Seagrass mapping using Landsat TM data
Mapping Seagrasses by Satellite
As with most operations, there are advantages and disadvantages with the use of satellite data. Some limitations of acquisition and processing of satellite information include: spatial resolution; cloud cover; satellite passover time and tide; water turbidity and depth: expensive computer trained staff to process and understand data.
Some advantages in the use of Landsat Thematic Mapper (TM) satellite data for seagrass mapping are: coast effectiveness: timeliness; quickly sensitivity; monitoring capability; large area mapped quickly; quantitative information obtainable; potential reduction in fields work; and cartographic product easily produced. It is for these reasons that coastal remote sensing has potential in coastal resource management.
The most significant of these advantages is perhaps the coast effectiveness of using satellite data. The cost of mapping a large area such as Great Sand Strait using Landsat TM satellite data has been
estimated to be US $6700.00 ( Queensland Department of Geographic Information charges fro purchase of data US $1900, image processing charges US $3400, and labour US $1350. Comparing this to the cost of surveying the strait by conventional means US $53200 (estimate made by Department of Primary Industries, 1987 fro use of equipment and lobour), clearly demonstrates the cost effectiveness of using satellite data where possible. However, this methodology has a significant time frame advantage as wel. THe image processing component of mapping Great Sandy Strait seagrasses using Landsat TM data was performed in approximately 15 days. The subsequent field check of the maps that were produced required another 18 days ( three persons for six days) giving a total of 33 days. To produce similar results by conventional field survey methods has been estimated to take somewhere between six and twelve months (Queens) nand Department of primary Industries estimate, 1987).
The Landsat TM image used for this study was chosen because it was captured close to low tide. It is most probable that more accurate information will be obtainable about seagrasses exposed exposed above the tide. than seagrasses below water level, where the variables associated with delineation of the beds. The satellite image (path 90, Row 77 centred at G) was captured at 9:18am on 21st September, 1988, by Landsat 5. Low tide did not correspond with the satellite passover at any place in the strait. However, this particular passover was the best and closest to low tide for many months. One of the main problems associated with using satellite data, namely the problem of cloud cover, is evident here. To match satellite passover time in the area of interest with optimum low tide and clear weather conditions is not a common event.
The six band TM image (excluding the thermal band, band 6) was then rectified on an image processing system to a 1:50 000 scale hydrographic chart of the Great Sandy Strait area.
The two environments where seagrasses occur, (Exposed and submerged), one a wet mud or sandbank covered with a varying depth of water and with seagrass vegetation standing upright, are too different physically and spectrally for treatment together. The differing environments require separate selection of bands and separate treatment of the two environments in image processing. In making the band selections, it was necessary to investigate the range of data in each band. Exposed seagrass areas were examined by first creating smaller images covering only those areas of exposed banks with both known seagrass areas and known bare substrates and then calculating correlation matrices of the spectral bands for these small areas. An examination of the correlation matrices revealed that in all three areas examined, TM bands 4 (near infrared), 5 ( near infrared) and 7 (middle infrared) were the least correlated. The use of these bands for image processing or to create false colour composites should give the greatest spectral separation of features in the exposed intertidal zone. Band selection for use in submerged areas below water areas (the blue, green and red bands [bands 1, 2 and 3]), these bands must be included in the mapping of submerged areas. False colour composite image were created using the optimum bands for both exposed and submerged. Sea grass areas. These false colour images may be useful to resource managers in interpretation of sea grass areas. However, a more quantifiable result is provided from image classification.
Classification of Exposed Seagrass
Image classification allows for the image to be transferred into some quantifiable form with classes developed to represent the features on the ground. An appropriate technique in image processing before classification is to mask from the image data those areas that are not the primary concern of the task at hand. In this case the area of the image below the tide level at the time ( all water pixele), and the terrestrail vegetation areas leaving only the exposed intertidal areas. The new reduced or masked image allows greater classification accuracies and enables the use of smaller numbers of classes in the classification. An unsupervised Nearest Neighbour classification algroithm was performed of this new masked image using the least correlated TM bands, bands 4, 5 and 7, and the ratio of bands 4.3. Bands 4, 5 and 7 gave the best differentiation in exposed intertidal zones as determined in band selection. The infrared/red ratio was included to assist in the delineation of seagreasses in the exposed intertidal zones as determined in band selection. The infrared/red ratio was included to assist in the delineation of seagrasses in the exposed intertidal zones since the ratio is known to exhibit a good relationship to biomass for vegetation with is known to exhibit a good relationship to biomass for vegetation with simple structures such as grasses (Budd and Milton 1982). The spectral classes obtained were analyzed using the micro BRIAN software analytical techniques such as canonical Variates (CV) analysis, the Minimum Spanning Tree (MST) and clustering methods. Only two classes occupied areas in the exposed intertidal zone and were assumed, by visual interpretation of the false colour imagery and aerial photography of the area, to be possible seagrass area.
