Sains Malaysiana 40(2)(2011): 83–88

Remote Sensing for Mapping RAMSAR Heritage Site

at Sungai Pulai Mangrove Forest Reserve, Johor, Malaysia

(Penderiaan Jauh untuk Pemetaan Tapak Warisan RAMSAR

di Hutan Simpan Bakau di Sungai Pulai, Johor, Malaysia)

I. Mohd Hasmadi*, H.Z. Pakhriazad & K. Norlida

Forest Surveying and Engineering Laboratory, Faculty of Forestry

Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

Received: 5 November 2009 / Accepted: 6 August 2010

ABSTRACT

The Sungai Pulai Mangrove Forest Reserve (SPMFR) is the largest riverine mangrove system in Johore. In 2003 about 9,126 ha of the Sungai Pulai mangrove was designated as a RAMSAR site. RAMSAR sites are wetland areas that are deemed to have international importance and are included in the List of Wetlands of International Importance. The SPMFR plays a significant socio-economic role to the adjacent 38 villages. Satellite remote sensing is a useful source of information where it provides timely and complete coverage for vegetation mapping especially in mangroves where the accessibility is difficult. This study was carried out to identify and map land cover types using SPOT-4 imagery at the Sungai Pulai-RAMSAR site and its surrounding areas. Through unsupervised classification technique a total of seven classes of land cover type were mapped, where about 90% mapping accuracy was gained from the accuracy assessment. Later, vegetation densities were classified into five levels namely very high, high, medium, low and very low based on crown density scale using vegetation indices model such as NDVI, AVI and OSAVI. Results from NDVI and OSAVI model were almost similar but AVI model detected more on medium vegetation which did not show the real ground condition. The study concludes that SPOT-4 imagery was able to discriminate mangrove area clearly from other land covers type. Vegetation indices model can be used as a tool for mapping vegetation density level in the SPMFR and its surrounding area. Therefore VI’s models from remote sensing are useful to monitor and manage the mangrove forest for sustainable management and preserve the SPMFR as a RAMSAR site in Peninsular Malaysia.

Keywords: Conservation Management; mangrove mapping; RAMSAR site; remote sensing

ABSTRAK

Hutan Simpan Paya Bakau Sungai Pulai (SPMFR) merupakan sistem hutan paya bakau terbesar di negeri Johor. Pada tahun 2003, kira-kira 9,126 ha kawasan paya bakau Sungai Pulai telah diberikan taraf sebagai tapak RAMSAR. Tapak RAMSAR adalah kawasan tanah lembap yang mempunyai kepentingan antarabangsa dan termasuk dalam Senarai Kepentingan Tanah Lembap Antarabangsa. SPMFR memainkan peranan penting dalam sosio-ekonomi kepada 38 kampung yang berdekatan. Penderiaan jarak jauh merupakan sumber maklumat yang bermanfaat kerana ia menyediakan liputan masa yang tepat dan lengkap untuk pemetaan tumbuhan paya bakau terutama di kawasan yang sukar. Kajian ini dijalankan untuk mengenal pasti dan memeta jenis litupan tanah menggunakan imej SPOT-4 di kawasan Sungai Pulai dan kawasan sekitarnya. Dengan menggunakan teknik pengelasan tidak terselia, tujuh kelas tumbuhan telah dihasilkan dan kira-kira 90% hasil pemetaan adalah tepat. Kemudian kepadatan tumbuhan dikelaskan kepada lima iaitu sangat padat, padat, sederhana padat, kurang padat dan sangat kurang padat berdasarkan skala kepadatan silara menggunakan model Indek Tumbuhan (VI’s) seperti NDVI, AVI dan OSAVI. Keputusan daripada model NDVI dan OSAVI adalah hampir sama tetapi AVI lebih tertumpu kepada tumbuhan berkepadatan sederhana dan tidak menggambarkan keadaan sebenar di lapangan. Kajian ini jelas menunjukkan data SPOT-4 mampu membezakan kelas hutan bakau daripada tumbuhan yang lain. Indeks tumbuhan boleh digunakan untuk menghasilkan peta kepadatan tumbuhan. Oleh itu, model indeks tumbuhan daripada data deriaan jarak jauh boleh membantu dalam pemantauan dan pengurusan hutan paya bakau secara berterusan serta mengekalkan SPMFR sebagai tapak RAMSAR di Semenanjung Malaysia.

Kata kunci: Pemetaan paya bakau; penderiaan jarak jauh; pengurusan pemuliharaan; tapak RAMSAR

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*Corresponding author; email: mhasmadi@putra.upm.edu.my