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  1. Suhartono Nurdin, Muzzneena Ahmad Mustapha, Tukimat Lihan, Mazlan Abd Ghaffar
    Sains Malaysiana, 2015;44:225-232.
    Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a) improves our understanding on the variability and productivity of the marine environment, which is important for exploring fishery resources. Monthly level 3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the archipelagic waters of Spermonde Indonesia were used to investigate the relationship between SST and chl-a and to forecast the potential fishing ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive correlation was also found between SST and chl-a with the catch of R. kanagurta (R=0.7, p<0.05). The potential fishing grounds of R. kanagurta were found located along the coast (at accuracy of 76.9%). This study indicated that, with the integration of remote sensing technology, statistical modeling and geographic information systems (GIS) technique were able to determine the relationship between SST and chl-a and also able to forecast aggregation of R. kanagurta. This may contribute in decision making and reducing search hunting time and cost in fishing activities.
  2. Ahmad Mustapha, Gandaseca, Seca, Ahmad Hanafi, Siti Nurhidayu, Mohammad Roslan, Khan, Waseem, et al.
    MyJurnal
    The objectives of this review are to determine the types of indices to use, to assess the current sediment quality index (SQI) of a mangrove forest and to select the appropriate index to describe the mangrove sediment quality index. Amongst the many indices considered in this review are the enrichment factors (EFs), the geo-accumulation index (Igeo), the pollution load index (PLI), the marine sediment pollution index (MSPI) and sediment quality index (SQI). The different indices give diverse perspectives of the status of mangrove sediment quality. This review also highlights the appropriate parameters that need to be used in assessing sediment quality, such as the physical, chemical and biological properties. As the comparison review, the sediment quality can be utilized for Mangrove quality index (MQI) development like to assess the heavy metal, complete laboratory parameters and a classification following the Interim Sediment Quality Guidelines ISQG, PCA and HACA. For the heavy metal content of sediment, the suggested parameters are Pb, Zn, Cu, Co and Mn. Lastly, for the indices, the enrichment factor (EFs), geo-accumulation index (Igeo), pollution load index (PLI) and marine sediment pollution index (MPSI) are used in develop SQI on mangrove forest.
  3. Tukimat Lihan, Nur Fatin Khodri, Muzzneena Ahmad Mustapha, Zulfahmi Ali Rahman, Wan Mohd Razi Idris
    Sains Malaysiana, 2018;47:2241-2249.
    Aktiviti guna tanah di kawasan lembangan adalah salah satu faktor yang mendorong kepada kemerosotan kualiti air
    sungai akibat daripada hakisan tanih. Potensi hakisan tanih di kawasan lembangan Sungai Bilut, Raub, Pahang yang
    menjadi sumber bekalan air minuman utama di daerah Raub boleh ditentukan dengan menggunakan integrasi model
    Semakan Semula Persamaan Kehilangan Tanih Universal (RUSLE) dan Sistem Maklumat Geografi (GIS). Kajian ini
    bertujuan untuk menentukan potensi hakisan tanih dan faktor utama yang mempengaruhi kadar hakisan tanih. Kajian ini
    melibatkan penggunaan data sekunder yang terdiri daripada data hujan, data siri tanih dan topografi bagi menghasilkan
    faktor kehakisan hujan (R), kebolehhakisan tanih (K), serta panjang dan kecuraman cerun (LS). Faktor litupan tumbuhan
    (C) dan amalan pemuliharaan (P) pula dijana daripada imej satelit Landsat 8 (2014). Keputusan kajian menunjukkan
    nilai faktor R di kawasan kajian ialah 8927.68-9775.18 MJ mm ha-1 jam-1 tahun-1, nilai K ialah 0.036-0.500 tan jam-1
    MJ-1 mm-1, nilai LS ialah 0-514, nilai C ialah 0.03-0.80 dan nilai P ialah 0.1-0.7. Kawasan yang mempunyai potensi
    hakisan sangat rendah hingga rendah meliputi 81%, manakala potensi hakisan tanih sederhana hingga sangat tinggi
    meliputi 19% daripada keseluruhan kawasan kajian. Model yang dihasilkan mempunyai ketepatan sebanyak 81%. Faktor
    utama yang mempengaruhi berlakunya hakisan tanih di kawasan kajian adalah faktor topografi, litupan tumbuhan dan
    kebolehhakisan tanih. Keputusan menunjukkan analisis integrasi RUSLE dan GIS berpotensi dalam penentuan potensi
    hakisan tanih untuk kawasan luas yang mempunyai pelbagai jenis guna tanah, topografi dan jenis tanih.
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