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Profiling Human-Induced Vegetation Change in the Horqin Sandy Land of China Using Time Series Datasets

Author

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  • Lili Xu

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Zhenfa Tu

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yuke Zhou

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Guangming Yu

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

Discriminating the significant human-induced vegetation changes over the past 15 years could help local governments review the effects of eco-programs and develop sustainable land use policies in arid/semi-arid ecosystems. We used the residual trends method (RESTREND) to estimate the human-induced and climate-induced vegetation changes. Two typical regions in the Horqin Sandy Land of China were selected as study areas. We first detected vegetation dynamics between 2000–2014 using Sen’s slope estimation and the Mann–Kendall test detection method (SMK) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series, then used RESTREND to profile human modifications in areas of significant vegetation change. RESTREND was optimized using statistical and trajectory analysis to automatically identify flexible spatially homogeneous neighborhoods, which were essential for determining the reference areas. The results indicated the following. (1) Obvious vegetation increases happened in both regions, but Naiman (64.1%) increased more than Ar Horqin (16.8%). (2) Climate and human drivers both contributed to significant changes. The two factors contributed equally to vegetation change in Ar Horqin, while human drivers contributed more in Naiman. (3) Human factors had a stronger influence on ecosystems, and were more responsible for vegetation decreases in both regions. Further evidences showed that the primary human drivers varied in regions. Grassland eco-management was the key driver in Ar Horqin, while farming was the key factor for vegetation change in Naiman.

Suggested Citation

  • Lili Xu & Zhenfa Tu & Yuke Zhou & Guangming Yu, 2018. "Profiling Human-Induced Vegetation Change in the Horqin Sandy Land of China Using Time Series Datasets," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1068-:d:139459
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    References listed on IDEAS

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    1. Walter Musakwa & Shuai Wang & Fangli Wei & Olgah Lerato Malapane & Masala Makumule Thomas & Sydney Mavengahama & Hongwei Zeng & Bingfang Wu & Wenwu Zhao & Nesisa Analisa Nyathi & Zama Eric Mashimbye &, 2020. "Survey of Community Livelihoods and Landscape Change along the Nzhelele and Levuvhu River Catchments in Limpopo Province, South Africa," Land, MDPI, vol. 9(3), pages 1-21, March.
    2. Yuyang Chang & Geli Zhang & Tianzhu Zhang & Zhen Xie & Jingxia Wang, 2020. "Vegetation Dynamics and Their Response to the Urbanization of the Beijing–Tianjin–Hebei Region, China," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    3. Le’an Qu & Zhenjie Chen & Manchun Li, 2019. "CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 11(20), pages 1-23, October.

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