IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v220y2009i15p1810-1818.html
   My bibliography  Save this article

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China

Author

Listed:
  • Xie, Yichun
  • Sha, Zongyao
  • Yu, Mei
  • Bai, Yongfei
  • Zhang, Lei

Abstract

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSEr=39.88% for the training set, and RMSEr=42.36% for the testing set) than MLR (RMSEr=49.51% for the training, and RMSEr=53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147g/m2, respectively.

Suggested Citation

  • Xie, Yichun & Sha, Zongyao & Yu, Mei & Bai, Yongfei & Zhang, Lei, 2009. "A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China," Ecological Modelling, Elsevier, vol. 220(15), pages 1810-1818.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:15:p:1810-1818
    DOI: 10.1016/j.ecolmodel.2009.04.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009002786
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.04.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiruhan, & Shiyomi, Masae & Akiyama, Tsuyoshi & Wang, Shiping & Yamamura, Yasuo & Hori, Yoshimichi & Ailikun,, 2014. "Long-term prediction of grassland production for five temporal patterns of precipitation during the growing season of plants based on a system model in Xilingol, Inner Mongolia, China," Ecological Modelling, Elsevier, vol. 291(C), pages 183-192.
    2. Xiumei Wang & Jianjun Dong & Taogetao Baoyin & Yuhai Bao, 2019. "Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    3. Alberto Rodríguez-Maturino & José Hugo Martínez-Guerrero & Isaías Chairez-Hernández & Martín Emilio Pereda-Solis & Federico Villarreal-Guerrero & Marusia Renteria-Villalobos & Alfredo Pinedo-Alvarez, 2017. "Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert," Land, MDPI, vol. 6(4), pages 1-14, October.
    4. Lu Jiang & Tengfei Cui & Hui Liu & Yong Xue, 2022. "Remote Sensing Monitoring and Analytical Evaluation of Grasslands in the Muli Region of Qinghai, China from 2000 to 2021," Land, MDPI, vol. 11(10), pages 1-15, October.
    5. Danni Wang & Changjian Qiao & Sijie Liu & Chongyang Wang & Ji Yang & Yong Li & Peng Huang, 2020. "Assessment of Spatial Accessibility to Residential Care Facilities in 2020 in Guangzhou by Small-Scale Residential Community Data," Sustainability, MDPI, vol. 12(8), pages 1-23, April.

    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:eee:ecomod:v:220:y:2009:i:15:p:1810-1818. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.