H U Solanlki, R M Dwivedi & S R Nayak

Transcription

1 Indian Journal of Marine Sciences Vol. 30, September 2001, pp Application of Ocean Colour Monitor chlorophyll and AVHRR SST for fishery forecast: Preliminary validation results off Gujarat coast, northwest coast of India H U Solanlki, R M Dwivedi & S R Nayak Marine and Water Resources Group, Space Applications Centre (ISRO), Ahmedabad , Gujarat, India and J V Jadeja, D B Thakar, H B Dave & M I Patel Commissionerate of Fisheries, Fisheries Department, Gujarat State, Gandhinagar , Gujarat, India Received 20 September 2000, revised 27 April 2001 Quantitative analysis of sea surface temperature (SST) and chlorophyll improve our understanding of circulation and distribution of phytoplankton population in water masses. In this study near synchronous IRS P4 Ocean Colour Monitor (OCM) derived chlorophyll and NOAA AVHRR derived SST are used for exploring fishery resources. Ocean Colour Monitor data were atmospherically corrected using long wavelength approach. Ocean Chlorophyll 2 (OC2) bio-optical algorithm was applied to corrected radiance to calculate chlorophyll concentration. Multi channel sea surface temperature (MCSST) approach was adopted for estimation of SST from split window of NOAA AVHRR. Both data sets were geometrically corrected and registered with master image of north westcoast of India so as to generate geo-coded chlorophyll and SST images. Sea surface temperature contours generated and overlaid on chlorophyll images to generate composite images. These composite images were interpreted for generating experimental fishery forecast along the Gujarat coast. Different types of the oceanic features like, coastal fronts, fringe fronts, rings, mushrooms, meanders and features shaped as tongue and mushroom, jet etc. were selected for forecast. The information was disseminated to Porbandar for validation. Gillnetters were used for experimental fishing. Suggested areas were located using global positioning system (GPS). The feedback received from fishing sites were analysed. The catch per gill net operation was normalised and compared with month wise normal catch available around Porbandar. About 2-3 fold increase in the catch in potential fishing zones (PFZ) area was observed. The frontal positions in chlorophyll image coincide with temperature boundaries at some locations. This coincidence indicates that physical and bio-chemical processes are closely coupled at these locations and can be used for identification of potential fishing zones (PFZs). Ocean colour is dependent on complex interrelationship involving biological, chemical and physical processes. Chlorophyll pigment concentration is a convenient index of phytoplankton biomass, which can be measured from space. Data from Coastal Zone Colour Scanner (CZCS) provided first view of the synoptic scale distribution of chlorophyll concentration. Gordon & Morel 1 used Nimbus-7 CZCS data to estimate chlorophyll concentration from space. Thermal infrared channels of AVHRR onboard NOAA satellites are operationally used to derive sea surface temperature (SST) in India 2. Arnone 3 used clustering technique using unsupervised classification to classify different water masses and to understand relationship between the satellite derived SST and ocean colour. The classification of water mass appears to be associated with different biological and physical processes. Muller & La Violate 4 used CZCS derived ocean color and NOAA AVHRR derived SST image to correlate the oceanic features observed in both images. It was observed that the oceanic features in SST and chlorophyll images are displaced horizontally by tens of kilometers and the colour features not necessarily coincide with temperature features. Barnard et al. 5 using five years ( ) time series of CZCS chlorophyll and SST mean field to study spatial and temporal variability, observed that along shelf the chlorophyll variability was highest in nearshore and decreases with distance towards offshore and the distribution of chlorophyll and SST seasonal changes are inversely correlated. Laurs et al. 6 used CZCS and NOAA AVHRR imagery to study tuna distribution and found that tuna population was distributed in the clear water. Solanki et al. 7 used IRS P3 MOS-B and NOAA AVHRR derived chlorophyll and SST images, respectively to understand the relationship between two parameters and its synergistic application for exploring fishery resources. Inverse relationship between pa-

2 SOLANKI et al.: FISHERY FORECAST 133 rameters was observed in the coastal waters. The frontal location in the chlorophyll images was coincided with the thermal boundaries. Quantitative analysis of SST and chlorophyll might improve our understanding of circulation and distribution of water masses containing food resources. The IRS-P4 satellite launched on 26 May 1999 by Indian Space Research Organisation (ISRO) carried two oceanographic payloads, i.e. Ocean Colour Monitor (OCM) and Microwave Scanning Multi-frequency Radiometer (MSMR). The first payload OCM is designed to measure ocean color, the spectral variation of water leaving radiance that can be related to concentration of phytoplankton pigments, suspended matter and coloured dissolved organic matter in coastal and oceanic waters, and the characterisation of atmospheric aerosols. At present operational fishery forecast in India is based on the features observed in the SST images 8,9. Satellite data provide synoptic and near synchronous SST and chlorophyll observations with reasonable accuracy which is not possible through shipboard observations. The objective of this study was to validate fishery forecast for pelagic resources. Preliminary results of the fishery forecast have been discussed in this paper. Materials and Methods The study area is located at Gujarat coast, off northwest India, which is a part of north Arabian Sea. This area has a wide continental shelf. The data of IRS P4 OCM and synchronous NOAA AVHRR data for January, February and March 2000 were used in this study. The details of the sensor parameters are given in the Table 1. Spatial resolution of OCM is higher i.e. 360 m as compared to Sensor SeaWiFS (1.1 km) on board Sea Star satellite. The high spatial resolution allows us to study small features and patches of chlorophyll distribution in coastal regions. The retrieval of ocean colour parameters such as phytoplankton pigment (i.e. chlorophyll) and sea surface temperature (SST) from satellite data includes series of steps. The processing steps are shown in form of flow chart in Fig. 1. The two main steps in chlorophyll retrieval are atmospheric correction of visible channels of OCM to obtain normalised water leaving radiance and application of bio-optical algorithm for retrieval of water parameters. Atmospheric correction of IRS-P4 OCM imagery - In case of oceanic remote sensing, the total signal received at the satellite altitude is dominated by radiance contribution through atmospheric scattering processes and only 8-10% signal corresponds to oceanic reflectance. Therefore it has become mandatory to correct for atmospheric effect, to retrieve any quantitative parameter from space. Atmospheric correction was carried out using approach suggested by Mohan et al. 10. Application of bio-optical algorithms for OCM imagery - A number of bio-optical algorithms for chlorophyll retrieval have been developed to relate measurements of ocean radiance to the in situ concentrations of phytoplankton pigments. An empirical algorithm 11 (also known as Ocean Chlorophyll 2 or OC2) has been used with IRS-P4 OCM data, on the basis of results of a study done on inter-comparison of different bio-optical algorithms. It was possible to retrieve chlorophyll values with 20% error. The algorithm operates with five coefficients. Table 1 Technical characteristics of OCM and NOAA AVHRR payload Parameter OCM NOAA AVHRR Swath 1420 km 2700 km Equatorial crossing time 12 noon 0230 and 1430 hrs Spectral range nm μm Quantisation 12 bits 10 bits No. of channels 8 5 Spatial resolution 360 m 1.1 km Wavelengths Channel 1: nm Channel 2: nm Channel 3: nm Channel 4: nm Channel 5: nm Channel 6 : nm Channel 7: nm Channel 8: nm Repetitive 2 days daily Channel 1: μm Channel 2: μm Channel 3: μm Channel 4: μm Channel 5: μm

3 134 INDIAN J. MAR. SCI., VOL. 30, SEPTEMBER 2001 Fig. 1 Schematic presentation of methodology adopted for data analysis C=10^( *R+2.811*R *R 3 +( 0.040) (1) where C = chlorophyll concentration (mg/m 3 ) and R = log 10 [R rs (490)/R rs (555)], where R rs is remote sensing reflectance. NOAA AVHRR data analysis - The brightness temperature sensed at satellite height is influenced mainly by atmospheric moisture. The signal loss due to water vapour absorption is proportional to the radiance difference in the measurement made at two different channels of the thermal infrared. The MCSST approach suggested by McClain 12 was used to compute SST from AVHRR thermal infrared channels i.e. Ch # 4 ( μm) and Ch # 5 ( μm). Preliminary geometric correction was carried out according to Narayana et al. 13. This approach uses the satellite ephemeris. The precise geometric corrections were carried out using a set of ground control points (GCPs) located both on an image and a Naval Hydrographic Office (NHO) bathymetric map. This georeference master image was used for image-to-image registration of AVHRR channels in order to generate geo-reference data set. Colour-coded SST images were generated which indicate the distribution of SST in the northwest Arabian Sea. Same colour scheme was applied to both chlorophyll and SST images to make easy comparison between two parameters. Co-registration of OCM and NOAA AVHRR images - Geometric registration of both satellites data is a major concern, particularly when the comparison between two different sensor data is to be made. For this, a master image was generated using GCPs located on an image as well as on a map. This was followed by map-image registration. Output master images were used to co-register chlorophyll and SST images in order to generate geo-coded images. Generation of chlorophyll and SST composite - In order to visualise both parameters in the single product, chlorophyll and SST composite product was generated. For this, SST images were filtered to remove the noise using low pass filter. These filtered images were used to generate contours showing different SST. The contours were converted into vectors. These vectors were overlaid on chlorophyll images to generate chlorophyll SST composite showing chlorophyll and SST features/values. Generation of fishery forecast and dissemination - Common features observed in both images were monitored and located in the composite image. Features like eddies, front with large gradients, meanders, etc. were selected. The position of such features in terms of latitude and longitude were read from composite image. The depths at these locations were read from the hydrographic maps. This information was disseminated to Porbander base through FAX for experimental fishing. Experimental fishing and feedback analysis - Gill net fishing operation was selected for fishing in pelagic region off the Gujarat coast. The purpose for selecting gill net fishing was to explore pelagic region for selective fishing of some pelagic species like pomfret (Pempus argentus), ribbon fish (Trichiurus sp.), catfish (Trachysurus caelatus) etc., so that signatures observed on images can be compared with particular species. The professional gill-netters were used for experimental fishing. Global poisoning system (GPS) was used for locating the position of the suggested area. A total of nets were used, each having 25 m length, 7 m height and 15 cm mesh size. Feedback consists of catch composition with weight of each species, actual fish area, fishing depth and number of nets used for fishing. The fish catch was normalised for 1200 m i.e. 48 gill nets. The normal catch for gillnetters per operation for the month was taken from census survey for comparison with experimental fishing in potential fishing zone (PFZ) areas. The fishing points were plotted on images as per geometric location of fishing sites.

4 SOLANKI et al.: FISHERY FORECAST 135 Results and Discussion Synergistic application Ocean colour from space provides a measure of the areas of enhanced biological production which occurred/accumulated at the front, topographic structure, upwelling and eddies. The zooplankton and fish population are known to accumulate for feeding and spawning at these locations. Remotely sensed SST explains oceanic environment suitable for enhanced production. The use of both parameters would explain the oceanic environment and food resource availability in an ecosystem for exploring fishery resources. Oceanographic features found on SST and chlorophyll were fronts, meanders, rings, eddies, upwelling, mushroom shaped features, etc. Fronts with high SST gradients and chlorophyll concentration gradients are considered as good areas for fish accumulation because they indicate the nutrient enriched areas leading to high bio-mass production. Meanders and mushroom shaped features indicates larger aerial extend in relatively smaller area. They form enclosed pockets, hence confines the resources. Eddies are the indicator of deep mixing of water masses leading to high production. Rings are the derivatives of eddy or meanders, hence they are productive and indicator of the development of localised ecosystem. Upwelling areas were known to be productive zones leading high biomass production. A composite image of chlorophyll and SST was used for experimental forecast. Figure 2 shows typical composite image generated from sea surface temperature and chlorophyll images of February 29, 2000 and February 15, Sea surface temperature contours overlaid on the chlorophyll indicate the matching features in images of both optical and thermal infrared sensors. Cool water is an indicator of high nutrient containing waters. In such areas probability of enhanced production is more as compared to stratified warm waters. Areas of matching features were suggested for fishing. The coincidence of chlorophyll and SST features indicates that physical and bio-chemical processes are closely coupled at these locations. High catch points were observed in the vicinity of thermal as well as colour persistent features. Thus coincidence of ocean colour and thermal features can be utilised for exploring fishery resources. Monthly averaged normal catch per gill net operation recorded during the survey for for Porbander base was considered for comparison. Figure 3 indicates the comparison of experimental fishing in suggested area and monthly average catch. On an average 2-3 fold increase in catch was observed in potential fishing zones. Ocean colour is dependent on complex interrelationship involving biological, chemical and physical processes. The synergistic analysis of different parameters i.e. bio-chemical (chlorophyll) and physical parameter (temperature) allows us to understand biology as well as oceanic environment supporting the food chain in the dynamic medium. The use of both parameters would explain the oceanic environment and food resource availability in an ecosystem for exploring fishery resources. Pattern and persistence of oceanographic features Pattern and persistence of colour and thermal features were analysed for the month of January March The comparable features were marked in the serial number in both sensor images (Fig. 4). Ocean colour features observed in the chlorophyll images were coinciding with thermal boundaries observed in SST images. On average high chlorophyll concentration was observed in the comparatively cold-water mass. This shows that the cold-water mass consists of nutrients in euphotic zone, which enhances the primary productivity. Feature types observed on chlorophyll and SST images were 1) rings, 2) mushroom shaped features, 3) tongue shaped features, 4) meanders, 5) coastal jets and 6) fringe fronts. On an average chlorophyll images showed many well-developed and well-defined features exhibiting water circulation patterns and distribution of biomass. Some SST features are matching and have relevance in shape, pattern and persistence with chlorophyll features. This is an indicator of the suitability of the environment for phytoplankton production. Localised wind, tidal currents and bottom topography may play major role in the formation as well as distribution of features.

5 136 INDIAN J. MAR. SCI., VOL. 30, SEPTEMBER 2001 e in presence and absence of lactic acid

6 SOLANKI et al.: FISHERY FORECAST 137 Fig. 3 A comparison of normal catch and normalised fish catch per gill net operation in the PFZ area. Fig. 4 A typical image of February 21, 2000 showing pattern and persistence of oceanographic features in the study area Some features are visible on both SST and chlorophyll infrared sensors. The infrared sensor can detect images, although all the features seen in the chlorophyll radiation emitted from the sea surface only, hence it images are not visible in the SST images. The reasons provides only surface information. The ocean colour could be the characteristic of optical and thermal sensor has the ability to visualise the marine structure

7 138 INDIAN J. MAR. SCI., VOL. 30, SEPTEMBER 2001 originated from down to one attenuation depth. Hence the colour sensor can distinguish the water masses based on the different column production. The coexistence of colour and temperature features with some horizontal displacement and with some difference in the fine morphology of the features may be due to cross frontal diffusion or different observation time. Though both sensors see most remarkable structures, in some cases the location of the structure differ from one sensor to other. There are several reasons for such horizontal displacements such as time required for phytoplankton growth, delay in the growth of phytoplankton, supply of nutrients, different scanning time etc. Therefore, colour features have been dynamically related to biological processes under influence of parameters like photosynthesis, wind direction, wind speed etc. apart from the temperature alone. Feedback of fishing operation indicates increase in catch by two to three folds in PFZ areas as compared to monthly normal catch. Oceanographic features like, coastal fronts, rings, mushroom shaped feature, tongue shaped feature, jets, fringe fronts were delineated on OCM derived chlorophyll and NOAA AVHRR derived SST images and compared. The persistent cooccurrence of colour and thermal features indicate the close coupling between the physical and biological parameters. A chlorophyll feature appears to be well defined and contain more information as compared to the SST features. High fish catch points were observed in the vicinity of colour and thermal features. The synergistic analysis of the data from two-different sensor types makes possible to describe the complex structure of phytoplankton field structures. Hence, the synergistic applications of chlorophyll and SST images would be useful to improve the methodology for exploring living marine resources. Acknowledgement The authors express their sincere gratitude to Dr R.R. Navalgund, then Deputy Director, Remote Applications Area, Space Applications Centre for valuable suggestions and guidance during the course of the study. We would like to place on record our thanks to Mr. N.A. Vohra, then Commissioner of Fisheries and officials from Fisheries Dept, Gujarat State for their help during validation cruises. References 1 Gordon H R & Morel A., Water color measurements - an introduction. in Oceanography from space, edited by J.F.R. Gower, (Plenum, New York), 1980, pp Nath A., Rao M V, Rao T & Daniel N E, Retrieval of sea surface temperature from NOAA AVHRR data some results, Mahasagar Bull Nat Inst Oceanogr, 22 (1989) Arnone R A, Satellite-derived color-temperature relationship in the Alboran sea, Rem. Sen. Env., 23 (1987) Mueller J L & La Violate P E, Colour and temperature signatures of ocean fronts observed with Nimbus 7 CZCS, in Oceanography from space, edited by J.F.R. Gower, (Plenum, New York) 1980, pp Barnard A H, Stegmann P M & Yoder J A, Seasonal surface ocean variability in the South Atlantic Bight derived from CZCS and AVHRR imagery, Cont Shelf Res, 17 (1997) Laurs R M, Fielder P C & Montgomery D R, Albacore tuna catch distribution relative to environmental features observed from satellite, Deep Sea Res., 31 (1984) Solanki H U, Dwivedi R M & Nayak S R, Relationship between IRS MOS-B derived chlorophyll and NOAA AVHRR SST: A case study in the North West Arabian sea, India, in Proceedings of 2 nd International workshop on MOS-IRS and Ocean Colour, (Institute of Space Sensor Technology, Berlin, Germany) 1998, pp Solanki H U, Raman M, Kumari B, Dwivedi R M & Narain A,. Seasonal trends in the fishery resources off Gujarat : Salient observations using NOAA-AVHRR Indian J. Mar Sci, 27 (1998) Solanki H U, Raman M, Dwivedi R M, Kumari B & Narain A, Seasonal variability in the fishery resources off Gujarat: Some preliminary observation using NOAA-AVHRR data, in Proc. Nat. Symp. on Remote Sensing for Sustainable Development, (Lucknow) 1992, pp Mohan M, Chauhan P, Mathur A & Dwivedi R M, Atmospheric correction of MOS-B data using long wavelength and PCI based approaches, IRS-P3 MOS validation experiment: Ocean applications SAC Report No. SAC/RESA/MWRD/IRSP3/SN/02/98, (Space Applications Centre, Ahmedabad) 1998, pp O Reilly J E, Maritonena, Mitchell B G, Siegal D A, Carder K L, Graver S A, Kahru M & McClain C R, Ocean colour chlorophyll algorithms for SeaWiFs, J. Geophys. 103 (1998) McClain P C, Overview of satellite data applications in the climate and earth sciences laboratory of NOAA's National Environmental Satellite, data and information services, in Proc. of the US-India Symp Cum-workshop on Remote Sensing Fundamentals and Applications, (Space Applications Centre, Ahmedabad, India), 1985, pp Narayana A, Solanki H U, Krishna B G & Narain A, Geometric correction and radiometric normalisation of NOAA AVHRR data for fisheries application, Int. J. Rem Sen, 16, (1995)