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Sample translations submitted: 4
English to Malay: Detection of Potential Forest Fires Using Satellite Remote Sensing Techniques General field: Science Detailed field: Environment & Ecology
Source text - English In 1997/1998, Malaysia experienced one of the most severe forest fire episodes in history as a consequence of a prolonged dry season following the El- Niňo phenomenon. Since then, uncontrolled fires, atmospheric pollutions and haze remained as a common problem throughout the dry period in this region. The estimated cost of the damage caused by forest fires in Malaysia is about RM816.47 million a year. The loss by forest fire episodes has brought to light the importance of developing better tools for effective forest fire management systems. In this research, three sets of computer programmes were designed for: detecting hot spots; computing the fire risk index and generating spatial analysis for detected fires. Remote sensing and GIS techniques have both been integrated in this work. Eventually, a simple yet robust early warning system for forest fire detection in Malaysia has been devised. Thermal bands of MODIS (Moderate Resolution Imaging Spectroradiometer) were used to extract hot spot information and to generate a fire risk map. Proximity analysis was carried out using an extension in ArcView GIS software. The results from this research were compared with forest fire occurrence information from the Fire and Rescue Department of Malaysia
(FRDM) and information of rainfall and temperature from the Malaysian Meteorological Services (MMS). High correlation (R2=0.8) was found between temperature derived from MODIS and the temperature obtained from the MMS. Forest fire map generated from the study also gave a high accuracy (71%). Normalized Difference Vegetation Index (NDVI) values derived from MODIS were found to be highly correlated (R2=0.7 and R2=0.85) with rainfall and temperature data obtained from the MMS, respectively. Hence, the output of the research shows that this system can be used as an early warning system mechanism to mitigate forest fire incidence and can be upgraded into a more complex system to enhance its functioning.
Translation - Malay Pada tahun 1997/98, Asia Tenggara telah melalui episod kebakaran hutan yang paling ekstrem di dalam sejarah akibat
musim kemarau yang panjang kesan fenomena El-Niňo. Berikutan daripada peristiwa itu, pencemaran atmosfera dan jerebu telah menjadi masalah yang lazim pada musim kemarau di sini. Anggaran kerugian daripada kebakaran hutan di Malaysia ialah sebanyak RM816.47 juta setahun. Kerugian yang dialami akibat daripada episod kebakaran hutan tersebut telah menyedarkan banyak pihak tentang kepentingan untuk membina sistem pengurusan kebakaran hutan yang efektif. Di dalam kajian ini, 3 set program komputer telah direka untuk: mengesan titik panas; mengira indeks risiko kebakaran, dan menjana analisa spatial. Teknik remote sensing dan GIS telah digabungkan di dalam kajian ini. Dengan itu, sebuah sistem
amaran awal yang ringkas tetapi efektif untuk mengesan kebakaran hutan telah dicipta. Jalur termal dari MODIS (Moderate Resolution Imaging Spectroradiometer) telah digunakan untuk mengekstrak informasi titik panas dan menjana peta risiko kebakaran hutan. Analisa spatial dilakukan dengan menggunakan fungsi dari perisian ArcView. Hasil dari kajian ini dibandingkan dengan data kebakaran hutan dari Jabatan Bomba dan Penyelamat, Malaysia, dan maklumat taburan hujan serta suhu dari Jabatan Kajicuaca, Malaysia. Nilai korelasi yang tinggi (R2 = 0.8) telah diperolehi diantara suhu yang diekstrak dari MODIS dengan suhu dari Jabatan Kajicuaca. Peta kebakaran hutan yang diperolehi juga mempunyai ketepatan yang tinggi (71%). Nilai Normalized Difference Vegetation Index
(NDVI) yang diperoleh dari MODIS juga mencatatkan korelasi yang tinggi (R2 = 0.7 and R2 = 0.85) dengan data jumlah hujan dan suhu dari Jabatan Kajicuaca. Dengan itu, hasil dari kajian ini menunjukkan bahawa ianya boleh digunakan sebagai satu mekanisma sistem amaran awal untuk mengurangkan kejadian kebakaran hutan dan boleh diperkembangkan lagi menjadi sebuah sistem yang lebih kompleks untuk meningkatkan lagi fungsinya.
English to Malay: SEA SURFACE SALINITY RETRIEVAL BASED ON LEVENBERG MARQUARDT ALGORITHM USING SATELLITE DATA General field: Science Detailed field: Geography
Source text - English Soil Moisture Ocean Salinity satellite exploits the frequency of 1.4 gigahertz which represents the best conditions for salinity retrieval. The new challenge is to interpret the observed brightness temperature into the salinity. The main objective of this study is to measure the sea surface salinity in the South China Sea using the Levenberg Marquardt algorithm. The methodology of this study involves the mapping of this algorithm to solve the non-linear least squares in order to obtain the salinity. The salinity was estimated based on the comparison between brightness temperature measured and brightness temperature simulated value of the successive iteration. The difference between both brightness temperature values is compared to the desired threshold at each iteration, this recursive process either updates the brightness temperature simulated or finally terminated if the brightness temperature difference is lower or higher than that threshold respectively. The salinity values estimated from the designed of Levenberg Marquardt algorithm tools were
assembled, thus maps of sea surface salinity were produced. Some accuracy analyses were carried out to identify the appropriateness of a Levenberg Marquardt algorithm for the salinity retrieval. The results of the regression analysis and Pearson Correlation Coefficient indicate that sea surface salinity measured performs high
correlation with the sea truth data, which is 0.9042 and ±0. 9509 psu, respectively. The analysis of variance by testing the hypothesis indicates that there is no substantial difference between the mean of sea surface salinity from the satellite and sea truth data. The root mean square error of measured sea surface salinity is smaller compared to the sea truth data values. In conclusion, the appropriateness of
Levenberg Marquardt algorithm in inverting the salinity in the non-linear technique proved as a solution for ill-posed inversion that estimates the sea surface salinity from the Soil Moisture Ocean Salinity brightness temperature.
Translation - Malay Satelit Kelembapan Tanah Kemasinan Laut mengaplikasi frekuensi sebanyak 1.4 gigahertz di mana ia merupakan jalur yang terbaik bagi penganggaran kemasinan. Cabaran baru ialah untuk mengadaptasi suhu kecerahan yang dicerap kepada kadar kemasinan. Objektif utama kajian ini adalah untuk menentukan kemasinan permukaan laut di Laut China Selatan menggunakan algoritma Levenberg
Marquardt. Kaedah digunapakai dalam kajian ini melibatkan penggunaan algoritma tersebut untuk menyelesaikan kuasa dua terkecil tidak langsung dalam menentukan kadar kemasinan. Nilai kemasinan dianggar berdasarkan perbandingan di antara cerapan suhu kecerahan dan simulasi suhu kecerahan untuk lelaran berterusan.
Perbezaan di antara kedua-dua suhu pencerahan dibandingkan dengan nilai ambang yang dikehendaki pada setiap lelaran dan proses rekursif ini samada akan mengemaskini semula nilai simulasi suhu kecerahan atau prosesnya ditamatkan sekiranya perbezaan suhu kecerahan lebih rendah atau lebih tinggi daripada nilai ambang masing-masing. Penganggaran kadar kemasinan daripada algoritma Levenberg Marquardt yang direka telah dikumpul, seterusnya menghasilkan peta kemasinan permukaan laut. Beberapa analisis ketepatan dijalankan bagi menilai kesesuaian algoritma Levenberg Marquardt terhadap penentuan kadar kemasinan. Hasil bagi analisis regresi dan Pekali Hubungan Pearson menunjukkan kadar
kemasinan laut memberikan perkaitan yang paling hampir dengan data lapangan, iaitu masing-masing merekodkan 0.9042 dan ±0.9509 psu. Analisis kepelbagaian dengan menguji hipotesis menunjukkan tiada perbezaan yang ketara di antara purata kadar kemasinan laut daripada data satelit dan data lapangan. Ralat punca min kuasa dua
bagi kadar kemasinan yang dicerap adalah lebih kecil berbanding nilai data lapangan. Kesimpulannya, kesesuaian algoritma Levenberg Marquardt dalam
penyongsangan kadar kemasinan bagi teknik tidak langsung terbukti sebagai satu kaedah penyelesaian untuk menentukadar kemasinan laut daripada suhu kecerahan
Kelembapan Tanah Kemasinan Laut.
English to Malay: Flood mapping of Northern Peninsular Malaysia using SAR images General field: Science Detailed field: Environment & Ecology
Source text - English Malaysia is one of the evident countries whose recurrence of floods proves that floods are getting worse. The northern peninsular of Malaysia has lost a lot of lives and property worth billions to the series of floods that have been occurring for many years. Many disaster management strategies have been adopted by the Malaysian government in handling these flood disasters but it is still a topic in the annual agenda. This research project aimed at using fusion techniques in mapping the flood extents in the northern peninsular Malaysia in order to contribute to the flood disaster eradication by extracting more and better information through the fusion of RadarSat 1 and TerraSAR-X images. The Principal Component Analysis was also used and compared with the fusion techniques which include the Hue Saturation and Value (HSV), the Brovey Transformation (BT), the Gram Schmidt (GS), and the Principal Component Spectral Sharpening (PCSS). The best principal component of the PCA, that is the PC2 which classified and compared with the classification of the other fusion techniques using Maximum likelihood (ML) and support Vector Machine (SVM). The results indicated BT technique has the highest overall accuracy of 70.9615% and kappa coefficient of 0.3418. This method showed relative improvement on the classification of the flooded and non-flooded area which was used to produce the flood extent Map that was further validated with the DEM data. The final results in this study showed that more information on the areas that are affected by the floods especially the extents, became more exposed after the classification of the fused images.
Translation - Malay Malaysia merupakan salah satu negara yang mengalami banjir yang semakin teruk. Di kawasan Utara Semenanjung Malaysia telah banyak kehilangan nyawa dan harta benda yang mencecah nilai berbilion-bilion akibat daripada banjir yang telah berlaku selama bertahun-tahun. Banyak strategi pengurusan bencana telah
dilaksanakan oleh kerajaan Malaysia dalam menangani bencana banjir ini tetapi ia masih menjadi satu topik dalam agenda tahunan. Kajian ini bertujuan untuk menggunakan teknik “fusion” dalam memetakan kadar banjir di Utara Semenanjung Malaysia untuk menyumbang kepada pembasmian bencana banjir dengan mengekstrak maklumat dengan lebih lanjut dan lebih baik melalui gabungan imej Radarsat 1 dan TerraSAR-X. “Principal Component Analysis” juga digunakan dan dibandingkan dengan teknik “fusion” yang mana merangkumi “Hue Saturation And Value” (HSV), “Brovey Transformation”(BT), “Gram Schmidt”(J), dan “Principal
Component Spectral Sharpening” (PCSS). Komponen utama terbaik PCA, iaitu PC2 yang diklasifikasikan dan dibandingkan dengan teknik kalsifikasi “fusion” yang lain dengan menggunakan “Maximum likelihood” (ML) dan “Support Vector Machine” (SVM). Keputusan menunjukkan teknik BT mempunyai ketepatan keseluruhan yang tertinggi iaitu 70,9615% dan kappa coefficient dengan nilai 0.3418. Kaedah ini menunjukkan peningkatan berbanding klasifikasi kawasan ditenggelami air banjir dan kawasan yang tidak ditenggelami banjir yang telah digunakan untuk menghasilkan peta limpahan banjir yang terus disahkan dengan data DEM. Keputusan akhir dalam kajian ini menunjukkan bahawa maklumat lanjut mengenai kawasan-kawasan yang terjejas oleh banjir terutamanya keluasan lebih dapat dikenal pasti selepas klasifikasi “fused” imej.
English to Malay: Forest cover change in Peninsular Malaysia using satellite remote sensing data General field: Science Detailed field: Forestry / Wood / Timber
Source text - English Forest balance the global ecosystem by maintaining sustainable interaction between living and non living things. Deforestation caused land to be fragile, and destroyed water catchment area. Soil will absorb large amount of water and eroded. The water that caught by the forest before flows unhindered to the river with the soil and cause extreme flood. Thus forest change should be monitored to maintain sustainable ecosystem. Sustainable forest management should be done to maintain future forest resources. Forest replantation have been done by forestry department. However, it is not sufficient to balance the ecosystem. Malaysia forest loss has been increased and recorded high rate of deforestation. Thus, continuous forest monitoring should be done. Remote sensing technology with the multispectral image that has
high spatial, temporal, and radiometric resolution, possible to monitor the forest cover change in short period of time. The aim of this study is to investigate the forest cover change in Malaysia between 1990 and 2010 using Landsat and ALOS Palsar satellite images. CLASlite software used Landsat data, while ALOS Palsar use threshold to classify forest. Besides, comparison of forest cover from Support Vector Machine and Maximum Likelihood Classifier is carried out. Then the result is validated with Forestry Department statistics. Comparison of total forest cover from CLASlite, ALOS Palsar, land use map and forestry statistics also was being made. The change of forest is detected by selecting Iskandar and Kuala Lumpur area. CLAlite also show the deforestation and disturbance area in Peninsular Malaysia. Results show that a forest loss value is high, compare to forest gain value. Iskandar have forest loss about 54 966 hectare and gain 21 411 hectare, while Kuala Lumpur loss about 2 521 hectare and gain 3 004 hectare of forest. The result of this study will be useful for Forestry Department to monitor the deforestation in Malaysia.
Translation - Malay Hutan penting dalam menyeimbangi ekosistem global dengan mengekalkan hubungan antara benda yang bernyawa dan tidak bernyawa. Penebangan hutan menyebabkan tanah menjadi rapuh, dan menghapuskan kawasan tadahan air. Tanah menyerap sebilangan besar air lalu terhakis. Air yang dahulunya diserap oleh hutan mengalir terus kedalam sungai bersama tanah dan mengakibatkan banjir. Maka, perubahan hutan harus dipantau agar dapat mengekalkan keseimbangan ekosistem. Pengurusan hutan yang mampan harus dilaksanakan supaya dapat mengekalkan sumber hutan pada masa akan datang. Penanaman semula hutan
telah dijalankan oleh jabatan perhutanan. Namun, tindakan ini tidak mencukupi bagi menyeimbangi kestabilan ekosistem. Kehilangan kawasan hutan di Malaysia semakin meningkat dan menunjukkan kadar penebangan hutan yang tinggi. Maka pemantauan hutan
secara berterusan harus dilaksanakan. Teknologi remote sensing dengan imej “multispectral” yang mempunyai resolusi "spatial”,“temporal” dan “radiometric” yang tinggi, ia berupaya memantau perubahan kawasan litupan hutan dalam tempoh yang singkat. Matlamat kajian ini adalah untuk mengkaji perubahan kawasan hutan di Malaysia diantara tahun 1990 sehingga tahun 2010dengan menggunakan imej satellite Landsat dan ALOS
Palsar. Bagi menganalisa perubahan, kawasan hutan perlu dikenal pasti. Perisian CLASlite menggunakan data Landsat manakala data ALOS menggunakan “threshold” bagi mengenalpasti kawasan hutan. Perbandingan kawsan hutan dari “Support Vector Machine” dan “Maximum Likelihood Classifier” dijalankan. Hasil kajian disahkan dengan statistik hutan dari Jabatan Perhutanan Malaysia. Jumlah kawasan hutan dari CLASlite, ALOS Palsar, peta guna tanah and forestry statistics juga dijalankan. Perubahan hutan dikenalpasti dengan memilih Iskandar dan Kuala Lumpur sebagai kawasan yang kurang dilitupi awan. CLASlite juga menunjukkan kawasan penebangan dan pengurangan hutan. Hasil kajian menunjukkan perbezaan ketara antara kehilangan hutan dan pertambahan hutan. Jumlah kehilangan hutan di Iskandar sebanyak 54 966 hektar dan peningkatan kawasan hutan sebanyak 21 411 hektar, manakala Kuala Lumpur kehilangan kawsan hutan sebanyak 2 521 hektar dan meningkat 3 004 hektar. Hasil kajian akan membantu pihak Jabatan Perhutanan bagi memantau penebangan hutan di Malaysia.
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Translation education
University Technology of Malaysia
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Years of experience: 6. Registered at ProZ.com: Aug 2017.
I have a solid education in Science and professional experience working in oil & gas industry. I offer service in translation, subtitling, proofreading, transcribing & localization.
Source & target translation languages: English & Malay
As a translator, I will help my client to:
~Convert concepts in the source language to equivalent concepts in the target language
~Compile information, such as technical terms used in legal settings, into glossaries and terminology databases to be used in translations
~Relay the style and tone of the original language
~Render spoken messages accurately, quickly, and clearly
Taking pride in my work, reliable proofreading service, deliver well-written translations to my clients and being capable of meeting deadlines, I am inviting you to work together in a professional and respectful manner for translations and proofreading or other tasks where you think my skills might be useful. I am seeking a position where I can maximize my accurate keyboard skills in a demanding work environment.
Translation means transferring text from one language into another. Translating a text means understanding it and analyzing it thoroughly. A translator has to be very careful in choosing the equivalent expression in the target language.
Looking forward to working together.
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Aidavera
(Impeccable Translator English-Malay)
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