Soil Conservation Dataset Covering National Ecological
Barrier Zone at 1-km Resolution (2000–2015)
Wang, Y.1 Wang,
X. F.2,3* Yin, L.C.4
1. School
of Earth Science and Resources, Chang’an University, Xi’an 710054, China;
2. The
College of Land Engineering, Chang’an University, Xi’an 710064, China;
3. The Key
Laboratory of Shaanxi Land Consolidation Project, Chang’an University, Xi’an
710064, China;
4. Key
Laboratory of Land Surface Pattern and Simulation, Institute of Geographic
Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
100101, China
Abstract:Ecosystem
services are the benefits that ecosystem provides to human beings. As one of
the main regulation services provided by the terrestrial ecosystem, soil
conservation is an important guarantee to prevent regional land degradation and
reduce the frequency of flood disasters, which is often expressed as the value
of soil conservation. The national ecological barrier zone (NEBZ), known as
“two barriers and three belts”, established the national ecological security pattern.
Exploring the spatiotemporal distribution of soil conservation in the barrier
area is of far-reaching significance for China’s ecological civilization
construction and sustainable development. Based on the revised universal soil
loss equation (RUSLE), the soil conservation dataset (2000-2015) with a
resolution of 1 km in NEBZ was quantitatively evaluated using MOD13A2 NDVI,
ASTER GDEM, meteorological station data and soil dataset of China, etc. The
data is archived in .tif format (unit: t·km‒2·a‒1).
The spatial resolution is 1 km, and data size of the compressed dataset is 168
MB.
Keywords:national
ecological barrier zone; ecosystem service; soil conservation; RUSLE model
Dataset Available Statement:
The dataset supporting this paper was
published and is accessible through the Digital
Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2020.03.19.V1.
1 Introduction
As an
important “directional flow service”,
soil conservation (SC) is the regulation and control capabilities of the
ecosystem to prevent soil erosion, and the ability to store and maintain
sediment in rivers, lakes, wetlands, and reservoirs[1]. Under the
joint influence of climate change and human activities, the risk of global soil
erosion is aggravating, and the soil conservation capacity is facing severe
challenges[2-3]. As a country with a large population and
agriculture, China is also one of the countries with the most severe soil
erosion in the world[4]. The increasingly serious soil erosion is
the concentrated manifestation of various ecological problems in China, and
poses an acute threat to food security, ecological security, and the
sustainable development of the social economy. Therefore, as an important
material basis for human survival, more and more attention has been paid to the
function of soil conservation that has become a research hotspot in the fields
of geography and ecology[5].
In December 2010,the State Council of China launched
the national ecological security strategic pattern project, in
which the construction framework of “two
barriers and three belts” was
mentioned. In response to the call of national policy, Fan et al.[6] proposed national
barrier plan on the basis of national major function oriented zoning. On the
basis of ensuring the integrity of counties, Fu et al.[7] described the scope of NEBZ and carried out
comprehensive assessment of ecosystem services from 2000 to 2010. The
geographical regions of NEBZ include the northern sand belt, the ecological
barrier of the Qinghai Tibet Plateau, Sichuan Yunnan-loess plateau ecological
barrier, the southern hill and mountain belt, and the northeastern forest zone.
Quantitative analysis of soil conservation with long time series in the barrier
area not only helps to reveal the spatiotemporal distribution and evolution of
soil conservation, but also provides theoretical basis for ecological
construction and the sustainable development of China. This dataset was
evaluated under the support of the national key research and development
project. The main purpose of this dataset is to construct the long time series
products of soil conservation in NEBZ, to carry out researches on trade-offs and synergies of
ecosystem services, and to ensure human rights and well-being. The 1-km
spatial resolution_2000-2015_SC product of national barrier zone product is an
important output of ecosystem service science in barrier area, and it is also
an important digital resource for monitoring and evaluating soil conservation
and evolution of ecological environment. In this paper, we aimed to introduce
detailed information of the data, the basic principle of the data algorithm and
the data results, and make a comparative analysis of the data, so as to
evaluate its accuracy.
2
Metadata of the Dataset
The name, short name, authors, geographical
region, data age, data resolution, data format, publisher and sharing policy,
and related information of the “National ecological barrier zone 1-km
resolution soil conservation dataset (2000–2015)”[8] are listed in Table
1.
3
Methods
3.1 Study Area
(1) NDVI data applied a 16-day composite product
of MOD13A2 1 km vegetation index from 2000 to 2015[10]. After format
conversion, annual maximum value composite, clipping and projection conversion,
the annual NDVI of the study area was obtained. This dataset was adopted to
calculate the vegetation coverage factor in RUSLE model.
(2) DEM data utilized ASTER global digital
elevation model data (ASTER GDEM)[11]. The spatial resolution is 90m. According to
the data, ArcGIS10.2 software was used to
calculate the slope and slope length.
(3) Monthly climate dataset was downloaded from
China meteorological data sharing network[12]. The
rainfall data was extracted from it, and the software ANUSPLIN [13]
was used for interpolation to obtain the rainfall raster data with a spatial
resolution of 1km and was used to calculate the rainfall erosivity factor in
RUSLE model.
Table 1 Metadata summary of the “Soil conservation grid yearly dataset in the national barrier zone of China
(2000-2015)”
Items
|
Description
|
Dataset full name
|
Soil conservation
grid yearly dataset in the national barrier zone of China
(2000-2015)
|
Dataset short name
|
NBZ_SC_1 km_2000-2015
|
Authors
|
Wang, Y. AAS-5036-2020,
School of Earth Science and Resources, Chang’an University,
wangyichangan134@163.com
Wang, X. F. AAS-5271-2020,
The College of Land Engineering; the Key Laboratory of Shaanxi Land
Consolidation Project, Chang’an University, wangxf@chd.edu.cn
Yin, L. C. AAS-4914-2020,
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences; University of Chinese Academy of Sciences, yinlichang3064@163.com
|
Geographical
region
|
The provinces include
Heilongjiang, Jilin, Qinghai, Gansu, Sichuan, Xinjiang, Inner Mongolia,
Hebei, Liaoning, Tibet, Ningxia, Yunnan, Guangxi, Guangdong, Guizhou, Hunan,
Jiangxi, and Shanxi
The northern sand belt (36°45¢N-45°06¢N,
75°50¢E-124°18¢E)
The ecological barrier of
the Qinghai Tibet Plateau (29°40¢N-38°10¢N, 82°50¢E-105°5¢E)
Sichuan Yunnan-loess plateau
ecological barrier (24°10¢N-38°50¢N, 99°05¢E-114°25¢E)
The southern hill and
mountain belt (22°45¢N-27°10¢N,
103°10¢E-119°15¢E)
The northeastern forest zone
(40°52¢N-53°34¢N,
118°48¢E-134°22¢E)
|
|
Year
|
2000-2015
|
Spatial resolution
|
1 km Data format .tif
|
Data size
|
168 MB (After compression) Projection
coordinate system WGS_1984_Albers
|
Foundations
|
Ministry of Science and
Technology of P. R. China(2018YFC0507300, 2019QZKK0405); Shaanxi
Province(2018JM4016)
|
Data
publisher
|
Global Change Research Data
Publishing & Repository, http://www.geodoi.ac.cn
|
Address
|
No. 11A, Datun Road,
Chaoyang District, Beijing 100101, China
|
Data sharing policy
|
Data from the Global Change
Research Data Publishing &Repository includes metadata, datasets (in the Digital Journal of Global Change Data Repository), and
publications (in the Journal of Global Change Data & Discovery). Data
sharing policy includes: (1) Data are openly available
and can be free downloaded via the Internet; (2) End users are encouraged to
use Data subject to citation; (3) Users, who are by definition
also value-added service providers, are welcome to redistribute Data subject
to written permission from the GCdataPR Editorial Office and the issuance of
a Data redistribution license; and (4) If Data are
used to compile new datasets, the ‘ten percent principal’ should be followed
such that Data records utilized should not surpass 10% of the
new dataset contents, while sources should be clearly noted in suitable
places in the new dataset[9]
|
Communication
and searchable system
|
DOI,DCI,CSCD,WDS/ISC,GEOSS,China
GEOSS,Crossref
|
(4) Soil data was collected from China soil
dataset (v1.1) of the harmonized world soil database (HWSD)[14]. Soil types
(sand, silt, clay) content and soil organic carbon content were extracted from
the data to calculate the soil erodibility factor in RUSLE model. All the above
data was uniformly re-sampled to 1 km×1 km, and the
projection coordinate system was Albers_ WGS_ 1984.
3.2 RUSLE Model
The revised universal soil loss equation (RUSLE)
model[15-16] was adopted to estimate soil conservation in NEBZ.
According to the principle of the model, the potential soil erosion (Ap) and actual soil erosion (Ar) of various land use types
were calculated under the conditions of bare land, vegetation cover, and other
engineering measure situation. The formula of soil conservation (Ac) is as follows:
(1)
where Ac
is the amount of soil conservation per unit area, and the units of Ac, Ap, andAr
refer to t·km‒2·a‒1; R represents the rainfall erosivity factor (MJ·mm·km‒2·h‒1·a‒1);
K marks soil erodibility factor (t·km2·h·km‒2·MJ‒1·mm‒1);
L and S stand for slope length and slope factor; C means vegetation coverage factor; P indicates conservation support practice factor. The
calculation of each factor is as follows:
(1) The empirical equation proposed by Wischmeier et al.[16] was applied to calculate rainfall erosivity
factor (R). The calculation equation
is as follows:
(2)
where p
marks the annual rainfall (mm), and pi
refers to the average monthly rainfall (mm).
(2) According to different soil particle composition content and organic
matter content, the soil erodibility factor (K) was calculated by Williams model[17]. The
calculation equation is as follows:
(3)
where SAN, SIL,
and CLA refer to sand, silt, and clay
content (%), respectively. TOC represents
organic matter content (%), and SNI =
1-SAN/100.
(3) The slope length
factor (L) was calculated with the
method proposed by Wischmeier
et al.[16], and the
calculation equation is defined as follows:

(4)

where λ
marks the slope length extracted from DEM; m
represents the slope length index, and θ
stands for the slope value extracted from DEM.
The slope factor (S) was
extracted by the slope equation proposed by Zhang et al.[18], and the specific calculation equation is as
follows:
(5)
where θ
represents the same as equation (4).
(4) C marks the vegetation
coverage factor. The calculation equation was proposed by Caiet al[19].
(6)
where NDVI represents the normalized
vegetation index and f means the fraction of vegetation.
(5) The soil and water conservation measure factor (P) was defined as follows[20-21]:
(7)
where α
refers to the slope index.
4 Data Results and Validation
4.1 Dataset Composition
The NBZ_SC_1km_2000-2015
dataset is the annual soil conservation data of NEBZ in ArcGIS TIFF format from
2000 to 2015. The spatial resolution is 1 km (unit: t·km‒2·a‒1), and the
projection coordinate system is WGS_1984_Albers. The total amount of compressed
data is 168 MB. After decompressing, the data can be applied in ArcGIS
software.
4.2 Data Results
The spatial
distribution of soil conservation at 1-km resolution in NEBZ from 2000 to 2015
is displayed in Figure 1. From 2000 to 2015, the average value is 2,996.49 t·km‒2·a‒1
that is higher in the southeast and lower in the northwest. That is, the
high-value areas are concentrated in the Sichuan Yunnan-loess plateau
ecological barrier and the southern hill and mountain belt, the middle value is
distributed in the northeastern forest zone and the southeast of the Qinghai
Tibet plateau ecological barrier, while the low-value areas are located in the
northwest of the Qinghai Tibet Plateau ecological barrier and the northern sand
belt.

Figure 1 Visualization Map of the national
ecological barrier zone soil conservation data (2015)
From 2000 to 2015, soil conservation increased in most of the study
area (84.7%). Apart from some parts of the ecological barrier area of the
Qinghai Tibet Plateau, the soil conservation of other sub barriers increased
significantly (p<0.05), and the
regions with higher growth rates were concentrated in the middle part of the
Sichuan Yunnan-Loess Plateau ecological barrier.
4.3 Data Validation
By consulting literatures,
we compared the same data (annual soil conservation in different years) in this
region to verify and evaluate the accuracy of soil conservation in NEBZ. A
total of 17 relevant data (Table 2) were collected, and their quantitative models
were all based on RUSLE. No.1-13 are
the annual average value of soil conservation in the Three-River Headwaters region
of the Qinghai Tibet Plateau ecological barrier from 2000 to 2012[22]. The last four are the annual soil
conservation values of the same region in 2000, 2005, 2010, and 2015[23]. The results prove that the absolute
value of relative error between the two data fluctuates from 6.98-993.32 t·km‒2·a‒1, and the percentage of
relative error is mostly less than 20%. After calculation, the RMSE of this dataset
is 431.16, and the overall accuracy is 82.74% (1 minus RMSE divided by the
average value of soil conservation simulation data). Therefore, the acquisition
of soil conservation results based on this technical process have high
correlation with similar data, which can accurately reflect the changing trend
of soil conservation in NEBZ in recent years from a macro perspective.
Table 2 Table of comparative analysis of data
validation for soil conservation
No.
|
Average annual value (t·km‒2·a‒1)
|
Dataset (t·km‒2·a‒1)
|
Relative error (t·km‒2·a‒1)
|
Relative error (%)
|
1
|
1,983.47
|
2,454.37
|
470.90
|
19.19
|
2
|
1,846.34
|
2,482.97
|
636.63
|
25.64
|
3
|
2,169.57
|
2,326.17
|
156.59
|
6.73
|
4
|
2,600.55
|
2,498.71
|
‒101.84
|
‒4.08
|
5
|
2,747.47
|
2,518.09
|
‒229.39
|
‒9.11
|
6
|
2,767.06
|
2,662.07
|
‒104.99
|
‒3.94
|
7
|
2,149.98
|
2,358.90
|
208.92
|
8.86
|
8
|
3,491.89
|
2,558.02
|
‒933.87
|
‒36.51
|
9
|
2,698.50
|
2,582.31
|
‒116.19
|
‒4.50
|
10
|
3,011.94
|
2,760.14
|
‒251.80
|
‒9.12
|
11
|
3,580.04
|
2,586.72
|
‒993.32
|
‒38.40
|
12
|
2,933.58
|
2,556.55
|
‒377.03
|
‒14.75
|
13
|
2,904.19
|
2,561.50
|
‒342.69
|
‒13.38
|
14
|
2,454.37
|
2,060.76
|
‒393.61
|
‒19.10
|
15
|
2,662.07
|
2,463.29
|
‒198.78
|
‒8.07
|
16
|
2,586.72
|
2,579.75
|
‒6.98
|
‒0.27
|
17
|
2,411.76
|
2,448.10
|
36.34
|
1.48
|
5 Conclusion
The RUSLE model was used to calculate the potential
and actual soil erosion, and soil conservation modeling research was carried
out, based on remote sensing, meteorology, topography, soil type, and other
data. Compared with the existing similar products, the consistency between them
is strong, indicating that the dataset has high accuracy and can meet the
design goals. The soil conservation dataset of national ecological barrier zone
with 1-km spatial resolution from 2000 to 2015 reveals the
spatiotemporal distribution of soil conservation in “two
barriers and three belts” and the soil conservation benefits of the barrier
area in recent years. It can provide reliable basic data and information for
the sustainable development of ecosystem in China.
Author Contributions
Wang, Y. designed
the overall dataset development, designed the model and algorithm, did the data
validation, and wrote this data paper. Wang, X. F. collected and processed the
data, and wrote this data paper. Yin, L. C. collected and processed the data.
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