Research article |
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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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Abstract 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.
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Received: 07 April 2021
Published: 31 January 2022
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Corresponding Authors:
* SUN Piling (E-mail: spling86@qfnu.edu.cn)
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