buletin geospatial sektor awam

BULETIN GEOSPATIAL SEKTOR AWAM 1


Sidang Pengarang

KANDUNGAN

DARI MEJA KETUA EDITOR

Assalamualaikum dan Salam Sejahtera,

Kajian Penentuan Lokasi Memorial

Park Menggunakan Aplikasi GIS

(Perkuburan Islam)

High Resolution Image For Forest

Classification

LHdnM Pilot Geographical

Information System (GIS)

Detecting And Mapping Of Langat

River And Its Tributaries Using Remote

Sensing

GIS SANA SINI

Persidangan dan Pameran

Antarabangsa MAP ASIA 2007,

di Kuala Lumpur Convention Centre

(KLCC), Kuala Lumpur pada

14 hingga 16 Ogos 2007

Galeri Foto

1

9

14

19

27

31

PenaungY.B. Dato’ Seri Azmi bin KhalidMenteri Sumber Asli dan Alam Sekitar

PenasihatY. Bhg. Datuk Suboh bin Mohd YassinKetua SetiausahaKementerian Sumber Asli dan Alam Sekitar

Ketua EditorFuziah binti Abu HanifahPengarahPusat Infrastruktur Data Geospatial Negara (MaCGDI)

EditorShaharudin bin Idrus - (LESTARI, UKM)Dr. Azhari bin Mohamed - (JUPEM)Hamdan bin Ab. AzizJawahiril Kamaliah binti MohamadDr. Zainal bin A. MajeedAnual bin Haji AzizRaja Abd. Aziz bin Raja AliMariyam binti MohamadNorazmel bin Abd. KarimWan Faizal bin Wan Mohamed

RekabentukHaji Muhammat Puzi bin Ahmat - (JUPEM)Ya’cob bin AbasSiti Sapura binti RafeeNurul Akmalina binti Khairuddin

JurufotoAzlina binti Mahad

PenerbitPusat Infrastruktur Data Geospatial Negara (MaCGDI)Kementerian Sumber Asli dan Alam Sekitar (NRE)Aras 7 & 8, Wisma Sumber Asli,No. 25 Persiaran Perdana, Presint 4,62574 Putrajaya, Malaysia.Tel : 603 - 8886 1111Fax : 603 - 8889 4851www.mygeoportal.gov.my

Setelah genap 50 tahun kita merdeka, pelbagai kemajuan dan pembangunan yang telah kita capai. Begitu juga dengan perkembangan Geographic Information System (GIS) di sektor awam. GIS sekarang menjadi salah satu mekanisma dalam penyelesaian tugasan harian. Pemangkin kepada pembangunan dan kemajuan GIS adalah dengan adanya Pusat Infrastruktur Data Geospatial Negara (MaCGDI). Infrastruktur maklumat geospatial negara telah dibangunkan bagi menyediakan perkhidmatan dan kemudahan ke arah penyebaran, perkongsian, penggunaan dan pengurusan maklumat geospatial di kalangan agensi-agensi sektor awam.

Seiring dengan kemajuan tersebut MaCGDI juga menekankan akan kepentingan dalam pendidikan GIS. Jika dilihat dari realiti semasa, bidang GIS masih bukan menjadi satu bidang pilihan utama di kalangan pelajar ketika melanjutkan pelajaran di institusi-institusi pengajian tinggi, tidak seperti bidang-bidang lain yang lebih ’glamor’ seperti kedoktoran, undang-undang, perakaunan, dan sebagainya. Masih ramai lagi yang tidak menyedari wujudnya bidang GIS dan peranannya di dalam pembangunan tamadun manusia walaupun bidang berkaitan pemetaan bumi adalah di antara profesion tertua di dunia. Selaras dengan itu, adalah menjadi misi kami di MaCGDI untuk ‘mempopularkan’ penggunaan GIS di dalam setiap aktiviti berkaitan pembangunan dan menerap ’kepercayaan’ bahawa GIS merupakan satu elemen penting di dalam pembangunan sesebuah negara. MaCGDI juga menyedari bahawa komuniti GIS di Malaysia masih lagi kecil dan dengan itu adalah menjadi satu cabaran bagi MaCGDI untuk membina satu komuniti GIS yang lebih besar, berdaya saing, berinovasi dan berperanan di dalam kemakmuran negara.

Sejarah GIS bermula kira-kira 15,500 tahun yang lampau dengan hanya lakaran-lakaran dan maklumat yang dilukis di dinding gua. Sejajar dengan perkembangan tamadun manusia, evolusi GIS mula berkembang hingga ke era perkomputeran pada masa kini. Kini, GIS bukan hanya paparan peta di atas skrin komputer sahaja, tetapi menggabungkan teknologi-teknologi lain seperti teknologi mudah alih (mobile), teknologi GPS, teknologi berasaskan web, teknologi internet, teknologi penderiaan jauh (remote sensing), teknologi pangkalan data dan sebagainya. Gabungan teknologi-teknologi tersebut dengan teknologi GIS berjaya menghasilkan inovasi dan aplikasi yang mampu membantu manusia di dalam pelbagai aspek kehidupan.

Akhir sekali diharap artikel yang dipaparkan pada kali ini akan memberi ilmu yang berguna kepada para pembaca mengenai perkembangan GIS sektor awam di Malaysia. Selamat membaca!

Penafian : Kesahihan dan ketepatan tulisan atau pendapat adalah tertakluk pada cetusan idea pengirim artikel.


KAJIAN PENENTUAN LOKASIMEMORIAL PARK MENGGUNAKAN

APLIKASI GIS (PERKUBURAN ISLAM)norazman Ismail1, Abdullah Hisham Omar2, Zainal A Majeed1

AbstrakKeperluan tanah perkuburan (Memorial Park) adalah satu kemudahan asas yang sama pentingnya dengan kemudahan

asas lain. Apabila memajukan sesuatu kawasan perumahan seringkali keperluan ini tertinggal dan menimbulkan masalah

kepada penduduk kerana tiada tanah perkuburan yang dirancang. Justeru itu kajian penentuan kesesuaian tapak baru

bagi kawasan perkuburan menggunakan aplikasi GIS dan pemetaan digital adalah salah satu alternatif kepada jalan

penyelesaiannya. Melalui analisis dan hasil akhir yang dicapai menggunakan aplikasi GIS pastinya dapat membantu

Pihak Berkuasa Tempatan untuk merancang sesuatu pembangunan seiring dengan keperluan penduduk setempat agar

lebih berkesan dan terancang. Seterusnya ianya dapat membantu pihak perancang mengenalpasti perancangan sesuatu

kawasan dan meminimumkan masalah kekurangan tanah untuk kawasan perkuburan di negara kita.

PENDAHULUAN

Matlamat perancangan bandar dan desa di negara ini adalah untuk mewujudkan penempatan manusia yang berkualiti di semua peringkat petempatan, baik di bandar mahupun di desa. Tahap kualiti sesebuah petempatan pula mempengaruhi dan sering dikaitkan dengan kualiti kehidupan manusia, yakni pencapaian suatu tahap keadaan persekitaran sosial, binaan manusia dan alam semulajadi yang membolehkan masyarakat hidup dengan selesa, sihat, produktif dan selamat. Di bawah Akta Perancangan Bandar dan Desa 1976, setiap Pihak Berkuasa Tempatan (PBT) perlu menyediakan pelan pembangunan (pelan struktur dan pelan tempatan) bagi mengawal guna tanah dan pembangunan kawasannya. Ini bererti setiap pelan pembangunan yang menjadi asas rujukan pembangunan perlu sentiasa dikemaskini. Proses ini pastinya memerlukan pembaharuan terhadap kebolehan proses perancangan menerima dan mengolah maklumat-maklumat terkini serta menggunakan kaedah dan peralatan moden.

Tanah perkuburan adalah salah satu kemudahan asas yang sama pentingnya dengan kemudahan asas yang lain. Apabila memajukan sesuatu kawasan perumahan seringkali keperluan ini tertinggal dan menimbulkan masalah kepada penduduk kerana tiada tanah perkuburan yang dirancang. Masalah ini menjadi lebih kritikal khususnya di kawasan kepadatan yang tinggi iaitu di bandar kerana berlakunya masalah kekurangan tanah. Faktor sosial seperti adab resam penduduk bandar yang berbilang kaum juga perlu diberi perhatian dalam merancang tanah perkuburan.

Justeru itu dengan adanya aplikasi Sistem Maklumat Geografi (GIS) ianya dapat membantu Pihak Berkuasa Tempatan untuk merancang sesuatu pembangunan seiring dengan keperluan penduduk setempat agar lebih berkesan dan terancang. Ianya dapat membantu pihak perancang untuk mengenalpasti kesesuaian kawasan dan meminimakan masalah kekurangan tanah untuk kawasan perkuburan di negara kita.


Peta Cadangan Lokasi Baru Perkuburan Islam

Mukim Cheras

Pemilihan Perkakasan & PerisianPemilihan Kawasan Kajian

Imej Satelit

Pra-Pemprosesan

Pengkelasan Imej

PenentuanKelas Pemberat

Pengrasteran

PengkelasanSemula

Permodelan Pengvektoran

Pengkelasan Semula

PetaKecerunan Penyuntingan

Operasi Buffering

Operasi Erase

Operasi Tindih Lapis

Analisis

Pengkelasan Semula

Peta Guna Tanah

Kontur

dEM

Sungai Utiliti Jalanraya

Data Digital Topografi Lot TanahPeta Tanah Tanih

Kawasan Kajian

Kajian ini merangkumi Mukim Cheras iaitu merupakan salah satu mukim di dalam Negeri Selangor di dalam Daerah Hulu Langat. Keluasannya dianggarkan lebih kurang 5,973 hektar dan mempunyai kepadatan penduduk seramai 99,700 orang (2000) yang terdiri daripada berbilang bangsa dan agama (Rajah 1.1).

Kriteria Pemilihan Tapak

Kajian ini berpandukan kepada Garis Panduan Perancangan Tanah Perkuburan Islam yang di keluarkan oleh Jabatan Perancang Bandar dan Desa (2000) iaitu mengikut kriteria-kriteria seperti berikut:

i. Bagi kawasan bandar besar (lebih 200,000 orang penduduk) keperluan tanah perkuburan ialah seluas 20 hektar. Sekiranya melebihi 20 hektar ia boleh diagihkan ke beberapa tempat;

ii. Perletakan berhampiran dengan masjid dan kawasan perumahan untuk memudahkan pengurusan jenazah untuk disembahyangkan dahulu sebelum dikebumikan serta memudahkan mereka menziarahi simati;

Metodologi

Dalam melaksanakan projek ini terdapat beberapa fasa telah dijalankan untuk mendapatkan hasil kajian yang memuaskan seperti ditunjukkan pada Rajah 1.0 di bawah:

Rajah 1.0 : Carta Alir Metodologi Kajian

Rajah 1.1 : Lokasi Kawasan Kajian


iii. Jenazah diwajibkan mengadap ke kiblat atau kaabah. Bagi tujuan ini, tanah yang rata adalah paling sesuai;

iv. Jarak di antara tanah perkuburan dan rumah kediaman yang terdekat ialah sekurang-kurangnya 30 meter;

v. Jenis tanah yang paling sesuai ialah tanah terbuka jenis poros; dan

vi. Hendaklah jauh dari kawasan sungai, saliran atau lain-lain punca air. Ia sesuai ditempatkan di pinggir kawasan pembangunan dan berhampiran dengan rumah ibadat.

Jadual 1.0 : Kelas Kesesuaian Guna Tanah Mukim Cheras

Jadual 1.1 : Keluasan dan Peratusan Guna Tanah Rajah 1.2 : Peta Guna Tanah Mukim Cheras

HASIL DAN ANALISIS

Bagi mencapai hasil yang diharapkan, berbagai analisis telah dijalankan seperti pengkelasan (classification), pengkelasan semula (reclassify), pemodelan (modeling), tindihan (overlay), gabungan (union) dan sebagainya telah dilaksanakan.

Analisis Pengkelasan

Peta Guna tanah Mukim Cheras dihasilkan melalui pemprosesan imej satelit QuickBird Mukim Cheras, bertarikh 15 October 2004 dan 18 Mac 2003. Proses pengkelasan imej dilakukan menggunakan kaedah pengkelasan berpenyelia (supervised classification) seperti ditunjukkan di dalam Jadual 1.0 di bawah.

Pemberat Jenis Guna Tanah Kesesuaian

1 Unclassified/Air/Awan Amat Tidak Sesuai

2 Bangunan (Perumahan, komersial) Tidak Sesuai

3 Cleared Land (kawasan yang sedang dibangunkan) kurang Sesuai

4 Hutan dan pelbagai tanaman Sesuai

5 Kawasan Lapang Paling Sesuai

Guna Tanah Luas

Peratus Kawasan (Ha)

Air, Awan 769.5 12.8

Bangunan 1682.3 28.2

Cleared Land 703 11.8

Hutan dan 1910.8 32

Pelbagai Tanaman

Tanah Lapang 907.4 15.2

Luas Keseluruhan 5972.6 100

Berdasarkan kepada Peta Guna Tanah Mukim Cheras yang dihasilkan Rajah 1.2, keluasan setiap kelas bagi guna tanah adalah seperti paparan di dalam Jadual 1.1.


Jadual 1.2 : Garis Panduan Pemajuan Tanah Di Kawasan

Berbukit

Rajah 1.4 : Peta Kecerunan Mukim Cheras

Rajah 1.3 : Graf Keluasan dan Kelas Kesesuaian

Analisis Kecerunan

Analisis kecerunan dijalankan berdasarkan kepada muka bumi kawasan kajian yang terdiri daripada bentuk rupa bumi yang berbeza. Bagi kajian ini Garis Panduan Bagi Pemajuan Tanah Untuk Pembangunan Di kawasan Berbukit oleh Jabatan Alam Sekitar telah digunakan seperti ditunjukkan dalam Jadual 1.2 di bawah.

Daripada hasil penjanaan Digital Elevation Model (DEM) menggunakan data vektor kontur dengan sela ketinggian 20 meter, proses pengkelasan dilakukan bagi tujuan mendapatkan kesesuaian cerun di mana setiap pengkelasan adalah mengikut pemberat seperti yang telah ditetapkan.

Rumusan daripada hasil pengkelasan ini didapati kelas paling sesuai yang berkecerunan 0˚ – 10˚ mempunyai keluasan 82.4 % iaitu meliputi hampir keseluruhan Mukim Cheras. Darjah kecerunan ini adalah dianggap sebagai paling sesuai untuk kawasan perkuburan Islam kerana kecerunannya adalah dianggap rata. Kawasan ini meliputi keluasan 115,361,200 meter persegi yang mana secara keseluruhannya meliputi kawasan sebelah barat, selatan dan tengah Mukim Cheras. Gambaran jelas dapat dilihat seperti di dalam Rajah 1.3 dan 1.4.

Analisis Pemodelan

Analisis pemodelan ini dijalankan berasaskan kepada pemodelan Weighted Linear Sum (Voegd,1983), di mana formula yang digunakan adalah seperti berikut:

S = (Wj Xij)

Di mana S = Output (Tapak Kesesuaian Perkuburan) Wj = Pemberat Parameter/Variasi (Pemberat bagi tanah-tanih, cerun dan guna tanah) Xij = Pemberat kelas bagi parameter (Pemberat kelas bagi tanah-tanih, cerun dan guna tanah)

Pemodelan ini dijalankan dengan menggabungkan tiga lapisan data iaitu peta kecerunan, peta guna tanah dan peta tanah-tanih. Tiga lapisan data tersebut dihasilkan melalui proses pengkelasan bagi mendapatkan kelas yang terbaik bagi tujuan kajian. Parameter (Wj) adalah nilai pemberat parameter iaitu antara 3 (paling sesuai) dan 1 (tidak sesuai) dan nilainya ditentukan daripada setiap pemberat kelas parameter (Xij) seperti ditunjukkan di dalam Jadual 1.3.

Kecerunan Kelas

< 12˚ Risiko Rendah

12˚- 20˚ Risiko Sederhana

> 20˚ Risiko Tinggi

0

20000000

40000000

60000000

80000000

100000000

120000000

Keluasan (meter persegi)

G r a f K e lu a s a n T a n a h d a n K e s e s u a ia n

Sangat Tidak Sesuai

Tidak Sesuai

Kurang Sesuai

Sesuai

Paling Sesuai


Wj = Pemberat Parameter / Variasi Xij = Pemberat Kelas Bagi Parameter

Tanah 3 2 1 1 1

Kecerunan 5 4 3 2 1

Penggunaan Tanah 5 4 3 2 1

Kelas Luas (Ha) Peratus

Paling Sesuai 246.7 4.5

Kurang Sesuai 1367.1 24.5

Paling Tidak Sesuai 3976.3 71.0

Data Jarak Penampan (Meter)

Jalan 30

Sungai 300

Surau 500,1000,1500,2000

Masjid 500,1000,1500,2000

Jadual 1.3 : Nilai Pemberat Parameter dan Pemberat Kelas Parameter

Rajah 1.5 : Peta Kesesuaian Tanah Mukim Cheras Rajah 1.6 : Operasi Erase

Jadual 1.4 : Jadual Hasil Pengkelasan Jadual 1.5 : Data Spatial dan Jarak Penampan

Hasil yang diperolehi, kelas yang paling sesuai adalah mewakili kawasan tanah lapang di mana tanah tersebut tidak dilakukan sebarang pembangunan. Kecerunan pula mewakili kecerunan 0o - 10o yang dikategorikan sebagai tanah rata. Bagi jenis tanah yang mengandungi kandungan pasir yang tinggi iaitu struktur tanah yang poros akan memudahkan laluan air dan udara kesemua arah. Tanah jenis ini adalah membolehkan pereputan mayat dengan lebih cepat berbanding dengan tanah lain. Keluasan dan peratusan bagi kelas yang dipilih adalah ditunjukkan seperti Jadual 1.4 dan Rajah 1.5.

Analisis Tindih Lapis (Overlay)

Daripada hasil analisis empirical model, kajian lebih ditumpukan kepada kelas paling sesuai bagi mendapatkan kawasan perkuburan Islam. Melalui operasi buffering dan operasi union data-data seperti sungai, jalan raya, masjid dan surau diasingkan bagi tujuan mendapatkan zon penampan bagi kawasan perkuburan seperti di dalam Jadual 1.5, Rajah 1.6,1.7 dan 1.8.


Rajah 1.7 : Hasil Operasi Erase

Rajah 1.8 : Operasi Penampan Kawasan Masjid dan Surau

Proses terakhir untuk mendapatkan hasil kajian yang diharapkan adalah melaksanakan analisis tindih lapis (overlay). Analisis tindih lapis ini dijalankan adalah antara data lot kadaster Mukim Cheras dengan kawasan yang paling sesuai bagi Cadangan Tapak Perkuburan Islam Di Mukim Cheras seperti ditunjukkan di Rajah 1.9. Tujuan analisis ini dijalankan adalah untuk mendapatkan maklumat lot tanah bagi kawasan tersebut.

Rajah 1.9 : Hasil Operasi Tindih Lapis


Rajah 2.0 : Cadangan Tapak Baru Perkuburan Islam

Rajah 2.1 : Peta Cadangan Tapak Perkuburan Islam Mukim Cheras

Hasil akhir daripada kajian ini didapati bahawa kawasan tapak baru bagi perkuburan Islam di Mukim Cheras adalah memiliki keluasan seluas 801,423.00 meter persegi bersamaan dengan 80.14 hektar. Sebanyak 116 lot tanah terletak di atas kawasan tersebut. Keluasan ini adalah mengikut kriteria yang digariskan di dalam Perancangan Tanah Perkuburan Mengikut Hierarki Petempatan bagi Kawasan Bandar dan Pekan. Mengikut kriteria tersebut, penduduk yang melebihi 200,000 orang, keperluan tanah perkuburan adalah memerlukan seluas 20 hektar atau lebih dan ia boleh diagihkan ke beberapa tempat.

Kawasan ini adalah dianggap terbaik disebabkan berdekatan dengan jalan raya, kawasan perumahan serta rumah ibadat (masjid) yang memudahkan pengangkutan jenazah ke tanah perkuburan. Begitu juga dengan ciri-ciri topografi yang terletak di kawasan rata dan tidak bercuram serta jenis tanah yang poros seperti ditunjukkan di dalam Rajah 2.0. Hasil akhir kajian ini adalah Peta Cadangan Perkuburan Islam Mukim Cheras seperti ditunjukkan di dalam Rajah 2.1.

Secara keseluruhannya ia menepati kriteria yang telah ditetapkan oleh Jabatan Perancang Bandar dan Desa melalui Garis Panduan Perancangan Perkuburan Islam dan Bukan Islam Bil.17/1997.

Cadangan Tapak Kubur

Sungai Utama

LOT_TANAH_CHERAS

Nama Taman

Jalan_Bandar

Highway

Sempadan_Cheras


KESIMPULAN

Secara keseluruhannya matlamat dan objektif kajian ini telah tercapai di dalam Kajian Penentuan Kesesuaian Tapak Baru Perkuburan Islam Menggunakan Aplikasi Sistem Maklumat Geografi dan Pemetaan Digital bagi Mukim Cheras. Keputusan ini adalah berpandukan kepada kriteria-kriteria tertentu yang telah ditetapkan oleh Jabatan Perancang Bandar Dan Desa Malaysia. Berdasarkan kepada keupayaan yang dilaksanakan ini, maka diyakini ia dapat membantu pihak perancang mahupun pemaju-pemaju perumahan dalam merancang sesuatu pembangunan khususnya tapak baru perkuburan Islam dengan lebih efektif di samping memberi penekanan kepada konsep pembangunan mapan.

CADANGAN

Berdasarkan analisis daripada kajian yang dijalankan, beberapa cadangan dapat diberikan untuk memperbaiki lagi analisis-analisis yang akan datang khususnya analisis yang berkaitan dengan pembinaan tapak baru perkuburan antaranya:

1. Hasil daripada kajian ini didapati terdapat kelemahan dari segi kriteria-kriteria yang tidak spesifik yang dikeluarkan oleh agensi yang berkaitan. Diharapkan agar Jabatan Perancang Bandar dan Desa dapat mengkaji dan menetapkan kriteria yang lebih spesifik contohnya darjah kecerunan bagi perkuburan Islam dan bukan Islam. Begitu juga dengan meletakkan nilai jarak yang minimum bagi jalanraya, kawasan perumahan, kawasan industri mahupun rumah ibadat daripada kawasan perkuburan.

2. Cadangan kajian seterusnya perlu menitikberatkan kepada pembangunan pangkalan data bagi kawasan Mukim Cheras khususnya dalam data-data banci, pelan rancangan struktur dan juga pelan rancangan tempatan bagi membolehkan analisis yang dijalankan lebih berkesan. Di samping itu juga, pangkalan data yang dibangunkan perlu diperkemas dengan metadata yang terperinci.

RUJUKAN DAN BIBLIOGRAFI

Garis Panduan Perancangan Tanah Perkuburan Islam dan Bukan Islam (2002). Jabatan Perancang Bandar dan Desa.

Haniah binti Zakariah (2003). Jangkaan Perubahan Gunatanah 5 Tahun Akan Datang Di Rancangan Rizab Krau Menggunakan Aplikasi Remote Sensing Dan GIS.

Jabatan Perancang Bandar dan Desa Negeri Selangor (1999). Projek Strategi Pembangunan Mampan dan Agenda 21 Selangor.

Nor Aizam, Zaharah, Pauziyah (2005). GIS Technique For Incinerator Site Suitability study.

Noraisah Yahya (2001). “Planning For Human Settlement.” Habitat Malaysia – Human Settlements Journal of Malaysia.

1 Pusat Infrastruktur data Geospatial negara (MaCGdI),Kementerian Sumber Asli dan Alam SekitarEmail: [email protected]

2 Faculty Of Geoinformation Science & Engineering University Of Technology Malaysia Email: [email protected]

Penyediaan Rancangan Pemajuan, Rancangan Cheras (1997-2010). Jabatan Perancang Bandar dan Desa 2000.

Safiza Suhana, (2002). Analisa Penggunaan Sistem Maklumat Geografi Untuk Pembangunan Mapan Pulau Tioman.

Email: [email protected]


AbstractUnsupervised classification is also known as clustering because it is based on natural grouping of pixels in the image

data when the pixels are plotted in the feature space. Clusters are defined with a clustering algorithm, which often uses

all or many of the pixels in the input data file for its analysis. ISODATA (Iterative Self-Organizing Data Analysis Technique)

are examples of unsupervised classification methods. The aim of this study was to apply the unsupervised classification

method for forest mapping using high resolution QuickBird image. The study site was located at the Forest Research

Institute Malaysia (FRIM), Kepong. The overall accuracy for the unsupervised classification was 77.50% while the kappa

statistics, 0.8182.

INTRODUCTION

Mapping forest resources has become one of the most important activities in sustainable forest management. However, mapping heterogeneous and high density tropical rain forests is a heavy task. Nevertheless, advancement of satellite remote sensing technologies and image processing techniques has improved the situation. One of the basic characteristics of optical remote sensing is the extensive use of quantitative algorithms for estimating natural resources. Recently, satellite imagery has become available at spatial resolution nearly comparable to aerial photographs, with the added advantage of digital multi-spectral information more complete even than those provided by digital orthophotographs (DOQs). QuickBird is one of the high resolution earth observation satellites (EOS) which is commercially available for Earth surveillance usage. QuickBird imagery consists of four different multispectral bands and one panchromatic band. The best resolutions for the multispectral and panchromatic bands of QuickBird imagery are 2.44 m and 0.61 m, respectively (DigitalGlobe Inc. 2002). Using pan-sharpen technique, the resolution for multispectral bands can be improved to 0.61 m.

Tan Sek Aun1, Nurul Fariza Abd. Azis2, nor Zahrah Suboh2, Azhan Idris1 & Rodziah Hashim1

Various image processing techniques have been introduced to improve results derived from remote sensing data. The technique used in this study is the Unsupervised Classification method or also known as clustering. Clustering technique is appropriate for classifying the types of features on Earth. The common clustering techniques are k-means, ISODATA, hierarchical, fuzzy c-mean and expectation maximum. ISODATA is the abbreviation for Iterative Self-Organizing Data Analysis Technique. It uses the minimum spectral distance formula to form clusters. The ISODATA utility repeats the clustering of the image until either a maximum number of iterations have been performed or a convergence threshold has been reached between iterations. (Erdas 1997). In this study, the rules of 10% canopy cover and area coverage not less than 0.5 ha were followed in order to apply the classification method.

OBJECTIVE

The main objective for this study was to apply the unsupervised classification to forested areas using high resolution QuickBird satellite image.

HIGH RESOLUTION IMAGE FOR FOREST CLASSIFICATION


METHODOLOGY

Study AreaForest Research Institute Malaysia (FRIM) is a regenerated secondary forest which is located 16 km northwest of Kuala Lumpur. Total coverage of the area is 530 ha and is divided into various zones according to the dominant plant species. This study was carried out in these zones which contain different dominant species.

DataThe remote sensing data used in this study is the multispectral and panchromatic QuickBird imagery. This data was acquired from the Malaysian Centre for Geospatial Data Infrastructure (MaCGDI) through its data sharing and exchange policy among government agencies. Besides the remote sensing data, the study also collected other forms of data and information such as Global Positioning System (GPS) and forest composition.

Data ProcessingFigure 1 shows the flow chart of the methodology applied in this study. The steps involved were image processing, ground observation and results analysis.

In order to verify the QuickBird data with ground features such as trees and buildings, the image was registered to the ground collected GCPs (ground control points). High accuracy Differential Global Positioning System (DGPS) device was used to collect the GCPs at FRIM compound. Table 1 shows the GCPs collected from the DGPS equipment.

Ground features such as road junctions and sharp building edges were collected as GCPs. These features were first identified from the QuickBird image before the GCPs were collected on the ground. The GPS-finder software was used to post-processed the GCPs for acquiring better accuracy. The QuickBird geocoding process was carried out using Image Geometric Correction function in ERDAS Imagine Version 9.1. The image projection was set to RSO Peninsular Malaysia map projection with Modified Everest as the spheroid and Kertau 1948 as the datum. The image was then subset to a smaller image window size which only covered the study site.

The Normalized Difference Vegetation Index (NDVI) was used to differentiate forested areas from the non-vegetation features such as buildings, bare land, cloud and shadow. NDVI is an indicator to represent the vividness of vegetation. Green or healthy vegetation will give higher values of NDVI. Non-vegetated features always show lower NDVI values. The general algorithm is shown as below:

Figure 1 : Methodology flow chart

Table 1 : DGPS collected GCPs

Points Easting Northing

#1 404527.01 357498.28

#2 404649.12 357854.52

#3 404174.08 357761.65

#4 404072.33 357940.75

#5 404333.57 357790.10

#6 404151.48 358102.29

⎟⎠

⎞⎜⎝

⎛

+

−=

RNIR

RNIRNDVI

where NIR = near infra red band R = red band

P re -p r o c e s si n g - Ge o met r ic corr e ct i on

N or m aliz e d D if f er e n c e Ve g eta tio n I n di c e s ( ND V I )

A na l y s is - Ac cur a cy

a s s e s s m e nt

Fie l d v eri f ic ati o n

U ns u pe r v i se d c la ssi f ic at i on

Re -a pp l y u n s u p er v i s e d cl a s s if i cat i on

Q ui c k B ir d I m a g e GC P s ( u s i ng D G P S)

M a s k ing

Sub s et

QuickBird Image

Subset

Masking

Unsupervied classification

Field

Re-apply unsupervisedclassification

Analysis - Accuracy

Normalized DifferenceVegetation Indices (NDVI)

GCPs (using dGPS)

Pre-processing-Geometric


The image was then masked to differentiate the high NDVI from low NDVI values. The masking process was applied by using modeller. The image was then classified using Unsupervised Classification (ISODATA) to differentiate various forest classes within the forested areas. Unsupervised classification technique is a computer-based process that requires no specific information to be supplied to the process for classification. The output will be generated based on default setting. ISODATA is an iterative technique for choosing a threshold to be applied in the classification process.

To improve the preliminary classification results, field verification was carried out in this study. The image was then re-classified and labelled based on ground information. The number of classes was reduced from ten to five classes, due to the highly heterogeneous conditions in the study area. Accuracy assessment was conducted for data analysis.

RESULTS AND ANALYSIS

In order to differentiate the non-vegetation features such as building, cloud and shadow from the forested areas, the QuickBird image was indexed and masked. The NDVI process was used mainly to separate non-vegetation features from forested areas. Meanwhile the masking process was applied to cluster all non-vegetation features into one single class in order to ease the classification process for forested areas. Consequently, the process will minimize the

misinterpretation between non-vegetation features and forested areas and thus increase the final results accuracy. Figure 2 shows the output of NDVI and masking process of non-vegetation and vegetation areas. Bright areas were the vegetated areas, while the black pixels, non-vegetation areas.

Figure 3 shows the preliminary unsupervised classification results with 10 classes including shadow and building; while Figure 4 shows the five classes map after the re-classification process. The 10-classes map is relatively difficult to be interpreted due to the patches information from the image classification. The map is relatively less helpful in forest management practices due to no detailed information such as species grouping. Thus, the numbers of classes were then reduced to five classes for better forest composition mapping. The map was re-classified into four different classes for forest species groups and one non-vegetation class such as buildings, cloud and shadow. The re-classification process was aided with the ground truthing and existing tree plot information.

Figure 3 : Classification using 10 classes

Figure 2 : NDVI and masking results


From the ground truthing observation, the re-classification map was labelled by the dominant tree species. The classes were labelled as kapur, mahogani, and meranti. The highly mixture forested area was labelled as “pure mixed trees”. The buildings, cloud and shadow were labelled as “unclassified features”. The cyan color represents the kapur class with a mix of other tree species, black represents unclassified features, green represents the meranti dominated group with a mix of other trees, yellow represents the mahogani with a mix of other trees and orange represents mix trees species (Figure 4).

Figure 5 shows the percentage of tree distribution for each tree species. Among the four forest classes, Class D had the highest coverage which was dominated by meranti species while Class B which was dominated by the mahogani species showed the lowest percentage of the tree distribution in the study area. Although the study area was a plantation forest, the results from this study showed that the area was a mixture of other tree species.

Figure 5 : Percentage graph of tree distribution

Figure 4 : After reduced classes

REFERENCES

DigitalGlobe, Inc. (2002). QuickBird imagery products: Product guide, Revision 3.3. Colorado’ Longmont.

Erdas. (1997).Erdas Field Guide. Georgia, Erdas, Inc.

J. R Jensen. (1996). Introductory to Digital Image Processing Second Edition, Prentice-Hall International Limited London.

Jindong Wu, Dong Wang, Marvin E. Bauer (2005), Image-based Atmospheric Correction of QuickBird Imagery of Minnesota Cropland. Remote Sensing of Environment. Pg. 315-325.

The overall accuracy for the re-classification in this study was 77.50% and the kappa statistic was 0.8182. Kappa coefficient is widely used in comparing the results of an image classification with results of a random classification (Erdas 1997). In this study, the 0.8182 kappa coefficient implied that the result was generated with the effective agreement of 81.28% in a completely random classification process. From the kappa statistic it can be concluded that high kappa coefficient shows that there are mutual agreements between reference data and the image texture data.

CONCLUSIONS

The overall classification accuracy in this study was relatively high with 77.50% using QuickBird satellite image. However, no individual tree species classification was performed due to the highly mixed heterogeneous conditions. Different remote sensing technologies and tools could be integrated in order to successfully classify the tropical rain forest. Better spatial and spectral resolution tools such as laser and hyperspectral are considerably useful to achieve the goal.

ACKNOWLEDGEMENTS

The author thanks the Malaysian Centre for Geospatial Data Infrastructure (MaCGDI) for the courtesy of QuickBird image. The author would also like to thank the staff of Geoinformation Unit (UGI) for their supports and help throughout this study.


Flashmapping.org is a collection of open source tools for Adobe Flash that supports the development of professional mapping applications. Flashmapping.org is a collection of open source tools for Adobe Flash that supports the development of professional mapping applications. Interactive maps on the web are in big demand these days. End of 2005 a big boom of GIS started since Google and Yahoo producing public map services and viewers. This boom creates already a higher demand from end users. Server based scripting for mapping services (such as .NET,.ASP) are not user friendly enough because of the constant page loads and the need to wait for the server after one user action. Every interaction with a map demands a request towards the server. These days there are several good solutions to achieve that. Solutions such as AJAX, JAVA clients and Flash macromedia are the most common options these days. They run on the client what makes the user experience much better.

Why Using Flash

The files are small.

Designers and developers can work easily on

the same project.

Flash has been for years the tool of designers.

It’s easier then learning .NET or JAVA.

You can build components and classes in flash.

Flash is the number one in add-on for browsers.

It’s platform independent. Linux, Windows, Unix,

Macintosh.

All major internet browsers can integrate the

flash plug in.

Backwards compatible to older browsers.

Flash has no difference on different platform

levels.

Integrating animation is easy.

Flash supports vector information.

source : www.flashmapping.org/

1Forest Research Institute Malaysia (FRIM)Email: [email protected]

2department of Remote Sensing, Faculty of Science Geoinformation and Engineering,Universiti Teknologi Malaysia.

John A. Richards. (1999). Remote Sensing Digital Image Analysis, 3rd edition, Springer.

Le Wang, Peng Gong, Geogory S. Boging. (2004). Individual Crown Delineation and Treetop Detection in High Spatial Resolution Aerial Imagery. Photogrammetric Engineering & Remote Sensing.

Le Wang, Wayne P. Sousa, Peng Gong, Geogory S. Boging. (2004). Comparison of IKONOS and QuickBird images for mapping mangrove species on the Carribean coast of Panama. Remote Sensing of Environment. Pg. 432-440.

Lund, H. Gyde (2006). Definitions of Forest, Deforestation, Afforestation, and Reforestation. Gainesville, VA: Forest Information Services.

M. Heurich, H. Weinecker. (2004). Automated Tree Detection and Measurements in Temperate Forests of central Europe using Laser Scanning Data. Internatioanl Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.

Robert A. Schowengerth. (1997). Remote Sensing Models and Methods for Image Processing, 2nd edition, Academic Press.


INTRODUCTION

Lembaga Hasil dalam negeri Malaysia (LHdnM) is strategically positioning its operations to improve the effectiveness and proficiency of its operations via advanced information technology. The development of LHDNM GIS system which was started in 2006 will assist all personnel, especially those that have of-site assignment, in carrying out their duties in more systematic, efficient and effective manner. The GIS is defined to be a system that would enable LHDNM to perform necessary querying, editing, reporting, analysis and maintenance administration in GIS environment based on the information residing in the LHDNM Data Warehouse. Manipulation of data and mapping the data to geographical location will make easy by using GIS system. It also provide assistance for LHDNM personnel in executing its compliance activities.

The main objective of LHDNM GIS system is basically to provide geographical information to LHDNM personnel. Having GIS system, the location of all information that was made available will be

LHDNM PILOT GEOGRAPHICAL INFORMATION SYSTEM (GIS)

Figure 1: Sample screen of LHDNM GIS

Mohamad Fauzi bin Saat

analyzed, displayed and mapped accordingly for the purpose of conducting various compliance activities such as;

• business survey for expansion of tax base; • audit function; and • revenue collection.

The proposed implementation of the LHDNM Pilot GIS system shall covers the area of Majlis Perbandaran Petaling Jaya (MPPJ) that is under the jurisdiction of Petaling Jaya LHDNM Branch Office. Subsequent location shall be done in stages based on project extension or availability of location data layer. To establish a complete GIS database for the whole nation will be very time consuming as the data belongs to various external authorities. To gather all those data may require to follow reacquisition process and it is dependent on the availability of the data since not all the areas or towns having the same level of information detail and some areas may not yet to have the digital cadastral lot.


SYSTEM OVERVIEW

LHDNM GIS system application requires two sets of data; namely tax payer information data and spatial data (geographic and location data). As part of LHDNM Data Warehouse Project, the LHDNM pilot GIS system is depending on tax payer information data from various internal LHDNM systems (DB2

The overview of GIS system in the environment of LHDNM Data Warehouse Project is explained by the following diagram:

Figure 2: Data Synchronization between Oracle Spatial and DB2 Data Warehouse

Figure 3: Overview of the LHDNM Pilot GIS System in Data Warehouse Environment

LHdn GIS System data Warehouse Application

Oracle

Spatial

database

dB2 data Warehouse

database

Ar cSd E

Se r ve r

d a ta Extr a ctio n & Syn ch r o n izin g C o m p o n e n ts

`

LHdn GIS C lient

Ba tch M o d e

Data Warehouse) as an input to GIS application. The Oracle Spatial Database on the other hand creates, maintain and manage the spatial and textual data requirements. The following diagram depicts the synchronization between DB2 Data Warehouse database and the Oracle Spatial database.


GEO-CODING LEVEL

The Geo-coding level will determine the LHDNM GIS application zooming level. Geo-coding level (zooming level) determination of tax payer in this pilot project can be achieved base on: 1. Lot Level 2. Road Level 3. Enumeration Block Level

Base on this zooming level, the higher the zooming level, the better application will serve the LHDNM needs. The Ultimate aim is to geocode the tax payer up to lot level. However, some constraints are –

• Mechanism to find the most practical (automated) way to geo-code the tax payer information with the spatial lot;

• There are no direct links between the tax payer information from Data Warehouse with the cadastral lot map available at JUPEM or local authorities;

• The availability of data varies from one state to another state, one town to another town. Not all towns in Malaysia have digital cadastral lot and some that have might not be up-to-date.

• Therefore, the highest zooming level that can be perform automatically for the pilot project is up to road level which both of the databases contains road information – the tax payer information in Data Warehouse contains road information in their address system and in GIS layers, the road layers is available from Majlis Perbandaran Petaling Jaya (MPPJ).

The following are the possible approaches for tax payer geo-coding in the LHDNM GIS pilot project:

1. Corporate and Sole Proprietor Tax payer:• Using State as the national level of

grouping.• Using post-code boundary or mukim

boundary to group within state level.• Using commercial area/factory area

to narrow down to the smaller area coverage.

• Using road to further locate the company location.

• Using lot number (if Data Warehouse data contain the information) to geo-code the tax payer information with each of cadastral lot.

2. Individual (SG group)

• Using enumeration block map.• Using I/C No. to map the individual tax

payer with location.• Or using tax payer postal address to map

back to the State, district, mukim, and road or lot number of the geo-database information.

• Zoom level – up to enumeration block (80 – 100 tax payers per block).

Figure 4: Example screen of LHDNM GIS Zooming Level


BASE LAYER

The LHDNM Pilot GIS system shall utilize the base layer taken from the following authorities such as;

• Cadastral Lot - JUPEM/Local Authorities; • Road – JUPEM/JKR; • Enumeration Block – Statistic Department;

and • Administrative Boundary – JUPEM.

As for the pilot implementation the following set of base layer will be created for the LHDNM Pilot GIS system usage:

• Country Boundaries covers Semenanjung Malaysia, Sabah and Sarawak.

• State Boundaries • District Boundaries • LHDNM Branch Boundaries • LHDNM Zone Boundaries • Postcode

However, additional GIS layer can be added using the Common Utility Module by System Administrator as when the layer is required in the future.

FUNCTION AND BENEFIT OF LHDNM GIS SYSTEM

With the implementation of LHdnM GIS system, it is expected that the solution can meet LHDNM immediate needs of geographical visualization and analysis aid. Overall, all tax payer information which

Figure 5: Search the tax payer by tax payer’s location address

is available at LHDNM Data Warehouse database can be displayed in geographical manner and the system will be able to:

• Locate taxpayer; • Be used as one of strategic planning tools in

locating high potential areas of operations; • Identify areas with high potential taxpayer,

but low in tax registration; • Display tax-payer location and other tax

payer attributes using GIS; • Display information about premise tenant and

premise owner profile; • Show type of industries in selected area; • Display the tax payer density information

within the area; • Display the high risk taxpayer information in

geographical manner.

With all the functionalities and capabilities, LHDNM GIS system will help managers:

• To plan and program the compliance activity; • To strategize and develop the action plan for

compliance activity; • To plan, execute, observe and develop

computerization and operation system in relation to the compliance activity; and

• To control and monitor the execution of compliance activity.


The GIS system allows the users to locate tax payer according to the identified location according to the attributes shown in the below Table 1.

NO Attributes Description

1 Region Search the tax payer by LHDN region.

2 Zone Search the tax payer by LHDN zone.

3 Branch Search the tax payer by LHDN branch.

4 Tax Payer Name Search the tax payer by tax payer’s name itself.

5 Address Search the tax payer by tax payer’s location address.

6 Post Code Search the tax payer by tax payer’s address postcode.

7 State Search the tax payer by tax payer’s address state.

8 Industry Type Search the tax payer by tax payer’s type of industry.

9 Land Use Type Search the tax payer by tax payer’s land use type.

10 Income Wages From Search the tax payer by tax payer’s income which de

termines by range.

From certain amount to certain amount.

11 Income Wages Search the tax payer by tax payer’s income which de

termines by range. From certain amount to certain famount.

12 Year Of Assessment Search the tax payer by tax payer’s year of

assessment.

13 Have Report Search the tax payer by tax payer’s report whether

have report or not.

14 Total Report Search the tax payer by tax payer’s total number of

reports.

15 Registered In LHDN Search the tax payer by tax payer’s registration with

LHDN whether registered or not registered.

16 File Type Search the tax payer by tax payer’s LHDN file type.

17 Compliance With Search the tax payer by LHDN form compliance.

18 LHDN File No Search the tax payer by tax payer’s LHDN file type.

19 Total Payroll Search the tax payer by tax payer’s total payroll.

20 Registered In SSM Search the tax payer by tax payer’s registration with

SSM whether registered or not registered.

21 Business Registration Search the tax payer by tax payer’s business

registration.

Example: business as sharing

22 Business Type Search the tax payer by tax payer’s business type.

Example: mini market

23 Registration Year Search the tax payer by tax payer’s year of registration in SSM.

Table 1: GIS system allows the users to locate tax payer

Jabatan PematuhanLembaga Hasil dalam negeri MalaysiaEmail : [email protected]


Mohd. Hasmadi Ismail

DETECTING AND MAPPING OF LANGAT RIVER AND ITS TRIBUTARIES USING REMOTE SENSING

Abstract

Rivers are precious living systems and it is important that these resources be protected to ensure continuous and safe

utilization, both now and in future. Land denudation is perhaps the most basic culprit of all river pollutants. Agriculture,

mining, industrial, urbanization and improper forest harvesting are major potential causes of degradation to the river

system. Sound management of land use is fundamental both to preserve stable river ecosystem and control pollution.

Satellite remote sensing data provides an economic and excellent starting point for mapping and monitoring our river

system and their changes. Remote sensing data also act both as a base map and thematic map, which are useful for

national and regional planning. This study was carried out to test the capability of Landsat TM to map the Langat River and

its tributaries as well as the surrounding areas in an attempt to extract vital information necessary for river management.

This paper also describes how Landsat TM together with specific image processing method can be used for Langat River

mapping and management of its surrounding watershed.

INTRODUCTION

The river system in Malaysia is an integral part of the water resources system. There are more than 1,800 rivers and 100 river systems in Peninsular Malaysia and 50 river systems in Sabah and Sarawak. River systems as a whole, with or without impounding reservoir, are estimated to contribute almost 96 percent of raw water supply source. Moreover, about 97 percent of the required water comes from forested areas in the headwater ridges (Mohd. Hasmadi and Kamaruzaman, 1999). Water has always been a significant factor in the development of any area since long time ago where the first settlements were usually located near water bodies or river. When population expands and the consequent demand for land increases, more water development had been planned to fulfill this society’s demand. Agriculture, mining, industrial, urbanization, recreation and forest harvesting are major potential causes of degradation to the river systems.

The contribution of remotely sensed data to the management and development of forest resources in Malaysia has been significant (Kamaruzaman and Haszuliana, 1998). Hence, the possible use of satellite imageries for mapping and monitoring river pattern in the country is being tested. Satellite image data enable direct observation of the earth’s surface at repetitive intervals and therefore allow mapping of the extent, and monitoring of the changes in land use

(Mohd. Hasmadi and Kamaruzaman, 1999). Suroso (1985) stated that the advancement of computer aided remote sensing technology with computer aided has not only enhanced the capability of handling data but has also given a powerful information which consist of spatial and geo-thematic information that can be used in management of land resources.

Satellite remote sensing is an evolving technology with the potential for contributing to studies of the river and water change by making globally comprehensive evaluations of many human actions possible. Moreover, satellite remote sensing data provides an economic and excellent starting point for mapping and monitoring of our river system and their changes. Remote sensing data also act as both a base map and thematic map, which are useful for national and regional planning. This study was carried out to test the capability of Landsat TM to map the Langat River and its tributaries as well as the surrounding areas in an attempt to extract present vital information necessary for river management.

METHODOLOGY

Description of Study Area

The study area is located in the South-East of Selangor Darul Ehsan. The area is situated within latitudes 1010 46’E to 1010 59’E and longitudes 020 58’N to 030 17’N. This area was chosen as a study


The climate of Hulu Langat area is typically tropical. The mean annual temperature is 320C with a mean maximum and minimum of 330C, respectively. However, the mean temperature at night ranges from 240C to 280C. Annual precipitation in the area is approximately 2,316.5 mm – 4,223.4 mm. Rainfall mainly occurs in February to April and September to November.

Generally, the physical features of the Hulu Langat area are distinctive with undulating lowland, hill and mountains culminating in the highest mountain of Gunung Nuang (4,899m). The mean elevation is 21m Above Sea Level (a.s.l) and the maximum relief of the area is over 1000m. The catchments fall into the hydrological regional of Kuala Krai-Ulu Langat as demarcated by the Drainage and Irrigation Department (Goh, 1974). Batangsi River, Congkak River, Lawing River and Lui River are the main streams in this basin. The four streams are tributaries of the Langat River

Figure 1: Location of study area

whose headwater drain the western flank of the main range and flow southwest into the Straits of Malacca. Another stream is Semenyih River which is located at the South of the study area.

Method

Landsat TM images scene 127/58 (path/row) of 8 February 1998 was used for the study. The scene has less than 5 percent cloud cover and was obtained from the Malaysian Centre for Remote Sensing (MACRES) in the form of computer disk with a spatial resolution of 30m. The TM data was initiated by selecting the representative subsection of the scenes that was covered by the experimental area. The ancillary data used in this study were topographical (1:50,000) and land use maps (1:50,000). These maps were used as a preliminary abstraction of land use information and assist in image interpretation. The landsat TM data was processed using a computer based PCI/EASIPACE, available in the Faculty of Forestry, Universiti Putra Malaysia for digital analysis and classification. The work flow is illustrated in Figure 2.

site because of the existing natural resources and two important namely Semenyih Dam and Langat Dam. Figure 1 illustrates the general river system available in the surrounding watersheds.


Image Processing

The Landsat TM image was displayed using the true color composite with a combination of several false colors composite (FCC). Using histogram equalization, contrast enhancement and spatial filtering, the image was manipulated to generate informative data set(s). Mode filtering and Edge filtering of 3 x 3 sizes was performed to reduce the noise and improved the data quality to assist data interpretation. In this pre processing steps, clouds showed a higher irradiance value than ground data. Moreover, the amount or irradiance varies depending on whether the clouds are white, gray, black or combination of different shades. This factor adversely influenced the classification and analysis of imagery data. However, this problem was minimized by creating shadow mask using a histogram based on data derived from Channel 1, 2 and 3 TM.

Image Interpretation

In the visual interpretation method, different types land uses are separated depending on the tonal variation. In addition, the visual interpretation technique is subjective and depends on the field knowledge and aptitude

Figure 2: The Diagram of Study

of the interpreter. Image enhancement produced a new enhanced image that is display on False Color Composite for visual interpretation. By using linear enhancement, image was clearly displayed and easy to classify and interpret than the original image. In the visual interpretation method different types and coverage were separated depending on the total variation. The visual interpretation techniques have been widely used for forest covering mapping (Anon, 1983: Anon, 1993: Porwal et. al., 1994). However, the visual interpretation technique is subjective and highly depends on the field knowledge and aptitude of the interpreter. The visual interpretation of Landsat TM data identified road, river, plantation and other land cover types. This image also provided land cover type boundaries, and the areal extent of roads and rivers. Figure 3 showed the result of visual interpretation using 5-4-2 (RGB) channel to be used in land cover types classification.

Ground verification was conducted on 24 and 25 February 1999 to verify the results obtained from the preliminary image analysis, and to determine the accuracy of assessment. GPS-Trimble GeoExplorer II was used to locate the actual land cover of the study area based on longitudes and latitudes.


Figure 3: Visual Interpretation using Channel 5-4-2 (RGB) of Landsat TM

Figure 4: Land Cover Map 1998 of Hulu Langat, Selangor after Supervised Classification

RESULT AND DISCUSSION

Land Use Classification

Land use cover was carried out using supervised classification technique. After the training areas were selected, statistical calculation was done. The classifier used in this classification was the Maximum Likelihood Classifier (MLC). Figure 4 showed the land cover classification result of the study area.


Figure 5: Edge Detection Technique showed the Extraction of River Pixel from Landsat TM Image

Table 1: Confusion Matrix for Five Classes of Land Cover in Hulu Langat

Apart from the advantages of using this data, there are two major problems encountered such as to differentiate some land cover such as rubber and oil palm (Hassan et al., 1997) and orchards (Mohd. Hasmadi, 1998) which are having almost similar spectral responses. The other ones are difficult to discriminate the river tributaries and skid trail in the forest. The accuracy of classification was assessed on a pixel basis and an error matrix was computed (Card, 1992; David and Goetz, 1990; Congalton, 1991). The numbers of correctly classified pixels, meaning the major diagonal in the error matrix for the five classification result are listed in Table 1. The data was processes to enhance the classification accuracy for all land cover classes with the error matrix; an overall accuracy of about 87.69 percent was archived. The overall accuracy is higher than the minimum accuracy value of 85 percent which is the basic requirement for digitally classified image (Paul, 1991). From the classification, it can be deduced that forest areas were over exploited principally for agriculture (oil palm and rubber) and resettlement.

River Mapping

As river appear black or dark, edge detection technique is quite suitable to extract river pixels from the image (Figure 5). Meanwhile, by using band 2 of Landsat TM with linear enhancement and contrast stretching, river segments appears as white but two of the dams were not well separated. As river display a linear pattern, it implies that applying line-edge detector using 3 x 3 and 5 x 5 processing windows is more appropriate. In this study the main river such as Lui River, Langat River

Theme

Forest

Agriculture

Urban/Cleared Land

Water

Clouds

Percent Pixels Classified by Class

Accuracy (%) 1 2 3 4 5

89.37 89.37 9.98 0.61 0.03 0.01

76.45 17.33 76.45 6.22 0.00 0.00

71.13 0.21 28.24 71.13 0.00 0.42

96.50 1.40 0.70 1.40 0.00 0.00

99.06 0.00 0.30 0.63 0.00 99.06

Class

1

2

3

4

5

Pixels

7576

1189

478

143

640

Average Accuracy 86.50%

Overall Accuracy 87.69%

Kappa Coefficient 0.72412

Standard Deviation 0.00721

Confident Level 99 +/- 0.01859

95 +/- 0.01412

90 +/- 0.01185

and Tekali River are easily detected in the scene. Small rivers like Pangsun River, Chongkak River, Peredak River and Mukau River are also quite easily identified. Unfortunately, the other tributaries like Lawing River, Segambir River, Limbat River, Pagoh River, S. Rephen, Semungkis River and etc. are difficult to identify since they size factor are very small. River network extraction consisted of edge detection followed by the construction of river segments through delineating were illustrated in Figure 6.


Characterizing river in satellite imagery is a very hard task due to the low resolution of satellite imagery such as those of the Landsat TM. According to Ichoku et al. (1996) computerized detection of one or more specific features from such images are not an easy task. The process of the noise in the image and the gap between river pixels caused

Figure 6: Landsat TM Data Showing the Main River and Their Tributaries in Hulu Langat, Selangor

by overhanging trees in the river make the linking process of river pixel very complex. Any water bodies less than 5 m wide will not be immediately detected due to the canopy closure which tends to cover up the water surfaces. By using ancillary map (topography map), some small rivers can be delineated.


CONCLUSION

In order to extract a complete river network, its tributaries segments need to be linked. Unfortunately, linear features extracted in the study are not capable to connect all rivers. Some of them may be roads, mountain valley’s skid trail and others. It can be concluded that thematic maps extracted from satellite imageries provide a vital source of information for river network mapping and monitoring, especially by the river authorities and town councils. The river network were delineated based on color separation on the TM image through visual interpretation and were corrected by combination of digital analysis of TM data and the data obtained from ground verification. Although there are some limitations of this technique, the results obtained can give a general view of the river system in Langat River basin. Development of Hulu Langat District is expected to increase in commencement of agro-based industrial operation and development of urban centers. This rapid development has caused a great change on the river quality such as discharged polluted water to the river from industrial activities. Hulu Langat and its surrounding hill areas are very important source of water supply for downstream users, as part of any new initiatives, an integrated programme of process hydrology needs to be implemented for a better understandings of within drainage basin effects arising from forest area manipulation or conversion.

RECOMMENDATIONS

Several recommendations that can be deduced from this study area as follows:

1. Higher classification accuracy can be achieved by using higher resolution of satellite data such as SPOT where accurate measurement of land cover classification can be obtained.

2. The use of other software such as ENVI, ER Mapper, ERDAS and ILWIS are necessary for comparison purposes.

3. Future research should emphasis on the effectiveness of the satellite data to predict river quality such as sediment impounding and consider the response of rivers in term of scour and deposition by any new development area.

REFERENCES

Anon, 1993. Rehabilitation of Logged Over Forest in Asia/Pacific Region. Final Report of Sub Project II International Tropical Timber Organization-Japan Overseas Consultant Association, pp 1-79.

Anon, 1983. Nationwide Mapping of Forest and Non Forest Areas using Landsat False Color Composite for the Period 1972-75 and 1980-82. National Remote Sensing Agency, Hyderabad, Technical Report, Vol. 1, pp 1-36.

Card, D. H. 1982. Using Known Map Category Marginal Frequencies to Improved Estimates at Thematic Map Accuracy. Photogrammetric Engineering and Remote Sensing, 48: 431-439.

Congalton, R. 1991. Review of Assessing the Accuracy of Classification of Remotely Sensed Data. Remote Sensing Environment, 37: 35-46.

Davis, F.W. and Goef, Z.S. 1990. Modelling Vegetation Pattern Using Digital Terrain Data. Landscape Ecology, 4: 69-80.

Goh, K.S. 1974. Hydrology Region of Peninsular Malaysia. Drainage and Irrigation Department, Ministry of Agriculture Malaysia. Water Resources Publication. No. 2, Kuala Lumpur, Malaysia.

Hassan. Z.A, Ramli, K.M.M, I. Selmat, and K. F. Loh. 1997. Complementary Nature of SAR and Optical Data for Land Cover/Use Mapping in the State of Johore, Malaysia. Prof. of the 18th Asian Conference on Remote Sensing, 20-24 October 1997, Hotel Nikko, Kuala Lumpur. pp F1-1-F1-9.

Ichoku, C, Karnieli, A, Meisels, A and J. Chorowicz. 1996. Detection of Drainage Channel Networks on Digital Satellite Images. Int. J. of Remote Sensing, 17, 9: 1659-1678.

Kamaruzaman Jusoff and Haszuliana Mohd. Hassan. 1998. An Overview of Satellite Remote Sensing for Land Use Planning with Special Emphasis in Malaysia. J. remote Sensing Reviews, (16): 209-231.

Mohd. Hasmadi Ismail and Kamaruzaman Jusoff. 1999. The Potential of Remote Sensing/GIS


Technology in Watershed Management. Paper Presented at Seminar of Water: Forestry and Landuse Perspective. 31 March – 1April 1999, Forest Research Institute of Malaysia (FRIM), Kepong, Kuala Lumpur, Malaysia. 19p.

Mohd. Hasmadi Ismail and Kamaruzaman Jusoff. 1999. Use of Satellite Remote Sensing in Forest Resource Management in Malaysia. Paper Presented at Second Malaysian Remote Sensing and GIS Conference, 16-18 March, 1999, ITM Resort & Convention Centre, Shah Alam, Selangor, Malaysia. 24p.

Mohd. Hasmadi Ismail. 1998. Integration of Remote Sensing and GIS Technique for Urban Forest Landscaping at Kuala Lumpur International Airport (KLIA), Sepang, Selangor. B. Sc. (Forestry) Thesis, Faculty of Forestry, Universiti Putra Malaysia, Serdang, Selangor, Malayisa. 114p.

Paul, M. 1991. Computer processing of Remotely Sensed Images. An introduction. Biddley Limited Publication. 352p.

Porwal, M.C., Dabral, S.L and Roy, P.S. 1994. Revision and Updating of Stock Maps using Remote Sensing and GIS. Proc. ISRS Silver Jubilee Symposium, Dehradun, India, pp: 334-342.

Suroso. 1985. The Application of Remote Sensing in Support of Land Use Planning for the Development of Physical Agricultural Infrastructure (Case Study of Asahan River Basin, North Sumatera, Indonesia). Proc. Workshop on Land Use Planning in a Watershed Context, 17-24 April 1885, Jakarta, Indonesia. Pp: 78-119.

Survey and Engineering LaboratoryDepartment of Forest Production, Faculty of ForestryUniversiti Putra [email protected]

Calculating the Distance Between Points

Carter Vickery, our local GIS guru offers expert advice on using ArcGIS to create maps and do spatial analysis. Scenario: You have a set of points. You’d like to know the best way to calculate the distance between points found in a layer and a specific point (other than using the measure tool). For example, if these points represent cities, and you would like to know the distance between each of the cities and the capital, this tutorial will illustrate how to calculate the distances.

http://www.mentorsoftwareinc.com/CC/gistips/TIPSarch.HTM

http://www.planweb.co.uk/tip1.htm

http://www.urban-research.info/resources/GIS%20Skill%20Builder-distance1.shtml

GIS TIPS


PERSIDANGAN DAN PAMERAN ANTARABANGSA MAP ASIA 2007,DI KUALA LUMPUR CONVENTION CENTRE (KLCC),KUALA LUMPUR PADA 14 HINGGA 16 OGOS 2007

LATAR BELAKANG

Persidangan dan Pameran Antarabangsa Map Asia adalah siri Persidangan dan Pameran dalam bidang Geographic Information System (GIS) di peringkat antarabangsa yang dianjurkan secara tahunan. Pada tahun ini ia telah diadakan di Kuala Lumpur dari 14 hingga 16 Ogos 2007 dan dianjurkan secara bersama di antara Kementerian Sumber Asli dan Alam Sekitar (NRE) dengan GIS Development, India. Fokus utama Persidangan dan Pameran ini adalah kepada perkembangan dan penggunaan teknologi GIS dalam mentadbir urus perancangan dan pembangunan negara serta kaedah-kaedah pemetaan yang terkini yang berkaitan. Sasaran jumlah peserta persidangan dan pameran adalah 1500 orang peserta dari dalam dan luar Negara.

Di Malaysia, Jabatan Ukur dan Pemetaan Malaysia (JUPEM) dan Pusat Infrastruktur Data Geospatial negara (Malaysian Centre for Geospatial data Infrastructure) (MaCGDI) merupakan agensi penting di bawah NRE yang saling menggembleng tenaga

menggunakan GIS, Global Positioning System (GPS) dan Remote Sensing untuk memangkin perancangan dan pembangunan negara serta mencapai visi NRE. NRE melalui Jabatan dan Agensi berkenaan sangat giat mempromosikan penggunaan ketiga-tiga teknologi ini bagi semua pengurusan dan pentadbiran alam sekitar serta sumber asli. Seterusnya, penggunaannya dikembangkan ke lain-lain agensi pembekal dan pengguna data GIS di Malaysia bagi membawa perubahan dan mempertingkatkan jentera perkhidmatan awam ke tahap yang lebih efisien.

Persidangan ini merupakan platform di mana berbagai komuniti bertaraf superlative yang terlibat dengan penyediaan dan penggunaan maklumat geospatial mempamerkan, berinteraksi dan bertukar fikiran serta mempersembahkan aktiviti dan perlaksanaan GIS, GPS dan Remote Sensing.

Y.B Dato’ Seri Azmi bin Khalid sebagai tetamu kehormat Persidangan dan Pameran Antarabangsa Map Asia 2007,

Kuala Lumpur.

Yaacub bin Yusoff

UCAPTAMA Y.B. MENTERI Y.B. Menteri menyampaikan ucapan terima kasih kepada GIS Development, India selaku penganjur kerana telah memberikan kepercayaan kepada Kerajaan Malaysia melalui NRE untuk menjadi tuan rumah kepada Persidangan Map Asia 2007. Selaras dengan hasrat kerajaan mempromosikan Malaysia sebagai destinasi pelancongan dunia lebih-lebih lagi sekarang adalah tahun melawat Malaysia. Di samping itu, Kerajaan Malaysia sememangnya memberikan keutamaan kepada perkembangan teknologi ICT yang akan memacu negara ke arah taraf negara maju dalam tahun 2020.


Y.B Dato’ Seri Azmi bin Khalid memotong reben bagi merasmikan pameran GIS, GPS dan Remote Sensing di

Dewan Pameran

PERSIDANGAN DAN PAMERAN

Persidangan ini yang terdiri daripada sesi-sesi seminar, telah memberi banyak faedah dari segi pendedahan kepada pembangunan dan perancangan terkini teknologi geospatial oleh pakar-pakar dari negara membangun dan negara maju. Pembentang-pembentang kertas kerja telah mempersembahkan pengalaman dan kajian kes yang baik untuk diikuti oleh para peserta. Sesi seminar telah mempamerkan pengalaman dan kesungguhan para profesional dan pakar yang telah bergiat lama dalam penyelidikan dan pembangunan di dalam bidang GPS, GIS dan Remote Sensing dan organisasi yang berjaya mempeloporinya.

Kebanyakan kandungan kertas pembentangan mempunyai kaitan rapat dengan core business yang dijalankan oleh JUPEM, MaCGDI dan juga Jabatan-Jabatan di bawah NRE. Para peserta boleh meningkatkan keupayaan mereka dengan mengambil iktibar dan pengetahuan terhadap idea dan kajian kes yang dibentangkan. Di samping itu, nama pakar GIS dan Remote Sensing serta organisasi GIS yang dikenali dapat digunakan untuk rujukan dan pertanyaan jika berbangkit selepas menghadiri persidangan ini.

Sementara itu pula, sesi pameran telah menyaksikan penyertaan syarikat-syarikat antarabangsa dan

Y.B. Menteri menyatakan bahawa Map Asia diperakui sebagai persidangan dan pameran terbesar Asia dalam bidang Geographic Information System (GIS), Global Positioning System (GPS), Aerial Photography dan Remote Sensing di rantau Asia Tenggara dan Pasific. Persidangan Map Asia adalah platform kepada komuniti di rantau Asia bagi memanfaat, mengiktiraf dan mewar-warkan kemajuan penyebaran maklumat geospatial. Sehubungan itu, ia juga dapat menjana keperluan masyarakat geo-informasi supaya bersatu dalam perkongsian pengunaan teknologi maklumat geospatial.

Y.B. Menteri juga menyentuh keperihatinan Malaysia mengenai isu alam sekitar seperti pemanasan global, pelupusan toksid, biological diversity perlu ditangani secara berhemah dan bertanggungjawab. Tema Persidangan Map Asia 2007 adalah ‘Maponomics’ iaitu gabungan perkataan pemetaan dan ekonomi yang memberi maksud merangsang ekonomi melalui teknologi geo-informasi yang berperanan memajukan ekonomi negara melalui keputusan dan polisi secara good governance yang menjurus kepada K-economy.

Seterusnya Y.B. Menteri telah merasmikan Pameran GIS, GPS dan Remote Sensing di ruang Dewan Pameran. Y.B. Menteri telah diiringi oleh Dr. Krishnaswamy Kasturirangan, Ahli Parlimen India untuk mengadakan lawatan ke tapak pameran. Y.B. Menteri telah diberi pendedahan dengan berbagai teknologi, perisian, aplikasi dan showcase yang menarik dan inovasi di tapak pameran.


tempatan yang bergiat hebat dalam perisian dan aplikasi teknologi informasi geospatial. Banyak manfaat dan pendedahan kepada pelawat telah dapat diambil bagi mengenal pasti dan memperbaiki teknologi dan penyelidikan yang dijalankan oleh agensi pelawat.

Di dalam Sesi Key Notes, seramai 15 orang pakar dari dalam dan luar negara dari syarikat dan organisasi berprestij telah membentangkan kertas-kertas yang bertemakan Asian Economics: The Spatial Perspective, Infrastructure Development and Engineering, Technology Trends dan Return on Investment (ROI) in Geo-Spatial Technology Applications. Sesi teknikal pula telah menyaksikan pembentangan 61 kertas penyelidikan dan 24 poster yang telah dibahagi kepada tema-tema seperti Earth Resources Management, Space Program and Remote Sensing, Water Resources, Disaster Mitigation and Management, Photogrammetry 3D Visualisation, Web and Internet GIS, Education, Demographic Studies and SDI, Land Information and Infrastructure Management, Urban and Town Planning, Marine and Coastal GIS, Environment, Forestry and Precision Farming, Technology: Trends and Applications dan Surveying and Global Positioning & Navigation Systems.

Sebanyak dua (2) bengkel telah diadakan. Bengkel pertama menumpu kepada isu dan halatuju teknologi pengumpulan data geospatial menggunakan Light Detection and Ranging (LIDAR) yang dianjurkan Syarikat AAMHatch, Malaysia, manakala bengkel kedua membincangkan permasalahan dalam proses

pemantapan Spatial Data Infrastructure (SdI) anjuran MaCGDI, di mana lima (5) negara telah membuat pembentangan.

Bagi sesi Technology Track telah mempamerkan projek, perisian dan aplikasi yang dibangunkan oleh syarikat-syarikat gergasi geospatial. Tiga (3) buah syarikat berkenaan yang terlibat adalah merupakan leader dalam teknologi informasi geospatial iaitu ESRI, Digital Globe dan Fugro Earth Data.

Syarikat Bentley Systems telah mengadakan satu perjumpaan meja bulat untuk membincangkan isu dan masalah kini teknologi informasi geospatial. Syarikat Bentley telah menunjukkan potensi untuk berkecimpung dengan komuniti geospatial di Malaysia.

Sebanyak 46 syarikat dan organisasi dari dalam dan luar negara telah menyertai booth pameran. Pameran ini telah disertai oleh syarikat-syarikat bertaraf world-class dan berpengalaman tinggi dalam memajukan teknologi informasi geospatial. Beberapa showcase, produk, perkhidmatan, nasihat pakar telah ditunjukkan di tapak pameran yang telah dapat menarik ramai pelawat untuk melihat sendiri teknologi terkini dan kemajuan yang dicapai. Pameran berkenaan telah mengambarkan trend pasaran dan komuniti geospatial yang ada di serata dunia.

Keseluruhannya jumlah kehadiran delegasi sebanyak 1491 orang yang telah menyertai Persidangan dan Pameran Antarabangsa Map Asia 2007 dengan pecahannya terdiri daripada 39 buah negara dengan 580 orang peserta, 84 orang pembentang, 71 orang pelajar, 64 orang peserta jemputan, 527 orang pelawat luar dan 165 orang untuk sesi pameran.


i) Mendapat pendedahan yang meluas berkaitan teknologi geospatial terkini dan perkembangan aplikasi GIS, GPS dan Remote Sensing;

ii) dapat menimba ilmu melalui pengalaman-pengalaman serta maklumat pembentangan kajian kes dari dalam dan luar negara yang boleh dijadikan sebagai sumber rujukan;

iii) Dapat meningkatkan jaringan di peringkat antarabangsa iaitu antara negara-negara maju dan membangun sambil dapat bertemu dan berinteraksi secara langsung dengan pengguna-pengguna aplikasi GIS, GPS dan Remote Sensing di serata dunia;

iv) dapat menimba pengalaman perkongsian penggunaan aplikasi teknologi geospatial di beberapa peringkat pengurusan bencana, penjagaan alam sekitar dan pengurusan sumber asli serta berpeluang didedahkan kepada kaedah praktikal terbaik untuk mengawal bencana, penjagaan alam sekitar yang lestari dan pengurusan sumber asli yang moden dan canggih; dan

v) Dapat mencetuskan idea/cadangan penambahbaikan yang praktikal untuk meningkatkan infra-struktur pangkalan data spatial dalam organisasi dan negara sendiri.

FAEDAH DARI PENGANJURAN PERSIDANGAN

Melalui maklumat yang telah diperolehi pada persidangan ini, antara faedah-faedah yang telah dicapai oleh para peserta adalah seperti berikut:

KESIMPULAN

Persidangan dan pameran Map Asia 2007

yang dianjurkan bersama NRE telah mencapai

matlamat yang dikehendaki. Antaranya ialah

jumlah penyertaan yang ditetapkan sebanyak

1500 penyertaan, dan juga faedah dan

pendedahan yang comprehensive mengenai

teknologi maklumat geospatial telah dapat

dicapai. Penyertaan dari delegasi amat baik dan

kebanyakan pembentangan kertas kerja dan

sesi technology tracks telah mendapat sambutan

audience yang baik walaupun telah dijalankan

secara serentak di beberapa bilik persidangan

(parallel session).

Melalui sesi-sesi pembentangan kertas keynote dan

teknikal yang bertemakan ’Maponomics’, kesedaran

dan pendedahan terhadap pentingnya kaedah

pemetaan dan teknologi informasi geospatial yang

dikaitkan dengan perkembangan dan pembangunan

ekonomi telah dapat diterima oleh delegasi yang

hadir. Pembangunan infrastruktur negara, sumber

asli dan kesejahteraan alam sekitar tidak boleh

dipisahkan dengan pengumpulan hasil negara dan

ekonomi nasional, dan semua pembangunan ini

memerlukan informasi geospatial dan teknologi

yang digabungkan dengannya untuk menuju ke arah

yang lebih berinovasi dan berteknologi tinggi.

Pusat Infrastruktur data Geospatial negara (MaCGdI)Kementerian Sumber Asli dan Alam Sekitar (NRE)E-mel: [email protected]


Taklimat ini telah diadakan di Tabin Wildlife Resort, Lahad Datu, Sabah pada 22 – 27 Februari 2008

Seminar ini telah diadakan di Hotel Impiana Casuarina, Ipoh Perak pada 20 – 21 Februari 2008.

Taklimat MyGDINegeri Sabah

Galeri Foto

Seminar MyGDI Negeri Perak


Program ini telah berlangsung di Dewan Tun Abdul Razak, Baling, Kedah selama 4 hari bermula dari 15 – 18 Februari 2008 dan dirasmikan oleh Y.B. Datuk Dr Mashitah bt Ibrahim.

Pameran ini telah diadakan di Pusat Dagangan Dunia Putra (PWTC) pada 21 – 22 November 2007.

World TownPlanning Day 2007

Program Perkampungan Sains, Teknologi dan Pendidikan Baling 2008


Lawatan Sambil Belajar Peserta Kursus “Environment

Protection For CLMVT Countries” Anjuran Bersama

INTAN/JICA ke MaCGDIpada 6 Disember 2007

Lawatan Sambil BelajarPelajar-Pelajar Bachelor

REMOTE SENSING dan GIS Universiti Putra Malaysia (UPM)ke MaCGDI pada 6 Ogos 2007

Lawatan Sambil Belajar Peserta Kursus “Integrated Environment Planning &

Management” Di Bawah Kerjasama Program Kerjasama Teknikal Malaysia (MTCP),

INTAN/EPU ke MaCGDI pada 28 Ogos 2007


International Symposium & Exhibition On Geoinformation & International Symposium On GPS

(ISG/GNSS 2007)

Pada 5 hingga 7 November 2007 telah berlangsung seminar ISG/GNSS 2007 di Kompleks Persada Johor yang mengetengahkan pencapaian, penemuan baru dan produk-produk berkaitan Remote Sensing.

Persidangan dan Pameran ini telah berlangsung pada 12 hingga 16 November 2007 di PWTC, Kuala Lumpur dan dirasmikan oleh Timbalan Menteri Sains, Teknologi dan Inovasi (MOSTI). Ia merupakan persidangan dan pameran tahunan dalam bidang remote sensing di rantau Asia.

The 28th Asian ConferenceOn Remote Sensing (ACRS)


Pada 20 hingga 25 Julai 2007 telah berlangsung Minggu Sains, Teknologi dan ICT Negeri Johor (MISTI 2007) yang telah dianjurkan oleh Unit Sains, Teknologi dan ICT Negeri Johor di Kompleks Persada Johor yang telah dirasmikan oleh YAB Dato’ Hj Abdul Ghani bin Othman.

Minggu Sains Teknologi dan ICT Negeri Johor 2007 (MISTI Johor 2007) Johor Darul Takzim

Majlis telah dirasmikan oleh YAB Tuan Guru Dato’ Menteri Besar Kelantan, Tuan Haji Nik Abdul Aziz Nik Mat. Ia merupakan acara tahunan yang dilaksanakan oleh Kerajaan Negeri Kelantan di Dewan Kompleks Balai Islam Lundang, Kelantan.

Karnival Sains & Teknologi Peringkat Negeri Kelantan 2007 Bersempena Program Pembangunan Sains & Teknologi,

(PPST ’07) Peringkat Negeri Kelantan.


Dirasmikan oleh YB Dato’ Dr. Abdul Latiff bin Awang dan dianjurkan bersama oleh Kerajaan Negeri Terengganu dari 11 hingga 14 Julai 2007.

Pameran Tunas Saintis Kebangsaan MRSM Se-Malaysia

Peringkat Menengah Rendah Kali Ke 16

SAMBUTAN HARI GIS SEMPENA HARI GIS SEDUNIA DI MAKTAB RENDAH SAINS MARA, KUALA KELAWANG PADA 14 NOVEMBER 2007 DI DEWAN SEMARAK, MRSM KUALA KELAWANG, NEGERI SEMBILAN

Maktab Rendah Sains Mara Kuala Kelawang, Negeri Sembilan telah di pilih yang mana sasaran melibatkan semua pelajar sekolah tersebut. Menerusi program ini para pelajar berpeluang melihat dan memahami kaedah-kaedah pengurusan sumber alam dengan menggunakan data-data geografi yang di tadbir urus oleh agensi-agensi kerajaan. Program ini telah berjaya berlangsung pada 14 November 2007 di Dewan Semarak, MRSM Kuala Kelawang, Negeri Sembilan.

Sebahagian daripada pelajar MRSM di beri penerangan.


Buletin Geospatial Sektor Awam diterbitkan oleh Pusat Infrastruktur Data Geospatial Negara (MaCGDI). Sidang Pengarang amat mengalu-alukan sumbangan sama ada berbentuk artikel atau laporan bergambar mengenai perkembangan Sistem Maklumat Geografi di Agensi Kerajaan, Badan Berkanun dan Institusi Pengajian Tinggi.

Garis Panduan Untuk Penulis

Manuskrip boleh ditulis dalam Bahasa Melayu atau Bahasa Inggeris.Setiap artikel yang mempunyai abstrak perlu ditulis dengan huruf condong (italic).Format manuskrip adalah seperti berikut:

Jenis huruf (font) : Arial

Saiz huruf bagi tajuk : 12

Saiz huruf : 10

Langkau (spacing) : single

Margin : Atas, bawah, kiri & kanan = 2.5cm

Justifikasi teks : Kiri

Lajur (column) : Satu lajur setiap mukasurat

Sumbangan hendaklah dikemukakan dalam bentuk softcopy dalam format Microsoft Word.

Semua imej grafik hendaklah dibekalkan dalam format .tif atau .jpg dengan resolusi tidak kurang daripada 150 d.p.i.

Segala pertanyaan dan sumbangan hendaklah dikemukakan kepada :

Ketua Editor

Pusat Infrastruktur data Geospatial negara (MaCGdI)Kementerian Sumber Asli dan Alam Sekitar (NRE)Aras 7 & 8, Wisma Sumber Asli,No. 25 Persiaran Perdana, Presint 4,62574 PUTRAJAYA,Malaysia.

Tel : 603-8886 1209

Fax : 603-8889 4851

Email : [email protected]

FORMAT DAN GARIS PANDUANSUMBANGAN ARTIKEL

BULETIN GEOSPATIALSEKTOR AWAM

Sebarang Pertanyaan Mengenai Kursus-kursus GIS Anjuran MaCGDI, sila hubungi:Encik Muhamad Zamri bin Mustapha

Tel: 603-88861177Email: [email protected]

*******************

buletin geospatial sektor awam

Documents