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Detecting and quantifying degraded forest land in Tanah Merah forest district, Kelantan using Spot-5 image


Ismail, Mohd Hasmadi and Abdul Malek, Ismail Adnan and Bebakar, Suhana (2008) Detecting and quantifying degraded forest land in Tanah Merah forest district, Kelantan using Spot-5 image. Pertanika Journal of Tropical Agricultural Science, 31 (1). pp. 11-17. ISSN 1511-3701; ESSN: 2231-8542


In sustainable forest management, information on the extent and types of degraded forest sites is essential and crucial. It enables planning of appropriate remedial strategies. This study was carried out to detect and quantify degraded forest land in Tanah Merah District, Kelantan using remotely sensed data. Spot-5 satellite data (Path/Row: 269/339) was acquired from MACRES, which covered part of three forest reserves ie. Sungai Sator, Gunung Basor and Gunung Stong. The ERDAS IMAGINE software version 8.7 was used to enhance the image for better visualization using band combination and spatial filtering techniques. This was followed by "Supervised Classification" of the image using "Maximum Likelihood Classifier" to detect and classify degraded forest features into pre-determined classes. The four classes detected were primary forest, degraded forest, gap and water bodies. Results showed that the degraded forest class constituted the largest area (57,878 ha), followed by primary forest gap (20,686 ha) and gap (3,488 ha). Degraded forest types were represented by road, agriculture, plantation areas. Based on the accuracy assessment, the overall classification accuracy obtained was 89% and showed that the remote sensing technique was able to detect and map degraded forest sites.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Forestry
Publisher: Universiti Putra Malaysia Press
Keywords: Remote sensing; Degraded forest detection; Quantifying
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 18 Jun 2015 02:41
Last Modified: 18 Sep 2015 00:47
URI: http://psasir.upm.edu.my/id/eprint/13923
Statistic Details: View Download Statistic

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