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Contents lists available at ScienceDirect

Resources, Conservation and Recycling

journal homepage: www.elsevier.com/locate/resconrec

Full length article

Monitoring wind farms occupying grasslands based on

remote-sensing data from ’s GF-2 HD satellite—A case study of

Jiuquan city, province, China

a a b a a a a

Ge Shen , Bin Xu , Yunxiang Jin , Shi Chen , Wenbo Zhang , Jian Guo , Hang Liu ,

a a,∗

Yujing Zhang , Xiuchun Yang

a

Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of

Agricultural Sciences, , 100081, China

b

Key Laboratory of Digital Agricultural Early-warning Technology of the Ministry of Agriculture, Institute of Agricultural Information, Chinese Academy of

Agricultural Sciences, Beijing, 100081, China

a r t i c l e i n f o a b s t r a c t

Article history: Wind power is a clean and renewable resource, and it is rapidly becoming an important component of

Received 14 April 2016

sustainable development and resource transfer. However, the construction of wind farms impacts the

Received in revised form 13 June 2016

environment and has been the subject of considerable research. In this study, we verified whether China’s

Accepted 30 June 2016

GF-2 HD satellite (GF-2) could be used to monitor the 10 million kilowatt wind power grassland construc-

Available online xxx

tion area in City, Gansu Province. Monitoring was performed by comparing the imaging results

from the Landsat 8 OLI and China’s GF-1 HD satellite (GF-1). We performed an interactive interpretation

Keywords:

of the remote sensing images and verified the accuracy of these interpretations using measured field data.

Wind farm 2

We evaluated 354 pieces of wind turbine equipment with an average construction density of 0.31 km

Grassland monitoring

2

per device. The construction of a single wind turbine was found to damage nearly 3000 m of grassland.

China’s GF-2 HD satellite

2 2

Gansu province The average area of grassland damaged by 3 MW and 1.5 MW turbines was 5757 m and 2496 m , respec-

2

tively. Approximately 2.44 km of farmland was occupied by wind power construction and accounted for

approximately 2.2% of the study area. Roads covered 60.6% of the farmland occupied by wind power con-

struction. The average difference between the measured and calculated GF-2 image data was 0.09, and

the overall interpretation accuracy was approximately 84%. Therefore, the use of comprehensive imag-

ing analyses and GF-2 image data are feasible for monitoring grasslands under construction for wind

power. In addition, the impacts of wind farm construction on vegetation destruction and soil erosion are

discussed. In this study, grassland wind farms are explored using remote sensing tools to guide decision

making with regards to the rational use of grassland resources and their sustainable development.

© 2016 Elsevier B.V. All rights reserved.

1. Introduction unique advantages, wind power is rapidly developing as impor-

tant part of sustainable development and resource transfer in many

Wind power is a clean and renewable resource. Compared with countries (Cui et al., 2009; IEA, 2013a,b). According to the Global

coal and other conventional energy sources, wind power does not Wind Energy Council, the capacity of newly installed wind power

rely on external energy sources, avoids fuel price risk, and mit- reached 51,477 MW in 2014. Since 2003, the average annual growth

igates environmental costs, such as carbon emissions (Piepers, rate of China’s wind power installed capacity has increased to more

1981; Apergis and Payne, 2011; Dai et al., 2016). Because of these than 70%. As of 2015, the cumulative installed wind power capac-

ity in China reached 145 million kilowatts, which represents a

26.6% increase over the values in 2014. However, an imbalance

can be observed in regional wind power development, with rapid

∗

Corresponding author. development occurring in the northeast, north, and, northwest (the

E-mail addresses: shenge [email protected] (G. Shen),

“Three Northern Regions”), as well as along the southeast coast

[email protected] (B. Xu), [email protected] (Y. Jin), [email protected] (S. Chen),

(Deng, 2002). The northwestern province Gansu has abundant wind

[email protected] (W. Zhang), [email protected] (J. Guo),

energy resources and theoretical reserves of 237 million kilowatts

liuhang [email protected] (H. Liu), [email protected] (Y. Zhang),

[email protected] (X. Yang). (7.3% of the total reserves, 5th highest in the country) (Wang, 2009).

http://dx.doi.org/10.1016/j.resconrec.2016.06.026

0921-3449/© 2016 Elsevier B.V. All rights reserved.

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Table 1

Jiuquan City, which is located in the western end of the Hexi Cor-

Study area location information.

ridor, has reserves exceeding 40 million KW over an area of nearly

2

10,000 km . Therefore, Gansu Province has proposed the construc- Study area Latitude(N) Longitude(E)

tion of a wind power zone equivalent to an onshore ◦ ◦ ◦ ◦ a 40.33389 –40.34821 96.84701 –96.87339

◦ ◦ ◦ ◦

Three Gorges in the west. b 40.2463 –40.29484 96.83449 –96.904

◦ ◦ ◦ ◦

Because wind power bases are mostly located in the fragile c 40.17078 –40.24287 96.81527 –96.88903 ◦ ◦ ◦ ◦

d 40.16292 –40.24882 96.85096 –96.95114

inland northwest Gobi Desert area, wind power development and

construction will inevitably cause ground destruction and soil ero-

sion. Once the environment is damaged, it is difficult to restore, and

tools to inform decisions regarding the rational use of grassland

erosion and desertification in the region will likely be exacerbated

resources and their sustainable development.

(Sun, 2011). Therefore, understanding the impact of wind farms on

the environment prior to construction is essential. The main effects

2. Study area

of such damage include soil and vegetation damage, biomass loss,

and soil erosion (Wang, 2015). Studies have explored the impact

Based on the principles of representativeness, operability and

of wind farm construction on wildlife, such as birds (Plonczkier

data validity, we selected four areas of the Yumen wind power

and Simms, 2012; Parsons and Battley, 2013), and qualitatively and

base within the 10 million kilowatt wind-power base in Jiuquan

quantitatively evaluated the environmental impacts including cli-

City, Gansu Province, which are defined as areas a, b, c, and d

mate change by using the relevant models (Ruotolo et al., 2012;

2

(Fig. 1, Table 1). The total study area was 109.33 km . Yumen City

Chias and Abad, 2013; Phillips, 2015; Abbasi and Abbasi, 2016;

is a county-level city under the administration of Jiuquan City in

Bouman et al., 2016; Nagashima et al., 2016). Studies from China

Gansu Province. Yumen City is rich in grassland resources, and its

have produced qualitative descriptions and established a quanti-

flora consists of the typical Asia central shrubs, small shrub desert

tative evaluation system for investigating the effects of wind farm

steppe and some swampy and flat meadow steppes. Its grassland

construction, although the studies are primarily based on sample

is divided into grassland, meadow grassland and desert grassland,

plots using measured statistical data. Studies have evaluated the

and temperate desert steppe is the main grassland type. There is

impact of wind farms using chromatographic analyses of vegeta-

2

a total of 11 grasslands, with an area of nearly 11,333 km . This

tion biomass, ecosystem productivity, soil physical and chemical

area has a typical inland arid desert climate zone and is between

composition, as well as other variables (Yun, 2014). Complex eval-

1200 and 2042 m above sea level, and it has approximately 3200 h

uation systems can fully characterize the impacts of wind farm

of annual sunshine for an average of 71% sunshine and is frost-free

construction on the environment; however, because of the myr- ◦

for 147 days. The average temperature is 8 C, the maximum and

iad factors involved, they are not practically applicable. The most ◦

minimum temperatures over the past five years were 38 C and

direct impact of wind farm construction on grassland environments ◦ ◦

−24 C, respectively, the summer diurnal temperature is 14–17 C

is grassland damage. Because wind farms cover large areas, obtain-

and the annual average precipitation is 63.3 mm. These values are

ing actual measurements of this destruction is impractical. With

typical of an agricultural and animal husbandry area.

recent advances in remote sensing technology and the rapid devel-

opment of remote sensing data processing methods, high spatial

3. Materials and methods

and temporal resolution remote sensing data can provide highly

accurate information on ground features and enable the large-scale

3.1. Data sources and processing

monitoring of wind farms.

In this study, we used remote sensing to monitor grassland

3.1.1. Remote sensing data

damaged by wind farm construction in the 10 million kilowatt

GF-2 is the first civilian optical remote sensing satellite inde-

wind-power base in Jiuquan City, Gansu Province based on image

pendently developed by China, and it has a spatial resolution better

data from China’s GF-2 HD satellite (GF-2). We verified the accuracy

than 1 m, is equipped with two high-resolution 1 m panchromatic,

of the interpreted results using measured indicator data from the

4 m multi-spectral cameras, and features sub-meter spatial reso-

field and by comparing the imaging results from Landsat 8 OLI and

lution, high position accuracy, and fast maneuverability (Table 2).

China’s GF-1 HD satellite (GF-1) and GF-2. We discussed the fea-

We used an image from GF-2 acquired on August 17, 2015 (image

sibility of using GF-2 image data to monitor grassland wind farms

row/column No. 55/137) with overall cloud cover of less than 5%,

and defined grassland areas occupied by wind farms to include the

which meets the application requirements. Preprocessing of the

area of grassland damaged by wind turbine construction and by

GF-2 image data, such as radiation calibration, atmospheric correc-

roads. We discussed the impact of wind farm construction on the

tion, orthorectification, and image enhancement, was performed

local grassland environment and comprehensively and accurately

using ENVI software. A panchromatic image was used to perform

assessed grassland wind farms in real time using remote sensing

band fusion of the multi-spectral band image to obtain a fusion

image with a spatial resolution of 1 m. The fusion result retained

Table 2

GF-2 satellite payload technology index.

Parameter 1 m resolution panchromatic/4m-resolution multi-spectral camera

␮ Spectral range Panchromatic 0.45-0.90 m

Multi-spectral 0.45-0.52 ␮m

0.52-0.59 ␮m

0.63-0.69 ␮m

0.77-0.89 ␮m

Spatial resolution Panchromatic 1 m

Multi-spectral 4 m

Width 45 km

Revisit period (when sideway) 5 days

Coverage period (not side to side) 69 days

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Fig. 1. Wind power study area.

Table 3

both sides of the turbine are easily established; in the image, the

Wind power field survey sample information table.

texture has a regular shape and elongated strip and is more promi-

Sample Type Latitude Longitude Altitude nent in color. During field research, we observed and analyzed the

◦ ◦ environment of the area surrounding the wind farm. The areas

1 Wind power (1.5 MW) 40.26066 N 96.88469 E 1525.47 m

◦ ◦

2 Wind power (1.5 MW) 40.29077 N 96.88473 E 1536.43 m selected for wind power construction were mostly desert with

◦ ◦

3 Wind power (1.5 MW) 40.17852 N 96.91972 E 1466.42 m sparse vegetation, and the topographic features were relatively

◦ ◦

4 Wind power (3 MW) 40.33563 N 96.87126 E 1455.89 m

◦ ◦ simple. However, through field research and analysis, we found

5 Wind power (3 MW) 40.34572 N 96.84743 E 1448.90 m

that differences occurred in the geographic features between the

areas damaged by wind power construction and the surrounding

the multi-spectral characteristics in color and the full-color image areas without construction. The surface of the damaged construc-

features in texture. In addition, to assess the applicability of the tion areas was all gravel with almost no vegetation cover, and clear

GF-2 image data, we also used Landsat 8 OLI image data (spatial boundaries were observed between the substrate types. To accu-

resolution of 30 m) and fusion GF-1 image data (spatial resolution rately determine the boundaries in the remote sensing images,

of 2 m). which is an important indicator for identifying the area damaged by

wind power construction, we enhanced the GF-2 image by image

3.1.2. Field measured data stretching and vegetation index methods.

We performed a field survey on the Yumen wind farm in Image stretching is the most basic image processing method

Gansu Province in August 2015 using a GPS to record the lati- and mainly used to improve the contrast of the displayed image.

tude, longitude, and altitude (Table 3). We used a tape and other Enhancement is performed by processing a single pixel value

field measuring devices to measure the equipment specifications, (stretching the image increases the color contrast, provides more

including the wind turbine tower perimeter, the radius of its base detailed information regarding the feature boundaries, and pro-

perimeter, the road width, and the width of the area damaged. vides more prominent boundaries) (Fig. 2).

We established remote-sensing interpreting marks typical of the A vegetation index (VI) is a characteristic index developed using

region. Using ArcGIS 10.0 software to interactively interpret the linear and nonlinear combinations of remote sensing satellite data,

remote sensing images, we obtained an interpretation graph of the and such indices reflect the status and distribution of green vege-

area of damaged grassland (i.e., the area around the sites of wind tation growth (Pettorelli et al., 2005). Numerous vegetation indices

turbines) through the construction of wind towers, wind turbine are currently used. In this study, we determined the boundaries

foundations, substations, and frame poles. between area that were damaged and undamaged by wind power

construction by vegetation boundaries. The Normalized Difference

3.2. Methods Vegetation Index (NDVI) is an index that indicates the plant growth

status and spatial distribution density of vegetation, and it has a

3.2.1. Establishment of interpreting marks linear correlation with vegetation distribution density:

Establishing the correct interpreting marks is the basis of

−

remote-sensing image interpretation. In this paper, the study sub- NIR R

NDVI = (1)

ject is the area of grassland damaged by wind power construction NIR + R

and the area of the access road. The interpreting marks required

for the study were established by combining field research and where R is the reflectance of visible light red band (GF-2 image

processed remote sensing images. Road marks and roads built on is the 3rd data image band, 0.63-0.69 ␮m) and NIR is the near

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Fig. 2. Comparison chart before and after the remote sensing stretch.

Fig. 3. Schematic of the relevant wind power development measurement indices.

infrared band reflectance (GF-2 image is the 4th data image band, aged grassland area, road polygon) and linear features (i.e., distance

0.77-0.89 ␮m). between the relevant indicators). The interpretation process was

The results indicate that the NDVI values of the damaged areas performed in ArcGIS 10.0.

have clear boundaries that separate them from the surrounding

area, which is consistent with the results of our field survey.

3.2.3. Accuracy verification

The accuracy verification was designed to test the effects of

3.2.2. Interactive interpretation of the remote sensing images image interpretation and ensure reliability of the inter-

Man-machine interactive remote sensing image interpretation pretation results. The conventional verification method conducts

is a remote sensing interpretation and analysis method that com- a field verification for the pre-interpreted results to determine

bines the visual interpretation of remote sensing images with whether the interpretation of the object type is correct. This method

computer analyses and processing (Chen and Dai, 1998). Using pre- is usually applied to determine the object type. Because the object

processed GF-2 images as background images, rough positioning of the study is relatively simple and easy to determine, in this study

is performed via field survey points. The wind farm information is we chose to use indicators field measured distance indicators to val-

provided from local region maps and Google Maps. We then identify idate the accuracy of the interpretation results. We validated the

ground features and interpreting marks by processing enhanced interpretation results for wind turbine tower foundation perime-

images and assign properties along the edge of the image features, ters, grassland damaged widths, and the distance between the wind

as well as class boundaries. The contents were divided into two turbine tower to substation, frame poles and access road, respec-

categories: polygon elements (i.e., wind power equipment, dam- tively, using field measured data (Fig. 3). The width of the grassland

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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To verify the accuracy of the results, we also interpreted the rel-

evant indicators of five wind farm field survey sampling points in

the corresponding GF-2 image data, including the tower founda-

tion perimeter, width damaged by turbine construction, distance

from tower to substation, distance from tower to frame poles, and

distance from tower to access roads (Table 5). Samples 1 and 2 were

located at the Datang Changma wind farm in Yumen, which has a

1.5 MW wind turbine. The equipment indicators for the two sam-

pling points (except for the tower foundation perimeter) was 0.989

based on the Pearson correlation coefficient, which was signifi-

cant at the 0.01 level (unilateral). Sampling points 4 and 5 contain

3 MW wind turbines. Except for the tower foundation perimeter,

the equipment indicator was 0.990 based on the Pearson corre-

lation coefficient, which showed significant correlations at 0.01

(one-sided). This result demonstrates a standard for similar wind

power equipment constructed by the same manufacturer. The field

survey found large differences in the circumference of the founda-

tion of the tower, which was mainly caused by terrain conditions.

For low-lying locations, barriers piled with earth were built around

the wind power equipment to prevent damage to the wind power

equipment from summer rain siltation. Sampling point 3 (CECEP

Wind-power Corporation Changma hybrid power generation field)

Fig. 4. Flowchart of methods.

has a 1.5 MW turbine and indicates different results from the other

sampling points.

area damaged by wind turbine construction refers to the horizon- To analyze the area of grassland damaged by the construction

tal distance between the wind turbine center position of the tower of different types of turbines (excluding access roads), we analyzed

foundation and the farthest position of the damaged area and the the grassland damaged by a number of similar turbines around

distance between the center position of the tower foundation and field survey sampling points. Our results indicate that 3 MW wind

2

substation, frame poles, and access road is the horizontal distance. turbines damaged an average of 5757 m , the 1.5 MW turbine at

2

The accuracy of interpretation is reflected by three indicators: the Datang Changma damaged an average of 2778 m , and the 1.5 MW

2

degree of difference (DD), the root mean square error (RMSE), and at CECEP Changma damaged an average of 2214 m . Thus, 3 MW

the mean relative error (MRE): wind turbines damage approximately 2.3 times more grassland

− than the 1.5 MW wind turbines.

ID MD

DD = (2)

MD

 4.2. Accuracy verification

2

(MD − ID)

RMSE = (3)

N 4.2.1. Correlation and verification of the multi-source remote



sensing data imaging results

 2

− (MD ID⁄ID) To fully discuss the suitability of the GF-2 image data appli-

=

MRE (4) cations for monitoring grassland occupied by wind farms, we

N

compared Landsat 8 OLI image data (spatial resolution of 30 m),

where ID is the interpretation of the data, MD is the measured data, GF-1 image data (spatial resolution of 2 m), and GF-2 image data

and N is the number of verification data points (25). Fig. 4 presents (spatial resolution 1 m) after performing the same preprocessing

the whole process of study methods. and enhancement (Fig. 7).

White bright spots appeared in the wind power equipment con-

4. Results struction area of Landsat 8 OLI image data, and these were caused

by the high reflectivity of the turbine cement foundation. Except

4.1. Interpretation of grasslands under wind power development for the white bright spots, additional details on the wind power

equipment could not be obtained, including for access road con-

We summarized the interpretation results from the GF-2 image struction and turbine construction areas. In addition, the color was

data, which were obtained via an output map from Arc GIS10.0, and monotonous, which increases the difficulty of identifying features.

used the statistics function of ArcGIS 10.0 to calculate the grassland Therefore, Landsat 8 OLI image data have a lower resolution for the

area damaged by turbine construction and access roads for the four remote-sensing monitoring of grasslands occupied by wind farms

locations (Table 4; Figs. 5 and 6). and are unsuitable for monitoring grassland areas occupied by wind

2

In the 109.33 km Yumen study area, there were 354 wind tur- farms.

bines, including 9 in Area a, 96 in Area b, 127 in Area c, and 122 in Wind power equipment, including towers and turbine blades,

2

Area d. The average construction density was 0.31 km per device, can be clearly observed in GF-1 images, and the color of the area

2 2 2

or 0.36 km in Area a, 0.30 km in Area b, 0.29 km in Area c, damaged by turbine construction and access roads can be clearly

2

and 0.33 km in Area d. The area of grassland damaged (excluding distinguished from other surface features. However, the overall the

2 2

roads) by turbine construction was 0.96 km , or nearly 3000 m per image texture is relatively indistinct and the type of edge features

turbine; and the interpretation results indicated that there were in the image are not obvious, which leads to the inaccurate deter-

2

1.48 km of road. The wind power construction area included areas mination of category boundaries during interpretation. In addition,

damaged by turbine construction and access roads; the area of the tower foundations, substations, poles and other key elements

2

grassland occupied by wind farms was 2.44 km , or approximately are unclear.

2.2% of the area of the study area, and roads covered 60.6% of the In the GF-2 images, the wind power equipment is clearly iden-

farmland occupied by wind power construction. tified and has regular shapes. Moreover, clear boundaries are

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Fig. 5. The remote sensing image interpretation results of study area b.

Fig. 6. The remote sensing image interpretation results of sample 2.

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Table 4

Interpretation of the remote sensing results for the wind power development in the study area.

Item Area of grassland damaged by wind turbine construction Access road

Number of polygons 354 67

2 2

Area 0.96 km 1.48 km

Table 5

Remote sensing-based interpretation of wind power equipment related indices.

Tower foundation Turbine construction Distance from tower to Distance from tower to Distance from tower to

perimeter damaged area width substation frame poles road

(m) (m) (m) (m) (m)

Sample ID ID ID ID ID

1 29.86 41.16 10.49 21.93 21.43

2 37 37.81 10.68 23.37 23.48

3 65.66 36.52 7.8 18.01 7.67

4 35.42 75.95 15.59 28.59 24.94

5 62.78 62.27 15.07 26.86 28.87

Fig. 7. Comparison of multi-source remote sensing data imaging.

observed between areas damaged by turbine construction, access ples). However, because of the relative regularity of the objects, we

roads, and surrounding features. The type boundary is clearly dis- believe our validation is indicative (Table 6).

tinguishable, and thus the grassland area occupied by wind power The mean difference between the measured and interpreted

construction can be easily extracted. Circular tower foundations, wind turbine tower foundation perimeter was 0.03, and it ranged

substations, and poles are clear and detailed and provide rich infor- from 0.0003 to 0.09; the mean difference in the damaged width of

mation. the turbine construction was 0.16, and it ranged from 0.001 to 0.37;

Overall, the GF-2 image data have considerable advantages for the mean difference in the distance from the tower to substation

monitoring grasslands occupied by wind farms. was 0.14, and it ranged from 0.05 to 0.23; the mean difference in

distance from the tower to the frame poles was 0.06, and it ranged

4.2.2. Comparison and verification of interpreted and measured from 0.001 to 0.12; and the mean difference in the distance from

indicator results the tower to the road was 0.06, and it ranged from 0.001 to 0.16.

The GF-2 image data were determined to be the best for this The overall mean difference was 0.09, which was low based on the

application, and we verified these data using field measurements. environment of the monitored area. Most of the local grassland

Because of time and environmental limitations, the number of was desert steppe and Gobi Desert (sand), whereas certain areas

field survey samples was relatively small (only five effective sam- evolved to calamine, which has similar surface features to the area

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

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Table 6

Comparison of the measured and interpreted data for the wind farm field survey sample indicators.

Tower foundation perimeter (m) Turbine construction damaged area width (m) Distance from tower to substation (m)

Sample MD ID DD MD ID DD MD ID DD

1 29.52 29.86 0.01 39 41.16 0.06 9.6 10.49 0.09

2 35.80 37 0.03 50 37.81 −0.24 13.2 10.68 −0.19

3 64.3 65.66 0.02 58 36.52 −0.37 6.3 7.8 0.23

4 32.38 35.42 0.09 76 75.95 −0.001 13.8 15.59 0.13

5 62.8 62.78 −0.0003 72 62.27 −0.13 14.4 15.07 0.05

Distance from tower to frame poles (m) Distance from tower to road (m)

Sample MD ID DD MD ID DD

1 20.6 21.93 0.06 20 21.43 0.07

2 21 23.37 0.11 22 23.48 0.07

3 16.1 18.01 0.12 6.6 7.67 0.16

4 28.55 28.59 0.001 25 24.94 −0.002

5 26.2 26.86 0.02 29 28.87 −0.001

tion has less of an impact on land use, water quality, air emissions,

and waste generation than coal, hydro, and nuclear power. How-

ever, such construction requires a large area of land, and the areas

suitable for wind farm construction are primarily distributed in

grassland regions. The environments in most of these areas are

fragile and are easily affected and destroyed; thus, the ecological

environment should be considered a key factor in the assessment

of wind farm projects.

5.1. Biomass

Construction of wind farms will inevitably damage vegetation.

In the study area, the Gobi Desert is prominent; therefore, native

vegetation species will not be significantly influenced. Biomass loss

and reduction are the main vegetation changes caused by wind

farm construction.

To quantitatively analyze the impact of wind farm construc-

Fig. 8. Scatter diagram of the measured and interpreted values.

tion, we estimated the unit grass production of the local temperate

desert steppe using 12 locally available data plots from six sam-

damaged by turbine construction. We considered these results in pling points based on remote-sensing interpretation results of the

the actual measurement process, although they were excluded in area damaged by turbine construction and access roads. The aver-

2

the interpretation process, which led to certain errors. age dry grass yield based on 12 sample plots was 86,852 kg/km . In

2

Therefore, the root mean square error (RMSE) and mean relative the study area, wind power construction occupies 2.44 km and the

error (MRE) were also used to verify the interpreted results. The estimated reduction in hay production is 211,919 kg. The impact of

results showed that the GF-2-based interpretation corresponded wind farm construction on the dry grass yield is minor; however,

with the measured data (Fig. 8). The points generally fell on or because the area is mostly the Gobi Desert, which is subject to water

near the 1:1 line; the RMSE was 5.48; the MRE was 0.16; and the shortages, vegetation cannot be quickly restored. Our field survey

interpretation accuracy was 84%. also found that a certain area around the turbine site was bare land

Overall, the GF-2 image data were accurately interpreted found with a large amount of gravel and little vegetation.

to be useful for monitoring grassland occupied by wind farms.

5. Discussions

5.2. Soil erosion

Wind energy resources are inexhaustible clean energy, and

The geomorphic features of the area include flat open terrain,

the development of wind power is important for adjusting the

sparse vegetation, low precipitation, and a large number of days

energy structure and reducing environmental pollution. More-

with strong wind. The construction process, including turbine and

over, such sustainable new energy is encouraged and supported by

transformer foundation excavation and backfilling, aerial cable pole

local governments. Wind power can reduce conventional energy

foundation construction, underground cable trench excavation and

consumption, save water, and reduce water pollution with zero

backfilling, booster station construction, access road construction

atmospheric pollutant emissions (Liu, 2014).

and expansion, will cause vegetation damage, surface disturbances,

However, wind power development and operations have

and soil structure destruction, which results in a decrease in soil

direct and indirect impacts on the environment, including the

erosion resistance and an increase in the sensitivity to wind and

electromagnetic environment, acoustic environment, water envi-

hydraulic action leading to soil erosion. Soil erosion by wind farms

ronment, atmospheric environment, and ecological environment

has certain telltale characteristics, including the coexistence of

(Tremeac and Meunier, 2009; Wang and Sun, 2012; Marimuthu

point and linear erosion, as well as wind and water erosion.

and Kirubakaran, 2013; Reimers et al., 2014). Wind power construc-

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026

G Model

RECYCL-3303; No. of Pages 9 ARTICLE IN PRESS

G. Shen et al. / Resources, Conservation and Recycling xxx (2016) xxx–xxx 9

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This study was supported by the National Natural Science

of wind power base in Gansu. Energy 25, 43–47 (in Chinese).

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&Technology Cooperation Program of China (2013DFR30760), the environment and restoration measures. Environ. Prot. Sci. 41, 105–108 (in

Chinese).

Agricultural Scientific Research Fund of Outstanding Talents and

Yun, B.T., 2014. Environmental Impact Index Assessment of Wind Power Plant

the Open Fund for the Key Laboratory of Agri-informatics, Min-

Project. North China Electric Power University, Beijing (in Chinese).

istry of Agriculture, China (2015003) and the Agricultural and Rural

Resource Monitoring Statistics (Grassland Monitoring) Project.

We acknowledge the assistance of the Grassland Monitoring and

Supervision Center, Ministry of Agriculture, PRC. And we also

acknowledge units such as the grassland management stations in

the Gansu Province of China, which provided extensive support and

assistance during the field survey.

Please cite this article in press as: Shen, G., et al., Monitoring wind farms occupying grasslands based on remote-sensing

data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China. Resour Conserv Recy (2016), http://dx.doi.org/10.1016/j.resconrec.2016.06.026