Marine Pollution Bulletin 93 (2015) 298–300
Contents lists available at ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Correspondence Response to Svejkovsky et al.
can be purchased off-the-shelf. All other sensors require special order and, often, development’’ (Fingas and Brown, 2011).
This is in response to the rebuttal by Svejkovsky et al. First, It should be pointed out that the Fingas and Brown, 2014, article in MPB is a new and original review. This review was carried out over a two-year period using more than 1000 references. These references were then summarized using about 160 references to fit into the MPB format. In terms of the literature review, it should be noted that the authors relied on peer-reviewed articles. The survey is intended to focus the reader on newer developments in the field. In addition it should be noted that combined, the authors have more than 60 years of practical and research experience in remote sensing. This includes developing sensors such as UV/IR sensors, fluorosensors, space and airborne SAR applications to oil, multispectral sensors, a three-laser thickness sensor, and various airborne sensors and packages. Operational experience includes operating several systems during the Kurdistan, Nestucca, and Exxon Valdez spills plus several other incidents as well as performing demonstration flights on routine surveillance. For 15 years, the authors had two aircraft dedicated to developing and evaluating sensors. Over the years the authors were involved with more than 8 aircraft in terms of oil spill remote sensing, practice and research. Further it should be noted that neither author sells any of these sensors nor data resulting from these sensors. Svejkovsky et al. note that several review articles were presented but include two articles (Brown and Fingas, 2005; Brown and Fingas, 1999) which were on a different topic (international aircraft systems). Examination of these references would reveal significant differences between these reviews. In particular Svejkovsky et al. might wish to consult the Fingas and Brown (2011) reference as this contains more historical and more operational information than the Marine Pollution Bulletin article of 2014. Surely between 1997 and 2014 there are enough developments to warrant two updates on technology. During this time there were an additional 1000 peerreviewed papers published on the topic. Svejkovsky et al. note that remote sensing is an important part of oil spill response. Fingas and Brown agree with this and highlight this importance several times in the paper. Svejkovsky et al. go on to say that Fingas and Brown said that the IR technique was ‘useless’. This is not true, in fact in the paper says ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ (p. 13, Fingas and Brown, 2014). In fact, the other reference that is noted says ‘‘A cheap sensor recommended for oil spill work is an infrared camera. This is the cheapest but undiscriminating device. This is the only piece of equipment that
1. The Role and Usefulness of Remote Sensing and Visual Observations
q
DOI of original article: http://dx.doi.org/10.1016/j.marpolbul.2015.01.005
http://dx.doi.org/10.1016/j.marpolbul.2015.02.003 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.
Svejkovsky et al. suggest that the IR/UV technique is used to identify the areas of the thick and thin areas of the oil around the Baltic and North Seas. This is different than the statement made by Fingas and Brown which clearly states that the technique does not yield thickness measurements relevant to oil spill countermeasures as opposed to measuring areas of thick or thin. These are two completely different things. Fingas and Brown are merely stating that the thin and thick indications noted by IR/UV are not useful as a thickness measure. Svejkovsky et al. suggest that the thickness stated to be useful for countermeasures in the MPB paper are too thick and that 0.100 mm is sufficient for dispersant treatment. It must be noted that the stated effective thicknesses for in-situ burning is 2 mm, for skimming about 0.5–10 mm (Goodman, 2009). Skimming is most common world-wide. In fact, in the example used in the rebuttal, the Baltic Sea, dispersants are not used. The thickness of 0.1 mm is not a commonly- stated desirable thickness for dispersants either. 2. Visible Remote Sensing Svejkovsky et al. note that they disagree with the conclusions that the only reliable thicknesses indications are for thin sheen and rainbow portions, however do not provide any evidence to the contrary. They also note that the generalized absorbance figures in 1–3 are flawed because they do not specify what type of water is measured or what state the oil is in or the high UV reflectivity of oil in daytime. The references noted for the absorbance figures should be consulted. These references are from carefully-conducted scientific studies on pure water. As noted in the Fingas and Brown (2014) there are few studies on this. Further when we reference studies on water, this is noted and does not include chlorophyll. There are studies on chlorophyll and this is not the topic of the paper in question. Svejkovsky et al. note that thickness of the oil changes the visual reflectance, again this statement is not supported. Further they note that UV is a prime method for distinguishing biogenic versus oil slicks. Again this statement is not supported and no physics given for why this might be. 2a. The use of subsidiary references. Svejkovsky et al. note that the reference Fingas (2012) is used extensively in the Fingas and Brown (2014). The Fingas (2012) paper is in the peer-reviewed AMOP proceedings and contains more than 80 references dealing with the important topic of oil
Correspondence / Marine Pollution Bulletin 93 (2015) 298–300
spill thickness measurement. Svejkovsky et al., go on to state that the Bonn appearance code, apparently the point of dispute here, was published in some papers and cite some grey literature for this. Svejkovsky et al. also use Lehr (2010) to support their case; however the intent of the Lehr paper is opposite to that. Lehr (2010) in his conclusion states that ‘‘. . .in most cases since the majority of the oil will be in the optically thick portion, which cannot be accurately estimated by visual observation’’. While thickness measurements are certainly a topic of active discussion, it should be noted that few ‘solutions’ that have been scientifically tested and reviewed and published in the peerreviewed literature. Fingas and Brown both note the absence of measurements of slick thickness on spills in peer-reviewed literature or for a well-documented spill. This is certainly a work in progress. Svejkovsky et al. state that visual surveillance remains the most commonly used method of detecting and quantifying illegal oil discharges and to help guide oil spill response methods. Fingas and Brown feel that these two uses should be separated. For surveillance of illegal oil discharges, there is no doubt that the most common technique is that of radar, typically confirmed by using visual means. For direct oil spill countermeasures, visual means are certainly the most common and Fingas and Brown (2014) state that ‘‘Overall, the visible area remains an active research area as well as a practical means of monitoring oil spills.’’ 3. Use of multispectral analyzers Svejkovsky et al. suggest that multispectral analyzers can be used to quantify oil despite the fact that Fingas and Brown note that documentation and explanations are lacking and that the statement is not back up by peer-reviewed literature. 4. Thermal remote sensing Svejkovsky et al. incorrectly state that Fingas and Brown (2014) dismiss the usefulness of thermal imaging to detect oil. In fact, Fingas and Brown say ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ (p. 13, Fingas and Brown, 2014). Fingas and Brown (2014) do say that infrared is not capable of providing thickness indications. Svejkovsky et al. state that Fig. 5 is actually a night time image that and Fingas and Brown incorrectly stated that this was a daytime image. They note that the Aster sensor is part of the Terra satellite which also houses MODIS and then misinterpret the Figure 5. In fact MODIS itself is a day-time sensor. In fact the NASA website says ‘‘‘‘MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra’s orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth’s surface every 1–2 days, acquiring data in 36 spectral bands, or groups of wavelengths’’ (MODIS, 2014). Svejkovsky et al. incorrectly state the interpretation of the image. Fingas and Brown (2014) took the interpretation of the image directly from the NASA website which states ‘‘The image is a thermal image, with the coldest surfaces appearing dark, and the warmest appearing white. The city of Pascagoula, Miss., is visible in the upper right corner; at night the land is colder (darker) than the Gulf waters. Offshore islands also appear dark compared to the surrounding ocean. The black dots and patches are small clouds, particularly in the southern half of the image. The thickest parts of the oil spill appear as dark grey, filamentous masses in the
299
southern part of the image, extending off of the bottom. Other dark-light swirl patterns are water currents where different temperature water masses are visible’’ (ASTER, 2013). Svejkovsky et al. criticize the fact that a 20-year old reference is used to note that under some circumstances emulsions cannot be detected by infrared. They then go on to note the improvements in infrared sensors and state that infrared is often used to identify thicker and emulsified slicks in the North Sea. Fingas and Brown note that there is no peer-reviewed literature on the topic other than the 20-year old one used in the paper, and that this is an area where more research might be done. Svejkovsky et al. also criticize another reference used by Fingas and Brown, that of Brown et al. (1998) (not one of the present authors) which shows that infrared cannot be used to measure oil thickness. Svejkovsky et al. suggest that because of the age of the infrared camera, that the test was invalid and that modern digital equipment might see the difference in infrared signal with oil thickness. This is not correct because if there were a difference in infrared emission with thickness, the 25–38 grey-scale would have detected it as well as a 256-grey scale device. No references are given to support their arguments that infrared indicates oil layer thickness. No studies have shown that infrared emission is related to oil thickness. Svejkovsky et al. suggest that Fingas and Brown (2014) did not correctly use the Shih and Andrews (2008) reference and that this reference actually notes that there is a correlation between infrared emission and oil thickness. This is not correct. Shih and Andrews (2008, 2009) clearly show that there is not a correlation between oil thickness and emission – in fact they show that thinner layers are sometimes more emissive than thick layers depending on day or night. This is the reverse of what Svejkovsky et al. suggest. Other reviewers have noted this lack of infrared/thickness as well (Goodman, 2009). Svejkovsky et al. also suggest that Fingas and Brown (2014) suggest that infrared is not useful for oil spill countermeasures. The Fingas and Brown paper actually says that infrared is not useful to measure the kinds of thicknesses necessary for oil spill countermeasures. In actual fact in the paper says ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ 5. Synthetic Aperture Radar Svejkovsky et al. suggest that in reviewing radar processing techniques and suggest that one reference (Garcia-Pineda et al., 2008) was misclassified. They also suggest that a newer paper was not incorporated into the study and that this paper showed that emulsions were detected with no wind (Garcia-Pineda et al., 2013). In fact this paper does not say that and the following is a quote from the abstract of this paper: ‘‘The algorithm performs efficiently for all radar incidence angles when wind conditions are above 3 m/s. When low wind conditions are present, the performance of the neural network classification is limited. . .’’ (Garcia-Pineda et al., 2013). This is the opposite of what Svejkovsky et al. suggest. Svejkovsky et al. also suggest that Fingas and Brown (2014) do not cover emulsions in their study and thus have not got the latest technology incorporated into their paper. This is not correct and even Fig. 8 shows a radar image of the oil from the Deep Water Horizon, oil which was emulsified. It is quite clear that radar techniques are indifferent to the state of the oil. Svejkovsky et al. suggest that Fingas and Brown do not include technological advances and misrepresent some technologies. Fingas and Brown have shown, on the contrary, that many of the points raised by Svejkovsky et al. are incorrect and are misquoted.
300
Correspondence / Marine Pollution Bulletin 93 (2015) 298–300
Further we should remind Svejkovsky et al. that the paper was a based on more than one thousand papers. Fingas and Brown continue to strive to present that latest and proven technology to readers based on peer-reviewed literature as well as extensive and unique experience. We will continue to provide this information in an unbiased fashion as users of remotely-sensed data and sensors. References ASTER, file:///E:/Aatransfer/JPL%20_%20Space%20Images%20_%20ASTER%20Views% 20the%20Gulf%20of%20Mexico%20Oil%20Spill%20in%20Infrared%20%28May% 207%29.htm, accessed 2013. Brown, H.M., Baschuk, J.J., Goodman, R.H., 1998. Infrared sensing and the measurement of oil slick thickness. In: Proceedings of Environment Canada Arctic and Marine Oil Spill Program Technical Seminar (AMOP) Proceedings, vol. 2, Ottawa, ON, pp. 805–810. Brown, C.E., Fingas, M.F., 2005. A review of current global oil spill surveillance, monitoring and remote sensing capabilities. In: Proceedings of the Twentyeighth Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 789–798. Brown, C.E., Fingas, M.F., 1999. Oil spill surveillance, monitoring and remote sensing. In: Proceedings of the Twenty-Second Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 387–401. Fingas, M., Brown, C.E., 2011. Oil Spill Remote Sensing: A Review. Gulf Publishing Company, NY, NY, pp. 111–169 (Chapter 6, in Oil Spill Science and Technology). Fingas, M., 2012. How to measure oil thickness (or not). In: Proceedings of the Thirty-fifth Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 617–652. Fingas, M., Brown, C., 2014. Review of oil spill remote sensing. Mar. Pollut. Bull. 83 (1), 9–23.
Garcia-Pineda, O., MacDonald, I.R., Li, X., Jackson, C.R., Pichel, W.G., 2013. Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA). IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 6 (6), 2517–2525 (Art. No. 6507630). Garcia-Pineda, B., MacDonald, I.R., Zimmer, B., 2008. Synthetic aperture radar image processing using the supervised textural-neural network classification algorithms. IGARSS IV (1), IV1265–IV1268, 4779960. Goodman, R.H., 2009. Why does it Work or Nor Work? In: Proceedings of the Thirty-Second Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 515–520. Lehr, W.J., 2010. Visual observations and the Bonn agreement. In: Proceedings of the Thirty-third Environment Canada Arctic and Marine Oil Spill Program Technical Seminar (AMOP) Proceedings, pp. 669–678. MODIS. http://modis.gsfc.nasa.gov/, accessed November 2014. Shih, W.-C., Andrews, A.B., 2008. Modeling of Thickness Dependent Thermal Contrast of Native and Crude Oil Covered Water Surfaces, IGARRS. Shih, W.-C., Andrews, A.B., 2009. Infrared contrast of crude-oil-covered water surfaces. Proc. Optics Lett. 3019–3021.
Merv Fingas Spill Science, Edmonton, Alberta T6W 1J6, Canada Tel.: +1 780 9896059. E-mail address: fi[email protected] Carl Brown Environmental Science and Technology Section, Environment Canada, Ontario K1A OH3, Canada E-mail address: [email protected] Available online 5 March 2015
Contents lists available at ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Correspondence Response to Svejkovsky et al.
can be purchased off-the-shelf. All other sensors require special order and, often, development’’ (Fingas and Brown, 2011).
This is in response to the rebuttal by Svejkovsky et al. First, It should be pointed out that the Fingas and Brown, 2014, article in MPB is a new and original review. This review was carried out over a two-year period using more than 1000 references. These references were then summarized using about 160 references to fit into the MPB format. In terms of the literature review, it should be noted that the authors relied on peer-reviewed articles. The survey is intended to focus the reader on newer developments in the field. In addition it should be noted that combined, the authors have more than 60 years of practical and research experience in remote sensing. This includes developing sensors such as UV/IR sensors, fluorosensors, space and airborne SAR applications to oil, multispectral sensors, a three-laser thickness sensor, and various airborne sensors and packages. Operational experience includes operating several systems during the Kurdistan, Nestucca, and Exxon Valdez spills plus several other incidents as well as performing demonstration flights on routine surveillance. For 15 years, the authors had two aircraft dedicated to developing and evaluating sensors. Over the years the authors were involved with more than 8 aircraft in terms of oil spill remote sensing, practice and research. Further it should be noted that neither author sells any of these sensors nor data resulting from these sensors. Svejkovsky et al. note that several review articles were presented but include two articles (Brown and Fingas, 2005; Brown and Fingas, 1999) which were on a different topic (international aircraft systems). Examination of these references would reveal significant differences between these reviews. In particular Svejkovsky et al. might wish to consult the Fingas and Brown (2011) reference as this contains more historical and more operational information than the Marine Pollution Bulletin article of 2014. Surely between 1997 and 2014 there are enough developments to warrant two updates on technology. During this time there were an additional 1000 peerreviewed papers published on the topic. Svejkovsky et al. note that remote sensing is an important part of oil spill response. Fingas and Brown agree with this and highlight this importance several times in the paper. Svejkovsky et al. go on to say that Fingas and Brown said that the IR technique was ‘useless’. This is not true, in fact in the paper says ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ (p. 13, Fingas and Brown, 2014). In fact, the other reference that is noted says ‘‘A cheap sensor recommended for oil spill work is an infrared camera. This is the cheapest but undiscriminating device. This is the only piece of equipment that
1. The Role and Usefulness of Remote Sensing and Visual Observations
q
DOI of original article: http://dx.doi.org/10.1016/j.marpolbul.2015.01.005
http://dx.doi.org/10.1016/j.marpolbul.2015.02.003 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.
Svejkovsky et al. suggest that the IR/UV technique is used to identify the areas of the thick and thin areas of the oil around the Baltic and North Seas. This is different than the statement made by Fingas and Brown which clearly states that the technique does not yield thickness measurements relevant to oil spill countermeasures as opposed to measuring areas of thick or thin. These are two completely different things. Fingas and Brown are merely stating that the thin and thick indications noted by IR/UV are not useful as a thickness measure. Svejkovsky et al. suggest that the thickness stated to be useful for countermeasures in the MPB paper are too thick and that 0.100 mm is sufficient for dispersant treatment. It must be noted that the stated effective thicknesses for in-situ burning is 2 mm, for skimming about 0.5–10 mm (Goodman, 2009). Skimming is most common world-wide. In fact, in the example used in the rebuttal, the Baltic Sea, dispersants are not used. The thickness of 0.1 mm is not a commonly- stated desirable thickness for dispersants either. 2. Visible Remote Sensing Svejkovsky et al. note that they disagree with the conclusions that the only reliable thicknesses indications are for thin sheen and rainbow portions, however do not provide any evidence to the contrary. They also note that the generalized absorbance figures in 1–3 are flawed because they do not specify what type of water is measured or what state the oil is in or the high UV reflectivity of oil in daytime. The references noted for the absorbance figures should be consulted. These references are from carefully-conducted scientific studies on pure water. As noted in the Fingas and Brown (2014) there are few studies on this. Further when we reference studies on water, this is noted and does not include chlorophyll. There are studies on chlorophyll and this is not the topic of the paper in question. Svejkovsky et al. note that thickness of the oil changes the visual reflectance, again this statement is not supported. Further they note that UV is a prime method for distinguishing biogenic versus oil slicks. Again this statement is not supported and no physics given for why this might be. 2a. The use of subsidiary references. Svejkovsky et al. note that the reference Fingas (2012) is used extensively in the Fingas and Brown (2014). The Fingas (2012) paper is in the peer-reviewed AMOP proceedings and contains more than 80 references dealing with the important topic of oil
Correspondence / Marine Pollution Bulletin 93 (2015) 298–300
spill thickness measurement. Svejkovsky et al., go on to state that the Bonn appearance code, apparently the point of dispute here, was published in some papers and cite some grey literature for this. Svejkovsky et al. also use Lehr (2010) to support their case; however the intent of the Lehr paper is opposite to that. Lehr (2010) in his conclusion states that ‘‘. . .in most cases since the majority of the oil will be in the optically thick portion, which cannot be accurately estimated by visual observation’’. While thickness measurements are certainly a topic of active discussion, it should be noted that few ‘solutions’ that have been scientifically tested and reviewed and published in the peerreviewed literature. Fingas and Brown both note the absence of measurements of slick thickness on spills in peer-reviewed literature or for a well-documented spill. This is certainly a work in progress. Svejkovsky et al. state that visual surveillance remains the most commonly used method of detecting and quantifying illegal oil discharges and to help guide oil spill response methods. Fingas and Brown feel that these two uses should be separated. For surveillance of illegal oil discharges, there is no doubt that the most common technique is that of radar, typically confirmed by using visual means. For direct oil spill countermeasures, visual means are certainly the most common and Fingas and Brown (2014) state that ‘‘Overall, the visible area remains an active research area as well as a practical means of monitoring oil spills.’’ 3. Use of multispectral analyzers Svejkovsky et al. suggest that multispectral analyzers can be used to quantify oil despite the fact that Fingas and Brown note that documentation and explanations are lacking and that the statement is not back up by peer-reviewed literature. 4. Thermal remote sensing Svejkovsky et al. incorrectly state that Fingas and Brown (2014) dismiss the usefulness of thermal imaging to detect oil. In fact, Fingas and Brown say ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ (p. 13, Fingas and Brown, 2014). Fingas and Brown (2014) do say that infrared is not capable of providing thickness indications. Svejkovsky et al. state that Fig. 5 is actually a night time image that and Fingas and Brown incorrectly stated that this was a daytime image. They note that the Aster sensor is part of the Terra satellite which also houses MODIS and then misinterpret the Figure 5. In fact MODIS itself is a day-time sensor. In fact the NASA website says ‘‘‘‘MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra’s orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth’s surface every 1–2 days, acquiring data in 36 spectral bands, or groups of wavelengths’’ (MODIS, 2014). Svejkovsky et al. incorrectly state the interpretation of the image. Fingas and Brown (2014) took the interpretation of the image directly from the NASA website which states ‘‘The image is a thermal image, with the coldest surfaces appearing dark, and the warmest appearing white. The city of Pascagoula, Miss., is visible in the upper right corner; at night the land is colder (darker) than the Gulf waters. Offshore islands also appear dark compared to the surrounding ocean. The black dots and patches are small clouds, particularly in the southern half of the image. The thickest parts of the oil spill appear as dark grey, filamentous masses in the
299
southern part of the image, extending off of the bottom. Other dark-light swirl patterns are water currents where different temperature water masses are visible’’ (ASTER, 2013). Svejkovsky et al. criticize the fact that a 20-year old reference is used to note that under some circumstances emulsions cannot be detected by infrared. They then go on to note the improvements in infrared sensors and state that infrared is often used to identify thicker and emulsified slicks in the North Sea. Fingas and Brown note that there is no peer-reviewed literature on the topic other than the 20-year old one used in the paper, and that this is an area where more research might be done. Svejkovsky et al. also criticize another reference used by Fingas and Brown, that of Brown et al. (1998) (not one of the present authors) which shows that infrared cannot be used to measure oil thickness. Svejkovsky et al. suggest that because of the age of the infrared camera, that the test was invalid and that modern digital equipment might see the difference in infrared signal with oil thickness. This is not correct because if there were a difference in infrared emission with thickness, the 25–38 grey-scale would have detected it as well as a 256-grey scale device. No references are given to support their arguments that infrared indicates oil layer thickness. No studies have shown that infrared emission is related to oil thickness. Svejkovsky et al. suggest that Fingas and Brown (2014) did not correctly use the Shih and Andrews (2008) reference and that this reference actually notes that there is a correlation between infrared emission and oil thickness. This is not correct. Shih and Andrews (2008, 2009) clearly show that there is not a correlation between oil thickness and emission – in fact they show that thinner layers are sometimes more emissive than thick layers depending on day or night. This is the reverse of what Svejkovsky et al. suggest. Other reviewers have noted this lack of infrared/thickness as well (Goodman, 2009). Svejkovsky et al. also suggest that Fingas and Brown (2014) suggest that infrared is not useful for oil spill countermeasures. The Fingas and Brown paper actually says that infrared is not useful to measure the kinds of thicknesses necessary for oil spill countermeasures. In actual fact in the paper says ‘‘Infrared sensors are reasonably inexpensive, however, and are currently a tool used by the spill remote sensor operator. Infrared cameras are now very common and commercial units are available from several manufacturers.’’ 5. Synthetic Aperture Radar Svejkovsky et al. suggest that in reviewing radar processing techniques and suggest that one reference (Garcia-Pineda et al., 2008) was misclassified. They also suggest that a newer paper was not incorporated into the study and that this paper showed that emulsions were detected with no wind (Garcia-Pineda et al., 2013). In fact this paper does not say that and the following is a quote from the abstract of this paper: ‘‘The algorithm performs efficiently for all radar incidence angles when wind conditions are above 3 m/s. When low wind conditions are present, the performance of the neural network classification is limited. . .’’ (Garcia-Pineda et al., 2013). This is the opposite of what Svejkovsky et al. suggest. Svejkovsky et al. also suggest that Fingas and Brown (2014) do not cover emulsions in their study and thus have not got the latest technology incorporated into their paper. This is not correct and even Fig. 8 shows a radar image of the oil from the Deep Water Horizon, oil which was emulsified. It is quite clear that radar techniques are indifferent to the state of the oil. Svejkovsky et al. suggest that Fingas and Brown do not include technological advances and misrepresent some technologies. Fingas and Brown have shown, on the contrary, that many of the points raised by Svejkovsky et al. are incorrect and are misquoted.
300
Correspondence / Marine Pollution Bulletin 93 (2015) 298–300
Further we should remind Svejkovsky et al. that the paper was a based on more than one thousand papers. Fingas and Brown continue to strive to present that latest and proven technology to readers based on peer-reviewed literature as well as extensive and unique experience. We will continue to provide this information in an unbiased fashion as users of remotely-sensed data and sensors. References ASTER, file:///E:/Aatransfer/JPL%20_%20Space%20Images%20_%20ASTER%20Views% 20the%20Gulf%20of%20Mexico%20Oil%20Spill%20in%20Infrared%20%28May% 207%29.htm, accessed 2013. Brown, H.M., Baschuk, J.J., Goodman, R.H., 1998. Infrared sensing and the measurement of oil slick thickness. In: Proceedings of Environment Canada Arctic and Marine Oil Spill Program Technical Seminar (AMOP) Proceedings, vol. 2, Ottawa, ON, pp. 805–810. Brown, C.E., Fingas, M.F., 2005. A review of current global oil spill surveillance, monitoring and remote sensing capabilities. In: Proceedings of the Twentyeighth Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 789–798. Brown, C.E., Fingas, M.F., 1999. Oil spill surveillance, monitoring and remote sensing. In: Proceedings of the Twenty-Second Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 387–401. Fingas, M., Brown, C.E., 2011. Oil Spill Remote Sensing: A Review. Gulf Publishing Company, NY, NY, pp. 111–169 (Chapter 6, in Oil Spill Science and Technology). Fingas, M., 2012. How to measure oil thickness (or not). In: Proceedings of the Thirty-fifth Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 617–652. Fingas, M., Brown, C., 2014. Review of oil spill remote sensing. Mar. Pollut. Bull. 83 (1), 9–23.
Garcia-Pineda, O., MacDonald, I.R., Li, X., Jackson, C.R., Pichel, W.G., 2013. Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA). IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 6 (6), 2517–2525 (Art. No. 6507630). Garcia-Pineda, B., MacDonald, I.R., Zimmer, B., 2008. Synthetic aperture radar image processing using the supervised textural-neural network classification algorithms. IGARSS IV (1), IV1265–IV1268, 4779960. Goodman, R.H., 2009. Why does it Work or Nor Work? In: Proceedings of the Thirty-Second Arctic and Marine Oil Spill Program Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 515–520. Lehr, W.J., 2010. Visual observations and the Bonn agreement. In: Proceedings of the Thirty-third Environment Canada Arctic and Marine Oil Spill Program Technical Seminar (AMOP) Proceedings, pp. 669–678. MODIS. http://modis.gsfc.nasa.gov/, accessed November 2014. Shih, W.-C., Andrews, A.B., 2008. Modeling of Thickness Dependent Thermal Contrast of Native and Crude Oil Covered Water Surfaces, IGARRS. Shih, W.-C., Andrews, A.B., 2009. Infrared contrast of crude-oil-covered water surfaces. Proc. Optics Lett. 3019–3021.
Merv Fingas Spill Science, Edmonton, Alberta T6W 1J6, Canada Tel.: +1 780 9896059. E-mail address: fi[email protected] Carl Brown Environmental Science and Technology Section, Environment Canada, Ontario K1A OH3, Canada E-mail address: [email protected] Available online 5 March 2015
