 45-mm sinking line, or additional slope greater than
0.5� (inclination is equal to 9 mm m-1), or the VCLS value
at 0.6; it is feasible for the criterion to be the reference basis
for the boundary demarcation.
Keywords Damaged cultivated land � Boundary
demarcation � Coal mining subsidence � Damage
evaluation indicator system � Chemical pollution � Visual
cultivated land sensitivity
Introduction
Coal is the main energy resource in China; the extraction and
utilization of coal play an important role in the development
of the national economy; however, this mining can cause
serious land subsidence and damage to the cultivated land.
Assuming that the land subsidence rate from coal mining is
2–3 9 10-3 km2 (104)-1t and annual output of the raw coal
is 25 9 109t, the area of the national new land subsidence
will be more than 400 km2 each year and that of the subsi-
dence cultivated land will be up to 120 km2 (cultivated land
accounts for 30 % of the total area). The eastern plain coal
mining region of China provides a typical example where
crop cultivation and coal extraction overlap. In some areas,
more than 90 % of the subsurface is underlain by coal. In
some localities, this results in a sharp conflict of interests
between the extraction companies and farmers; this situation
is intensified by the human need for development versus the
need for cultivated land (Hu 1996; Hu et al. 2007). In order
to relieve this tense situation and resolve the subsidence
land dispute, crop compensation and land requisition are
being enforced according to the Chinese Law of Land
Management. It is also imperative to take measures to
implement land reclamation. However, the outstanding
problem remains, how to determine where the cultivated
land is damaged and then to evaluate the degree of damage?
Currently, there are no criterion for determining the
boundaries of damaged cultivated land. According to the
existing mining specification for subsidence which includes
X. Xu (&) � Y. Zhao � Z. Hu (&) � Y. Yu � F. Shao
Institute of Land Reclamation and Ecological Restoration,
China University of Mining and Technology (Beijing),
Beijing 100083, China
e-mail: [email protected]
Z. Hu
e-mail: [email protected]
X. Xu
State Key Laboratory of Coal Resources and Safe Mining,
China University of Mining and Technology (Beijing),
Beijing 100083, China
123
Bull Eng Geol Environ
DOI 10.1007/s10064-013-0495-2
‘‘mining under the three objects’’ (namely, buildings, rail-
ways, and water bodies), the subsidence basin is defined as the
area whose subsidence depth is greater than 10 mm, and that
this subsidence is considered to impact on one or all of the
three objects. In some areas, where the subsidence depth is
greater than 10 mm, there has been no observed detrimental
effect on agricultural yield; consequently, the subsidence
criterion of 10 mm has not been used to demarcate damaged
cultivated land. Nevertheless, the need for a boundary
demarcation remains. There is some literature that shows the
influence of research on the crop yield, such as the effective
specific soil moisture capacity is evaluated in the case of
transient unsaturated flow in stratified soils using a three-
dimensional stochastic approach (Mantoglou 1987). Other
researches have shown that crop yield potential has been
reduced by 25 % after 80 seasons of irrigation furrow erosion
on approximately 1 million ha of furrow-irrigated land (Carter
1990), and (Fernandez-Gomez et al. 2004) discussed irriga-
tion-induced soil erosion. The recent advances in irrigation
technology have made inroads in the cultivation of vegetables
and horticultural crops. The spatial representation of hydro-
logical and geomorphological processes using terrain analysis
techniques is integrated with the development of a field
sampling and soil–landscape model building strategy, and
these techniques provide appropriate methodologies for spa-
tial prediction and understanding of soil–landscape processes
(Gessler et al. 1995). Strong correlations between these
measured soil–landscape variables and explanatory digital
terrain attributes are used to develop quantitative soil–land-
scape models (Gessler et al. 1999). The sensitivity of the
distributed hydrological SWAT model to the preprocessing of
soil and land use data was tested for modeling rainfall–runoff
processes in the Thyle catchment in Belgium (Romanowicz
et al. 2005). Based on the analysis of the impact of mining-
induced subsidence, this paper describes the development of a
new method and assessment criterion for determining the
boundaries of the damaged cultivated land caused by coal
mining subsidence.
The organization of this paper is as follows. Section
‘‘Background of the research area’’ presents a short intro-
duction of the research area. Section ‘‘Demarcation criterion
of the damaged cultivated land’’ focuses on the damage
evaluation indicator system, which is proposed based on the
analysis of the subsidence influence, and the establishment of
the demarcation criterion, which is achieved by compre-
hensively analyzing the influence of the groundwater depth,
surface inclination, chemical pollution, and the visual cul-
tivated land sensitivity (VCLS). Section ‘‘Boundary
demarcation of the research area’’ presents the generation of
the DEM and additional slope of the Yanzhou coal mining
area, followed by the classification of the damaged cultivated
land and experimental results. Finally, ‘‘Conclusions’’ sec-
tion contains the concluding remarks.
Background of the research area
The Yanzhou coal mining area, spans five cities and
counties, and is located in the southwest of Shandong
Province (see Fig. 1). The research area includes two
Fig. 1 Study area: Yanzhou mining area, Shandong province, China, with county boundaries
X. Xu et al.
123
coalfields with a total area of 458 km. The Yanzhou
coalfield is located within Yanzhou, Qufu, and Zoucheng
in the southwest of Shandong Province with a total area
of 258 km2. This coalfield includes six mines, namely
Nantun, Xinglongzhuang, Baodian, Dongtan, Beisu, and
Yangcun. The Jining coalfield is located in Rencheng and
Weishan with the total area of 200 km2: it includes the
Jining2 and Jining3 coal mines, respectively. The
approved production capacity is 38.65 million tons in
2007, and the designed annual raw washed coal is 20.8
million tons. By the end of 2007, the proved reserve of
the coal was 3.014 billion tons. The Yanzhou coal mining
area is located in the eastern plains where high-quality
land is cultivated; unfortunately, the Yanzhou coal
industry is one of the eight national key energy centres
(Hu 2007).
The land subsidence caused by underground mining in the
Yanzhou area is about 1 x 105 km2; this has resulted in a
huge amount of damage to cultivated land and impacted
badly on a large number of farmers. Thus, the conflict of
interests between people and land use is intensified, and the
need for cultivated land protection is a priority. Therefore, to
undertake reclamation measures and/or pay compensation, it
is imperative to accurately monitor the subsidence and
establish unequivocal boundaries for the damaged cultivated
land.
Demarcation criterion of the damaged cultivated land
Mining subsidence theory
According to the probability integration method of min-
ing subsidence, the entire mining area is subdivided into
an infinite number of tiny mining units, and the influence
of rock and ground surface caused by the mine exploi-
tation is equal to the sum influence of all the tiny mining
units. The subsidence basins caused by mining units
present normal distribution and consistent with the dis-
tribution of the probability density from stochastic
medium theory. Therefore, the subsidence profile equa-
tion caused by mine exploitation can be expressed as the
integral formula of the probability density function. The
subsidence basin can be expressed as the following
equation:
weðxÞ ¼1
re�px2
r2 ð1Þ
where r is the major influencing radius, which is mainly
related to the mining depth and major influence angle. It
can be seen from Eq. 1 that the functional form of the
subsidence basin is the same as the normal probability
density function (He 1991).
Analysis of the subsidence influence
In this region, crop yield reduction or out of yield is
attributed solely to the mining subsidence. Due to the rising
phreatic water level, the effective moisture of the soil in
this area will be changed, followed by salinization. Addi-
tionally, the waterlogging that occurs in the rainy season
will cause degradation of the land quality, soil erosion, and
crop yield reduction. When the subsidence depth falls
below the phreatic water level, the cultivated land is
inundated by perennial water and crop is totally out of
yield. Moreover, the soil that is located in the gentle slope
area of the subsidence basin will suffer the soil erosion;
meanwhile, crop irrigation is another problem. Thus, the
impact of subsidence on the cultivated land are as follows
(Hu 1996; Li et al. 1998):
1. Reducing land elevation and rising phreatic water
levels, seasonal land water logging, salinization, and
declining soil fertility.
2. Serious soil erosion and soil and water loss due to the
changing inclination of the cultivated land.
3. Changing soil structure, e.g., porosity and density.
4. Surface cracks around the subsidence margins.
Damage grade classification
As some areas have experienced subsidence greater than 10
mm with no ill affects on agricultural yield, the subsidence
line of 10 mm has not been used to determine the boundaries
of damaged cultivated land. However, there needs to be some
criterion for determining such boundaries. It is proposed that
the area between the 10 mm subsidence line and the
boundary of unaffected yield-cultivated land can be called
the non-damaged area and does not need to be considered in
the classification scheme. Beyond this and depending on the
degree of subsidence, the damaged cultivated land is then
categorized into three grades, namely mild yield reduction
area, moderate yield reduction area, and out of yield area (Bi
and Ding 2003; Hu et al. 2007; Li and Lu 2004; Kong 2007).
To illustrate this, Fig. 2 provides a schematic diagram of the
mining subsidence in the plain area of China.
Mild yield reduction area. This encompasses large areas of
surface deformation caused by subsidence. The cultivated
ground exhibits a reduction in soil moisture and fertility which
affects agricultural activities and crop yields to a certain extent.
Moderate yield reduction area. The surface damage of
this area is more serious because of the subsidence and
seasonal water logging; land salinization will likely happen
in this area, leading to crop yield reduction or out of yield.
Out of yield area. The central area of the subsidence basin
will be permanently covered by water because the ground
surface has subsided below the groundwater level. This area
Boundary demarcation of the damaged cultivated land
123
will be formed in the original waterlogged area caused by
mining subsidence. Cultivation yield is totally lost in this
area.
It is noted that the area with light destruction, including
the center non-water area of the subsidence basin and the
smaller slope edge area, is the non-damaged area, which is
not the goal of this paper. Those areas can be used without
bringing influence to the crop yield.
Damage evaluation indicator system
After setting up the three-grade classification, the key
problem is how to determine reasonable classification
indicators to create a usable system for demarcating the
boundaries of damaged cultivated land. The boundary for
the mild damage area is the most important one as it
directly affects the interests of the farmers. The study noted
that aside from any other criterion used to award com-
pensation, the subsidence and crop compensation process
always included a visual assessment of the sensitivity of
the cultivated land. Therefore, in addition to the crop
reduction and out of yield categories described above, the
scheme must include a visual assessment of the impact of
the subsidence on the land; this can be abbreviated as
VCLS. Cultivated land that has a poor visual impact
because of the land damage is considered to belong to one
of the damaged categories. Figure 3 shows the flow chart
underpinning the evaluation scheme used for the Yanzhou
mining subsidence area; it takes the groundwater depth,
surface inclination, and VCLS as the first-level indicators
(Gao et al. 2009; Xiong 1996). The second-level evaluation
indicators include soil effective moisture, soil salinization,
soil erosion, crop irrigation, relative slope, conspicuity, and
special area.
Evaluation analysis of the groundwater depth
Ground subsidence leads to a relative rise of the phreatic
water level; continued subsidence eventually results in
seasonal or permanent water logging. These areas are prone
to crop yield reduction or out of yield cultivation. As
previously noted, the main factors affecting cultivated land
and crops are soil effective moisture, soil salinization,
degree of soil erosion, and submerged depth of the crops
(Xiong 1996).Fig. 2 Schematic diagram showing the categories of cultivated land
damage in a subsidence area
Fig. 3 A flow chart of damage evaluation criterion for cultivated land in subsidence areas
X. Xu et al.
123
Soil salinization
The primary reason for soil salinization is intense evapo-
ration of shallow levels of groundwater; this leads to the
degradation of water quality. With sustained evaporation of
ground water, the process of capillarity brings mineral salts
to the upper layers of the soil profile, leading to the
accumulation of salts in the soil. High groundwater level,
high soil moisture with a low infiltration rate, means that
little salt is removed naturally; soil desalination is also
difficult to achieve artificially (Menahem 2004). Ground-
water depth is used to judge whether soil salinization is
occurring or not because of the close relationship between
the soil desalination, salification, and groundwater depth.
Salinization will affect major soil degradation processes
such as soil dispersion, increased soil erosion, and engi-
neering problems. When soil salinization is assessed in
economic terms, reasons to be concerned about it become
more apparent.
Assuming minimum depth of ground water, guarantee-
ing root layer of the crop will not suffer salinization, called
groundwater critical depth and expressed by hcritical, so
Hcritical ¼ raising height of the capillary water
þ safety coefficient
When the groundwater level is less than the critical
depth, the salt in the ground water will be taken to the
surface by capillarity, leading to the soil salinization.
Therefore, the key factors for soil salinization are the
surface subsidence, raising phreatic water level, and
smaller groundwater level, which is no more than the
critical depth. The relationship between surface subsidence
and groundwater level is:
hdepth ¼ H0 � Hq � Wsubsidence ð2Þ
where H0 is the surface elevation, Hq is the elevation of the
phreatic water level, Wsubsidence is the surface subsidence
value, and hdepth is the elevation of the groundwater level.
To avoid the soil salinization, it has to meet the following
equation:
hcritical [ hdepth ð3Þ
The factors affecting the soil critical depth are soil
texture and groundwater mineralization. According to the
influence of the elevation of the groundwater level after
mining activities, such as mineralization, the influence
degree of the subsidence to soil salinization is divided into
four grades, which is shown in Table 1.
Soil effective moisture
Surface subsidence and the relative rise of phreatic water
level in the areas with high phreatic water level will
increase soil moisture content, leading to less crop roots
and the difficulty in taking root (Loheide 2011). So the
growth and yields of the crops are directly affected by the
high moisture in the soil. The relationship between the crop
yields and the depth of the groundwater level is shown in
Table 2.
Assuming hsuitable is the suitable depth of the ground-
water level for crop growth, in order to meet the require-
ment of the subsidence cultivated land that is not influenced
by the elevation of the groundwater level, there is:
hdepth [ hsuitable ð4Þ
From Table 2, it can be found that there is little
difference in the influence of groundwater depth on wheat
Table 1 Influence grades based on soil salinisation, groundwater depth and impact on crop yields
Influence grade
(Day)
Groundwater depth
hdepth (m)
Mineralization
(gL-1)
Influence of the soil
salinization
Influence
depth (m)
Crop influence
1 [3 \2 No obvious salinization [1.5 No influence
2 2–3 2–5 Mild salinization 1–1.5 Mild crop yield reduction
3 1–2 5–10 Serious salinization 0.5–1 Moderate crop yield reduction
4 \1 [10 Both floods and
salinization
\0.5 Serious crop yield reduction or
out of yield
Table 2 The relationship between the crop yields and the depth of the groundwater level
Crops The relative yield in different groundwater depths (m) (%) The relative of
100 % (kg l-1)
Winter 0.4 0.60 0.90 1.20 1.50
Wheat 0.58 0.77 0.89 0.95 1.00 267
Soybean 0.58 0.80 0.89 0.95 1.00 273
Pea 0.50 0.90 1.00 1.00 1.00 183
Boundary demarcation of the damaged cultivated land
123
and soybeans. The crop yields decline rapidly from 60 %
when the groundwater level is less than 0.5 m, and crop
yield is up to 80–90 % for 1 m. However, when
groundwater depth is over 1.5 m, the crop yield can be
100 %, which implies that at this depth and beyond, no
detrimental influence can be attributed to groundwater. The
other arid crops have a similar relationship. The four
grades shown in Table 1 are reflected in Table 2.
Comprehensive evaluation of groundwater depth
Crop growth and yields are influenced by soil salinisation and
soil moisture content, which are changed by the rising
groundwater depth in mining subsidence areas. Considering
these two factors, in order to ensure that the soil moisture will
not affect the growth and yields of the crops without soil
salinization, it has to meet Eqs. 2, 3, and 4, so
hcritical [ hdepth ¼ H0 � Hq �Wsubsidence [ hsuitable ð5Þ
For the mild yield reduction boundary, the Eq. 5 has to
meet H0-Hq-Wsubsidence C 3. So it can be true, that the
land subsidence depth which meets Wsubsidence C Hq-H0-
3, belongs to the scope of the subsidence area. On the
contrary, the subsidence will not have an impact on
agricultural production when Wsubsidence B Hq-H0 ? 3.
Similarly, the boundaries of the moderate area and out of
yield area have to meet the following equation,
respectively:
Wsubsidence�Hq � H0 � 2 Wsubsidence�Hq � H0 � 1 ð6Þ
Evaluation analysis of the surface inclination
Soil erosion
Soil erosion is a process where the soil (or other ground
material) is removed by external force, including erosion,
destruction, dispersion, separation, transportation, and
deposition (Rockwell 2011). Subsidence changes the
slope angle of the cultivated land; the greater the slope,
the greater the surface runoff and the more serious the
soil erosion. Soil erosion intensity reflects the amount of
the soil erosion in per-unit area, which is usually
expressed by soil erosion modulus Ms (unit is t (km2)-1
a) (or it can be expressed by Ds, the average erosion
thickness in the upper layer each year, the unit is mm
a-1). China’s Ministry of Water Resources developed a
classification criterion of the erosion intensity by using
the soil erosion modulus (Xiong 1996; Zhang 2001),
shown in Table 3.
The relationship between the ground slope and the
amount of the erosion is:
Qs ¼ 0:483þ 3:6ða0 � aÞ ð7Þ
where Qs is the amount of the soil erosion, a0 is the surface
slope, which can be regarded as 0� in the plain mining
areas, and a is the additional slope of the inclination caused
by mine activity. Therefore, the degree of erosion of cul-
tivated land resulting from ground subsidence can be cal-
culated based on the values of the stable surface slope after
mining subsidence.
Crop irrigation
The match between the need of the crop and the quantity of
water supplied is very important for the crop growth. The
decrease in the availability of water for agriculture, cou-
pled with the requirement for the higher agricultural pro-
ductivity, means that there is no option but to irrigate the
crop. This has to include an efficient utilization of available
water which otherwise would evaporate or percolate from
the root zone of the soil (Kumar et al. 2009). The additional
slope caused by subsidence enhances the surface declina-
tion; crops in the lower ground will be most likely be
inundated when irrigated, if this depression becomes sig-
nificant. There is a certain inundation time and inundation
depth limit for the crop growth, and the crop growth and
crop yields will be affected when the inundation time or
inundation depth exceeds the limitation, resulting in crop
yield reduction or out of yield. The height difference for a
piece of land can be obtained by the average inclination
and the slope length of the main inclination surface:
Dh ¼ i� Dið Þ � L ð8Þ
where Dh is height difference, i is the value of the original
inclination, which can be regarded as 0 in the plain areas,
Di is average additional slope generated by subsidence, and
L is the slope length of the main inclination surface. ‘‘?’’
represents the same direction with the original slope, and
‘‘-’’ represents the contrary direction with the original
slope. Table 4 shows the influence rank of subsidence on
cultivated land irrigation.
Table 3 Soil erosion type and intensity
Grades Ms (t(km2)-1a) Average erosion
thickness each
year
Slight or no significant
erosion
\200, 500, 1,000 \0.16 or 0.4 or 0.8
Mild erosion (200, 500, 1,000)–
2,500
0.16 or 0.4 or 0.8–2
Moderate erosion 2,500–5,000 2–4
Serious erosion 5,000–8,000 4–6
Extremely serious erosion 8,000–15,000 6–12
Strong erosion [15,000 [12
X. Xu et al.
123
Comprehensive evaluation of surface inclination
The growth and yields are influenced by soil erosion and
crop irrigation, resulting from surface inclination in mining
subsidence. Considering these factors, the following two
requirements must be simultaneously met for the mild
boundary of the damaged cultivated land:
Qs ¼ 0:483þ 3:6a Dh ¼ Di� L
Qs\5:48 kg ha�1 Dh\0:1 mð9Þ
where L can be considered to be the ratio of the maximum
subsidence value W and cosa and Di is the half value of the
maximum subsidence value W. Equation 9 can be
simplified as:
0:483þ 3:6a\5:48 Di� L\10: ð10Þ
So, a\ 1.388� and a\ arccos (W2(20)-1). Assuming
(W2(20)-1) = cos 1.388�, it can be calculated that
w = 4.48. As the function f = arccosx is a decreasing
function, the requirements for the mild boundary of the
damaged cultivated land are:
a[ arccos W2ð20Þ�1� �
when W [ 4:48
a[ 1:388� when W\4:48:ð11Þ
Similarly, the boundaries of the moderate area and out
of yield area have to meet the following requirement,
respectively:
a[ arccos W2ð30Þ�1� �
when W\5:48
a[ arccos W2ð40Þ�1� �
when W\6:32:ð12Þ
Evaluation analysis of the chemical pollution
Coal mining activities create huge waste piles. Where coal
waste is exposed to the atmosphere and rainfall, pollution is
inevitable caused by surface runoff and harmful leachates
percolating through to the aquifer, endangering human
health and normal growth of the plants and animals (Jiang
2012; Xiao and Ji 2007). The main pollution factors include
water hardness, SO4-2, Cl-, soluble solid, permanganate
index, NO3–N, and Na?. Pyrite (FeS2) is always deposited in
the bottom coal seam, and oxidation of sulfide can bring the S
into water, which is difficult to be dissolved in water. Thus,
the ground water in such area often contains sulfate ion
content (so4-2). Sulfate ion (so4
-2) is the most abundant ion for
the medium-mineralized water, just below the chloride ion
content (cl-), ranging from a few milligram/liter to tens of
gram/liter. Chemical reactions and biological degradation in
the ground water seriously effect on the cation–anion bal-
ance of the groundwater regime and groundwater quality
(Liu 2003). The classification standards of the groundwater
quality are shown in Table 5.
Assuming the factor set and classification standards set
are A and B, respectively, m and n are the number of the
two sets. So
A ¼ a1; a2; . . .; am½ �; B ¼ b1; b2; . . .; bn½ �
For this research, A = [water hardness, SO4-2, Cl-,
soluble solid, permanganate index, NO3–N, Na?], B = [1,
2, 3, 4, 5]. According to A and B, the fuzzy relation matrix
R can be established by the following equation:
R ¼
r11; r12; . . .r1n
r21; r21; . . .r2n
..
.
rm1; rm1; . . .rm1
2664
3775: ð13Þ
And rij represents the possibility that the value of the
environmental pollutant quality factor i may be rated to the
j-class environmental quality. Since the contribution of
each evaluation factor to the complex environment,
different weights should be given to each factor based on
the role of environmental quality assessment. And the
weight of the environmental pollutant quality factor i can
be given that:
Table 4 Influence of the classification of the crop yield on the sur-
face inclination
Influence
grade
Influence of the crop yield Dh m-1
1 Slight impact, mild yield reduction 0.05–0.1
2 Moderate impact, moderate yield
reduction
0.1–0.15
3 Serious impact, severe yield reduction 0.15–0.2
4 Out of yield [0.2
Table 5 Classification standards of the groundwater quality (GB/T14848-93) mg/L
Influence grade Water hardness SO4-2 Cl- Soluble solid Permanganate index NO3–N Na?
1 B150 B50 B50 B300 B1.0 B2.0 B9.3
2 B300 B150 B150 B500 B2.0 B5.0 B10
3 B450 B250 B250 B1,000 B3.0 B20.0 B20
4 B550 B350 B350 B2,000 B10.0 B30.0 B30
5 [550 [350 [350 [2,000 [10.0 [30.0 [30.0
Boundary demarcation of the damaged cultivated land
123
wi ¼Ci=KiPm
i¼1 ðCi=KiÞ; Ki ¼
1
n
Xn
j¼1
ðkijÞ ð14Þ
where Ci is the monitoring concentration of the pollutant i
and Ki is the environmental quality basis point value. So,
the fuzzy weight vector of the evaluation factors W can be
obtained as W = [w1, w2,…,wm]. The purpose of the
evaluation is to obtain the correct result by considering all
factors, determining the classification of the environmental
quality. Assuming T = (t1, t2,…tn) is the fuzzy subset in
the evaluation domain B, the fuzzy comprehensive
evaluation can be given that:
T ¼ W � R: ð15Þ
And the normalized value T is the comprehensive
evaluation result of the environmental quality.
Evaluation analysis of the VCLS
As previously noted, in the mining subsidence compensa-
tion process, the subsidence and compensation areas are
always determined on the basis of an assessment of the
visual cultivated land sensitivity (Yu 1991; Simon 2001).
Areas with slight damage but high VCLS will have a great
visual impact, and thus will be included within the scope of
the subsidence area. The indicator of VCLS defined in this
paper is a measure of the public attention given to damaged
cultivated land, as well as a measure of degree of notice of
the damaged cultivated land. The VCLS supplements the
two indicators above.
Quantitative and classification of the VCLS
Relative slope and visual cultivated land sensitivity The
greater the disparity between the surface slope and the
observer’s line of sight, the more likely larger areas of
damaged ground will be seen and noticed—resulting in a
higher attention rate. Thereby, the projected area (per-unit
area land) along the line-of-sight direction is used to
measure the VCLS in this paper. Assuming the surface of
the land and the line of sight of the observer is b, the
sensitivity (projected area) is:
Sa ¼ sin b ð0 b 90�Þ: ð16Þ
When b = 90�, the value of the projected area is the
maximum one, so is the VCLS. By contrast, the projected
area and VCLS have the minimum value when b = 0�. In
other cases, the value of Sa is distributed between 0 and 1.
In normal circumstances, the angle b is actually the slope
of the terrain when observer looks at the horizontal
direction, and it can be acquired through the
establishment of a triangulated irregular network (TIN)
model in Arcgis software (Environmental Systems
Research Institute, Inc., Redlands, CA, USA) based on
the elevation data of the mine area. Or Sa can also be
calculated by the following formula:
Sa ¼ sin ðtan�1 H W�1Þ ð17Þ
where H is the vertical interval between contours and W is
the horizontal interval between contours. Therefore, the
VCLS at any point can be calculated by Eq. 16 directly
from the TIN map.
Conspicuity degree of the damaged cultivated
land Conspicuity degree is determined by the contrast
degree (including contrast of shape, line, and texture)
between the damaged cultivated land and environment,
which can be expressed by Sc. The higher the contrast
degree, the more sensitive the damaged cultivated land, and
the stronger the VCLS. For the Yanzhou mining areas, the
areas with high sensitivity include the land of independent
use type and the border zone between two different land
types, such as cultivated land and woodland, cultivated
land and water, and cultivated land and roads. According to
the specific circumstances, various types of the special
damaged cultivated land mentioned above are divided to
the highly sensitive area.
In this paper, independent cultivated land is designated as
the first-class sensitive area, the cultivated land near wood-
land and road is designated as the second class, and the
cultivated land near ditch and rivers is designated as the third
class. The weight values are 0.2, 0.4, and 0.6, respectively.
Special area The special cultivated land, such as the
excellent vegetable base land and the flower base land,
requires serious consideration of the VCLS. Generally
speaking, these areas have high sensitivity degree, which
can be expressed by using St. According to the damaged
cultivated land that is a special area or not, the weight value
St could be assigned with 1 and 0.
Comprehensive evaluation of the VCLS
As the VCLS is influenced by the three factors in ‘‘Quanti-
tative and classification of the VCLS,’’ section it should be a
function of the components of the various factors:
S ¼ f ðSa; Sc; StÞ ð18Þ
where Sa, Sc, and St are relative slope of damaged cultivated
land, conspicuity degree, and sensitivity component of the
special area, respectively. The weight values of the sensitivity
component Sa, Sc, and St are determine d by the expert scoring
method, which are 0.5, 0.3, and 0.2, respectively:
S ¼ 0:5Saþ 0:3Scþ 0:2St
¼ 0:5 sin ðtan�1 H W�1Þ þ 0:3Scþ 0:2St: ð19Þ
X. Xu et al.
123
Establishment of the demarcation criterion
Part 3 focused on the impact of coal mining subsidence on
divided indicators and various indicators of quantitative
methods of groundwater depth, surface inclination, chemical
pollution, and visual cultivated land sensitivity. Evaluation
operation of these four indicators of coal cultivated land can
use computers and geographic information systems to assist
the completion. At this time, the division criterion can be
converted into defined four-indicator factors corresponding to
a threshold value, thereby achieved by the discriminant
function F.
F ¼ f ðWsubsidence; a; T; SÞ ð20Þ
The meaning of the function is that the four indicators,
respectively, are compared with a threshold value, then the
logical or operation.
For example, taking the mild-damage boundary and pro-
viding the corresponding threshold values for the four indica-
tors—as long as any indicator is greater than the corresponding
threshold value, then that area comes within the subsidence
scope. Conversely, if the indicator is less than the threshold
value, then that area does not belong to subsidence scope.
Information from the comprehensive collection and ana-
lysis of data are presented in Table 6. These include phreatic
levels, ground elevation data, subsidence values, additional
slope, the monitored pollutant data, and the VCLS values. As
a general observation, phreatic levels were found to vary
throughout the Yanzhou coal mining area.
Boundary demarcation of the research area
DEM establishment
In order to acquire the subsidence information needed, a
digital elevation model (DEM) has to be established for
different time periods. In this study, because of time con-
straints and cost, non-remote sensing data and two kinds of
remote sensing data, including topographic maps in 1995
and 2000, stereo aerial images in 2005, and stereo IRS-P5
images in 2007, were selected as data sources for this
research.
Digital elevation model (DEM) is the finite sequence of
the three-dimensional vector to express a region, with the
function of the form described as:
V ¼ ðXi; Yi; ZiÞ i ¼ 1; 2; . . .; n
where Xi and Yi are the plane coordinates and Zi is the
corresponding elevation value of (Xi, Yi). When the vector
sequences are arranged in a regular grid, the plane coor-
dinates can be omitted; simultaneously, DEM can be
simplified as one-dimensional vector sequence {Zi,
i = 1,2,3,…,n}. According to the features of collected
data, two methods are used to generate the DEM in this
paper, including the methods of digitizing topographic
maps and digital photogrammetry (Zhao 2000). However,
the remote sensing images need to be preprocessed to
eliminate distortion firstly before DEM establishment, and
the pretreatment in this study are mainly geometric cor-
rections, radiometric corrections, and image enhancement
processing (Hu et al. 2012).
Method of digitizing topographic map
The 1:2,000 topographic maps data for the years 1995 and
2000 were used in this research. In order to extract early
topographic information of the mine area, the topographic
map should be corrected first, and then the irregular tri-
angular network (TIN) is created by the elements of the
points, lines, and polygon, which is digitized according to
the corrected topographic map. The elements of the TIN
are mainly contour lines and elevation points, and the
values of property of elevation information are added. The
Table 6 Damage criteria of the cultivated land in Yanzhou mining area
Classes Mild yield reduction area Moderate yield reduction area Totally lost areas
1 2 3 1 2 3 1 2 3
Xinglongzhuang 56 0.6 0.73 160 1.4 0.96 255 2 1.10
Baodian 54 0.5 0.86 152 1.4 1.06 245 2 1.22
Dongtan 57 0.5 0.88 161 1.5 1.06 250 2 1.24
Nantun 47 0.45 0.67 139 1.3 0.83 230 1.5 1.02
Beisu 45 0.45 0.49 135 1.2 0.62 210 1.4 0.73
Jier 50 0.5 0.86 146 1.3 1.15 250 1.5 1.27
Jisan 54 0.5 0.84 143 1.3 1.16 255 1.5 1.30
1 Represents the criteria of the subsidence depth, and the unit of the subsidence depth is mm
2 Represents the criteria of the additional slope, and the unit of the additional slope is 0
3 Represents the criteria of the visual cultivated land sensitivity, which is a non-dimensional parameter
Boundary demarcation of the damaged cultivated land
123
vector- and attribute-editing work is done in Mapgis soft-
ware, and then the DEM is generated in the 3D Analyst
module of the Arcgis9.1 once the data are converted to
Shap format data.
Digital photogrammetry
The DEM of the years 2005 and 2007 is generated by using
VirtuoZo which is a digital photogrammetric system based on
high-resolution aerial images and three-dimensional relative
satellite images (Xu 2007). DEM production is a submodule
of VirtuoZo digital photogrammetric system, and DEM can
be generated automatically through the following steps: (1)
data preparation; (2) interior orientation, relative orientation,
and absolute orientation; (3) control point encryption and
epipolar image generation; (4) matching preprocessing and
image matching; (5) editing the results of matching; (6) DEM
generation automatically and splicing; (7) DEM editing; and
(8) DEM results exporting (Xu 2005). There are many editing
modules that affected the accuracy in DEM production pro-
cess, and the proper consolidated one was needed because of
the different editorial approach of these modules.
Taking the year 2005 as an example, the DEMs of the
Yanzhou and Jining mine areas are shown in Figs. 4 and 5.
Generation of the additional slope
According to the existing topographic maps and mining
subsidence prediction data, the additional slope schematic
diagrams of the Yanzhou coal mining area are acquired, as
shown in Fig. 6.
Results of the demarcation
The subsidence information data in different times are
acquired by employing the analysis of overlay and differ-
ences in the DEM raster images at different phases in
Arcgis. Based on the land use map, the values of the VCLS
in different coal mines can be automatically calculated by
the spatial topological relationship elements. Coupled with
the additional slope, the damaged cultivated land of
Yanzhou mining area is divided by using Eq. 16 and the
defined four-indicator factor’s corresponding threshold in
Table 6, achieving classification map of the damaged cul-
tivated land in Yanzhou mining area, including the distri-
bution, location, and quantity. The results are shown in
Fig. 7 and Table 7 (the measurement data are collected by
the end of the year 2007). The mild yield reduction area,
moderate yield reduction area, and out of yield area are
53.64, 18.63, and 27.66 km2, respectively, and the total
area is 99.93 km2.
Accuracy assessment
DEM accuracy
Assuming that the sampling error is ignored, in order to
have a complete understanding of the accuracy of the DEMFig. 4 DEM of the Yanzhou mine area for year 2005
Fig. 5 DEM of the Jidong mine area for year 2005
X. Xu et al.
123
images, the last comprehensive observation data from
observation station of Yanzhou mining area are taken as
the true value and the reference value. Twenty-five test
points are selected for the DEM accuracy assessment from
the entire study area in this paper, and the mean square
error is calculated by Eq. 18 (Xu 2005):
Fig. 6 Classification of the
additional slope in Yanzhou
mining area (a Yanzhou mining
field; b Jining mining field)
Fig. 7 Classification map of the
damaged cultivated land in
Yanzhou mining area
(a Yanzhou mining field;
b Jining mining field)
Table 7 A comparison of the areas of damaged cultivated land acquired by using the new demarcation criteria against areas determined by
actual measurements (unit km2)
Type Xinglong Baodian Dongtan Nantun Beisu Jining2 Jining3
New criterion 17.68 14.17 14.75 12.94 11.15 15.23 14.01
Measurement 17.72 14.00 14.54 13.12 11.39 15.03 13.85
Error 0.04 -0.17 -0.21 0.18 0.24 -0.20 -0.16
a The measurement data are collected by the end of the year 2007
Boundary demarcation of the damaged cultivated land
123
r ¼ �
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1 Ri � Zið Þ2
n
sð21Þ
where r is the mean square error of DEM, n is the sampling
point, Ri is the true value of the checkpoint, and Zi is the
DEM elevation interpolated. In the view of error distribu-
tion for the 25 points, the elevation interpretation differ-
ence in the P5 data distributes at the range of -4.834 to
4.708 m, and the true error of the checkpoint elevation r1
is ±2.582 m; simultaneously, the elevation interpretation
difference in the aerial data distributes at the range of
-1.383 to 1.571 m, and the true error of the checkpoint
elevation r2 is ±0.767 m. According to the actual subsi-
dence situation of the research area, the results of DEM
difference with this accuracy can be used as a data source
for the subsidence detection.
Area validation of the results
Generally speaking, the size of the minimum polygon area
should be independent of the resolution of the different
remote sensing images, as well as the accuracy of the final
detection results. The higher the resolution, the higher the
accuracy of the results would be. The area size of the
requisition and compensation land is taken as the reference
value to evaluate the accuracy of the area size of the
damaged cultivated land, measured based on the new cri-
terion. In accordance with the Chinese Law of Land
Management, coal mining enterprises must implement the
requisition and compensation for the damaged cultivated
land, which is executed according to the yield reduction of
the crops, resulted from the damage of the subsidence.
According to real measured data (Hu 2007), the total area
of the land requisition of Yanzhou mining caused by
mining subsidence is 74.08 km2, crop compensation is
34.31 km2, and the total area is 108.39 km2.
Comparison graphs of the results are shown in Fig. 8.
The results show that the total area of the damaged culti-
vated land, based on the new criterion proposed in this
paper, is less than the actual expropriation and compen-
sation land area. Equation 18 is used to evaluate the
accuracy of the above area results; the calculation shows
the mean square error of the damaged cultivated land area
is 0.18 km2. For the criterion indicators, including subsi-
dence depth and additional slope, the values of the Nantun
and Beisu minefields are less than the other mines; how-
ever, for the area difference assessments between the two
methods, these two minefields have the bigger errors.
There are two possible reasons for the difference
between the measurement data and the data resulting from
the new criterion, namely 1. that the four indicators of the
damage evaluation indicator system proposed in this paper
may introduce calculation errors, such as an error with the
subsidence value calculated by the subsidence prediction
software MSPS and/or an error within the slope elevation
data, 2. that errors may be introduced during the data
acquisition process for the evaluation indicator system.
Conclusions
In order to determine zones of damaged cultivated land for
crop compensation, land requisition, and land reclamation
in the eastern plain coal mining area of China, this paper
presents a new method and criteria for assessing the
boundaries. From data relating to subsidence-induced land
damage, the impact of subsidence on cultivated land is
analyzed and presented along with a proposed damage
Fig. 8 Comparison and analysis of the results (a comparison between the measurement data and the data resulted from the new criterion;
b comparison between the area error and the different criterion values)
X. Xu et al.
123
evaluation indicator system. Appropriate demarcation cri-
teria are established after comprehensively analyzing the
influence of the groundwater depth, surface inclination,
chemical pollution, and VCLS values. This paper takes the
Yanzhou coal mining area as a worked example to dem-
onstrate the practicality of the proposed system. Using the
system, the DEM and additional slope of the Yanzhou coal
mining area are generated, and the information was used to
sub-divide the damaged cultivated land into 1. mild yield
reduction areas, 2. moderate yield reduction areas, and 3.
out of yield areas. Compared with the size of the existing
area of the land requisition and crop compensation, the
mean square error of the calculated area result is 0.18 km2.
For Yanzhou coal mining area, the results reveal that the
most important boundary, for the mild yield reduction
category, can be determined by the 45-mm subsidence line
or, additional slopes greater than 0.5� (i.e. inclination is
equal to 9 mm m-1) or, VCLS values at 0.6. It is suggested
that these parameters be the reference basis for boundary
demarcation.
It is acknowledged that there are some errors in the
demarcation result presented. However, with further
research, the demarcation criteria can be optimized leading
to improved accuracy—these parameters should provide
the important, future reference markers needed to relieve
the stressful process of land requisition, crop compensa-
tion, and land reclamation assessment. Hopefully, such an
improved system will result in fewer land subsidence
disputes.
Acknowledgments This study was partially financed by the project
of Time–space development and governmental strategy of the land of
subsidence and Reclamation in Yanzhou Mining Area. The aerial
images, topographic map data, and P5 images are provided by the
Yanzhou Coal Mining Company Limited and the Beijing Earth
Observation Inc. Additionally, Qinhuan Huang is thankfully
acknowledged for providing the technical support in the establish-
ment of the criterion.
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