IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v3y2009i1p42.html
   My bibliography  Save this article

Mapping and Quantification of Land Area and Cover Types with LandsatTM in Carey Island, Selangor, Malaysia

Author

Listed:
  • J Hj. Kamaruzaman,
  • I Mohd Hasmadi,

Abstract

Information about current land cover type is essential at a certain level to ensure the optimum use of the land resources. Several approaches can be used to estimate land cover area, where remote sensing and Geographic Information System (GIS) is among the method. Therefore, this study was undertaken to evaluate how reliable these technologies in preparing information about land cover in Carey Island, Selangor of Peninsular Malaysia. Erdas Imagine 9.1 was used in digital image processing. A primary data of Landsat TM, with spatial resolution of 30 m was acquired from scene 127/58 on July 2007. Area estimate was calculated using direct expansion method from samples proportion of each segments of land cover type (1 km by 1 km sample size). In this study, four classes of land cover type have been identified and the areas were oil palm, mangrove, water bodies and urban/bare land area. The area estimate for all classes are 11039.28 ha (oil palm), 5242.86 ha (mangrove), 4894.92 ha (water bodies), and 4751.96 ha (urban/bare land), respectively. The overall classification accuracy obtained for this study is 96%. The results showed that the use of direct expansion method for estimating land cover type area is practical to be used with remote sensing approaches.

Suggested Citation

  • J Hj. Kamaruzaman, & I Mohd Hasmadi,, 2009. "Mapping and Quantification of Land Area and Cover Types with LandsatTM in Carey Island, Selangor, Malaysia," Modern Applied Science, Canadian Center of Science and Education, vol. 3(1), pages 1-42, January.
    • Handle: RePEc:ibn:masjnl:v:3:y:2009:i:1:p:42
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/658/635
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/658
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Igor Esau, 2010. "On Application of Artificial Neural Network Methods in Large-eddy Simulations with Unresolved Urban Surfaces," Modern Applied Science, Canadian Center of Science and Education, vol. 4(8), pages 1-3, August.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:masjnl:v:3:y:2009:i:1:p:42. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.