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Journal of Arid Land  2022, Vol. 14 Issue (1): 1-13    DOI: 10.1007/s40333-022-0001-4
Research article     
Spatiotemporal variation of forest land and its driving factors in the agropastoral ecotone of northern China
WANG Shiqing1,2, TAO Zefu1,2, SUN Piling1,2,3,*(), CHEN Sijia1,2, SUN Huiying1,2, LI Nan1,2
1School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
2Rizhao Key Laboratory of Territory Spatial Planning and Ecological Construction, Rizhao 276962, China
3College of Land Science and Technology, China Agriculture University, Beijing 100193, China
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As an important natural resource, forest land plays a key role in the maintenance of ecological security. However, variations of forest land in the agropastoral ecotone of northern China (AENC) have attracted little attention. Taking the AENC as an example and based on remote-sensing images from 2000, 2010 to 2020, we explored the spatiotemporal variation of forest land and its driving factors using the land-use transfer matrix, spatial autocorrelation analysis and spatial error model. The results showed that from 2000 to 2020, the total area of forest land in the AENC increased from 75,547.52 to 77,359.96 km2 and the changes were dominated by the transformations among forest land, grassland and cropland, which occurred mainly in areas with the elevation of 500-2000 m and slope of 15°-25°. There was obvious spatial agglomeration of forest land in the AENC from 2000 to 2020, with hot spots of forest land gathered in the southern marginal areas of the Yanshan Mountains and the low mountainous and hilly areas of the Loess Plateau. The sub-hot spots around hot spots moved southward, the sub-cold spots spread to the surrounding areas and the cold spots disappeared. The spatiotemporal variation of forest land resulted from the interactions of natural environment, socioeconomic and policy factors from 2000 to 2020. The variables of average annual precipitation, slope, terrain relief, ecological conversion program and afforestation policy for barren mountains affected the spatial pattern of forest land positively, while those of annual average temperature, slope and road network density influenced it negatively.

Key wordsforest land      spatiotemporal variation      driving factors      spatial error model      agropastoral ecotone      northern China     
Received: 07 April 2021      Published: 31 January 2022
Corresponding Authors: SUN Piling     E-mail:
Cite this article:

WANG Shiqing, TAO Zefu, SUN Piling, CHEN Sijia, SUN Huiying, LI Nan. Spatiotemporal variation of forest land and its driving factors in the agropastoral ecotone of northern China. Journal of Arid Land, 2022, 14(1): 1-13.

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Fig. 1 Location of study area and sample points
Driving factor Explanatory variable Interpretation
Natural environment factor Climate condition Average annual
Average annual precipitation of each unit was obtained by spatial interpolation with ArcGIS software (mm)
Annual average
Annual average temperature of each unit was obtained by spatial interpolation with ArcGIS software (°C)
Topographic condition Elevation Digital elevation model (DEM) of each unit was obtained by neighbor analysis with ArcGIS software (m)
Slope Annual average temperature of each unit was obtained by spatial interpolation with ArcGIS software (°C)
Aspect Aspect was extracted from DEM and obtained by neighbor analysis with ArcGIS software
Terrain relief Terrain relief was extracted from DEM and obtained by neighbor analysis with ArcGIS software (m)
Socioeconomic factor Economic density Gross domestic product was divided by the total regional area (×109 CNY/km2)
Population density Total population was divided by the total regional area (people/ km2)
Road network density Road mileage was divided by the total regional area (km/km2)
Regional policy factor Ecological conversion
If the ecological conversion area in one unit was 0, then the value of ecological conversion program was 0; if it was greater than 0, then the value was 1.
Afforestation policy
for barren mountains
If the afforestation area in one unit was 0, then the value of afforestation policy for barren mountains was 0; if it was greater than 0, then the value was 1.
Table 1 Explanatory variables relevant to spatiotemporal variation of forest land
Fig. 2 Spatiotemporal variation of forest land in the agropastoral ecotone of northern China (AENC) in 2000 (a), 2010 (b) and 2020 (c)
Fig. 3 Conversation between forest land and other land types in the AENC from 2000 to 2020. (a), 2000-2010; (b), 2010-2020.
Fig. 4 Vertical distribution of forest land in the AENC in different elevations (a) and slopes (b). DEM, digital elevation model.
Fig. 5 Distribution trend of forest land in the AENC in 2000 (a), 2010 (b) and 2020 (c). N, north; E, east.
Year Global Moran's I E(Gi*) Z(Gi*) P
2000 0.328 -0.004 8.952 0.000
2010 0.333 -0.004 9.090 0.000
2020 0.323 -0.004 8.818 0.000
Table 2 Global Moran's I of forest land in the AENC from 2000 to 2020
Fig. 6 Spatiotemporal variation of forest land in the AENC in 2000 (a), 2010 (b) and 2020 (c)
Variable Impact factor in 2000 Impact factor in 2020
Coefficient P Coefficient P
Constant -13,799.200 0.000 -26374.700 0.000
Average annual precipitation 7.235*** 0.000 12.376*** 0.000
Annual average temperature -241.243*** 0.000 -483.495*** 0.000
Elevation 1.497 0.433 1.747 0.629
Slope 2169.820*** 0.000 3901.600*** 0.000
Aspect -13.154** 0.020 -26.441*** 0.000
Terrain relief 30.720 0.393 138.368*** 0.000
Economic density -3.299 0.995 0.184 0.786
Population density -0.022* 0.071 -3.421 0.311
Road network density -0.711*** 0.000 -2.856*** 0.001
Ecological conversion project 3953.950 0.513 9634.830*** 0.000
Afforestation policy for barren mountains 311.692*** 0.000 2298.230** 0.020
R2 0.805 0.793
Log likelihood -70,786.733 -75,596.840
Akaike information criterion 141,597 151,218
Schwarz criterion 141,679 151,299
Table 3 Factors driving spatiotemporal variation of forest land in the AENC
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