Monitoring active fires in the Lower Paraná River floodplain: analysis and reproducible reports on satellite thermal hotspots
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Morandeira, Natalia Soledad
Floodplain wetlands play a key role in hydrological and biogeochemical cycles and comprise a large part of the world's biodiversity and resources. The exploitation of remote sensing data can substantially contribute to monitoring procedures at broad ecological scales. In 2020, the Lower Paraná River floodplain (also known as Paraná River Delta, Argentina) suffered from a severe drought, and extended areas were burned. To monitor the wildfire situation, satellite products provided by FIRMS-NASA were used. These thermal hotspots —associated with active fires— can be downloaded as zipped spatial objects (point shapefiles) and include recent and archive records from VIRRS and MODIS thermal infrared sensors. The main aim was to handle these data, analyze the number of hotspots during 2020, and compare the disaster with previous years' situation. Using a reproducible workflow was crucial to ingest the zip files and repeat the same series of plots and analyses when necessary. Obtaining updated reports allowed me to quickly respond to peers, technicians, and journalists about the evolving fire situation. A total of 39,821 VIIRS S-NPP thermal hotspots were detected, with August (winter) accounting for 39.8% of the whole year’s hotspots. MODIS hotspots have lower spatial resolution than VIIRS, so the cumulative MODIS hotspots recorded during 2020 were 8,673, the highest number of hotspots of the last 11 years. Scripts were written in R language and are shared under a CC BY 4.0 license. QGIS was also used to generate a high-quality animation. The workflow can be used in other study areas. An R workflow to obtain reproducible reports on active fires monitored with satellite products is presented. The work is an ecological application of spatial analyses conducted with open-source software (R, QGIS). By presenting this approach and results, I aim to highlight: the importance of using remote sensing data and ancillary geographic data to monitor large-scale disasters; how generating reproducible workflows can facilitate and improve geospatial analyses, and lastly, I want to show the usage of open-source geospatial software to account all these tasks. Wildfires are a current environmental topic in South American wetland environments. The case study area is the Paraná River floodplain.
 Good morning or good afternoon or good evening depending on where you are. My name is Marco Mingini. I will be the chair of the next talk of the academic track and I'm happy to introduce the next speaker. Natalia Morandera who is a doctor in biological sciences. She's a researcher at the National Scientific and Technical Research Council working at the Institute of Research and Environmental Engineering of the University of San Martín. A researcher focuses on landscape ecology and wetland plant ecology aided by remote sensing and geographic information systems tools. And today she's presenting a talk titled monitoring active fires in the lower Paraná River floodplain. Analysis and reproducible reports on satellite thermal hotspots. Natalia, the floor is yours. Thank you Marco for introducing me and thank you all for coming to my talk. Here today I'm sharing some of my work during last year and my slides are available. I'm sharing the link through the chat. So first I want to give some background on the environmental topic. Floodplains and wetlands cover more than 20% of South America of the continent. And among these wetlands, large areas are covered by floodplain wetlands such as those associated to the Amazonas, the Orinoco and the Paraná River. The dynamics of these large floodplain wetlands depend on floodpasses and climatic conditions. In this picture I'm showing an aerial view of the Paraná River floodplain in a high water level condition in the year 2010. And you can see that freshwater marshes are very green and there are a lot of shallow lakes with open water. However, last year and this year too, the area, the Paraná River is extremely low and the floodplain is very dry. So in this aerial view of the Paraná River floodplain in Argentina, this is a picture that was taken last year, you can see that the freshwater marshes are very dry. There was a lot of dry biomass. And here are some native forest and water is reduced to permanent shallow lakes, open water. So in this context, the area was affected by extended fires and at least 329,000 hectares were burned. This area corresponds to 14% of the Paraná River Delta and about half of the area belongs to natural protected areas. So last year I aimed to monitor the wildfires using spatial data. In particular, I used satellite thermal hotspots. So now I'm sharing some physical background on this special data. The energy emitted by the Earth in the thermal infrared wavelengths can be related to surface temperature. So we can measure this emitted energy with remote sensing sensors on board of satellites can measure the thermal infrared emissivity. And thermal hotspots are very hot pixels that are probably related to active fires. The NASA publishes fire hotspot products within three hours of the acquisition of the satellite emissaries. And these data are freely accessible through the fire information for our source management system. The data can be downloaded as point vector layers and you can also visualize it online like this red hotspot. So these products are generated from two different sensors which differ in their sensor resolution. Sensor resolution needs to be taken into account when interpreting the results. So if we imagine a fire like the one that is shown in this picture, if this fire is monitored with a low resolution sensor system, probably few hotspots are detected, each one corresponding to a large hot area. While if the same fire is monitored with a medium resolution sensor, we probably will detect more hotspots, each one corresponding to a smaller hot area. So this needs to be taken into account when interpreting results and comparing fire activities throughout the years. So my aim during last year was to process these point vector layers and I constructed the workflow that I'm presenting here to reproduce the same analysis and automatize most of the steps and to generate bilingual reports. The aim was to do quick analysis and summarize the information of the on fire situation mainly because peers and journalists were asking us for updated information and also the lockdown and the fires prevented us from conducting fieldwork. So we had to work with the satellite information and also we have our background in the studio area because in our lab we have been conducting studies in the Paranario Delta for almost 20 years. So that was the general situation and it seemed fine but the problem was that we needed to repeat the same analysis once and again to write dissemination articles. Here is a talk at our University by Patricia Candus, post in social media, write these articles, respond to journalists and that's why I need to reproduce the workflow. In the first month in June I designed the QCCIS modeler to process the special data. So in this modeler the input layers are the hotspot products from beer satellite and modi satellite. You download four layers, one is the beer's current data, beer's archive data, modi's current data, modi's archive data and with the modeler I merged the data, clipped the data to the studio area, and then we reprojected them and export output layers with all the active fire records in a single shape file. This is how the modeler is seen from the view of an end user and one problem was that I found a source of error that was manual layer selection. You can note here you have to select each of the layers and for example in this screenshot I misselected the same layer twice. So this is a source of error and also it is time demanding and seeping the shape files, selecting the layers and then the layer needs to be further analyzed and exported to AR to construct the plots and summarize information. Cushies has an advantage that it can generate very nice maps and for example I generated this animation, this is an example of an animation included in a dissemination article co-authorized with Patricia Candus and Priscila Minotti. And this animation was generated with the plugin time manager that recently was replaced by an integrated temporal control function and is included in Cushies. So next I wrote a code in AR to account for all these steps and to produce the plots and the reports and first I show what you need if you want to use the script as an end user. You only need to have a polygon layer of your studio area and save that polygon layer in a specific folder. Next you go to the Firm's NASA web page and download the Archive data and here you can download the data with a very broad bounding box, just drawing a polygon. And then you save the SIP files to a specific folder in your AR project. Next you just need the Archmarked Veyron code. These are the three steps to run the code and now I'm sharing what are the processing steps that are included. The code is written using mainly the libraries Tidyverse, SF, CCplot2 and Armarkdown and it includes file and geometric operations such as reading the SIP files that were saved in a given folder and SIPing the data, reading the hotspot point shape files and creating spatial objects, look for string patterns in the name files to create the hotspot objects. So this avoids the source of error of manual selection of the pond layers. Then all the geometric operations, the same that were included in the Cushy's Modeler that include merging the objects, reprosection clipping to the studio area, next you can obtain an interactive map of the hotspot of the current year and export the final layers to Shell Package. Also other steps include data cleaning, data tidying and producing the plots and the report. So this is general data cleaning and data tidying processes on the attribute tables of the layers. And then you also obtain plots in English and English Plans with showing daily hotspots, cumulative hotspots and also the image, you can export the image of the plots. And I'm showing next some of these plots and the annual comparisons and historical activity comparisons you can obtain. This is an example of a report in Spanish. The processing steps for both the geometric operations and the data tidying and the, and obtain the reports is less than two minutes in my laptop. So to illustrate the workflow, I'm showing some results of the situation last year in the Paranar River Delta. This is a screenshot of the thermal hotspots from Beers Data. In the Paranar River Delta, you can, in the interactive map, you can click any of these points and obtain information on them. And next, you obtain a plot summarizing the number of Beers hotspots, of daily Beers hotspots per day. And here I use to monitor what was happening last year. I use Beers hotspots because they have a medium resolution, the pixel has 375 meters and is better than modest. So here you can see that the months with the highest number of hotspots was August, which accounted for almost 40% of the total hotspot of the year. Next, you can also obtain a plot showing the cumulative number of hotspots all over the year. And the number of Beers hotspots that is recorded in the year, the total number was almost 40,000. And also, you can compare the historical fire activity for your studio area. And here, for example, using Beers Data, that has this resolution of 375 meters of the pixel. Here you can see that the total number of hotspots was the highest in the last nine years. Beers Data are available since 2012. So if we want to analyze the historical fire activity from previous year, we need to use modest data. Modest data are available since November 2001 and the resolution is of 1 kilometer. So the number of Modest hotspots during last year was almost 9,000 and were the highest since 2009. This is a plot that shows the number of Modest hotspots recorded per year. And you can see that there was a high number of Modest hotspots last year. But in 2008, that was also a very dry year and the primary floodplain was dry. In 2008, the number of hotspots was larger. Lastly, I want to include an update on the fire activity during this year because you may know that the Paraná River remains with very low hydrometric levels and fire activity continues. So I updated the analysis yesterday and I obtained that this year more than 11,000 of big hotspots were recorded. And this is a lot but is much less than the fire activity that was observed in 2020. This plot shows the monthly number of hotpots, of real hotpots in 2020 and this year. So our future work will include an analysis of the relation between the historical fire activity and the hydroclimatic trends. And I'm also working on the estimation of the barnet areas because more than fire hotspots are barnet areas which is important to analyze the ecological impact of the fires. So here you can see for example an area of grassland that was burned in the Paraná River floodplain. And an idea was to use the thermal hotspots at seeds and grow regions starting from these hotspots. I'm using the Arsaga library for growing these regions and each region starts in an area with the hotspot and grows according to the spectra similarity. I'm working now with an index called normalize barn ratio that is computed from Sentinel-2 in Mashary. Thanks for your attention. Muchas gracias. Here are my contact details. Here is the link to the full article in the phosphor sheet proceedings shown now. Here is also the GitHub repo of this project. And I also want to share that most of the pictures that I featured in my slides belong to a photographic ESAI project by Sebastián López Brach that is founded by Natcio. I'm grateful to Sebastián and also I want to share his Instagram account for you to see more of his work. Thank you. Thanks a lot Natalia for this exciting talk and for most of all an excellent research. I just want to make just one short note just before opening the floor for questions and it is about a word that is in the title of your call that is reproducible. This is really a keyword and thanks for making your research reproducible. This is a very, it should become a standard practice. Unfortunately, this is not always the case. So thanks for that. The link to the repository is in the slides. If you need the link now, I think we can paste it in the chat now or you can even more easily just open or find Natalia's paper that is associated to this talk and that is published in the phosphor sheet proceedings on the ISPRS archives. So if you look for ISPRS archives for phosphor G 2020. Lots of additional details. Thanks also for the pictures and the link to the Instagram account. Of course, we don't like so much to see fires, but we like to see pictures. So let's clearly hope that the situation will will improve. There is one question that was actually also some question that I wanted to ask that is about the how these research, how this data. Or the outcome of it, especially also regarding the historical analysis will be used by any governmental institution or anyone else, any civil protection organizations, fire brigades or anyone. Because that's very important and I hope that this will be the case. Thanks for the question. Our environmental ministry, Ministerio Nacional de Ambiente is using fire records from Firms NASA. Not my analysis, but they are conducting their own processing and analysis. So my results were used mainly. I use them mainly to disseminate what was happening and to analyze which areas we are being burned to detect. Maybe to disseminate what was happening in social media. And also we work with some non-governmental organizations that were reporting intentional fires or accidental fires followed by land use changes. So that was the use of my analysis. Thanks a lot. This definitely answers the question. We have other questions from the audience. Do you correct for errors in active fire products? No, for the moment I didn't correct for errors in the active fire products. I had ground truth data related both to conversations with local settlers and with journalists. I have also some reference points obtained with flights. I used that ground data to check if the active fires were really fires or not. But I didn't correct the data. My aim was to conduct quick analysis during the situation. Thanks for the question. Questions keep appearing in the venue. So the next one, did you compare the active fire records of the two sources that you use, modis and veers? And do they show any difference? The difference that showed was that veers, since it has a better special resolution, detected smaller fire areas. If you use veers data to detect burn-in areas, you may detect the burn-in areas better because of the better resolution. So veers data showed more hotspots and small fires were easily detected, done with modis. Thanks Natalia. We can quickly go through the last question. Can you use the results for projects to reforest or restore burned areas? It's a good question. The problem in the area is that some of the areas that were burned were changed to land use. For example, they were converted from natural areas or areas with cattle to agricultural areas. So that is the same general problem. I don't know if reforesting is the main issue here in the area. But probably you can use the data to locate which areas are being more seriously affected and to take actions in the ground. Thanks a lot Natalia for answering the questions and for once again giving a very interesting presentation. We need to close here. I would like also to thank the audience for the good questions and the input. I hope and I'm sure also you like the talk. If this is the case, please let us know and let Natalia know using the up-close button in Vanu-les. Thanks a lot and I wish you all a good continuation of PosterG 2021. Thank you Marco. Bye bye.
 Good morning or good afternoon or good evening depending on where you are.  My name is Marco Mingini.  I will be the chair of the next talk of the academic track and I'm happy to introduce the next speaker.  Natalia Morandera who is a doctor in biological sciences.  She's a researcher at the National Scientific and Technical Research Council working at the Institute of Research and Environmental Engineering of the University of San Martín.  A researcher focuses on landscape ecology and wetland plant ecology aided by remote sensing and geographic information systems tools.  And today she's presenting a talk titled monitoring active fires in the lower Paraná River floodplain.  Analysis and reproducible reports on satellite thermal hotspots.  Natalia, the floor is yours.  Thank you Marco for introducing me and thank you all for coming to my talk.  Here today I'm sharing some of my work during last year and my slides are available.  I'm sharing the link through the chat.  So first I want to give some background on the environmental topic.  Floodplains and wetlands cover more than 20% of South America of the continent.  And among these wetlands, large areas are covered by floodplain wetlands such as those associated to the Amazonas, the Orinoco and the Paraná River.  The dynamics of these large floodplain wetlands depend on floodpasses and climatic conditions.  In this picture I'm showing an aerial view of the Paraná River floodplain in a high water level condition in the year 2010.  And you can see that freshwater marshes are very green and there are a lot of shallow lakes with open water.  However, last year and this year too, the area, the Paraná River is extremely low and the floodplain is very dry.  So in this aerial view of the Paraná River floodplain in Argentina, this is a picture that was taken last year,  you can see that the freshwater marshes are very dry.  There was a lot of dry biomass.  And here are some native forest and water is reduced to permanent shallow lakes, open water.  So in this context, the area was affected by extended fires and at least 329,000 hectares were burned.  This area corresponds to 14% of the Paraná River Delta and about half of the area belongs to natural protected areas.  So last year I aimed to monitor the wildfires using spatial data.  In particular, I used satellite thermal hotspots.  So now I'm sharing some physical background on this special data.  The energy emitted by the Earth in the thermal infrared wavelengths can be related to surface temperature.  So we can measure this emitted energy with remote sensing sensors on board of satellites can measure the thermal infrared emissivity.  And thermal hotspots are very hot pixels that are probably related to active fires.  The NASA publishes fire hotspot products within three hours of the acquisition of the satellite emissaries.  And these data are freely accessible through the fire information for our source management system.  The data can be downloaded as point vector layers and you can also visualize it online like this red hotspot.  So these products are generated from two different sensors which differ in their sensor resolution.  Sensor resolution needs to be taken into account when interpreting the results.  So if we imagine a fire like the one that is shown in this picture, if this fire is monitored with a low resolution sensor system,  probably few hotspots are detected, each one corresponding to a large hot area.  While if the same fire is monitored with a medium resolution sensor, we probably will detect more hotspots, each one corresponding to a smaller hot area.  So this needs to be taken into account when interpreting results and comparing fire activities throughout the years.  So my aim during last year was to process these point vector layers and I constructed the workflow that I'm presenting here to reproduce the same analysis and automatize most of the steps and to generate bilingual reports.  The aim was to do quick analysis and summarize the information of the on fire situation mainly because peers and journalists were asking us for updated information  and also the lockdown and the fires prevented us from conducting fieldwork.  So we had to work with the satellite information and also we have our background in the studio area because in our lab we have been conducting studies in the Paranario Delta for almost 20 years.  So that was the general situation and it seemed fine but the problem was that we needed to repeat the same analysis once and again to write dissemination articles.  Here is a talk at our University by Patricia Candus, post in social media, write these articles, respond to journalists and that's why I need to reproduce the workflow.  In the first month in June I designed the QCCIS modeler to process the special data.  So in this modeler the input layers are the hotspot products from beer satellite and modi satellite.  You download four layers, one is the beer's current data, beer's archive data, modi's current data, modi's archive data and with the modeler I merged the data, clipped the data to the studio area,  and then we reprojected them and export output layers with all the active fire records in a single shape file.  This is how the modeler is seen from the view of an end user and one problem was that I found a source of error that was manual layer selection.  You can note here you have to select each of the layers and for example in this screenshot I misselected the same layer twice.  So this is a source of error and also it is time demanding and seeping the shape files, selecting the layers and then the layer needs to be further analyzed and exported to AR to construct the plots and summarize information.  Cushies has an advantage that it can generate very nice maps and for example I generated this animation, this is an example of an animation included in a dissemination article co-authorized with Patricia Candus and Priscila Minotti.  And this animation was generated with the plugin time manager that recently was replaced by an integrated temporal control function and is included in Cushies.  So next I wrote a code in AR to account for all these steps and to produce the plots and the reports and first I show what you need if you want to use the script as an end user.  You only need to have a polygon layer of your studio area and save that polygon layer in a specific folder.  Next you go to the Firm's NASA web page and download the Archive data and here you can download the data with a very broad bounding box, just drawing a polygon.  And then you save the SIP files to a specific folder in your AR project.  Next you just need the Archmarked Veyron code.  These are the three steps to run the code and now I'm sharing what are the processing steps that are included.  The code is written using mainly the libraries Tidyverse, SF, CCplot2 and Armarkdown and it includes file and geometric operations such as reading the SIP files that were saved in a given folder and SIPing the data,  reading the hotspot point shape files and creating spatial objects, look for string patterns in the name files to create the hotspot objects.  So this avoids the source of error of manual selection of the pond layers.  Then all the geometric operations, the same that were included in the Cushy's Modeler that include merging the objects,  reprosection clipping to the studio area, next you can obtain an interactive map of the hotspot of the current year and export the final layers to Shell Package.  Also other steps include data cleaning, data tidying and producing the plots and the report. So this is general data cleaning and data tidying processes on the attribute tables of the layers.  And then you also obtain plots in English and English Plans with showing daily hotspots, cumulative hotspots and also the image, you can export the image of the plots.  And I'm showing next some of these plots and the annual comparisons and historical activity comparisons you can obtain.  This is an example of a report in Spanish.  The processing steps for both the geometric operations and the data tidying and the, and obtain the reports is less than two minutes in my laptop.  So to illustrate the workflow, I'm showing some results of the situation last year in the Paranar River Delta.  This is a screenshot of the thermal hotspots from Beers Data.  In the Paranar River Delta, you can, in the interactive map, you can click any of these points and obtain information on them.  And next, you obtain a plot summarizing the number of Beers hotspots, of daily Beers hotspots per day.  And here I use to monitor what was happening last year.  I use Beers hotspots because they have a medium resolution, the pixel has 375 meters and is better than modest.  So here you can see that the months with the highest number of hotspots was August, which accounted for almost 40% of the total hotspot of the year.  Next, you can also obtain a plot showing the cumulative number of hotspots all over the year.  And the number of Beers hotspots that is recorded in the year, the total number was almost 40,000.  And also, you can compare the historical fire activity for your studio area.  And here, for example, using Beers Data, that has this resolution of 375 meters of the pixel.  Here you can see that the total number of hotspots was the highest in the last nine years.  Beers Data are available since 2012.  So if we want to analyze the historical fire activity from previous year, we need to use modest data.  Modest data are available since November 2001 and the resolution is of 1 kilometer.  So the number of Modest hotspots during last year was almost 9,000 and were the highest since 2009.  This is a plot that shows the number of Modest hotspots recorded per year.  And you can see that there was a high number of Modest hotspots last year.  But in 2008, that was also a very dry year and the primary floodplain was dry.  In 2008, the number of hotspots was larger.  Lastly, I want to include an update on the fire activity during this year because you may know that  the Paraná River remains with very low hydrometric levels and fire activity continues.  So I updated the analysis yesterday and I obtained that this year more than 11,000 of big hotspots were recorded.  And this is a lot but is much less than the fire activity that was observed in 2020.  This plot shows the monthly number of hotpots, of real hotpots in 2020 and this year.  So our future work will include an analysis of the relation between the historical fire activity and the hydroclimatic trends.  And I'm also working on the estimation of the barnet areas because more than fire hotspots  are barnet areas which is important to analyze the ecological impact of the fires.  So here you can see for example an area of grassland that was burned in the Paraná River floodplain.  And an idea was to use the thermal hotspots at seeds and grow regions starting from these hotspots.  I'm using the Arsaga library for growing these regions and each region starts in an area with the hotspot  and grows according to the spectra similarity.  I'm working now with an index called normalize barn ratio that is computed from Sentinel-2 in Mashary.  Thanks for your attention. Muchas gracias.  Here are my contact details. Here is the link to the full article in the phosphor sheet proceedings shown now.  Here is also the GitHub repo of this project.  And I also want to share that most of the pictures that I featured in my slides belong to a photographic ESAI project  by Sebastián López Brach that is founded by Natcio.  I'm grateful to Sebastián and also I want to share his Instagram account for you to see more of his work.  Thank you.  Thanks a lot Natalia for this exciting talk and for most of all an excellent research.  I just want to make just one short note just before opening the floor for questions and it is about a word that is in the title of your call that is reproducible.  This is really a keyword and thanks for making your research reproducible.  This is a very, it should become a standard practice.  Unfortunately, this is not always the case.  So thanks for that.  The link to the repository is in the slides.  If you need the link now, I think we can paste it in the chat now or you can even more easily just open or find Natalia's paper that is associated to this talk and that is published in the phosphor sheet proceedings on the ISPRS archives.  So if you look for ISPRS archives for phosphor G 2020.  Lots of additional details.  Thanks also for the pictures and the link to the Instagram account.  Of course, we don't like so much to see fires, but we like to see pictures.  So let's clearly hope that the situation will will improve.  There is one question that was actually also some question that I wanted to ask that is about the how these research, how this data.  Or the outcome of it, especially also regarding the historical analysis will be used by any governmental institution or anyone else, any civil protection organizations, fire brigades or anyone.  Because that's very important and I hope that this will be the case.  Thanks for the question.  Our environmental ministry, Ministerio Nacional de Ambiente is using fire records from Firms NASA.  Not my analysis, but they are conducting their own processing and analysis.  So my results were used mainly.  I use them mainly to disseminate what was happening and to analyze which areas we are being burned to detect.  Maybe to disseminate what was happening in social media.  And also we work with some non-governmental organizations that were reporting intentional fires or accidental fires followed by land use changes.  So that was the use of my analysis.  Thanks a lot.  This definitely answers the question.  We have other questions from the audience.  Do you correct for errors in active fire products?  No, for the moment I didn't correct for errors in the active fire products.  I had ground truth data related both to conversations with local settlers and with journalists.  I have also some reference points obtained with flights.  I used that ground data to check if the active fires were really fires or not.  But I didn't correct the data.  My aim was to conduct quick analysis during the situation.  Thanks for the question.  Questions keep appearing in the venue.  So the next one, did you compare the active fire records of the two sources that you use, modis and veers?  And do they show any difference?  The difference that showed was that veers, since it has a better special resolution, detected smaller fire areas.  If you use veers data to detect burn-in areas, you may detect the burn-in areas better because of the better resolution.  So veers data showed more hotspots and small fires were easily detected, done with modis.  Thanks Natalia. We can quickly go through the last question.  Can you use the results for projects to reforest or restore burned areas?  It's a good question.  The problem in the area is that some of the areas that were burned were changed to land use.  For example, they were converted from natural areas or areas with cattle to agricultural areas.  So that is the same general problem.  I don't know if reforesting is the main issue here in the area.  But probably you can use the data to locate which areas are being more seriously affected and to take actions in the ground.  Thanks a lot Natalia for answering the questions and for once again giving a very interesting presentation.  We need to close here. I would like also to thank the audience for the good questions and the input.  I hope and I'm sure also you like the talk.  If this is the case, please let us know and let Natalia know using the up-close button in Vanu-les.  Thanks a lot and I wish you all a good continuation of PosterG 2021.  Thank you Marco.  Bye bye.
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