IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i1p582-d718383.html
   My bibliography  Save this article

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Author

Listed:
  • Shengxin Lan

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Altay District Administration Office of Ili Autonomous Prefecture, Altay City 836599, China)

  • Zuoji Dong

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km 2 of sparse vegetation and 25,915.44 km 2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

Suggested Citation

  • Shengxin Lan & Zuoji Dong, 2022. "Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang," Sustainability, MDPI, vol. 14(1), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:1:p:582-:d:718383
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/1/582/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/1/582/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nana Luo & Dehua Mao & Bolong Wen & Xingtu Liu, 2020. "Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI," Land, MDPI, vol. 9(3), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoyan Tang & Yongjiu Feng & Chen Gao & Zhenkun Lei & Shurui Chen & Rong Wang & Yanmin Jin & Xiaohua Tong, 2023. "Entropy-weight-based spatiotemporal drought assessment using MODIS products and Sentinel-1A images in Urumqi, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 387-408, October.
    2. Oiliam Stolarski & João A. Santos & André Fonseca & Chenyao Yang & Henrique Trindade & Helder Fraga, 2023. "Climate Change Impacts on Grassland Vigour in Northern Portugal," Land, MDPI, vol. 12(10), pages 1-18, October.
    3. Yang Wang & Remina Shataer & Tingting Xia & Xueer Chang & Hui Zhen & Zhi Li, 2021. "Evaluation on the Change Characteristics of Ecosystem Service Function in the Northern Xinjiang Based on Land Use Change," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    4. Xiuhua Cai & Wenqian Zhang & Cunjie Zhang & Qiang Zhang & Jingli Sun & Chen Cheng & Wenjie Fan & Ying Yu & Xiaoling Liu, 2022. "Identification and Spatial-Temporal Variation Characteristics of Regional Drought Processes in China," Land, MDPI, vol. 11(6), pages 1-21, June.
    5. Yongchao Duan & Min Luo & Xiufeng Guo & Peng Cai & Fu Li, 2021. "Study on the Relationship between Snowmelt Runoff for Different Latitudes and Vegetation Growth Based on an Improved SWAT Model in Xinjiang, China," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    6. Xiuhua Cai & Wenqian Zhang & Xiaoyi Fang & Qiang Zhang & Cunjie Zhang & Dong Chen & Chen Cheng & Wenjie Fan & Ying Yu, 2021. "Identification of Regional Drought Processes in North China Using MCI Analysis," Land, MDPI, vol. 10(12), pages 1-19, December.
    7. Haochen Yu & Zhengfu Bian & Shouguo Mu & Junfang Yuan & Fu Chen, 2020. "Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China," IJERPH, MDPI, vol. 17(13), pages 1-25, July.
    8. Huaijun Wang & Zhi Li & Lei Cao & Ru Feng & Yingping Pan, 2021. "Response of NDVI of Natural Vegetation to Climate Changes and Drought in China," Land, MDPI, vol. 10(9), pages 1-24, September.
    9. Yu Zhang & Na Gong & Huade Zhu, 2023. "Vegetation Dynamics and Food Security against the Background of Ecological Restoration in Hubei Province, China," IJERPH, MDPI, vol. 20(2), pages 1-18, January.
    10. Wan-Jiun Chen, 2022. "Toward Sustainability: Dynamics of Total Carbon Dioxide Emissions, Aggregate Income, Non-Renewable Energy, and Renewable Power," Sustainability, MDPI, vol. 14(5), pages 1-27, February.
    11. Shuping Fan & Peng Li & Qi He & Jiaru Cheng & Mingfeng Zhang & Nan Wu & Song Yang & Shidong Pan, 2022. "Study on the Spatial-Temporal Evolution of Land Use Ecosystem Service Value and Its Zoning Management and Control in the Typical Alpine Valley Area of Southeast Tibet—Empirical Analysis Based on Panel," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    12. Xiuwei Xing & Jing Qian & Xi Chen & Chaoliang Chen & Jiayu Sun & Shujie Wei & Duman Yimamaidi & Zhahan Zhanar, 2022. "Analysis of Effects of Recent Changes in Hydrothermal Conditions on Vegetation in Central Asia," Land, MDPI, vol. 11(3), pages 1-27, February.
    13. Yan Li & Jie Gong & Yunxia Zhang & Bingli Gao, 2022. "NDVI-Based Greening of Alpine Steppe and Its Relationships with Climatic Change and Grazing Intensity in the Southwestern Tibetan Plateau," Land, MDPI, vol. 11(7), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:1:p:582-:d:718383. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.