FERY SUTYAWAN NRS 509 Concepts of GIS and RS in Environmental Science Annotated Bibliography

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1 The Application of GIS and Satellite Remote Sensing for Marine Fisheries Management Marine Fisheries is one of the fields that has been benefitted from the use of Geographic Information System (GIS) and Satellite Remote Sensing (SRS). In spite of the slow progress it made in the early years, Marine Fisheries nowadays has been extensively using both applications. Not only in providing factual yet informative spatial maps, but also the combination of GIS and SRS delivers near real-time information that is very useful in the decision-making process for managing highly migratory species of marine fisheries resources. In the early years ( ), the use of GIS in fisheries was limited in providing static spatial maps. It encompassed the Marine Mapping and Atlases, Habitat Mapping, Marine Productivity Mapping, Mapping the Human Impacts on Fishing Environment, Land Use or Classification, and the mapping of Aquaculture location and activities (Meaden 2000). One example of habitat mapping was a GIS Atlas of cephalopod distribution in the Southern Ocean (Xavier et al 1999). This paper demonstrates the ability of GIS in handling a large amount of data from different sources that expands over 100 years, from the Challenger Expedition in 1886 to the latest data collected in The results were 3 distribution maps for 21 species of squid from suborder Oegopsida. The Atlas contains 2497 geographic locations that portray species distribution in relation to the bathymetry, the oceanic fronts, and the winter/summer sea-ice extent. During the 2000 s the use of GIS developed significantly. The availability of environmental oceanic data obtained from SRS had shifted the use of GIS forward from merely providing the static maps or atlases. In this period, GIS used to match the suitable locations or condition with fisheries related activities. It included the site selection for marine aquaculture, matching fish distribution to environmental parameters, modeling the fish activity and movement, and analyzing catch and effort of specific fisheries (Meaden 2000). Furthermore, Meaden (2000) also predicted the further use of GIS in the future. It encompassed CPUE analysis in which the location and effort data will be obtained from e- fishing logbook using GPS technology, Evaluation on suitable locations for Essential Fish Habitat (EFH), marine reserve, stock enhancement, and deep-sea marine aquaculture, Predicting the suitable fishing zone for certain species, and the food web simulation. Meanwhile, the use of SRS in marine fisheries can be classified into 2 primary purposes, to identify the Potential Fishing Zones (PFZs) and to develop management measures (Saitoh et al 2011). Recently SRS combined with Vessel Monitoring System (VMS) also has been used for industrial fishing activities monitoring and surveillance, particularly as an effort in combating the Illegal, Unreported, and Undocumented Fishing (IUU Fishing) activities. Essentially, in marine fisheries, GIS and SRS have been used to match the environmental oceanic conditions with the results of fishing activities. The typical environmental oceanic data that have been used in predicting the suitable fishing grounds are the Sea Surface Temperature (SST), the Chlorophyll α (Chl α) concentration, the Sea Surface Height Anomaly (SSHA), the wind velocity, the Sedimentary Structure, the Sedimentation Rate, the Bottom Salinity, and the Depth of the sea. SST, Chl α concentration, SSHA, wind velocity are the data commonly obtained from the sensors of SRS. SST may be obtained from Aqua MODIS of TERRA satellite or from the Advanced Very High- Resolution Radiometer (AVHRR) sensor on NOAA satellite. Aqua MODIS of TERRA satellite and SeaWiFS sensor of NASA-the Goddard Space Flight Center (GSFC)/NASA are the examples of sensors providing the data for generating the Chlorophyll α (Chl α) concentration. Meanwhile, the SSHA are generated from 1

2 archived data (AVISO) of oceanographic satellites TOPEX/Poseidon, a joint satellite mission between NASA and CNES (French Space Agency) that mapping the ocean surface topography. Lastly, the wind velocity data were generated from the information collected by the scatterometers of European Remote Sensing Satellites (ERS-1 and ERS-2) (Zagaglia et al 2004, Solanki et al 2005, Zainuddin et al 2006, Saitoh et al 2011). The environmental oceanic data obtained from SRS are used to generate the suitable oceanic conditions and locations based on the previous study. Commonly the study will generate the suitable locations for feeding ground derived from Chlorophyll α (Chl α) concentration or the predicted migratory routes derived from SST and SSHA. For example, the previous study indicated that the optimum ranges of the environmental condition for Skipjack (Katsuwonus pelamis) are C of SST, mg m-3 Chlorophyll α (Chl α) concentration, and -8 to 12 m of SSHA (Mugo et al 2010 in Saitoh et al 2011). Therefore, the study will use ArcGIS to generate a map of the suitable geographic locations that have similar characteristics with the optimum ranges of the environmental conditions. Later the generated map will be compared with the results obtained from the field, either from collected data of fishing logbook or trawl sampling conducted specifically in the previously determined areas. In summary, largely the study investigates the suitability between environmental data and field results. The common fisheries data that used in this kind of study includes catch data derived from the fishing logbook, landing data from the adjacent fishing ports, vessels locations and movement obtained from the VMS. The amount of the catch and number of fishing efforts (number of fishing trips, frequency of hauling) are used to calculate the Catch per Unit Effort (CPUE), or Landing per Unit Effort (LPUE). Commonly the study classifies the size of CPUE and uses ArcGIS to mapping the CPUE into a specific size. ArcGIS divides the research locations into specific grids, i.e. 9-km x 9km grid, and illustrates the size of CPUE in each grid by dots or circles. A similar method is used to mapping the results from trawl sampling (Zagaglia et al 2004, Solanki et al 2005, Zainuddin et al 2006, Sanchez et al 2008, Martin et al 2009, Saitoh et al 2011) Meanwhile, the vessels locations and speed derived from VMS are used to characterize vessels activities and their adherence to fisheries conservation and management measures. The analysis on combined data of VMS and SRS is used to study skipper behavior relative to target species. This information improves not only the forecasting on fishing activities but also to improve the implementation of management measures. As mentioned earlier, the VMS s data is also can be used for fishing activities monitoring and surveillance. Recently public has an access to monitor global fishing activities derived from Satellite-based VMS data ( Later the GIS application will overlay the results obtained from the field (commercial fishing activities, trawl sampling) with the environmental oceanic conditions that have been generated previously. This overlay will provide us valuable information to support any decision-making process in regard to fisheries conservation and management measures. Based on a brief description above, it can be concluded that the combination of GIS and SRS has been benefiting the marine fisheries study. In some extent, GIS and SRS have eliminated the previously existed constraints that hindering data collection and analysis on the highly migratory species living in the vast area of the oceans. In summary, data resulted from both application are very useful for supporting the Stock Assessment (the abundance and distribution), supporting the fish behavior study, monitoring the climate changes that affects the fisheries, providing reliable and accurate information for 2

3 enforcing conservation and management measures, and monitoring and surveillance on global fishing activities (combating IUU Fishing). Saitoh, S-I., Mugo, R., Radiarta, I N., Asaga, S., Takahashi, F., Hirawake, T., Ishikawa, Y., In. T., and Shima, S Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture. _ ICES Journal of Marine Science, 68: This paper consists of 2 case studies illustrating Satellite Remote-Sensing (SRS) application in fisheries. The first study illustrates the application of satellite-based Vessel Monitoring System (VMS) and SRS data in analyzing fishing activity of skipjack tuna (Katsuwonus pelamis) fishery in the western North Pacific ( N E). The second study depicts the use of the information collected from remote sensing in analyzing climate change impacts on site suitability for scallop (Mizuhopecten yessoensis) aquaculture in Funka Bay (southern Hokkaido Island). The first case study, skipjack tuna fishery, uses 2 types of data, VMS and ocean environmental conditions data. High-resolution spatial (1-min logging interval) VMS data of a pole-and-line fishing vessel for the period were obtained via TOREDAS, a fishery information system and service provided by a private company, SpaceFish LLP ( This study uses ArcGIS 9.2 to calculate the distance between adjacent latitude and longitude positions. A spatial filter excluded all VMS data received from within 10 km of Japanese Coast to eliminate vessel s movement into and out of ports. In other word, it only retains vessel s movement during fishing activity. The aggregated VMS data which was transmitted between and with speed of knots. The ocean environmental condition data consists of Sea Surface Temperature (SST), Chlorophyll α (Chl α) and Sea Surface Height Anomaly (SSHA). SST and Chlorophyll α data are derived from monthly Aqua MODIS standard mapped images (resolution ~4 km), while SSHA of period was downloaded from AVISO. ArcGIS 9.2 mapped and re-grid the environmental data to uniform resolution matching with the VMS-derived fishing position. The analysis shows compatibility between environmental data (SST, Chlorophyll α, and SSHA) at VMS-derived fishing locations (17 to 29 0 C; 0.7 mg m -3, and -30 to 50 cm) and the optimum ranges of environmental condition for the species ( C; mg m- 3, and -8 to 12). In summary, this case study demonstrate that geographical locations of fishing ground match with the environmental conditions of targeted-species habitat. Zagaglia, C.R., Lorenzzetti, J.A., and Stech, J.L Remote sensing data and longline catches of yellowfin tuna (Thunnus albacares) in equatorial Atlantic. Remote Sensing and Environment 93 (2004): This study investigated the relationship between Catch per Unit Effort (CPUE) of yellowfin tuna (Thunnus albacares) fishery in the tropical Atlantic (15 0 S-15 0 N and 10 0 W-55 0 W) and environmental 3

4 variables obtained from remote sensors. It used statistical method of Generalized Additive Models (GAMs) to analyze the relationship. CPUE represented the number of tuna caught by 100 hooks (hook rate) for the period of in 10 latitude by 10 longitude square grid. CPUE data was calculated from the logbook of the northeast Brazilian tuna longline fleet fishing in that area. The environmental variables consisted of Sea Surface Temperature (SST), Chlorophyll-α concentration (Chl- α), Sea Surface Height Anomaly (SSHA), and wind velocity which were obtained from various sources. SST data were generated from the Advanced Very High Resolution Radiometer (AVHRR) sensor of NOAA satellite. Chl-α data were derived from SeaWiFS sensor which were produced by Distributed Active Archive Center (DAAC) of the Goddard Space Flight Center (GSFC)/NASA. SSHA data came from TOPEX and Poseidon altimeters which were produced and distributed by the PODAAC/JPL. Lastly, the wind velocity data were generated from information collected by the scatterometers of European Remote Sensing Satellites (ERS-1 and ERS-2). The study resulted an evidence of non-linear relationship between CPUE and environmental data. It assumed that the CPUE of Yellowfin Tuna was associated with the Intertropical Convergence Zone (ITCZ) position and its temporal variability. This result was supported by the highest correlation between CPUE and the meridional wind component. Although statistically significant, the environmental variables were considered as the secondary factors in controlling species abundance in study area. 4

5 Sanchez, P., Demestre, M., Recasens, L., Maynou, F., and Martin, P Combining GIS and GAMs to identify potential habitats of squid Loligo vulagaris in the Northwestern Mediterranean. Hydrobiologia (2008) 612: DOI /s This paper combined GIS and Generalized Additive Models (GAMs) to determine the most productive area for commercial squid Loligo vulgaris off the Catalan Coast. ArcGIS software produced maps of environmental preferences defining the suitable habitat for squid paralarvae. The maps were generated from 4 environmental data; the Sea Surface Temperature (SST in 0 C), Chlorophyll-α (CHLO in mg/m 3 ), Sea Level Anomaly (SLA in cm), and Photosynthetic Active Radiation (PAR in Einstein/m 2 /day). Those data were collected monthly from January 2000 December Later this paper combined the environmental data with Landing-per-unit-effort (LPUE) of squid landed in 16 fishing ports of Catalan Coast at the same period of time. The analysis of trends in time series of squid LPUE identified the best months for squid landing and the ports with higher yield. Its combination with the integration of SST and PAR data from the satellite presented the area distribution during the most productive months in relation to the spatiotemporal presence of paralarvae of Loligo vulgaris off the Catalan Coast. Generalized Additive Models (GAMs) combined the simultaneous analysis of the effect of different environmental explanatory variables derived from satellite imagery data to obtain the optimal model for squid s paralarvae. The proposed helped in identifying the potential Essential Fish Habitat (EFH) for squid paralarvae recruitment. The study suggested that Squid s paralarvae recruitment occur on May both in north and central of the Catalan Coast. These were the same location of fishing ports with the highest commercial yield of squid. In summary, this paper affirmed the correlation between the suitable environmental conditions for squid s paralarvae recruitment and the primary fishing bases of boat catching the adult squid. Xavier, J.C., Rodhouse, P.G., Trathan, P.N., and Wood, A.G Short note A Geographical Information System (GIS) Atlas of cephalopod distribution in the Southern Ocean. Antarctic Science 11 (1):61-62 This paper is an example of GIS application for marine resources management in the early years. It resulted the digitally stored Atlas of cephalopod distribution in the Southern Ocean. This study also demonstrated the GIS ability in handling the large amount of data collected from different sources. Data related to the cephalopod study were obtained from various sources expanding over more than 100 years of period, from the Challenger Expedition in 1886 to the latest data collected in The compiled data encompassed detailed information of the species. Geographical location (latitude and longitude) and species name were the minimum information required to be stored into Arc/Info (ESRI). The environmental data consisted of the bathymetry, oceanic fronts, and sea-ice extent were digitized and scanned manually from the previously existing maps. Finally, the study produced 3 distribution maps for 21 species of squid from suborder Oegopsida. The best represented species was 5

6 Galiteuthis glacialis with 625 geographic locations. The map contained 2497 geographic locations that portrayed species distribution in relation to the bathymetry, the oceanic fronts, and the winter/summer sea-ice extent which were digitally stored in Martins, D.E.G., Camargo-Zorro, M., Filho, P.W.M.S., Cintra, I.H.A., and Silva, K.C.A Spatial distribution of southern brown shrimp (Farfantepenaeus subtilis) on the Amazon continental shelf: a fishery, marine geology and GIS integrated approach. BJOCE. This paper investigated the spatial distribution of the southern brown shrimp (Farfantepenaeus subtilis) based on the correlation between the Catch per Unit Effort (CPUE) and the geological and oceanographic characteristics of the benthonic environment on the Amazon Continental Shelf. It used GIS to compare the relative abundance of brown shrimp (represented in CPUE) with the bathymetry, type of sedimentary structure, sedimentation rate and bottom salinity. Catch and fishing efforts data to calculate the CPUE were collected from 2000 to 2004 period. This study used 3,543 recods of fishing operations (hauling) with an average duration of 4.55±1.3 hours. In totally, the study analyzed 16,117,840 hours of fishing efforts. The data was obtained from the Center for Research and Management of Fishery Resources of the Northern Brazilian Coast which collected monthly samples from at least 2 boats of the industrial fishing fleet that operating at depth between 10 and 100 meter along the Amazon continental shelf. Geospatial information consisting the type of sedimentary structure, sedimentation rate and bottom salinity were spatialized based on literature studies. Meanwhile the Isobaths were obtained by digitizing the nautical charts provided by the Hydrography and Navigation Directorate (DHN) of Brazil that was prepared for the GEBCO Project (General Bathymetric Chart of the Oceans). These environmental factors were digitized using the WGS-84 projection. The fishing ground was divided into into 9-km x 9-km block with minimum 3 haulings/trawling per block. Hauling or Trawling frequency and the median CPUE were calculated for each block. The result were spatialized and were represented by dots that representing the size of CPUE. ArcGIS 9.3 program integrated the layers of environmental factors. The program used the identity command to integrate the layers in pairs. The study concluded that the CPUE is not uniformly distributed in space. Spatial analysis showed that commercial trawling efforts mostly occur in the subaqueous Amazon delta at depth of 40 to 60 m. The study also suggested the environmental characteristics that is suitable for the brown shrimp fishery, which were the muddy bottom having salinity >35 0 / 00 with sedimentation rate 2 cm/year. Solanki, H.U., Mankodi, P.C., Nayak, S.R., and Somvanshi, V.S Evaluation of remotesensing-based potential fishing zone (PFZs) forecast methodology. Continental Shelf Research 25 (2005):

7 This paper evaluated the Satellite-based potential fishing zone (PFZs) forecast by comparing it with the results from bottom trawling sampling off the Gujarat Coast in northern Arabic Sea. PFZs forecast was generated from suitable ocean environmental condition both for pelagic and demersal species. PFZs forecasts were generated using integration of Ocean Color Monitor (OCM) and Sea Surface Temperature (SST). Chlorophyll-α concentration and SST characterize the suitable oceanic environments for biological production. These 2 variables forecasted the area of zooplankton accumulation which also representing the feeding ground for the targeted species. OCM was derived from chlorophyll-α concentration, and SST was obtained from Advanced Very High-Resolution Radiometer (AVHRR) sensor on NOAA satellite. The validation of the PFZ forecast was exercised in 3 fishing seasons ( ) using Indian Fishery Survey vessels at m depth range. Species contributing 5kg/haul were considered as a significant species. CPUE per species, seasonal mean CPUE and standard deviation (SD) were computed for remotely sensed PFZs and the entire region. The study validated the mean CPUE in PFZs to the seasonal mean CPUE in other areas from a different habitat. It led to the conclusion that the pelagic species contribute fairly than the demersal ones in this validation. In other words, the PFZs forecast was more accurate for the pelagic species than the demersal ones. References: Martins, D.E.G., Camargo-Zorro, M., Filho, P.W.M.S., Cintra, I.H.A., and Silva, K.C.A Spatial distribution of southern brown shrimp (Farfantepenaeus subtilis) on the Amazon continental shelf: a fishery, marine geology and GIS integrated approach. BJOCE. Meaden, Geoff GIS in Fisheries Management. GeoCoast Vol (1) No. (1): Saitoh, S-I., Mugo, R., Radiarta, I N., Asaga, S., Takahashi, F., Hirawake, T., Ishikawa, Y., In. T., and Shima, S Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture. _ ICES Journal of Marine Science, 68: Sanchez, P., Demestre, M., Recasens, L., Maynou, F., and Martin, P Combining GIS and GAMs to identify potential habitats of squid Loligo vulagaris in the Northwestern Mediterranean. Hydrobiologia (2008) 612: DOI /s Xavier, J.C., Rodhouse, P.G., Trathan, P.N., and Wood, A.G Short note A Geographical Information System (GIS) Atlas of cephalopod distribution in the Southern Ocean. Antarctic Science 11 (1):61-62 Solanki, H.U., Mankodi, P.C., Nayak, S.R., and Somvanshi, V.S Evaluation of remote-sensing-based potential fishing zone (PFZs) forecast methodology. Continental Shelf Research 25 (2005):

8 Zagaglia, C.R., Lorenzzetti, J.A., and Stech, J.L Remote sensing data and longline catches of yellowfin tuna (Thunnus albacares) in equatorial Atlantic. Remote Sensing and Environment 93 (2004): Zainuddin, Mukti., Kiyofuci, Hidetada., Saitoh, Katsuya., and Saitoh, Sei-Ichi Using multi-sensor Satellite Remote Sensing and Catch Data to detect ocean hot spots for Albacore (Thunnus alalunga) in the northwestern North Pacific. Deep-Sea Research II 53 (2006): Global Fishing Watch at 8