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Photo-library method for mapping seagrass biomass
Estuarine, Coastal and Shelf Science 75 (2007) 559e563 www.elsevier.com/locate/ecss
Short Communication
Photo-library method for mapping seagrass biomass Tiit Kutser a,*, Ele Vahtma¨e a, Chris M. Roelfsema b, Liisa Metsamaa a b
a Estonian Marine Institute, University of Tartu, Ma¨ealuse 10a, 12618 Tallinn, Estonia Centre for Remote Sensing and Spatial Information Science, School of Geography, Planning and Architecture, University of Queensland, Brisbane, Qld 4072, Australia
Received 13 March 2007; accepted 23 May 2007 Available online 25 July 2007
Abstract The creation of seagrass biomass maps by diving/snorkelling is time-consuming and expensive. This paper presents a method for estimating seagrass dry weight using a photo-library of classes of differing seagrass biomass. Field data were collected at seagrass beds in Ngederrak Reef, Palau, in 2006. Photos of 25 25 cm quadrats were taken prior to the collection the above-ground biomass for determination of biomass dry weight. Fifteen classes of seagrass biomass and substrate type were identified. The dry weight for each class of seagrass was measured in laboratory. A photo-library was created for biomass classification where each in situ quadrat photo is accompanied with seagrass dry weight of the sample and a photo of the sorted sample taken in laboratory. The photo-library of quadrats was then used to estimate seagrass biomass on photos gathered along 100 m long transects at 2 m intervals. This procedure was conducted by three different observers. The seagrass dry weight estimates were consistent between interpreters even if one of the interpreters had no experience in seagrass research. This approach allows quick collection of seagrass dry weight data over large areas. The method can be used for creating seagrass biomass maps by snorkelling/diving and/or for calibrating and validating biomass maps created by remote sensing. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: seagrass; biomass; remote sensing
1. Introduction Seagrass habitats have valuable roles since they function as nursery ground, natural resource, and for biodiversity and coastal protection (Waycott et al., 2005). Management is needed to conserve these areas and part of management is the appropriate monitoring of the seagrasses’ biophysical characteristics: species composition, cover and biomass (McKenzie et al., 2001; Short and Coles, 2001; Larkum et al., 2006). The extent and the remote marine location of seagrass habitats creates the need for cost effective approaches to map these biophysical characteristics. Seagrass biomass is in general the most labour intensive value to determine in the field. The sampling method for estimating biomass depends on the
* Corresponding author. E-mail address: [email protected] (T. Kutser). 0272-7714/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2007.05.043
size of the area to be assessed, resource and time limitations, accuracy required, structure of the vegetation complex, and the vegetation components of interest (Catchpole and Wheeler, 1992). The restrictions associated with marine protected areas should be also taken into consideration and non-intrusive methods should be used in protected areas. The use of remote sensing in mapping of seagrass biomass has been investigated by Mumby et al. (1997a) and Green et al. (2000). There remains a need to collect in situ data for calibration and validation purposes of the remote sensing maps of seagrass biomass. Although a variety of methods have been developed to measure seagrass biomass, sampling designs will continue to be modified and improved. The most widely used method for estimating seagrass biomass is the manual harvest of macrophyte tissue within quadrats (Downing and Anderson, 1985; Duarte and Kirkman, 2001). Biomass is measured by harvesting either the aboveground or the total biomass within a sampling frame
560
T. Kutser et al. / Estuarine, Coastal and Shelf Science 75 (2007) 559e563
(Krause-Jensen et al., 2004). Samples may be collected randomly (Boer, 2000; Phillips et al., 2006) or by point sampling on a line transect (Lin and Shao, 1998; Tolan et al., 1997). To determine the patterns of seagrass abundance over a gradient of depth the transect method is recommended. The manual harvesting method provides a relatively precise measure of seagrass abundance, but has the disadvantages of being destructive, time consuming and expensive. If biomass estimation studies are carried out in marine protected areas, priority should be given to non-destructive methods. Mellors (1991) designed a non-destructive visual assessment method for estimating above-ground seagrass biomass, which allowed repeated monitoring, was less damaging to the environment and was generally faster than destructive methods. Mellors’ (1991) method used a linear scale of five biomass categories (1e5), which were assigned to seagrass samples in 0.25 m2 quadrats. The first quadrat is placed in the seagrass bed with the highest biomass (referred to as category 5) and the second quadrat was placed in an area with the lowest biomass (referred to as category 1). Quadrats for categories 2, 3 and 4 were placed by estimating biomass differences between categories 1 and 5. Each quadrat was photographed and the material therein harvested as a biomass sample. Using the photographs as a guide, observers proceeded to visually estimate seagrass biomass across a study site. Mumby et al. (1997b) extended the range of reference quadrats from five to six, since it seemed that surveyors were tempted to place disproportionate number of quadrats in the middle category. Calibration of the biomass scale was performed by harvesting multiple training samples by each observer. Use of the Mellors (1991) or Mumby et al. (1997b) method may be problematic in some situations. For example, the collection of training samples is usually prohibited or limited in marine protected areas. Furthermore, there may be time restraints on the duration of the fieldwork, limiting training of the observers. Classification with just six seagrass density classes may also be insufficient from a remote sensing point of view in such complex underwater environments where seagrass biomass has to be estimated on the background of sand, corals or macroalgae with highly variable colour and cover density. Calibration and validation of remote sensing imagery requires a large amount of geo-referenced field data. Georeferenced benthic photo transects are suitable for this purpose since they can be used to gather large geo-referenced field data sets through a non-intrusive approach (Roelfsema et al., 2005). In this paper we present an approach where seagrass biomass along a geo-located photo transect was determined using photos of quadrats with known seagrass dry weight, the photo-library method. 2. Method 2.1. Study area The study was carried out in the Republic of Palau at the Ngederrak Conservation Area (7 km2) in April 2006. The
Ngederrak Reef contains several habitats including seagrass beds, and the latter are known to be important feeding grounds for dugongs (Lanyon, 2003) The seagrass beds under investigation consisted of several seagrass species: Cymodocea rotundata, Halodule uninervis, Halophila ovalis, Thalassia hemprichii, Syringodium isoetifolium and Enhalus accroides. Canopy height of these beds can vary from a few centimetres for Halophila ovalis, to less than 20 cm for Enhalus accroides and over 30 cm for Thalassia hemprichii (Waycott et al., 2004). In April 2006 Thalassia hemprichii and Enhalis accroides were the most abundant seagrass species. The Ngederrak seagrass habitats are mixed with algae and corals. The algae species were mainly of genera: Laurencia, Caulerpa, Sargassum, Dictyota and Padina. Laurencia spp. was the most abundant species mixed with the seagrass, often forming dense and continuous cover below the seagrass canopy. Dense Padina spp. beds were also present in some areas. The seagrass/algae cover was mixed with mainly branching Acropora spp. and massive and branching Porites spp. Some of the habitats were covered with cyanobacterial mats overgrowing sand, seagrass, algae and coral.
2.2. Biomass samples The Ngederrak reef area was visually inspected prior the biomass sample collection to identify the range of seagrass biomass. We were allowed to collect a maximum of 15 samples in the marine protected area to cover the whole variety of habitats including dense seagrass beds, bare sand, dense macroalgal cover with some seagrass, dense coral cover with some seagrass, etc. Biomass of seagrass standing crop and algae were collected from 25 cm by 25 cm quadrats after photos of each quadrat were captured. The above ground contents of the quadrats were placed into mesh bags. At the laboratory the samples were cleaned of remaining sediments. Cleaned samples were separated into seagrass and macroalgae. Photo of each sorted sample was taken together with a tape measure. This allowed determination of the length of the seagrass shoots. The laboratory photos were also suitable for determining species composition of seagrass and macroalgae as the samples were sorted based on species composition. Epiphytes on the seagrass leaves were removed by soaking in hydrochloric acid. Each biomass sample was oven dried at 60 C for about 24 h. Dried samples were weighed immediately after removal from the oven, since they reabsorb moisture quickly. Dry weights of seagrass and macroalgae (if present) were measured separately for each quadrat. It must be noted that the macroalgae dry weight was not used in this study. As a result of the laboratory and in situ work, we obtained a photo-library where for fifteen classes we had in situ photos of the seagrass cover in a 25 25 cm quadrat accompanied with a photo of the sorted sample and the corresponding seagrass dry weight.
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2.3. Photo-transect method
Table 1 Biomasses (dry weight) of 25 25 cm calibration quadrats
The photo-transect method for creating benthic habitat maps for calibration and validation of satellite imagery was developed in the Centre for Remote Sensing and Spatial Information, University of Queensland (Roelfsema et al., 2006). Bottom types are identified based on still images taken along 100 m tape geo-located and fixed to the seafloor. The photos are captured at 2 m interval using a Sony PC10 camera in Marine Pack UW housing and external wide angle lens. A plumb line was used to position the camera at constant distance of 1 m from the bottom resulting in a 1 1 m area captured in each photo. A Garmin 72 GPS was towed by the photographer and recorded his track. Using GPS-Photo Link software from Geospatial Expert, the GPS track coordinates were used to derive the position where each photo was taken based on the GPS and camera time stamp. As a result all information derived for each photo, such as biomass, can be linked to coordinates as well.
Sample Seagrass dry Algae dry Substrate weight (g) weight (g)
2.4. Biomass estimation using the photo-library Seagrass dry weight estimates for the study areas were achieved by comparing transect photos with the photo-library. Transect photos were imported into Excel spreadsheet and divided into 16 squares (25 25 cm each) by drawing a grid on each image. Each of the 16 squares was visually examined and assigned a dry weight value based on comparing it with the photos in the photo library. The total biomass for each 1 1 m transect photo was then calculated as sum of biomasses of the 16 squares. This process of estimation was repeated independently by three different observers. Two of them participated in the field work and were familiar with the seagrass characteristics (e.g., 3D structure of the canopy) and environments. The third observer did not have any previous experience with seagrasses. The number of transects used in this study was four, each consisting of 50 photos. Bottom types along these four transects were extremely variable: from bare sand to 100% coral cover and from 100% of macroalgal cover to dense seagrass beds with canopy height up to 80 cm. The variability was often high also within the one square meter transect photos. 3. Results and discussion Of the 15 reference quadrats ten were over sand, three over coral and two over sand/coral mix. Three of the quadrats (#1, #2, and #13) had similar biomass (Table 1) and bottom type (seagrass-on-sand) despite our attempt to create as diverse a photo-library as possible. The number of classes we used in estimating biomass along the photo transects was therefore reduced to 13. It could have been possible to reduce the number of classes further based solely on the seagrass biomass values. For example classes #10 and #15 both have biomasses below 1 g and the substrate is sand in both cases. These two classes were however very different in benthic composition. One of them was almost bare sand with a few small Halodule
#15
0.56
5.33
#5 #10 #9 #8 #13 #2 #1 #7 #11 #4 #3 #6 #14 #12
0.62 0.68 1.72 3.75 4.15 4.22 4.23 7.62 7.64 12.83 13.82 13.91 17.31 24.98
5.43 e 21.47 20.96 e e e 11.34 10.24 46.62 1.32 e 4.08 0.75
Comments
Sand
Sand covered with cyanobacteria Sand/Rubble Lot of Padina Sand Coral Dense Laurentia Coral Dense Laurentia Sand Sand Sand Sand Laurentia and other algae Sand Dense cyanobacterial mat Sand Different macroalgae Sand Coral Sand Sand Substrate practically not visible
uninervis while the second class represents a bottom that was almost entirely covered with cyanobacteria, some algae (Laurencia, Padina) and different seagrass species (Halophila ovalis and Syringodium isoetifolium). The method we propose is based on visual assessment. Therefore, it is necessary to have a photo-library that covers the majority of bottom types occurring in the study area. Possible similarity in biomass values is not a reason to join classes in the photo-library. Visual biomass assessment methods proposed by Mellors (1991) and Mumby et al. (1997b) require underwater training of the observers and the taking of training samples. We let three observers estimate seagrass biomass along four transects. Two of the observers had collected the biomass samples in Palau and were familiar with the study site, but the third observer did not have any previous experience with seagrass studies. Seagrass dry weight estimates for one transect obtained by the three observers are shown in Fig. 1. Correspondence between all three observers is good. It is clearly seen that in case of higher biomass values the results by the observer #3 are slightly lower than those of first two observers. One explanation arises from the 3D structure of the seagrass canopy, a circumstance with which observer #3 would be unfamiliar. Observer #3 was more conservative in the case of photos with the highest biomass i.e. the sites with the tallest and densest seagrass canopy. The discrepancies between biomass estimates by the three observers became smaller as the work progressed. Fig. 2 illustrates correlation between the results obtained for four transects by observer #1 and observer #3. There are four photos where the observer #3 significantly underestimated seagrass biomass compared to observer #1, but these were all from the first transect observers worked on (Fig. 1). The general tendency was that observer #3 slightly underestimated the biomass values compared to the other two observers. Biomass estimates by observers #1 and #2 were not only closely correlated (R2 ¼ 0.95) but also the values in regression graph lie on the one-to-one line. These results allow us to assume that the
T. Kutser et al. / Estuarine, Coastal and Shelf Science 75 (2007) 559e563
562 300
Seagrass biomass (g/m2)
250
200
150
100
50
0 2
12
7
17
22
27
32
37
42
47
52
Number of photo along the transect line Observer 1
Observer 2
Observer 3
Fig. 1. Seagrass dry weight estimates along a photo transect made by three independent observers using photo-library of quadrats with know biomasses.
method is relatively independent of the observers experience with the seagrasses. The small difference in biomass estimate of experienced and inexperienced staff can be avoided with some training (that we did not perform) or by exploiting staff with previous in situ (seagrass) work experience in biomass assessment. The differences between dry weights of different classes used by us (Table 1) are variable and larger in case of higher biomasses. For example in the case of lower biomass classes the between class differences are in the range of 0.1e2 g per quadrat while the difference is 7.5 g per quadrat for two classes with the highest dry weight. It is therefore theoretically easy to get a larger discrepancy in biomass estimates made by different observers in the case of the classes with higher biomass. For example, one
observer might decide that all 16 quadrats in a transect photo belong to the 17.31 g seagrass dry weight class, but another observer might classify all quadrats belonging to the 24.98 g class (the highest biomass class). The difference would be unacceptably large, 276.96 g versus 399.68 g. This kind of discrepancy did not occur in our case. It may happen that the biomass in transect photos is higher than in the highest biomass class in photo-library used if the preliminary field work is not carried out carefully. For such situations we can only recommend ‘‘visual extrapolation’’ of biomass i.e. comparing the transect photo with photo-library photos and biomasses of 2e3 highest classes and estimating how much higher of the highest biomass class should the transect photo biomass be. The error made in such process should not be very high unless most of a transect photo is covered
Biomass, g/m2 observer #3
350 300
y = 0.8035x + 4.8624 R2 = 0.9378
250 200 150 100 50 0
0
50
100
150
200
250
300
350
Biomass, g/m2 observer #1 Fig. 2. Correlation between the seagrass dry weight estimate made by observer who collected the field data (observer #1) and observer with no previous seagrass study experience (observer #3). One to one line is indicated with the dashed line.
T. Kutser et al. / Estuarine, Coastal and Shelf Science 75 (2007) 559e563
with significantly higher biomass than the highest class in the photo-library. As a practical recommendation we suggest using dualmonitor computers as often used in remote-sensing laboratories. This allows both the transect photos and the photo-library photos simultaneously in the field of view of the observer making the biomass estimation process easier. We used the photo-library to estimate seagrass biomass along photo transects, but randomly taken geo-located photos can also be used. Our field experience suggests (taking into account the spatial accuracy of GPS) that the use of transect data is preferable, especially if purpose of the biomass estimates is the calibration/validation of high spatial resolution remote sensing imagery.
4. Conclusions The photo-library method is non-destructive and gives an opportunity to get large amounts of seagrass dry weight estimates over large areas within a relatively short time. The technique can be used as a stand-alone method for creating seagrass biomass maps based solely on in situ data, but it is also a very effective tool to collect data for calibration and validation of remote sensing imagery. In the latter case, relatively small numbers of biomass samples need to be collected to create a seagrass biomass photo library which then can then be used for interpretation of photo transect data. The advantage of the photo-library method is that it will reduce the amount of field time (diving/snorkelling) and laboratory time to gather and analyse seagrass samples. There is also no need to spend time on the training of dive staff for identifying different seagrass biomass classes under water. Our results show that staff with no previous seagrass study experience can be used to get the seagrass biomass estimates. The diving time usually needed for training the staff can thus be used to collect more photo transects over larger areas.
Acknowledgements The research for this paper was possible through funding through World Bank/GEF Coral Reef Targeted Research Project, and field support of the Palau International Coral Reef Centre and Dr Karen Brady. T.K.’s and E.V.’s participation in the fieldwork was funded by the Estonian Science Foundation grant 6051 and Estonian Basic Research grant 0712699s05. We wish to thank the reviewers for their constructive comments.
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References Boer, W.F., 2000. Biomass dynamics of seagrasses and the role of mangrove and seagrass vegetation as different nutrient sources for an intertidal ecosystem. Aquatic Botany 66, 225e239. Catchpole, W.R., Wheeler, C.J., 1992. Estimating plant biomass: a review of techniques. Australian Journal of Ecology 17, 121e131. Downing, J.A., Anderson, M.R., 1985. Estimating the standing biomass of aquatic macrophytes. Canadian Journal of Fisheries and Aquatic Sciences 42, 1860e1869. Duarte, C.M., Kirkman, H., 2001. Methods for the measurement of seagrass abundance and depth distribution. In: Short, F.T., Coles, R.G. (Eds.), Global Seagrass Research Methods. Elsevier Science, Amsterdam, pp. 141e153. Green, E.P., Mumby, P.J., Edwards, A.J., Clark, C.D., 2000. Remote Sensing Handbook for Tropical Coastal Management. In: Edwards, A.J. (Ed.), Coastal Management Sourcebooks 3. UNESCO, Paris, 316 pp. Lanyon, J.M., 2003. Distribution and abundance of dugongs in Moreton Bay, Queensland, Australia. Wildlife Research 30, 397e409. Larkum, A.W.D., Orth, R., Duarte, C.M., 2006. Seagrasses: Biology, Ecology and Conservation. Springer, Dordrecht, 691 pp. Lin, H., Shao, K., 1998. Temporal changes in the abundance and growth of intertidal Thalassia hemprichii seagrass beds in southern Taiwan. Botanical Bulletin of Academia Sinica 39, 191e198. Krause-Jensen, D., Quaresma, A.L., Cunha, A.H., Greve, T.M., 2004. How are seagrass distribution and abundance monitored? In: Borum, J., Duarte, C.M., Krause-Jensen, D., Greve, T.M. (Eds.), European Seagrasses: An Introduction to Monitoring and Management, 88 pp. http:// www.seagrasses.org/handbook/european_seagrasses_high.pdf. Mellors, J.E., 1991. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquatatic Botany 42, 67e73. McKenzie, L.J., Finkbeiner, M.A., Kirkman, H., 2001. Seagrass mapping methods. In: Short, F.T., Coles, R.G. (Eds.), Global Seagrass Research Methods. Elsevier, Amsterdam, pp. 101e122. Mumby, P.J., Green, E.P., Edwards, A.J., Clark, C.D., 1997a. Measurement of seagrass standing crop using satellite and digital airborne remote sensing. Marine Ecology Progress Series 159, 51e60. Mumby, P.J., Edwards, A.J., Green, E.P., Anderson, C.W., Ellis, A.C., Clark, C.D., 1997b. A visual assessment technique for estimating seagrass standing crop. Aquatic Conservation: Marine and Freshwater Ecosystems 7, 239e251. Phillips, R.C., Milchakova, N.A., Alexandrov, V.V., 2006. Growth dynamics of Zostera in Sevastopol Bay (Crimea, Black Sea). Aquatic Botany 85, 244e 248. Roelfsema, C.M., Phinn, S.R., Joyce, K.E., 2005. Evaluating benthic survey techniques for validating maps of coral reefs derived from remotely sensed images. Proceedings of 10th International Coral Reef Symposium, Okinawa, pp. 1771e1780. Roelfsema, C., Phinn, S., Joyce, K., 2006. Benthic Validation Photo Transect Method. University of Queensland, Brisbane, 29 pp. Short, F.T., Coles, R.G., 2001. Global Seagrass Research Methods. Elsevier, Amsterdam, 482 pp. Tolan, J.M., Holt, S.A., Onuf, C.P., 1997. Distribution and community structure of ichthyoplankton in Laguna Madre seagrass meadows: potential impact of seagrass species change. Estuaries 20, 450e464. Waycott, M., McMahon, V., Mellors, J., Calladine, A., Kleine, D., 2004. A Guide to Tropical Seagrasses of the Indo-West Pacific. James Cook University, Townsville, 72 pp. Waycott, M., Longstaff, B.J., Mellors, J., 2005. Seagrass population dynamics and water quality in the Great Barrier Reef region: a review and future research directions. Marine Pollution Bulletin 51, 343e350.
Short Communication
Photo-library method for mapping seagrass biomass Tiit Kutser a,*, Ele Vahtma¨e a, Chris M. Roelfsema b, Liisa Metsamaa a b
a Estonian Marine Institute, University of Tartu, Ma¨ealuse 10a, 12618 Tallinn, Estonia Centre for Remote Sensing and Spatial Information Science, School of Geography, Planning and Architecture, University of Queensland, Brisbane, Qld 4072, Australia
Received 13 March 2007; accepted 23 May 2007 Available online 25 July 2007
Abstract The creation of seagrass biomass maps by diving/snorkelling is time-consuming and expensive. This paper presents a method for estimating seagrass dry weight using a photo-library of classes of differing seagrass biomass. Field data were collected at seagrass beds in Ngederrak Reef, Palau, in 2006. Photos of 25 25 cm quadrats were taken prior to the collection the above-ground biomass for determination of biomass dry weight. Fifteen classes of seagrass biomass and substrate type were identified. The dry weight for each class of seagrass was measured in laboratory. A photo-library was created for biomass classification where each in situ quadrat photo is accompanied with seagrass dry weight of the sample and a photo of the sorted sample taken in laboratory. The photo-library of quadrats was then used to estimate seagrass biomass on photos gathered along 100 m long transects at 2 m intervals. This procedure was conducted by three different observers. The seagrass dry weight estimates were consistent between interpreters even if one of the interpreters had no experience in seagrass research. This approach allows quick collection of seagrass dry weight data over large areas. The method can be used for creating seagrass biomass maps by snorkelling/diving and/or for calibrating and validating biomass maps created by remote sensing. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: seagrass; biomass; remote sensing
1. Introduction Seagrass habitats have valuable roles since they function as nursery ground, natural resource, and for biodiversity and coastal protection (Waycott et al., 2005). Management is needed to conserve these areas and part of management is the appropriate monitoring of the seagrasses’ biophysical characteristics: species composition, cover and biomass (McKenzie et al., 2001; Short and Coles, 2001; Larkum et al., 2006). The extent and the remote marine location of seagrass habitats creates the need for cost effective approaches to map these biophysical characteristics. Seagrass biomass is in general the most labour intensive value to determine in the field. The sampling method for estimating biomass depends on the
* Corresponding author. E-mail address: [email protected] (T. Kutser). 0272-7714/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2007.05.043
size of the area to be assessed, resource and time limitations, accuracy required, structure of the vegetation complex, and the vegetation components of interest (Catchpole and Wheeler, 1992). The restrictions associated with marine protected areas should be also taken into consideration and non-intrusive methods should be used in protected areas. The use of remote sensing in mapping of seagrass biomass has been investigated by Mumby et al. (1997a) and Green et al. (2000). There remains a need to collect in situ data for calibration and validation purposes of the remote sensing maps of seagrass biomass. Although a variety of methods have been developed to measure seagrass biomass, sampling designs will continue to be modified and improved. The most widely used method for estimating seagrass biomass is the manual harvest of macrophyte tissue within quadrats (Downing and Anderson, 1985; Duarte and Kirkman, 2001). Biomass is measured by harvesting either the aboveground or the total biomass within a sampling frame
560
T. Kutser et al. / Estuarine, Coastal and Shelf Science 75 (2007) 559e563
(Krause-Jensen et al., 2004). Samples may be collected randomly (Boer, 2000; Phillips et al., 2006) or by point sampling on a line transect (Lin and Shao, 1998; Tolan et al., 1997). To determine the patterns of seagrass abundance over a gradient of depth the transect method is recommended. The manual harvesting method provides a relatively precise measure of seagrass abundance, but has the disadvantages of being destructive, time consuming and expensive. If biomass estimation studies are carried out in marine protected areas, priority should be given to non-destructive methods. Mellors (1991) designed a non-destructive visual assessment method for estimating above-ground seagrass biomass, which allowed repeated monitoring, was less damaging to the environment and was generally faster than destructive methods. Mellors’ (1991) method used a linear scale of five biomass categories (1e5), which were assigned to seagrass samples in 0.25 m2 quadrats. The first quadrat is placed in the seagrass bed with the highest biomass (referred to as category 5) and the second quadrat was placed in an area with the lowest biomass (referred to as category 1). Quadrats for categories 2, 3 and 4 were placed by estimating biomass differences between categories 1 and 5. Each quadrat was photographed and the material therein harvested as a biomass sample. Using the photographs as a guide, observers proceeded to visually estimate seagrass biomass across a study site. Mumby et al. (1997b) extended the range of reference quadrats from five to six, since it seemed that surveyors were tempted to place disproportionate number of quadrats in the middle category. Calibration of the biomass scale was performed by harvesting multiple training samples by each observer. Use of the Mellors (1991) or Mumby et al. (1997b) method may be problematic in some situations. For example, the collection of training samples is usually prohibited or limited in marine protected areas. Furthermore, there may be time restraints on the duration of the fieldwork, limiting training of the observers. Classification with just six seagrass density classes may also be insufficient from a remote sensing point of view in such complex underwater environments where seagrass biomass has to be estimated on the background of sand, corals or macroalgae with highly variable colour and cover density. Calibration and validation of remote sensing imagery requires a large amount of geo-referenced field data. Georeferenced benthic photo transects are suitable for this purpose since they can be used to gather large geo-referenced field data sets through a non-intrusive approach (Roelfsema et al., 2005). In this paper we present an approach where seagrass biomass along a geo-located photo transect was determined using photos of quadrats with known seagrass dry weight, the photo-library method. 2. Method 2.1. Study area The study was carried out in the Republic of Palau at the Ngederrak Conservation Area (7 km2) in April 2006. The
Ngederrak Reef contains several habitats including seagrass beds, and the latter are known to be important feeding grounds for dugongs (Lanyon, 2003) The seagrass beds under investigation consisted of several seagrass species: Cymodocea rotundata, Halodule uninervis, Halophila ovalis, Thalassia hemprichii, Syringodium isoetifolium and Enhalus accroides. Canopy height of these beds can vary from a few centimetres for Halophila ovalis, to less than 20 cm for Enhalus accroides and over 30 cm for Thalassia hemprichii (Waycott et al., 2004). In April 2006 Thalassia hemprichii and Enhalis accroides were the most abundant seagrass species. The Ngederrak seagrass habitats are mixed with algae and corals. The algae species were mainly of genera: Laurencia, Caulerpa, Sargassum, Dictyota and Padina. Laurencia spp. was the most abundant species mixed with the seagrass, often forming dense and continuous cover below the seagrass canopy. Dense Padina spp. beds were also present in some areas. The seagrass/algae cover was mixed with mainly branching Acropora spp. and massive and branching Porites spp. Some of the habitats were covered with cyanobacterial mats overgrowing sand, seagrass, algae and coral.
2.2. Biomass samples The Ngederrak reef area was visually inspected prior the biomass sample collection to identify the range of seagrass biomass. We were allowed to collect a maximum of 15 samples in the marine protected area to cover the whole variety of habitats including dense seagrass beds, bare sand, dense macroalgal cover with some seagrass, dense coral cover with some seagrass, etc. Biomass of seagrass standing crop and algae were collected from 25 cm by 25 cm quadrats after photos of each quadrat were captured. The above ground contents of the quadrats were placed into mesh bags. At the laboratory the samples were cleaned of remaining sediments. Cleaned samples were separated into seagrass and macroalgae. Photo of each sorted sample was taken together with a tape measure. This allowed determination of the length of the seagrass shoots. The laboratory photos were also suitable for determining species composition of seagrass and macroalgae as the samples were sorted based on species composition. Epiphytes on the seagrass leaves were removed by soaking in hydrochloric acid. Each biomass sample was oven dried at 60 C for about 24 h. Dried samples were weighed immediately after removal from the oven, since they reabsorb moisture quickly. Dry weights of seagrass and macroalgae (if present) were measured separately for each quadrat. It must be noted that the macroalgae dry weight was not used in this study. As a result of the laboratory and in situ work, we obtained a photo-library where for fifteen classes we had in situ photos of the seagrass cover in a 25 25 cm quadrat accompanied with a photo of the sorted sample and the corresponding seagrass dry weight.
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2.3. Photo-transect method
Table 1 Biomasses (dry weight) of 25 25 cm calibration quadrats
The photo-transect method for creating benthic habitat maps for calibration and validation of satellite imagery was developed in the Centre for Remote Sensing and Spatial Information, University of Queensland (Roelfsema et al., 2006). Bottom types are identified based on still images taken along 100 m tape geo-located and fixed to the seafloor. The photos are captured at 2 m interval using a Sony PC10 camera in Marine Pack UW housing and external wide angle lens. A plumb line was used to position the camera at constant distance of 1 m from the bottom resulting in a 1 1 m area captured in each photo. A Garmin 72 GPS was towed by the photographer and recorded his track. Using GPS-Photo Link software from Geospatial Expert, the GPS track coordinates were used to derive the position where each photo was taken based on the GPS and camera time stamp. As a result all information derived for each photo, such as biomass, can be linked to coordinates as well.
Sample Seagrass dry Algae dry Substrate weight (g) weight (g)
2.4. Biomass estimation using the photo-library Seagrass dry weight estimates for the study areas were achieved by comparing transect photos with the photo-library. Transect photos were imported into Excel spreadsheet and divided into 16 squares (25 25 cm each) by drawing a grid on each image. Each of the 16 squares was visually examined and assigned a dry weight value based on comparing it with the photos in the photo library. The total biomass for each 1 1 m transect photo was then calculated as sum of biomasses of the 16 squares. This process of estimation was repeated independently by three different observers. Two of them participated in the field work and were familiar with the seagrass characteristics (e.g., 3D structure of the canopy) and environments. The third observer did not have any previous experience with seagrasses. The number of transects used in this study was four, each consisting of 50 photos. Bottom types along these four transects were extremely variable: from bare sand to 100% coral cover and from 100% of macroalgal cover to dense seagrass beds with canopy height up to 80 cm. The variability was often high also within the one square meter transect photos. 3. Results and discussion Of the 15 reference quadrats ten were over sand, three over coral and two over sand/coral mix. Three of the quadrats (#1, #2, and #13) had similar biomass (Table 1) and bottom type (seagrass-on-sand) despite our attempt to create as diverse a photo-library as possible. The number of classes we used in estimating biomass along the photo transects was therefore reduced to 13. It could have been possible to reduce the number of classes further based solely on the seagrass biomass values. For example classes #10 and #15 both have biomasses below 1 g and the substrate is sand in both cases. These two classes were however very different in benthic composition. One of them was almost bare sand with a few small Halodule
#15
0.56
5.33
#5 #10 #9 #8 #13 #2 #1 #7 #11 #4 #3 #6 #14 #12
0.62 0.68 1.72 3.75 4.15 4.22 4.23 7.62 7.64 12.83 13.82 13.91 17.31 24.98
5.43 e 21.47 20.96 e e e 11.34 10.24 46.62 1.32 e 4.08 0.75
Comments
Sand
Sand covered with cyanobacteria Sand/Rubble Lot of Padina Sand Coral Dense Laurentia Coral Dense Laurentia Sand Sand Sand Sand Laurentia and other algae Sand Dense cyanobacterial mat Sand Different macroalgae Sand Coral Sand Sand Substrate practically not visible
uninervis while the second class represents a bottom that was almost entirely covered with cyanobacteria, some algae (Laurencia, Padina) and different seagrass species (Halophila ovalis and Syringodium isoetifolium). The method we propose is based on visual assessment. Therefore, it is necessary to have a photo-library that covers the majority of bottom types occurring in the study area. Possible similarity in biomass values is not a reason to join classes in the photo-library. Visual biomass assessment methods proposed by Mellors (1991) and Mumby et al. (1997b) require underwater training of the observers and the taking of training samples. We let three observers estimate seagrass biomass along four transects. Two of the observers had collected the biomass samples in Palau and were familiar with the study site, but the third observer did not have any previous experience with seagrass studies. Seagrass dry weight estimates for one transect obtained by the three observers are shown in Fig. 1. Correspondence between all three observers is good. It is clearly seen that in case of higher biomass values the results by the observer #3 are slightly lower than those of first two observers. One explanation arises from the 3D structure of the seagrass canopy, a circumstance with which observer #3 would be unfamiliar. Observer #3 was more conservative in the case of photos with the highest biomass i.e. the sites with the tallest and densest seagrass canopy. The discrepancies between biomass estimates by the three observers became smaller as the work progressed. Fig. 2 illustrates correlation between the results obtained for four transects by observer #1 and observer #3. There are four photos where the observer #3 significantly underestimated seagrass biomass compared to observer #1, but these were all from the first transect observers worked on (Fig. 1). The general tendency was that observer #3 slightly underestimated the biomass values compared to the other two observers. Biomass estimates by observers #1 and #2 were not only closely correlated (R2 ¼ 0.95) but also the values in regression graph lie on the one-to-one line. These results allow us to assume that the
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562 300
Seagrass biomass (g/m2)
250
200
150
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50
0 2
12
7
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Number of photo along the transect line Observer 1
Observer 2
Observer 3
Fig. 1. Seagrass dry weight estimates along a photo transect made by three independent observers using photo-library of quadrats with know biomasses.
method is relatively independent of the observers experience with the seagrasses. The small difference in biomass estimate of experienced and inexperienced staff can be avoided with some training (that we did not perform) or by exploiting staff with previous in situ (seagrass) work experience in biomass assessment. The differences between dry weights of different classes used by us (Table 1) are variable and larger in case of higher biomasses. For example in the case of lower biomass classes the between class differences are in the range of 0.1e2 g per quadrat while the difference is 7.5 g per quadrat for two classes with the highest dry weight. It is therefore theoretically easy to get a larger discrepancy in biomass estimates made by different observers in the case of the classes with higher biomass. For example, one
observer might decide that all 16 quadrats in a transect photo belong to the 17.31 g seagrass dry weight class, but another observer might classify all quadrats belonging to the 24.98 g class (the highest biomass class). The difference would be unacceptably large, 276.96 g versus 399.68 g. This kind of discrepancy did not occur in our case. It may happen that the biomass in transect photos is higher than in the highest biomass class in photo-library used if the preliminary field work is not carried out carefully. For such situations we can only recommend ‘‘visual extrapolation’’ of biomass i.e. comparing the transect photo with photo-library photos and biomasses of 2e3 highest classes and estimating how much higher of the highest biomass class should the transect photo biomass be. The error made in such process should not be very high unless most of a transect photo is covered
Biomass, g/m2 observer #3
350 300
y = 0.8035x + 4.8624 R2 = 0.9378
250 200 150 100 50 0
0
50
100
150
200
250
300
350
Biomass, g/m2 observer #1 Fig. 2. Correlation between the seagrass dry weight estimate made by observer who collected the field data (observer #1) and observer with no previous seagrass study experience (observer #3). One to one line is indicated with the dashed line.
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with significantly higher biomass than the highest class in the photo-library. As a practical recommendation we suggest using dualmonitor computers as often used in remote-sensing laboratories. This allows both the transect photos and the photo-library photos simultaneously in the field of view of the observer making the biomass estimation process easier. We used the photo-library to estimate seagrass biomass along photo transects, but randomly taken geo-located photos can also be used. Our field experience suggests (taking into account the spatial accuracy of GPS) that the use of transect data is preferable, especially if purpose of the biomass estimates is the calibration/validation of high spatial resolution remote sensing imagery.
4. Conclusions The photo-library method is non-destructive and gives an opportunity to get large amounts of seagrass dry weight estimates over large areas within a relatively short time. The technique can be used as a stand-alone method for creating seagrass biomass maps based solely on in situ data, but it is also a very effective tool to collect data for calibration and validation of remote sensing imagery. In the latter case, relatively small numbers of biomass samples need to be collected to create a seagrass biomass photo library which then can then be used for interpretation of photo transect data. The advantage of the photo-library method is that it will reduce the amount of field time (diving/snorkelling) and laboratory time to gather and analyse seagrass samples. There is also no need to spend time on the training of dive staff for identifying different seagrass biomass classes under water. Our results show that staff with no previous seagrass study experience can be used to get the seagrass biomass estimates. The diving time usually needed for training the staff can thus be used to collect more photo transects over larger areas.
Acknowledgements The research for this paper was possible through funding through World Bank/GEF Coral Reef Targeted Research Project, and field support of the Palau International Coral Reef Centre and Dr Karen Brady. T.K.’s and E.V.’s participation in the fieldwork was funded by the Estonian Science Foundation grant 6051 and Estonian Basic Research grant 0712699s05. We wish to thank the reviewers for their constructive comments.
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