NDVI Change Trend and Impact Factors Dataset in
Inner Mongolia (2000–2015)
Chen, K. Chao, L.
M.*
College of Ecology and Environment, Inner Mongolia
University, Hohhot 010021, China
Abstract: Based on SPOT/VEGETATION NDVI data with a resolution of 1 km per month
from 2000 to 2015, a dataset of annual NDVI data was generated using the
maximum value synthesis method. Then statistical yearbook data, trend analyses,
and a geographical detector model were used to calculate the trend of change in
the NDVI and to assess the factors influencing it for Inner Mongolia. The dataset
is categorized into GIS 1 km raster data; information on the degree of change
in the NDVI; data on natural phenomena such as precipitation, average
temperature, slope aspect, and so on; and human-related components such as
changes in rural populations, labor force, grain output, and per capita net
income of farmers and herdsmen, among others. Some data are
also provided in table format, including the area and proportion of change in
vegetation and the main influencing factors.
Keywords: geographical detector; vegetation NDVI; Inner Mongolia;
human factors; natural factors
Dataset Available Statement:
The dataset supporting this article is public available at: Chen, K., Chao,
L. M. Dataset of NDVI change trends and impact factors in Inner Mongolia
(2000–2015) [DB/OL/J]. Global Change Data Repository, 2020.
DOI: 10.3974/geodb.2020.05.07.V1.
1 Introduction
Vegetation is a vital part of terrestrial ecosystems that serves as a hub
of material circulation, energy flow, and information transmission[1–2].
Changes in vegetation coverage can indicate fluctuations or changes in whole
ecosystems to a certain degree[3].
Particularly in arid and semi-arid areas, changes in vegetation coverage are an
important indicator for monitoring and evaluating changes in ecology[4].
Therefore, it is of great significance to quantitatively analyze changes in
regional vegetation coverage and explore the driving factors.
Inner Mongolia
is a vast territory with a high diversity of ecosystems, although the main
landform is the Mongolian Plateau[5].
Due to this plateau, Inner Mongolia is an important ecological barrier in
northern China. The region has an arid and semi-arid climate, and has been
reported to be particularly sensitive to global climate change[4].
Regarding
human industry, the counties in the Inner Mongolia autonomous region are mainly
divided into pastoral, agricultural, semi pastoral, semi agricultural and urban
areas. Pasture area is dominated by grazing, where natural grassland is the
main vegetation type. Non-animal husbandry is the main vegetation type in other
counties, and farmland is the main vegetation type[6].
The dataset is developed on the SPOT/VEGETATION NDVI satellite remote
sensing database. Then we calculated the trend of change in the NDVI for this
area for 2000 to 2015 and assessed the natural and human factors influencing it
using statistical yearbook data, trend analyses, and a geographical detector model[7]. The dataset provides a
useful reference on the vegetation in this region, how it has changed, and the
main driving factors, and should help guide urban and rural developmental
decisions.
2 Metadata of the Dataset
The metadata of “Dataset of NDVI change trends and impact factors in Inner
Mongolia (2000–2015)” are shown in Table 1.
Table 1 Metadata summary of the Dataset
Items
|
Description
|
Dataset full name
|
Dataset of NDVI change trends and impact
factors in Inner Mongolia (2000–2015)
|
Dataset short name
|
NDVIChange.InnerMongolia_2000-2015
|
Authors
|
Chen, K., College of Ecology and Environment, Inner
Mongolia University, im_chk@163.com
Chao, L. M., College of Ecology and
Environment, Inner Mongolia University, colmvn@aliyun.com
|
Geographical region
|
Inner Mongolia
Temporal resolution 2000–2015
|
Data format
|
.tif, .xlsx, .shp Data
size 51.7 MB (after
compression)
|
Data files
|
Spatial data include (1) annual variation
trend of NDVI in Inner Mongolia from 2000 to 2015, 1 km raster data; (2) Categorize 1 km
raster data based on the degree of change annual variation trend of NDVI from
2000 to 2015; (3) Natural factor data, including 2000–2015 precipitation, average
temperature change trend, slope aspect, slope classification and vegetation
type 1 km grid data; (4) Human-factor GIS data based on county
units, including six attribute data from 2000 to 2015, including the trend of
change in rural population, the trend of change in rural household number,
the trend of change in rural labor force, the trend of change in grain
output, the trend of change in per capita net income of farmers and herdsmen,
and the trend of change in livestock quantity. Tabular data include the (1) area and proportion of vegetation change
grade divided based on vegetation NDVI change. (2) Main influencing factors and q
values of vegetation NDVI change in Pastoral banner county and non-pastoral
banner county in Inner Mongolia.
|
Foundations
|
National key RESEARCH and development programs
(2016YFC050604–4); National Natural Science Foundation of China (31060117)
|
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 (data products), and publications (in this case, 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 per cent 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
|
3 Data development
This study used time series SPOT/VEGETATION NDVI satellite remote sensing
data from 2000–2015[10] with the maximum synthesis method to
generate an annual NDVI. The natural factors considered were climate,
elevation, and vegetation. The climate data included annual precipitation and
annual average temperature with 1 km resolution calculated by interpolation.
Elevation with 1 km resolution was generated by resampling based on the latest
SRTM V4.1 data. Distribution data on vegetation types were derived from 1:1
million vegetation maps with a resolution of 1 km. Slope and aspect were
calculated from DEM data with a resolution of 1 km[11].
Data on human factors were taken from the “Inner Mongolia Statistical
Yearbook”[12]. The components
included data on rural population, rural labor force, number of rural
households, grain production, per capita income of farmers and herdsmen, and
number of livestock (in ‘sheep units,’ where a camel, horse, and cow are
equivalent to five sheep each, and a goat is the same as one sheep).
3.1 Algorithm
The trend line analysis method was used to analyze the trend of change in
the NDVI as well as climate and human factors for the study period[6].
In other words, taking time as an independent variable, the NDVI, annual
average temperature, annual precipitation, and six individuals were analyzed by
univariate linear regression. The correlation coefficient (R) between NDVI sequence and time was used to judge the degree and
nature of change in vegetation cover, and the magnitude of the coefficient was
used to judge the significance[6].
The critical value of significance was obtained from the critical value table
of correlation coefficient test (when the number of samples was 16, the
critical value of significance level was 0.468 and 0.590 at 0.01 and 0.05).
Vegetation change was divided into five categories, according to the trend
slope and critical value of NDVI: extremely significant degradation,
significant degradation, no significant change, significant improvement, and
extremely significant improvement.
The influences
of natural and human factors on the NDVI were analyzed using the geographical
detector model[7].
4 Data Results and Verification
4.1 Data Composition
The dataset includes 11 spatial data files (Table 2) and one statistical
table data (including the area and proportion data of NDVI of different grades
in Inner Mongolia; the main influencing factors and q value of NDVI changes in pastoral and non pastoral counties of Inner
Mongolia). Table 2 explains the data and describes the file or field names corresponding
to the data.
4.2 Results
Because the geographical detector model employs an algorithm for discrete
data, continuous variables (all data except vegetation type and slope aspect)
were discretized using the natural breakpoint method[13].
The slope was divided into 9 categories and the other factors were divided into
10 categories (Figure 1).
Table 2 Natural and human factors affecting variation in NDVI in Inner Mongolia (2000–2015)
Category
|
Factors
|
Details
|
Corresponding
file or field
|
Natural factors
|
Annual precipitation
|
Variation in precipitation from 2000 to 2015
|
IM_pre00_15slope.tif
|
Annual mean temperature
|
Average change in temperature from 2000 to 2015
|
IM_tem00_15slope.tif
|
Slope
|
Calculated from DEM data with a resolution of 1
km
|
IM_slope.tif
|
Aspect
|
Calculated from DEM data with a resolution of 1
km
|
IM_aspect.tif
|
Vegetation type
|
In 1995, a 1:1 million vegetation map of China
was digitally generated with a resolution of 1 km
|
IM_vegatation_
type.tif
|
Human factors
|
Change in rural population
|
Rate of change in the rural population from 2000
to 2015
|
Rp_slope
|
Change in number of rural households
|
Rate of change in number of rural households
from 2000 to 2015
|
house_slope
|
Change in rural labor force
|
Rate of change in rural labor force from 2000 to
2015
|
labor_slope
|
Change in grain yield
|
Rate of change in grain output from 2000 to 2015
|
grain_slope
|
Change in per capita net income of farmers and
herdsmen
|
Rate of change in per capita net income of farmers
and herdsmen from 2000 to 2015
|
Rgdp_slope
|
Change in number of livestock
|
Rate of change in livestock number from 2000 to
2015
|
sheep_slope
|

Figure 1 Spatial distribution of natural and human factors affecting the NDVI for Inner Mongolia during 2000–2015
During the 16
years from 2000 to 2015, the NDVI showed an overall increasing trend. In
general, it tended to decrease in the west and increase in the east and south;
in other areas, it changed very little (Figure 2). Overall, an area of about
249,842.65 km2 showed improved vegetation coverage, accounting for
21.88% of the total area of Inner Mongolia; about 64.38%, 10.88%, 1.88%, and
1.00% of the area experienced no change, a significant improvement, significant
degradation, and extremely significant degradation, respectively (Table 3).
These results indicate that the area of vegetation improvement was
significantly greater than that of vegetation degradation. Furthermore,
degraded areas were mainly distributed in
the northwestern region around individual towns. NDVI varying grade is shown in
Table 3. The vegetation of animal husbandry banner county was improved very
significant, accounting for 9.65% of the total area (significant improvement:
7.24%; no change: 79.34%; significant degradation: 2.56%; very significant
degradation 1.20%). The proportion of vegetation degradation area in animal
husbandry banner county exceeds that of the whole study area, which indicates
that vegetation degradation is more serious (and vegetation improvement is
lower) in pastoral counties than in other areas. The vegetation of non-pastoral
counties was improved very significant, accounting for 47.54% of the total area
(significant improvement: 18.52%, no change: 33.00%; significant
degradation: 0.35%; very significant degradation: 0.57%). Improved areas were mainly distributed in non-pastoral counties.

Figure
2 Spatial
distribution of vegetation change in Inner Mongolia from 2000 to 2015 based on
the interannual variation trend of NDVI and its significance
The factor detection of geographic detector model was used to assess the influences of natural and human factors on
the NDVI, expressed using q values.
Factor detection module is the core idea: geographic things always exists in a
certain place in space, affect the development and changes of environmental
factors on the space has the difference, if certain environmental factors and
changes in geographic objects in space with remarkable consistency, shows this
kind of environment factors on the occurrence and development of geographic
things decision significance[7].
Across the whole study area, the order of the degree of influence on NDVI was
as follows: annual precipitation > soil type > vegetation type
> grain yield > number of livestock > per capita net income of farmers
and herdsmen > number of rural households > rural population >
landform type > rural labor force > annual average temperature > slope
> slope aspect. The first three factors are natural factors and the following
two are human factors, which suggests that the
influence of natural factors is greater than that of human factors. Similarly,
for non-pastoral counties, the order was annual precipitation > soil type
> vegetation type > grain yield > per capita net income of farmers and
herdsmen > number of livestock > number of rural households > landform
type > rural population > rural labor force > annual average
temperature > slope > slope (Fig. 3), again showing that natural factors had a greater impact. In contrast, the
order for non-pastoral counties was grain output > rural labor force >
annual average temperature > soil type > annual precipitation > number
of rural households > per capita net income of farmers and herdsmen >
number of livestock > total rural population > landform type > slope
> vegetation type > slope aspect (Figure 3). These results indicate that
human factors were dominant in such areas.
Table 3 Area
and proportion of different NDVI varying grades
Region
|
Classification
|
Very significant degradation
|
Significant degradation
|
No change
|
Significant improvement
|
Very significant improvement
|
Total
|
All counties
|
Area (km2)
|
11,458.14
|
21,105.13
|
734,992.38
|
124,233.05
|
249,842.65
|
1,141,631.35
|
percentage (%)
|
1.00%
|
1.84%
|
64.38%
|
10.88%
|
21.88%
|
100%
|
Pastoral counties
|
Area (km2)
|
9,335.95
|
19,785.55
|
613,648.39
|
56,000.29
|
74,631.31
|
773,401.50
|
percentage (%)
|
1.20%
|
2.56%
|
79.34%
|
7.24%
|
9.65%
|
100%
|
Non-pastoral counties
|
Area (km2)
|
2,118.41
|
1,312.53
|
121,529.47
|
68,199.76
|
175,069.49
|
368,032.66
|
percentage (%)
|
0.57%
|
0.35%
|
33.00%
|
18.52%
|
47.54%
|
100%
|

Figure 3 q values of seven
natural factors and six human
factors leading to variation in the
NDVI in Inner Mongolia.
4.3 Data Validation
The precision of VEGETATION coverage represented
by NDVI lies in the spatial resolution of the metadata used. In this paper, the
SPOT/VEGETATION NDVI data with a resolution of 1 km is consistent with the
research results of the former[14].
Geographic detector method is useful for detecting spatial differentiation
and revealing the driving forces behind it. It is widely used in analyzing the
evolution of geographical element pattern and regional spatial differentiation[7]. However, it should
be pointed out that, as the maximum number of rows of data in the research area
exceeds the upper limit of the operation of the geographic detector model, the
use of the Create Random Points function of ArcGIS software to randomly extract
appropriate samples in proportion for factor detection may have a certain
impact on the final research results.
5 Discussion
It is of great significance to understand not only changes in regional
vegetation coverage but also their driving factors over time. We used linear
trend analyses and a geographical detector method to explore the impact of
natural and human factors on changes in the NDVI from 2000 to 2015. Our results
reveal obvious changes with an overall increasing trend in vegetation coverage.
Regionally, vegetation tended to decrease in the west and increase in the east
and south, with other areas showing little change. Across the entire region,
about 32.76% of the area showed improvement, whereas only 2.88% showed
degradation. The NDVI was affected more by natural factors than by human
factors, with precipitation and soil type having the main effects. However, in
non-pastoral counties, human factors had a greater impact, with grain yield being
the main effect. Our dataset provides a useful reference on the vegetation in
this region, how it has changed, and the main driving factors, and should help
guide urban and rural developmental decisions.
Author Contributions
Chao, L. M. designed the dataset and Chen, K. collected and processed the
data, and wrote the paper.
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