Pemilihan Lokasi Budidaya Japanese Kelp (Laminaria japonica) di Selatan

Hokkaido, Jepang, dengan Menggunakan Data Penginderaan Jauh Satelit

dan Model GIS

I Nyoman Radiarta, Sei-Ichi Saitoh, dan Hajime Yasui

Zhao, X. 2006. In situ target-strength measurement of young hairtail (Trichiurus haumela) in theYellow Sea. e ICES Journal of Marine Science, 63: 46-51.

PAPER SISTEM INFORMASI GEOGRAFI KELAUTAN

Oleh:

RIZQI RIZALDI HIDAYAT

C552120031

PROGRAM STUDI TEKNOLOGI KELAUTAN

INSTITUT PERTANIAN BOGOR

2013

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Pemilihan Lokasi Budidaya Japanese Kelp (Laminaria japonica) di Selatan Hokkaido,

Jepang, dengan Menggunakan Data Penginderaan Jauh Satelit dan Model GIS

Oleh: I Nyoman Radiarta, Sei-Ichi Saitoh, dan Hajime Yasui

Abstrak

Laminaria japonica adalah spesies penting dalam kegiatan budidaya perairan di Jepang.

Analisis daerah yang paling cocok untuk aktivitas budidaya di perairan Hokkaido Selatan

ditentukan dengan menggunakan model GIS dan pendekatan evaluasi multikriteria. Analisis

parameter fisik (suhu permukaan laut dan padatan tersuspensi dari SeaWiFS dan MODIS) dan

data batimetri menunjukkan bahwa sekitar 74% (1139 km2) dari total daerah dengan kedalaman

kurang dari 60 m adalah daerah berpotensi untuk budidaya Laminaria japonica. Sebuah analisis

sensitivitas menunjukkan bahwa parameter padatan tersuspensi (SS) lebih merupakan parameter

utama dalam mempengaruhi model output. Penelitian ini menunjukkan bahwa model GIS dapat

digunakan secara efektif untuk membangun model spasial untuk budidaya rumput laut.

Pendahuluan

Lebih dari 50 spesies rumput laut telah dilaporkan di seluruh dunia, sedangkan 20

diantaranya tumbuh di wilayah Asia-Pasifik (Scoggan et al., 1989). Japanese kelp (Laminaria

japonica) merupakan spesies asli dari pantai Pasifik Barat Laut dan dapat tumbuh perairan

subtropis dengan perairan yang relatif dingin (Scoggan et al., 1989). Japanese kelp adalah salah

satu spesies rumput laut bernilai ekonomis sehingga banyak dibudidayakan (Critchley, 1993).

Produktivitas Japanese kelp di dunia terus meningkat selama 17 tahun terakhir, dari 2,5 juta ton

pada tahun 1990 menjadi lebih dari 4,5 juta ton di tahun 2007 (FAO, 2009). Cina merupakan

produsen tertinggi dalam produksi Japanese kelp, diikuti oleh Jepang dan Korea. Di Jepang,

spesies ini terutama ditemukan di sepanjang Pulau Hokkaido dan pantai timur laut Honshu.

Secara tradisional, Japanese kelp dipanen dari alam, akan tetapi karena eksploitasi yang

berlebihan, spesies rumput laut ini menjadi terancam. Untuk itu diperlukan teknik budidaya agar

kelestarian Japanese kelp terus terjaga. Saat ini, lebih dari 36% produksi Japanese kelp di

Jepang adalah hasil dari budidaya (FAO, 2009), terutama di Hokkaido.

Lokasi dan jumlah kegiatan budidaya harus memperhatikan kebutuhan konservasi dan

ekonomi yang berkelanjutan (GESAMP, 2001). Penentuan akhir dari lokasi kesesuaian

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melibatkan pertimbangan faktor sosial, ekonomi, dan lingkungan. Dengan berkembangnya

sistem informasi geografis (GIS) dan ketersediaan data penginderaan jarak jauh satelit, sekarang

memungkinkan untuk memilih area yang cocok secara cepat dan sistematis. GIS telah banyak

digunakan dalam pengembangan budidaya, termasuk lokasi penentuan kesesuaian, zonasi,

analisis dampak lingkungan, perencanaan, inventarisasi, dan pemantauan budidaya dan

lingkungan (Arnold et al., 2000; Bacher et al., 2003; Perez et al., 2005; Corner et al., 2006;

Longdill et al., 2008; Radiarta et al., 2008). Telah banyak penelitian dilakukan dalam pemilihan

lokasi budidaya (Arnold et al., 2000; Perez et al., 2003; Radiarta et al., 2008), tetapi hanya

sedikit yang menggunakan data ocean-colour dari citra satelit untuk menganalisis lokasi

kesesuaian untuk budidaya rumput laut. Makalah ini menyajikan evaluasi kuantitatif wilayah

pesisir dengan menggunakan model fisik GIS untuk mengidentifikasi lokasi yang cocok untuk

pengembangan budidaya rumput laut Japanese klep di selatan Hokkaido, Jepang.

Bahan dan Metode

Daerah studi

Daerah penelitian meliputi periran dengangaris pantai 368 km dari Muroran ke Kikonai,

Hokkaido, antara 40,67o sampai 42,58o N dan 140,25 E sampai 141,25o (Gambar 1). Daerah

perairan tersebut dipengaruhi oleh masuknya dua massa air, yaitu Tsuguru air hangat dari musim

gugur ke musim dingin, dan air Oyashio (massa air laut Subarctic) dari musim semi ke musim

panas (Ohtani, 1971; Ohtani dan Kido, 1980; Takahashi et al., 2004). Suhu permukaan laut

(SST) berkisar dari, <5oC pada Maret menjadi >20oC pada bulan Agustus / September, dan

salinitas relatif stabil, dengan nilai berkisar 31-34 psu (Shimada et al., 2000). Tingkat klorofil

sangat tinggi (>3 mg m-3) selama musim semi pada bulan Maret, tetapi relatif rendah (<1 mg m-

3) selama musim panas (Radiarta dan Saitoh, 2008). Karakteristik di lingkungan perairan ini ini

memberikan keuntungan tersendiri untuk budidaya kerang (Mizuhopecten yessoensis) dan

rumput laut.

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Gambar 1. Lokasi penelitian

Identifikasi parameter dan akuisisi basis data spasial

Parameter utama yang digunakan dalam penelitian ini dijelaskan pada Tabel 1. Data SST

berasal dari citra Moderate Resolution Imaging Spectroradiometer (MODIS) dengan resolusi

spasial 1 km. Sedangkan untuk data konsentrasi Suspended Solid (SS) diperoleh dari citra

SeaWiFS dengan 1-km resolusi. Semua citra SST dan SS dari semua musim digabungkan untuk

menghasilkan peta komposit nilai rata-rata untuk masing-masing parameter (Perez et al., 2003).

Gambar-gambar ini kemudian direklasifikasi sesuai dengan indeks kesesuaian. Sebuah peta

batimetri dibuat dengan menggabungkan data dari Japan Oceanographic Data Center. Sebuah

digital terrain model (DTM) yang digunakan untuk membuat peta kontur akhir (baik sebagai

raster atau data vektor) untuk klasifikasi sesuai dengan indeks kesesuaian.

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Tabel 1. Parameter oseanografi untuk pengembangan budidaya

Kedalaman air yang cocok untuk budidaya rumput laut Jepang tergantung pada panjang

tali rumput laut yang digunakan dan rakit yang digunakan. Secara umum, tempat harus diseleksi

dengan kedalaman air minimum adalah 15-25 m. Dalam analisis ini, untuk meminimalkan biaya

operasi dan kesulitan dalam sistem mooring, kedalaman air lebih dari 60 m dieliminasi, sehingga

dihasilkan di daerah sebesar 1541 km2. Semua data spasial di wilayah studi, yang diperoleh dari

International Steering Committee for Global Mapping. Semua data yang digunakan dalam model

GIS dibangun pada WGS 84 UTM Zona 54N. Data parameter dipersiapkan untuk diinput ke

database GIS yang dibangun berdasarkan resolusi spasial 10 m × 10 m (Perez et al., 2005;

Radiarta et al., 2008).

Kerangka analisis dan konstruksi model

Tingkat Kesesuaian (skor) untuk setiap parameter yang digunakan untuk budidaya

rumput laut menggunakan teknik menggantung tertera pada Tabel 2. Nilai parameter yang

diurutkan dari nomor 1 yang paling tidak cocok sampai nomor 8 yang paling cocok (Radiarta et

al., 2008). Bobot parameter ditentukan dengan bedasarkan perbandingan sesuai dengan

Analytical Hierarchy Process dari Saaty (1977) untuk pengambilan keputusan. Kepentingan

relatif mereka diperoleh melalui kajian literatur dan pendapat para ahli (Tabel 3). Relatif

parameter penting dievaluasi pada sembilan poin, skala kontinu-rating dari 1 (paling tidak

penting) sampai 9 (yang paling penting). Pokok vektor eigen dari matriks perbandingan

berpasangan dihitung untuk menghasilkan yang terbaik untuk bobot total 1 fit. Selain itu, rasio

konsistensi matriks juga dihitung. Nilai ini menunjukkan kemungkinan bahwa penilaian secara

acak. Sebuah rasio konsistensi 0,10 atau kurang dianggap diterima (Saaty, 1977; Banai-Kashani,

1989). Setelah nilai dan bobot dari data spasial telah ditentukan, prosedur evaluasi multikriteria

(kombinasi linear tertimbang) tersedia dalam model fungsi pembangun ArcGIS telah diterapkan.

analisis sensitivitas. Analisis sensitivitas dilakukan untuk meneliti bagaimana bobot parameter

variabel waktu dapat mempengaruhi penentuan daerah pilihan. Batimetri dan kemiringan

diasumsikan konstan dan dikecualikan.

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Tabel 2. Penskoran untuk tiap parameter

Tabel 3. Matriks perbandingan untuk mengkaji tingkat kepentingan relatif dari tiap parameter

untuk pengembangan budidaya Japanese kelp (angka menunjukkan peringkat baris relatif

terhadap faktor kolom).

Banyak metode untuk analisis sensitivitas telah digunakan untuk model evaluasi (Hamby,

1994; Delgado dan Sendra, 2004). Penelitian ini menggunakan analisis sensitivitas lokal, karena

memberikan informasi yang lebih tentang parameter yang mempengaruhi variabilitas dari model

keluaran kesesuaian. Analisis dilakukan dengan memvariasikan setiap parameter dengan +5,

+10, dan +20% dari nilai acuan, tetapi meninggalkan semua orang lain konstan. Peta kesesuaian

untuk setiap interval nilai yang dihasilkan dan perubahan di daerah masing-masing nilai

kesesuaian dan ditentukan setiap skema pembobotan.

Hasil

Distribusi spasial kesesuaian

Distribusi luas lahan yang sesuai untuk kegiatan budidaya Japanese kelp berdasarkan

parameter yang digunakan dirangkum pada Tabel 4 dan diilustrasikan pada Gambar 2 dan 3.

Berdasarkan parameter SST, 43,8% luasan sangat potensial untuk melakukan budidaya (skor 8),

daerah ini sebagian besar terletak di barat (Gambar 2a). Untuk parameter SS, 67% dan 14% dari

wilayah memiliki skor 8 dan 7, dan hanya 8% dari total wilayah memiliki skor rendah (jumlah

skor 1, 2, dan 3). Daerah ini sebagian besar terletak di sepanjang pantai di dekat Sungai Yuurap

di wilayah Yakumo (Gambar 2b). Berdasarkan batimetri, sekitar 22% dari luas potensial

memiliki skor 8 (Tabel 4 dan Gambar 2c). Untuk karakteristik kemiringan, sebagian besar daerah

potensial memiliki skor kesesuaian tinggi 8 (99%) dan 7 (0,9%; Gambar 2d). Untuk hasil overlay

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dari semua parameter, model memprediksi bahwa 19% (292 km2) memiliki skor 8 (Tabel 4).

Gambar 3 jelas menunjukkan bahwa kesesuaian lahan terdapat pada bagian barat dan selatan

daerah penelitian.

Tabel 4. Luas area (km2) dan persentase dari total luas wilayah (%) dengan nilai kesesuaian yang

berbeda

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Gambar 2. Peta kesesuaian lahan budadiaya untuk paremeter (a) SST, (b) SS, (c) batimetri, dan

(d) kemiringan

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Gambar 3. Peta kesesuaian hasil overlay semua parameter

Analisis sensitivitas

Menurut Gittinger (1986), analisis sensitivitas (sensitivity analysis) dilakukan untuk

meneliti kembali kelayakan suatu proyek, agar dapat melihat pengaruh yang akan terjadi akibat

keadaan yang berubah atau ada suatu kesalahan dalam dasar perhitungan. Perubahan luasan dari

model untuk setiap variabel dalam kilometer persegi untuk masing-masing skor kesesuaian

diberikan dalam Tabel 5 dan Gambar 4. SS mempengaruhi model keseluruhan lebih kuat dari

SST. Model ini lebih sensitif lebih rendah dari nilai yang lebih tinggi dari SS dan SST.

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Tabel 5. Hasil analisis sensitivitas, yang menggambarkan perubahan di daerah kesesuaian (km2)

berdasarkan variasi SPL dan SSS.

Gambar 4. Hasil analisis sensitivitas yang menggambarkan perbedaan di wilayah (km) di antara

setiap faktor kepekaan dan model dasar untuk masing-masing nilai kesesuaian

Pembahasan

Pengembangan budidaya rumput laut dipengaruhi oleh banyak aspek, termasuk

lingkungan (fisik, biologi, dan kimia), serta faktor sosio-ekonomi (Scoggan et al., 1989; Largo

dan Ohno, 1993; Kingzet et al., 2002). Model seleksi yang digunakan dalam penelitian ini hanya

difokuskan pada parameter fisik penting dari SST, SS, batimetri, dan kemiringan.

Menggabungkan parameter ini melalui GIS memberikan analisis lebih relevan bagi para

pengambil kebijakan dari yang didasarkan pada parameter individu saja. Tidak mengherankan,

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daerah dengan potensi terbesar untuk budidaya rumput laut adalah daerah di mana memiliki nilai

parameter yang sesuai di tiap paremeternya.

Hasil dari model GIS hanya sebagian diverifikasi menggunakan produksi panen rumput

laut (Hokkaido Central Fisheries Experiment Station, 2009). Gambar 5 menunjukkan total panen

rumput laut untuk tiap daerah. Sebagai contoh, produksi tertinggi berada di daerah

Minamikayabe yang meruapakan daerah dengan skor tinggi pada penelitian ini, sedangkan

wilayah Kikonai paling tidak produktif memiliki karena memiliki skor rendah. Namun,

hubungan ini sangat kompleks, karena produksi rumput laut di dalam Funka Bay umumnya lebih

kecil karena prevalensi budidaya kerang di sana, bukan karena kurangnya daerah rumput laut

yang sesuai (Miyazono, 2006).

Pembobotan adalah salah satu tantangan utama dalam analisis situs-seleksi menggunakan

prosedur evaluasi multikriteria. Sedapat mungkin, bobot harus konsisten dengan preferensi

pembuat keputusan (Butler et al., 1997). Setelah bobot telah ditetapkan, analisis sensitivitas

harus dilakukan untuk menentukan pengaruhnya pada hasil keseluruhan. Dalam penelitian ini,

perubahan yang signifikan pada daerah yang cocok ditemukan ketika parameter bobot yang

bervariasi. Model itu sangat sensitif terhadap SS, tetapi juga untuk SST.

Data remote-sensing telah digunakan dalam kegiatan pemilihan lokasi budidaya selama

lebih dari 20 tahun (Kapetsky et al, 1987; Kapetsky dan Anguilar-Manjarrez, 2007). Hasil

analisa dengan teknik GIS modern yang bisa mengakibatkan alat manajemen yang efisien dan

hemat biaya. Ada potensi besar untuk penginderaan jauh, data ocean colour dalam

pengembangan budidaya laut (Grant et al, 2009.). Namun ada juga keterbatasan dan

ketidakakuratan potensial. Sebagai contoh, algoritma untuk SS dalam model saat ini

dikembangkan untuk perairan Korea (Ahn et al., 2001). Sebuah algoritma berdasarkan

karakteristik air Hokkaido lokal dapat memberikan output model yang lebih akurat dan harus

dikembangkan untuk penilaian di masa depan. Karakteristik lingkungan di wilayah studi

bervariasi temporal, serta spasial. Karena variabilitas temporal yang bisa mempengaruhi

pertumbuhan rumput laut (Radiarta et al., 2008), model ini dapat ditingkatkan lebih lanjut

dengan memasukkan bulanan (Eastwood et al., 2001) atau musiman (Vincenzi et al., 2006)

variabilitas parameter. Kemajuan lebih lanjut dalam GIS dan teknologi penginderaan jauh harus

memberikan kontribusi pada pengembangan kerangka evaluasi generik untuk perencana pesisir

dan pembuat kebijakan tertarik berkelanjutan budidaya

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Aquaculture site selection for Japanese kelp (Laminaria japonica)in southern Hokkaido, Japan, using satellite remote sensing andGIS-based models

I Nyoman Radiarta 1,2*, Sei-Ichi Saitoh 1, and Hajime Yasui 3

1Laboratory of Marine Bioresource and Environment Sensing, Faculty of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate,Hokkaido 041-8611, Japan2Center for Aquaculture Research and Development, Agency for Marine Affair and Fisheries Research and Development, Jl. Ragunan 20, PasarMinggu Jakarta Selatan 12540, Indonesia3Laboratory of Science and Technology on Fisheries Infrastructure System, Faculty of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho,Hakodate, Hokkaido 041-8611, Japan

*Corresponding Author: tel: +81 138 40 8843; fax: +81 138 40 8844; e-mail: radiarta@salmon.fish.hokudai.ac.jp; radiarta@yahoo.com.

Radiarta, I N., Saitoh, S-I., and Yasui, H. 2011. Aquaculture site selection for Japanese kelp (Laminaria japonica) in southern Hokkaido, Japan,using satellite remote sensing and GIS-based models. – ICES Journal of Marine Science, 68: 773–780.

Received 16 February 2010; accepted 19 July 2010; advance access publication 17 November 2010.

Japanese kelp (Laminaria japonica) is an important species cultured and harvested in Japan. The most suitable areas for hangingculture in southern Hokkaido were determined using geographic information system (GIS) models and a multicriteria evaluationapproach. Analyses of physical parameters (sea surface temperature and suspended solid from SeaWiFS and MODIS) and availablebathymetric data indicated that some 74% (1139 km2) of the total potential area with bottom depths ,60 m had the twohighest suitability scores. A local sensitivity analysis indicated that suspended solids were more important than temperature in affect-ing model output. This study demonstrates that GIS databases of different formats and sources can be used effectively to constructspatial models for kelp aquaculture.

Keywords: aquaculture site selection, GIS, Hokkaido, kelp, Laminaria japonica, remote sensing.

IntroductionMore than 50 species of kelp have been reported worldwide, of which�20 are present in the Asia-Pacific region (Scoggan et al., 1989).Japanese kelp [Laminaria japonica (“Ma-kombu”)] grows in the tem-perate, cold-water zone and is native to the northwest Pacific coast,occurring as far south as 368N (Scoggan et al., 1989). Globally, it isone of the most valuable cultured and harvested seaweed species(Critchley, 1993). Landings have increased consistently during thepast 17 years, from �2.5 × 106 t in the 1990s to more than 4.5 ×106 t in 2007 (FAO, 2009). China leads in Japanese kelp production,followed by Japan and Korea. In Japan, this species is mainly foundalong Hokkaido Island and the northeast coast of Honshu.Traditionally, kelps were harvested from wild stocks, but these aredeclining because of overharvesting. Recent advances in marine aqua-culture techniques have contributed significantly to kelp production.Currently, more than 36% of Japanese kelp production in Japan isfrom aquaculture (FAO, 2009), mainly in Hokkaido.

The location and amount of aquaculture activity must balancethe needs of conservation and economic return in a sustainablemanner (GESAMP, 2001). Final determination of site suitabilityinvolves careful consideration of social, economic, and environ-ment factors. Environmental suitability forms the basis for plan-ning exercises and management interventions.

With the development of the geographic information system(GIS) and availability of remote sensing data, it is now possible to

select environmentally suitable areas rapidly and systematically.GIS has been widely used in aquaculture development, includingsite suitability determination, zoning, environmental impacts, plan-ning, inventory and monitoring of aquaculture and the environ-ment, and competitive exploitation of common areas (Arnoldet al., 2000; Bacher et al., 2003; Perez et al., 2005; Corner et al.,2006; Longdill et al., 2008; Radiarta et al., 2008). Many studieshave been done on aquaculture site selection (Arnold et al., 2000;Perez et al., 2003; Radiarta et al., 2008), but few have used satelliteocean-colour data to investigate site suitability for kelp aquaculture.This paper presents a quantitative evaluation of coastal areas usingGIS-based physical models to identify suitable sites for Japanesekelp aquaculture development in southern Hokkaido, Japan.

Material and methodsStudy areaThe study area includes a 368-km coastline from Muroran toKikonai, Hokkaido, between 41840′ and 42835′N and 140815′

and 141815′E (Figure 1). The oceanography of the region isaffected by the inflow of two water masses: Tsuguru warm waterfrom autumn to winter, and Oyashio water (a Subarctic oceanicwater mass) from spring to summer (Ohtani, 1971; Ohtani andKido, 1980; Takahashi et al., 2004). Sea surface temperatures(SSTs) range from ,58C in March to .208C in August/September, and salinity is relatively stable, with values ranging

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ICES Journal of Marine Science (2011), 68(4), 773–780. doi:10.1093/icesjms/fsq163

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from 31 to 34 psu (Shimada et al., 2000). Levels of chlorophyll aare very high (.3 mg m23) during the spring bloom in March,but relatively low (,1 mg m23) during summer (Radiarta andSaitoh, 2008). These unique characteristics provide a favourableenvironment, making the region one of the most important culti-vation areas in Hokkaido for scallops (Mizuhopecten yessoensis)and kelp.

Identification of parameters and spatial databaseacquisitionThe main factors (Table 1) used in this study are described below.SSTs were derived from the Moderate Resolution ImagingSpectroradiometer (MODIS) Aqua sensor as level-2 data, with1-km resolution, from the Distribution Active Archive Centre/Goddard Space Flight Centre/National Aeronautic and Space

Administration (DAAC/GSFC/NASA; Savtchenko et al., 2004).In all, 287 images with good coverage were collected from June2002 to August 2004.

Suspended solid (SS) concentrations were determined fromremotely sensed, normalized, water-leaving radiance at 555 nauti-cal mile wavelength, nLw(555). Daily level-2 SeaWiFS data with1-km resolution (February 1998 to August 2004) were obtainedfrom DAAC/GSFC/NASA (Acker et al., 2002). The nLw (555)values were extracted from the daily SeaWiFS data. Monthlyaverage (g m23) images of nLw (555) were produced followingAhn’s equation (Ahn et al., 2001): SS ¼ 3.18nLw(555)0.95.

All SST and SS images from all seasons were combined to gen-erate composite maps of average values for each parameter (Perezet al., 2003). These images were then reclassified according to suit-ability scores. A bathymetric map was prepared by combining a

Figure 1. Location of the study area in southern Hokkaido, Japan, including major depth contours and place names.

Table 1. Parameter requirements for Japanese kelp aquaculture development in the southern part of Hokkaido Island, Japan.

Parameters Interpretation parameter Optimum Reference

Sea surface temperature Favourable temperature for kelp culture 9–128C Scoggan et al. (1989), Fang et al. (1996), Suzuki et al. (2008)Suspended solids Indicates level of water clarity (turbidity) ,2 g m – 3 Otero and Siegel (2004), Nezline et al. (2005)Bathymetry Favourable depth for hanging culture 10 –30 m Scoggan et al. (1989), Kawashima (1993), Fang et al. (1996)Slope Favourable slope for hanging culture ,108 Bushing (1995), Bekkby et al. (2009)

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scanned hydrographic chart (Japan Hydrographic Department1:150 000) and a digitized map of 500-m gridded bathymetricpoints (Japan Oceanographic Data Center, JODC, http://jdoss1.jodc.go.jp/cgi-bin/1997/depth500_file). A digital terrain model(DTM) was used (triangulated irregular network; Hutchinsonand Gallant, 2000) to create the final contour map (either asraster or vector data) for classification according to the suitabilitycriteria.

Suitable water depth for Japanese kelp aquaculture depends onthe length of kelp ropes used and the hanging-raft culture methodsemployed. In general, the sites should be selected where theminimum water depth is 15–25 m. In this analysis, to minimizeoperation costs and difficulty in mooring systems, water depths.60 m were excluded, resulting in a potential area of�1541 km2. The slope of the bottom (degrees) was obtainedfrom the DTM of the bathymetry image using the ArcGIS 9.2slope function that calculates the maximum rate change betweeneach cell and its neighbours.

All spatial data were registered to the study area’s coastline,obtained from the International Steering Committee for GlobalMapping (http://www.iscgm.org/cgi-bin/fswiki/wiki.cgi). Alldata used in the GIS models were built on a WGS 84 UTMZone 54 North coordinate system. Data on the above parametersprepared for input to the GIS database were built based on a10 m × 10 m pixel size (Perez et al., 2005; Radiarta et al., 2008).

Analytical framework and model constructionSuitability levels (scores) for each parameter were defined accord-ing to requirements for kelp aquaculture using the hanging tech-nique (Table 2). Parameter values were ranked and classifiedfrom 1 (least suitable) to 8 (most suitable) following Radiartaet al. (2008). Parameter weights were determined by pairwise com-parisons according to the Analytical Hierarchy Process of Saaty(1977) for decision-making. Their relative importance wasobtained through a literature review and experts’ opinions(Table 3). Relative parameter importance was evaluated on a nine-point, continuous-rating scale from 1 (least important) to 9 (mostimportant). The principal eigenvector of the pairwise comparisonmatrix was computed to produce the best fit for a total weightingof 1. In addition, the consistency ratio of the matrix was also cal-culated. This value indicates the probability that ratings were ran-domly assigned. A consistency ratio of 0.10 or less was consideredto be acceptable (Saaty, 1977; Banai-Kashani, 1989). Once thescores and weights of the spatial data had been determined, a mul-ticriteria evaluation procedure (weighted linear combination)available in the ArcGIS model builder function was applied.

Sensitivity analysisA sensitivity analysis was done to examine how the weighting oftime-variable parameters could affect the determination of

Table 3. Pairwise comparison matrix for assessing the relative importance of parameters for Japanese kelp aquaculture development in thesouthern part of Hokkaido Island, Japan (numbers indicate the rating of row relative to column factors).

Parameter Sea surface temperature Suspended solids Bathymetry Slope Weight

Sea surface temperature 1 2 3 4 0.46Suspended solids 1/2 1 2 3 0.28Bathymetry 1/3 1/2 1 2 0.16Slope 1/4 1/3 1/2 1 0.10

Consistency ratio (CR) ¼ 0.015, consistency is acceptable.

Table 2. Physical factor requirements and suitability scores for Japanese kelp aquaculture-site selection in southern Hokkaido, Japan.

Parameter

Suitability rating and score

8 7 6 5 4 3 2 1

Sea surfacetemperature (8C)

10–11 9–10 or 11– 12 8– 9 or 12–13 7–8 or 13– 14 6–7 or 14–15 5–6 or 15 –16 4–5 or 16–17 ,4 or .17

Bathymetry (m) 10–25 9–10 or 25– 30 8– 9 or 30–35 7–8 or 35– 40 6–7 or 40–45 5–6 or 45 –50 4–5 or 50–60 .60 or ,4Suspended solids

(g m – 3),1.6 1.6 –2.0 2.0–2.3 2.3 –2.6 2.6–2.9 2.9 –3.5 3.5–4.0 .4.0

Slope (8) ,5 5–10 10–15 15 –18 18–20 20 –23 23– 25 .25

Table 4. Area (km2) and percentage of total area (%) with different suitability scores for Japanese kelp aquaculture development in thesouthern part of Hokkaido Island, Japan, with depths ,60 m.

Physical model of siteselection

Suitability score

1 2 3 4 5 6 7 8

km2 % km2 % km2 % km2 % km2 % km2 % km2 % km2 %

Sea surface temperature 0.0 0.0 0.0 0.0 3.0 0.2 16.0 1.0 278.0 18.0 98.0 6.5 402.0 26.0 744.0 43.8Suspended solids 25.0 2.0 29.0 2.0 56.0 4.0 43.0 3.0 46.0 3.0 79.0 5.0 223.0 14.0 1 040.0 67.0Bathymetry 45.0 3.0 391.0 25.0 182.0 12.0 170.0 11.0 145.0 10.0 134.0 9.0 129.0 8.0 345.0 22.0Slope 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.1 10.5 0.9 1 530.0 99.0Overall model 0.0 0.0 0.0 0.0 1.0 0.1 14.0 0.9 64.0 4.0 323.0 21.0 847.0 55.0 292.0 19.0

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preferred areas. Bathymetry and slope were assumed to be tem-porally constant and excluded.

Many methods for sensitivity analysis have been used formodel evaluations (Hamby, 1994; Delgado and Sendra, 2004).This study used a local sensitivity analysis, because it providesthe most information about parameters influencing the

variability of the suitability model output. The analysis was con-ducted by varying each parameter by +5, +10, and +20% ofthe reference values, but leaving all others constant. Suitabilitymaps for every interval value were generated and the changein area for each suitability score and every weighting schemedetermined.

Figure 2. Suitability maps for environmental criteria used in physical modelling, masked to exclude depths .60 m, for Japanese kelpaquaculture: (a) SST, (b) SSs, (c) bathymetry, and (d) slope.

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ResultsSpatial distribution of suitabilityThe classifications of surface areas for each parameter are summar-ized in Table 4, and the corresponding spatial distributions of suit-ability sites are illustrated in Figures 2 and 3.

Based on SST, �43.8% of the potential area scored 8 (Table 4).These areas were mostly located in the west (Figure 2a). RegardingSS, �67 and 14% of the area had scores of 8 and 7, respectively.Only �8% of the area had low scores (sum of scores 1, 2, and3). These areas were mostly located along the coastline near theYuurap River in the Yakumo region (Figure 2b). Based on waterdepth, some 22% of the potential area scored 8 (Table 4 andFigure 2c). For slope characteristics, most of the potential areahad high suitability scores of 8 (99%) and 7 (0.9%; Figure 2d).

For all parameters combined, the model predicted that 19%(292 km2) of the potential area had a score of 8 (Table 4).Figure 3 clearly highlights the suitability of the western andsouthern part of the study area, because of the high quality ofwater properties, appropriate water depths, and slope

Figure 3. Overall site selection map for combined environmental criteria, depths ,60 m, for Japanese kelp aquaculture development insouthern Hokkaido, Japan.

Table 5. Results of local sensitivity analysis, illustrating the changein suitability areas (km2) as SSTs and SSs are varied by +5, +10,and +20% from the baseline model.

Parameter 1 2 3 4 5 6 7 8

Baseline model 0 0 1 14 64 323 847 292Sea surface temperature

+20 0 0 1 11 50 369 779 331+10 0 0 1 11 60 363 797 309+5 0 0 1 11 56 323 822 32825 0 0 1 13 65 323 918 221210 0 0 1 15 68 285 880 292220 0 0 1 26 70 470 774 200

Suspended solids+20 0 0 1 32 73 269 894 272+10 0 0 1 16 79 292 861 292+5 0 0 1 13 74 318 843 29225 0 0 1 11 114 495 681 239210 0 0 1 11 99 502 689 239220 0 0 1 9 140 518 570 303

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characteristics. Approximately 55% of the potential area scored 7,26% scored mid-scale (4–6), and ,1% scored 3. No area scoredeither 1 or 2 in suitability.

Sensitivity analysisChanges from the baseline model for each of the variables insquare kilometres for each suitability score are given in Table 5and Figure 4. SS affected the overall model more strongly thanSST. The model was more sensitive to lower than higher valuesof SS and SST.

DiscussionDevelopment of kelp aquaculture is affected by many aspects,including environmental (physical, biological, and chemical), aswell as socio-economic factors (Scoggan et al., 1989; Largo andOhno, 1993; Kingzet et al., 2002). The site-selection model usedin this study only focused on the important physical parametersof SST, SS, bathymetry, and slope. Combining these parametersthrough GIS provides a more relevant analysis for decision-makersthan the one based on individual parameters alone. Not surpris-ingly, areas with the greatest potential for kelp culture are thosewhere favourable parameter scores coincide.

The results of the GIS model could be only partly verified usingkelp landing production (Hokkaido Central Fisheries ExperimentStation, 2009). Figures 3 and 5 demonstrate that scores tend toreflect kelp production. For example, the highest production wasin the high-scoring Minamikayabe area, whereas the least pro-ductive Kikonai area had low scores. However, this relationshipis complex, because kelp production inside Funka Bay is generallysmaller because of the prevalence of scallop aquaculture there,rather than lack of suitable kelp areas (Miyazono, 2006).

Weighting is one of the primary challenges in site-selectionanalyses using multicriteria evaluation procedures. To the extentpossible, weightings should be consistent with decision-makerpreferences (Butler et al., 1997). Once weights have been assigned,a sensitivity analysis should be conducted to determine their influ-ence on the overall results. In this study, significant changes in

suitable areas were found when parameter weights were varied.The model was particularly sensitive to SS, but also to SST.

Remote-sensing data have been used in aquaculture site selec-tion for more than 20 years (Kapetsky et al., 1987; Kapetsky andAnguilar-Manjarrez, 2007). Their analysis with modern GIS tech-niques could result in an efficient and cost-effective managementtool. There is great potential for remotely sensed, ocean-colourdata in marine aquaculture development (Grant et al., 2009);however, there are also limitations and potential inaccuracies.For example, the algorithm for SS in the current model was devel-oped for Korean waters (Ahn et al., 2001). An algorithm based onlocal Hokkaido water characteristics could provide more accuratemodel output and it should be developed for future assessments.Environmental characteristics in the study region vary temporally,as well as spatially. Because temporal variability could influencekelp growth (Radiarta et al., 2008), the model could be improvedfurther by including monthly (Eastwood et al., 2001) or seasonal(Vincenzi et al., 2006) variability of parameters. Further progressin GIS and remote-sensing technology should contribute to the

Figure 4. Results of sensitivity analysis illustrating differences in area (km2) between each sensitivity factor and the baseline model for each ofthe suitability scores.

Figure 5. Kelp landings for southern Hokkaido, Japan, 1991–2006.

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development of a generic evaluation framework for coastal plan-ners and policy-makers interested in sustainable aquaculture.

AcknowledgementsWe thank the Distribution Active Archive Center at the NASAGoddard Space Flight Center for the production and distributionof the SeaWiFS and MODIS data. The study was supported by the2010 Hakodate Marine Bio Industrial Cluster Project of theRegional Innovation Cluster Program (formerly the KnowledgeCluster Program), Grants-in-Aid of University and SocietyCollaboration, Ministry of Education, Culture, Sports, Scienceand Technology (MEXT), Japan. Comments by two anonymousreviewers contributed greatly to improving the manuscript.

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