Well, welcome to our webinar, Remote Sensing Applications. Thank you to our both speakers, Dr. Wilma Zlanca and Dr. Betzabe de la Barre de la Autista. I'm just going to introduce briefly our two speakers. Will is a remote sensing scientist, recently working as the remote sensing specialist for the land-wise project with the University of Reading. His research focus on estimation and analysis of relative surface or moisture observations using Sivan satellite radar bascatter data for natural fluid management research. And also he uses the optical Sentinel-2 data for crop identification and verification purposes. His PhD focus on the observation and later modeling of passive microwave radiation from the natural snowpack at multiple frequencies. Observation took place during an eight-month field campaign and it was expected over two periods, two winter periods. And this was conducted at the Finnish Meteorological Institute Arctic Research Center in Finland. Our second speaker, Betzabe, is an Earth observation scientist using GIS and statistics for understanding the environment. Her main research interests are four. First one, to improve understanding of how vegetation responds to climatological events by exploring remote sensing data. The second one is monitoring these changes through time. The third one is to understand how ecosystem productivity is linked to environmental conditions and finally assessing vegetation conditions and land cover change. She's currently working as a research fellow in the Integrate Climate Resilience Understanding the LISP project, analyzing the impact of climate change into natural vegetation, agriculture, and livelihoods in this country. Betzabe is also involved in different projects, monitoring environmental impacts and changing changes with Earth observation data, including permafrost degradation. And finally, I'm just going to present our early career representative of the GI division, Dr. Mesjin Rasol. He is a post-doctoral research fellow at the Gustav Eiffel University. He's going to be a moderator of this webinar and myself, Veronica Escobar-Riz, which I work as a research support scientist at the University of Reading. Well, thank you so much for joining us and now I will allow Will to share his screen. I am Will Maslanka. I'm going to be presenting some work that I've done with the University of Reading on the land-wise project and specifically I'm going to be talking about my work on calculating relative surface soil moisture across the Thames Valley using Sentinel-1. And I'm going to be going through from data acquisition up to verification of the relative surface soil moisture product. So, outline of the presentation, I'm going to give a very brief introduction to the land-wise project as a whole. It's project motivations and where I sat within the project. I'm then going to talk about the method in which to calculate relative surface soil moisture. So, this is whenever I mention RSSM, I talk about relative surface soil moisture. And I'm going to be talking through the processing workflow that I've got to get to that point. And then I'm going to follow on by talking about some verification of the product. So, I'm actually going to be using talking about two separate independent data sources that I've used to verify the RSSM product. So, the land-wise project, as with every kind of academic product or academic project, they often have long acronyms with interesting names that they've managed to throw all the letters in together. So, land-wise stands for the land management in lowland catchments for integrated flood risk production. And basically, the project is aiming to evaluate and assess the effectiveness of realistic and scalable land-based natural flood management measures or NFM measures, aiming, looking at groundwater-fed lowland catchments. So, in our instance, we're looking for the river Thames in the UK. The reason that we're looking for this is that within the measurements of and within the research studies of NFM, a lot of work has gone into looking at leaky barriers and leaky dams and a lot of in-channel techniques and in-channel measures, but not a lot of work has actually gone in towards land-based measures. So, this project is having a lot to try and assess and get evidence behind the land-based measures so that we can begin to have them as evidence and we can include them in flood mitigation schemes. As with many big, with many academic projects, lots of different work packages and work pillars. Land-wise is made up of five. We've got one that's generating local knowledge. So, this is talking about talking to the farmers, talking to land owners, talking to policymakers, and those that really know the land and the areas as they're the experts in their field, if you'll excuse the pun. We then got another work package on field observations. So, this is with the UK Centre of Ecology and Hydrology where they have been taking soil samples from hundreds of fields across the Thames catchment and getting loads of soil properties. I sit within the third work pillar, which is the remote sensing and I've been looking at, as I'm going to be talking about, getting data from satellites for remote sensing purposes. All of that data then gets fed to our modellers who have been then doing a lot of the work, taking our institute data, taking the remote sensing data and local knowledge, and modelling different types of NFM measures on different catchments. That data and those results can get fed to our wet app development team who have been then basically giving new ways to show the results so that we're able to feed those back to the land owners and the farmers and policymakers and show them the results from our research to hope that we're able to actually give them information that they would actually use. So, the study area and the data that we have been looking at, so our study area is the Thames Valley in the UK and for those that are unfamiliar with the catchment, excuse me, it's the red polygon that's on the map on the right hand side and it can be summarised as the non-tidal part of the river Thames. Very basically and broadly, it's more rural to the west, much more urban to the east and you have a lot more rolling hills over in the kind of rural west area and the Thames itself flows west to east from those rural areas through then into the increasingly urban parts of the likes of Reading and Windsor and London. The data, myself that I've used, has been the central one constellation and I've been using level one infreferometric-wide Grammar Engine Tech's high resolution data at the vertical vertical polarization and I've been collecting data and I've been using data between October 2015 and September 2021, so there's a nice six-year time period of six water years that I've been able to actually have a look at the hydrological cycle across the catchment and I've been looking at the two ascending orbits, so orbits 30 and orbits 132. You can see from the map on the right hand side that the catchment sits quite nicely in between those two catchments so I'm able to use both of those ascending orbits to have a good catchment and a good idea of getting a good reliable data source. I've only been looking at the ascending orbits primarily due to data management constraints and to data storage constraints and for processing time because if we're using both ascending and descending orbits while it does double our number of available orbits and available scans, it does put our data processing up to and our data storage up to about three and a half terabytes which would take a long time for us to be processing so this is why I've been only focusing on the ascending orbits so bringing it down to about 0.7 terabytes. But a little bit going into the nitty-gritty of how I've kind of gone into this work. Hopefully you may have come across these before if you've been looking and trying to get with satellites remote sensing data but there are multiple different ways of actually accessing and downloading the data. The two that I looked at for this work primarily has been the open access hub of Copanicus which is the image on the top and that's the main hub for using Sentinel data and that's one of their main storage and downloading sensors and APIs for the Sentinel data but you can also use the Alaska build and that's an Alaska satellite facility which is the image on the bottom. Both of them have got very good user interfaces as you can see from the images and have API capabilities. Personally I've used the Alaska satellite facility because I found the downloading and acquisition a little bit more user-friendly but the Copanicus one is perfectly fine. Alternatively if you didn't want to be downloading terabytes of data to go and actually be using it and you just want to have a look at actually what the data what the actual satellite imagery is looking like and then you can use that within Google Earth Engine. That is a really good piece of kit. Personally I needed to actually bring the data down and have a look and play with it so I was using the Alaska satellite facility to actually download the data and make the data itself. Now unfortunately because I'm downloading the data in its level one format you can't actually just use the format of the data as it is especially for calculating relative surface soil moisture. It needs to be processed and you need to actually remove some of the artifacts and some areas that are in within the data just from observation techniques. So this needs to be going through a processing pipeline and so this kind of pipeline workflow I'm going to split just into three separate aspects just to kind of keep it simple. I've got one that I've called pre-processing which is going to involve a lot of the nitty-gritty orbits of the orbit corrections, calibrations, those sorts of things. I've got a part that I've called manipulation and normalization and this is where I'm actually sub-setting and cropping the data to the area that I'm looking for and then doing a little bit of instant angle normalization which I'll get into in a bit more detail and then once I've done that I'm actually able to get into this RSM calculation and actually get a relative surface soil moisture time series. Now, pre-processing there's lots of different steps to it, however very hopefully there's been other people that are actually already done this and have gone through and have got a published work close so I've been following a layout that's been proposed by the phony in 2019. There's a citation at the bottom that kind of goes through it in more detail but the very basic steps and work for processes and the kind of order is shown in the flow chart on the right hand side. This removes a lot of the geometric distortion so you can get rid of any slight variations in orbit, you can get rid of a lot of issues that come up with the border noise, you can get along with some of the stuff that is involved with the terrain correction that is involved when you're looking especially if you're looking at quite topography complex areas, luckily the Thames isn't that complex in its terrain, however it's able to remove some of the artifacts from that. In order to do that there's a couple of different things that you can use however I would highly recommend using the ESA Sentinel application platform or SNAP program as this is something that has been purpose built to use with Sentinel data. Again it has a really good user interface so it's really easy, it almost works like a GIS platform so almost like QGIS but it actually has a lot of the stuff in the background. It also because it's been built it also has a lot of the pre-processing steps already inbuilt as purpose built tools so here I've got the very easy calibration thing where you're able just to read in some data, run it through a calibration command and then write the data out but because it's able to have them as individual steps you can build them into work and create workflow pipelines so rather than having to do this all manually you're actually able to write it up as a bash script so this is kind of where bash scripting and using high-power computing is actually really important as it takes a fair bit of time to chug through a lot of data but it also allows you to set something off and let it go overnight for a couple of days for a week and that way it allows you to get on with other work while it is working in the background. Now the reason why it's really good as in so you can get on with the background is because say in my instance where I have six years worth of data you're looking at two orbits there's a lot of data that's there so as I mentioned before I've got 100 1164 raw tiles which would be the data that comes across that is any data that has been observed over the catchment area from those two orbits in some instances the catchment sits across two tiles so they need to be stitched together and then cropped. For some instances it sits in the middle of one tile so it doesn't need to be stitched so they require a little bit of data manipulation just to get the line up everything correctly. I've done that through MATLAB you could do that through many other different things Python is a perfectly good we'd be perfectly fine to do this I personally have used MATLAB just because that's my area of expertise but you're able to stitch these their tiles together and then do a bit of a visual check just to make sure that everything has worked properly and then remove any erroneous tiles or scenes because there has been some data that has been corrupted but hasn't been picked up in the bash scripting so in my instance I had one that as I said I had this part of 1100 raw tiles stitched together gave me about 640 scenes and then going through and doing a visual check there's this part of about 30 scenes that had erroneous data so I've removed those out and it gave me a total of 608 valid scenes so these would be for a given day the orbit has given me some data so a little bit now in the specifics about radar backscatter in the instance of the saw mush observation it's kind of backscatter is reliant and dependent on three separate properties you have saw moisture your surface roughness and then you have the interval of the look instance angle now the saw moisture is the variant of the dependent that we're after the one that we actually want to know the surface roughness we can assume to be constant throughout the year because of the scale that we're looking at across an entire catchment the actual any slight variations in surface roughness actually gets worked out and can be removed by spatial averaging so we can ignore that whereas the we can neglect that sorry whereas the look angle the look instance angle actually needs to be removed and we need to counter that and this can be done through normalization so I have the kind of normalization equation here where very briefly we've got a normalized backscatter as a given reference angle so this would be an angle that we would want to normalize the angle of the the backscatter to you have the actual backscatter so this is the one that has gone through the processing and the manipulation and the cropping and then you have the normalization parameter which is how we're able to actually adapt the backscatter and for this study we've used a reference angle of 40 degrees that's quite common in literature and it would seem to be quite easy just to stick it to 40 degrees and work with it from there now within backscatter normalization in literature there are two ways to calculate annual backscatter or annual beta you have a simple way which is just a direct linear relationship between the instance angle and backscatter so this is called beta d or the direct normalization and you have a complex way and this uses a multiple regression relationship so this is where it has beta r or the regression normalization now part of my work is I've actually investigated and investigated an extension to this and rather than just looking at annual normalization factors I've had a look to see if there are monthly variations and you can see from the image on the the graph on the right hand side from the pale red and pale blue curves actually there is an annual a slight annual curve which we believed to be to be part of a kind of some impacts from vegetation and when we have implemented this rather than implementing the annual backscatter we've seen there has been some improvements to rssm calculations when we've compared them to in-situ data sets and I had a paper that was published earlier this year that kind of goes into this into a lot more detail so if you want to have a read of that please feel free and then you'd be happy to hear final equation we can actually get into the calculation of rssm so this is where we've taken our normalized backscatter and we compare any given pixel to the rest of the pixels in that time series and can look at them at the largest and smallest backscatter values assuming they correspond to the wettest and driest so much of values which we've seen in this is a pretty valid assumption so it's a well documented model we actually don't use the largest and smallest values but we use the statistical model to remove outliers the citation at the bottom goes through this in a lot more detail and there's a really good paper into looking at how this rssm calculation actually works so I would highly recommend giving that a read as well but basically the equation we have our rssm value on the left hand side and it's just an index between normalized backscatter and these wet and dry thresholds or these largest and smallest backscatter values so that is all of my equations I can actually now go into map actually go and talk about what that data actually look like so here I'm showing normalized backscatter for the 11th of September in 2018 and you can see that there's in this there kind of central middle part of the catchment you've got this nice high or higher backscatter value return compared to lower backscatter values to the north and the south of it you'll also see that there's some kind of almost lodges of data that has been masked out and these are urban areas that I have removed and freshwater areas that I've removed from the analysis just because it doesn't actually hold any soil moisture value soil moisture data within that signal so I've just masked it for for ease and for necessity so we would take this backscatter data we then compare it to the dry threshold as you can see here this is a dry threshold of different calculators across the entire six years time series and a wet threshold and when we run it through the equation it gives us image like this this is then showing the kind of central swath where we had lower backscatter or higher backscatter sorry showing it as we've actually got quite a large but quite a high rssm value swath in the middle compared to two drier areas so once I've gone through this and done this calculation and done this created this time series verification it's quite key to see if this is valid this is true we start with the spatial verification so again the image on the back we've got is the rssm values for that 11th of september in 2018 and superimposed on top is two hourly animation of the two hourly data from the uk met office and it's their rainfall rate is their precipitation intensity data for the two hours preceding the orbit and we can see that it actually it spatially lines up fantastically that this kind of central swath really highlights it always cause because there's I mean it's pretty obvious when you look at it but it is cause because there's rainfall over the previous two years two hours so it means that because there's been rainfall over two hours that top couple of centimeters of soil is really wet so that shows us why the soil moisture has really increased whereas the areas where it hasn't rained is really dry because there is no precipitation so I've done this study for a number of different orbits a number of different observations and it the spatial parameterization spatial distribution of the rainfall and the rssm values lines up really nicely so looking at it spatially we're now going to look at it temporally we're going to have a look and actually see full given points have a look and see how that varies with time so for this I've used the uk center of an ecology and hydrology cosmic ray network so it's the cosmos uk network and they've got sensors up and down the country but they have a number of them across within the Thames catchment so I'm looking here at chimney meadows is one of the one of those in-situ sensors and that measures volumetric water content over the top 15 centimeters so of top the top 15 centimeters of the soil so what I've done for this is I've normalized that so that I'm able to compare a relative observation with relative observation I've applied a excuse me applied a moving average over that time series so I can remove some of the noise and then competitive a temporal analysis so you've got the on the left you've got the comparison between the cosmic race sensor which is the black trace that's the vwci and then you've got the central one data which is the rssm is the red trace and I've also included the a precipitation totals for a given day at the at the chimney meadows site using a pluvia rain gauge and you can see that actually generally there's quite good agreement and you can see that as well with the scatter plot on the right hand side that there is quite good agreement usually or the surface soil moisture and the volumetric water content in our agreement that it goes up over in the winter goes down in the spring and then comes back up again in the awesome there is an overestimation in the late summer and this is quite clear you can see in july 2018 and that's july august time where the volumetric water content decreases whereas the rssm value increases for those that are unfamiliar with the weather at the time in the uk in the summer of 2018 this was during our period of time where we actually had no rainfall over the best part of about 35 days so this is where we've actually having a look at some of the data looking at some videos of the sites and photos of the site and having a look at NDVI and Sentinel 2 data to have a look at optical data we actually think that this is due to vegetation growth and this is actually where the backscatter is becoming a contribution having an additional contribution from the vegetation and from the surface soil moisture and this is something that i'm have a going into and i've got a paper that i think i submitted last week that's looking into this in a bit more detail the cosmic ray network if you're interested in that i have a citation at the bottom that is really good to have kind of a brief overview of what the data what the network is and how it can be used for scientific data and scientific research so it's again another one that i'd hardly recommend reading so in summary i've gone through kind of a brief introduction to lamb wise it's in the workings and where i sit i've gone through the calculation of this rssm dataset i've gone through the data sources the where the the study area kind of some of the pre-processing steps how i've used them how i've done them so the manipulation and normalization of the data as well as the calculation and gone through the verification steps with the in-situ datasets from the Met Office and from UKCH so with that this is my last slide so i would like to thank everybody for listening and i think i'm going to open up for questions i've seen that there's some might be some on the chat yeah so if there's any if you have any questions please do um yeah ask them now i think thank you will for for this nice presentation and very interesting work actually i will ask audience and panelists if you have any question please go ahead before we uh listen uh BitSabi for her presentation or BitSabi if you have a question it will be an open discussion we can really go ahead and discuss ideas with Will because i have already a question well if no one have any i have i'm sorry go ahead BitSabi go ahead i just wanted to check like in your in your slide of the verification that you over post the precipitation data the animation one oh the animation one certainly yeah so just to understand that is rainfall yes so this is uh from the same time kind yes yes okay yeah so the orbits um for the ascending orbits for this area they come across at about uh 18 UTC um so what i've done is i've taken the um rainfall radar data across the UK and across the Thames catchment i've taken it for the 24 hours previous so i've gone from um 18 UTC on the 10th of September across the 18 UTC on the 11th and i've done some kind of spatial analysis of looking at when the uh how much rainfall falls in the area compared to this kind of spatial patterns and if they match so this kind of central swath uh really is highlighted over rainfall that happens over the previous two hours um you can see that there's a to the northwest of the plot of this image you can see that there's kind of like a higher um not quite a strong return but there is still quite a higher rssm value area compared to the areas to the south so i'm trying to think if i can roughly in this area to the um northwest um that area has actually had a uh rainfall that's passed over in the three to four hours that's happened and that rainfall has then since passed out of the catchment so this is why there is rainfall there is kind of still high rssm values there but not quite as high as where it has just rained yeah it's had a bit of time to sink in a little bit of evaporation a little bit of infiltration um and it's removed a little bit of the um that's kind of surface almost from the time and have you measured just for curiosity like what happened after the rain as in like how long does the sign now so this is yeah so we yeah so i'm able with this data i'm able to have a look and see what it would be like tomorrow so that i can see what you put one day after so it's had a whole day to go through um one thing that i've not yet managed to have a look to see exactly how much how would the rainfall of time as it would progress um that was something that as part of the project we had hoped that we would be able to go out and do but due to technical problems and with COVID restrictions we weren't actually able to get out into the field to do a bunch of measurements i know that Veronica has lost hours on building kit for um some stuff for this project that hasn't actually um that we weren't able to use because of restrictions um but we're aiming to yeah there's something that we would like to do and something that i think would be a fantastic piece of future work to actually have a look and see how the infiltration rate would work in relation to the soil moisture observation so we could see if it's rained six hours ago can we determine okay well that means then that we're going to have lost 20 percent of our relative surface soil moisture or something like that we can get a direct relationship between the two yeah we could say it would be really nice to see exactly how long does you have the signal for yeah that's really cool thank you so much it's really nice okay well thank you so much will um can I ask you just a question in the next slide I think it was the next one yes did you think that error that you were mentioning in July is related to your um normalized parameter in the RSSM or so we see this increase regardless of the normalization parameter that we use so I've done this analysis so you can see this one is for it says up in the title of the um the kind of busy line graph um that is too many memories regression monthly so this is where I've been using the beta R the monthly beta R I've done this analysis with uh annual monthly normalization factor annual uh sorry the simple annual the simple monthly the regression annual and the regression monthly so I've done it with as a kind of a combination um and this is where we see that there's this this kind of increase over the summer is prevalent in all of the data in all of regardless of the normalization factor there's actually in the literature and in with other people that have done this work at a more coarse resolution so this this data is done at a 100 meter spatial spatial averaging I've done this also at 100 meter 250 meter 500 meter and a kilometer grid resolution which is the traditional one that's used for this method um you see this this overestimation over the summer in all of that data and you see that it's it's a documented thing that is something it's kind of like a um cutting edge a bit of trying to remove this um vegetation index and it's a it is definitely because of vegetation growth over this time um and in some of the work that I've done um recently I've got different been looking at the soil moisture values for all the soil moisture return and the backscatter return for different crop types um across the Thames Valley and there are a number of crop types that actually due to the geometry of the fruit and the pods give a massive backscatter return because it massively increases the scattering um in the kind of canopy layer um so it's definitely this this increase is definitely due to vegetation rather than a um parameterization um there is a slight discrepancy because the the cosmos the volumetric water content measures over the first say 15 centimeters and the surface soil moisture measures over the top two centimeters so there is a slight lag but we've kind of removed that in part with the um moving average that I've applied across this the data so we can remove some of the noise and remove some of that discrepancy but yeah we definitely think that the this kind of increase over the summer is due to vegetation because it's something you can see annually uh you see it in you can see the roughness of the the vegetation roughness uh it's more it's not so much it's the roughness it's more that the vegetation itself has an additional contribution so it's then whereas we have over the winter when the vegetation isn't there the contribution is is is solely from the ground over the summer it is now a combination of the ground and the vegetation and it's that extra vegetation contribution that is in causing that increase okay okay well thank you um thank you a lot for this presentation very interesting that's my pleasure I have I just put in the chart uh your most recently paper if someone wants to check it which is related to this uh presentation okay well thank you thank you a lot I have a question very unique before you uh move with the betzabi well thank you well for the for the work very interesting actually what what interests me it's the part that you're doing the the two different seasons like for example dry and let's say rainy but what is relevant what I did before in term of seasonal chains of the water content which is poly channels uh I also used two different sessions seasons like for example during the summer and winter but not exactly during the rainy season after the rain as betzabi was discussing with you uh so the saturation is more uh as you you can know that and now I can prove that that the situation in Barcelona that time and what I see in your graph for the chimney midway so for the regression uh during the january to april you have uh the the peak period I guess it's all the years yes which is interesting because I had very similar but I was working with a ground penetrating rider uh with around 30 meters with low frequency what here I have a question regarding uh for example you were talking about box scattering yes so what uh you already answered very unique in term of the combination of two reflections that happen the vegetation and the soil uh characteristic let's say but in in in my case I had the active and an active uh underground streams let's say poly channels in this work you were only working on soil moisture or you were working on the water content also or you are planning to do something related to the water content to see the active uh poly channels or something like this with this uh what's so this work we're aiming to have so because I'm looking at the surface I'm only looking at the first the top two centimeters of the soil um so anything below your your ground penetrating radar is going to see a lot further into the ground than Sentinel will or Sentinel one will um so we're really looking at kind of that first that top couple of centimeters so that we can have a look and see how much we're aiming to have this data so we have passed this on to our modelers so they have a um quite a long a long time series of what the soil this that kind of top centimeters of soil moisture so we can have a look to see how that infiltrates because we're aiming to have a look to see if we can through changing farming practices um whether that be through changing crop cycles or changing um crop management schemes so if this we need going to be increased um plowing if increased um traffic on soils increasing or decreasing the compaction if we can alter the infiltration and alter the uh the actual storage of water in the soils on the on these farms um so hopefully we'll be able to then from that and we if looking at this is some stuff the models are currently doing enough in the process of running through their hectic models um when we have these this um when we have this rainfall that's passing through we can then try and keep the water in the soil rather than keeping it from either sinking down into the kind of active channels as you were saying or running off into um kind of the river network and the river courses the water courses themselves um because this then if we have a lot of if we were to have somewhere that has got quite a lot of compaction and there's not a lot of infiltration that's going in what that's going to do is just have a lot of water runoff and that means we're going to have a lot of soil erosion there's going to be a lot of pollutants that go any fertilizers that have been put onto the soil if it falls onto compacted land that water and that uh overland runoff is just going to wash that pollutants away into the water courses so we're going to increase a lot of pollution so that's kind of what we're ultimately hoping to do and i'm this this kind of surface centimeter first couple of centimeters of soil moisture data hopefully will then help to feed them feed the modelers with their anecdotal evidence and their anecdotal data so they can spin the models up so we can see okay we know what it has been let's see what happens in the future let's see what will happen okay thank you very much thank you very much um well should we now continue with Betza there but anyway uh well first thank you for the invitation to the seminar and uh thank you for a great presentation and i'm gonna talk so i'm Betza from La Barrera uh i'm working with all these people in in a project in the swedish arctic and also uh it's part of another project that is working with Peroni Kanki and the artists as well so the title is the mapping of permafrost in the in avisco in the swedish arctic with earth of the observation data so we know that the arctic is warming rapidly even like faster than any other part of the on the globe it is warming two or four times faster which this is bringing lots of consequences um so one of this is the throwing of permafrost which permafrost i'm gonna tell you a bit more about this but it's a permanent frozen ground and that creates subsidence in the ground and also the formation of thaw legs uh that also like with this uh subsidence you have an increase of the greenhouse gas emissions and also it impacts the the infrastructure of the of the that of everything that is above the permafrost and this increase in in greenhouse gas emissions has an a strong positive climate feedback resulting in greater rates of warming for the globe so it is a quite important topic and um and this is just for you to know what is permafrost and what is the active layer that is that i'm gonna talk about this all the time so the active layer is that this layer that i show you here that so like it goes as in it goes each summer and it has a lot of biological activity and the permafrost which is let's say this like this this layer which is the permanently frozen layer of the ground and it contains a high carbon so this permafrost is what we are trying to understand and what is happening and what research is showing is that with the ground temperatures uh increasing then you have like a thaw like you like sorry the thaw is great greater during summer so the active layer actually gets bigger and in some areas the permafrost is completely lost uh on the other hand the satellite data shows that the that there's an increasing on thaw lakes because you can measure lakes in the Arctic and that indicates area that that areas where permafrost has been lost or decreased and also you can see a lot of increases in moisture in areas where permafrost is losing and there's a lot of like they are really scary uh projections that the 30 to 70 percent of permafrost will be lost by 2100 how do you say 2100 um so when it's so the idea is to to measure this through remote sensing right but like normally like you cannot like the the traditional methods are field work methods and they cannot like cover the whole the whole Arctic and and you cannot quantify the rates and the extent of perma there of permafrost thaw like so what we are doing is understanding this thaw by subsidence using different methodologies and I'm gonna show you in a bit and this these technologies are a combination of optical uh SAR and drone data so I'm telling you this because well SAR is incredible useful in these areas because you don't have like the problems with clouds but also it is really important the optical part because you need to find and understand what is happening with the vegetation there and sometimes like in this case we are using drones to have a really high spatial resolution so the objective is just as I said is measuring the subsidence which is a vertical movement of the ground and uh and use vegetation changes as a prox as a proxy of this term of permafrost thaw and also we are trying to monitor methane emissions as the consequences this so this is just to tell you about um something really important to understand why the changes in vegetations are quite important for this area so this is like more or less how it works in the swedish Arctic and so you have palsa which is like a raised wetland and what it happens is when when you have permafrost below and it's intact you have a type of vegetation that normally is drier when they when the palsa collapse is because the permafrost is losing so you have a uh you have a decrease and it's this this kind of thing that you see here so you will have more water on the surface and that will create a lot of like biological biological processes that what we'll do is to start like changing the vegetation type but also the moisture levels so what you will have at the end is a much wetter vegetation and this picture shows like how more or less is the these transitions and this is really important because this type of vegetation emits as we have found that it emits lots of methane so just to show you where are we working is in the swedish Arctic in three areas in in the tourist station store flakket and store dalin which are like wetlands in this area and this picture is how it looks in reality and i haven't gone because of covid but like the swedish and actually veronica went there um so what we did again we used drones optical images historical orthophotos and sentinel one in sar and around the field data we used active layer depth uh emissions methane emissions and land cover so with that we created land cover maps de ends and we tried to understand these changes in the pulsars and understand it as well there subsidence so i'm going to talk a bit more on the i'm i'm dividing this into the different methodologies so that you can see and so first the orthophotos so we managed to get ortho historical orthophotos for the areas these look like that so you have an orthophoto here i just put them like the main ones there uh 1960 2008 and 2018 and this is just the zoom in of an area of a pulsar so you can see that this area is a rice area and this area the one that it has collapsed and this is just an example that how is retracting on time and uh this is another option and this is how it looks in reality a pulsar that it hasn't been well like how the lateral collapsing is looking and this is how it looks after collapsing uh then we used drones and we got uh flights that were planned in the area we had uh flights in 2017 and 2020 and 21 we had onboard the drone a multispectral camera and rgb as well and uh and then with px4d software which is a software that helps to kind of do all the like ortho rectifications and create mosaics of all the images that you take in the drones we use that to create then uh our mosaics that were the ones that we used to do vegetation maps with that we use a supervised classification in this case was super vector machine and then we also create digital elevation models that to see the difference between the 2020 and 27 and 2021 we didn't because um we just have for uh 21 um some of the data and so then we used in star which in star um i think you all know what is in star so i'm gonna uh but what it what it does is like it allows you to to measure changes in the land surface altitude so what you do is you have bunch of insert data that you layer all together you stack and then you create something called interferograms that it will tell you how the ground is the form from time one to time two and uh this is this is the way that we are doing we are doing a technique that it was that it was developed by terra motion which is a company and a collaborator of us where they use apsis technique that also i can give you the reference for good papers of that if you want later on and then we did the the in the field we measured the active layer so this is um this is literally you take us like a like stick to do like how do you say like you make hole in the ground until you uh until you get the permafrost and then you see how much is the active layer and how deep is the permafrost so we did that well i didn't like my colleague did and uh it was during the summer and the idea to do this it was across different vegetation types so that we can see if there was any difference of different vegetation um and now i'm going to show you really fast what we need to have then i'm just conscious of at the time but like um so this is the orthophotos so as i can uh as i've told you we digitized the the palsa mire like the edges of the palsa it was digitized by three different people so that we can reduce the error because it's a completely visual interpretation and in those areas where the where the difference between the three observers were uh not bigger than 50 centimeters i think uh we took that those areas and then we measured how we're changing on time so in these lines you can see uh 1960 2002 2008 and 2018 and you can see how uh the red line is 2018 and the black line is to uh 1960 you can see how much the palsa has been shrinked and what we did then is to have this a to have these rates of rates of shrinkage so what we did is just to calculate the rate of the of this collapsing and then we have found that actually from 2002 like the collapse is much faster which this is in this we found this paper so from these two guys in 2016 that they found that the the temperatures in ground and ground temperatures were increasing faster from 2000 2012 so sorry for 2004 it started like growing faster so then that that coincide exactly in that moment that we that we saw as well like like a rapid shrinkage of the palsas and now what we got with the orthophotos is a like the the land cover classifications and as i've told you these are the results and sorry they might just need to some scale and north and other elements of the maps but so i just wanted to show how they looked the maps and it was just really interesting to see for example like all the permafrost lost which are in these three types of vegetation and you can see that such wetlands are these like like really bright green and these are all the areas where the palsas actually collapsed and if you have if you take this image into your again keep it on your mind because and you see all these areas of uh of the bright green that they are the areas where the permafrost has been lost and you can see in another image but i will i will tell you that so what we found is like if we put these vegetation types and then we combine it with the active layer that i that i've told you that we measured we we found that the raised pile palsa vegetation types which are the ones that are more intact have a shallow active layer which means that the permafrost is still higher however in the fen vegetation types which are those areas that i've told you that are in the in the surroundings of the palsa you have a really uh deep active layer which means the permafrost is higher than 140 centimeters so again this is another another evidence that the permafrost in that areas are are like sinking and now these are the results from the from the drone data but the part of the digital elevation model i'm just showing you how it looks the digital elevation model from the processing drone images and you can see exactly where are the areas where are higher in the palsas and lower in the palsas and then what we did is the difference between 2020 and 2017 and we found and again as i've told you to remember these areas are areas where they are shrinking and they as in the the palsas are collapsing and these are an indication of permafrost though in this area and finally like the results with the insert data so we wanted to see like how actually the insert data will do it because we have like a really high spatial resolution and this is with insert which is as in in the drone data you are talking about centimeters and the centinal one you are talking about 20 meters by 20 meters pixel so we were just checking what were the differences and this is just to show you how it looks the the insert data and again even the insert data is showing how the palsas are subsided in the in this area we found difference between the two methodologies but it's obviously like this the spatial scale that we are looking at are quite different and in this case we are this is the average surface surface motion in three years and again we found that the the the raised palsas are the ones that are subsiding faster so that indicates that the insert data and this data is telling you what is happening now like a rate like it's showing like a like a rapid change of permafrost in the area and I promise this is the final one we just did the the methane emissions and this was measured in June 2021 and we just found that the areas where where that where the permafrost was lost we are seeing more methane methane concentration so yeah this is like the combination of all these methods is a great opportunity to to monitor permafrost you can have a panartic approach you can detect rapid changes and because this is really important to measure millimeters of this because permafrost is losing all over the place in different ways and different rates and all this data gives you the opportunity to combine and understand better the subsidence and yeah this is really important because yeah it is affecting people livelihoods global warming and we need just to take action and to understand better and to create better policies to conserve these areas and I think that's all for me thank you so much thank you so much that's a very interesting um that's how that presentation was related to her recent paper and I just put it in the chat if someone wants to check this paper is very interesting um if someone has any questions um please do it I do have a question if you compare in terms of the extension of the degradation of the permafrost between the inside and the drone did you see that the inside actually uh overestimate this permafrost degradation or uh over or underestimated yeah that's a good question because we found that like inside was uh yeah I would say overestimating uh some areas however and this is something that we need to understand we don't know if it's actually overestimating or the drone is underestimating it uh because what we what we found is like in all that areas that are like in the mires that are let me just show you a how do I go back sorry I just wanted to show you the um the picture of the where I so in these areas like the inside is detecting a lot of um subsidence and the drone doesn't but these areas like uh like Sophie went there and and she says that you can see like like that it was it is collapsing and it is collapsing and it is like uh quite recent collapsing so what we are thinking is that maybe also like uh inside is telling us like maybe a rapid change that you cannot that you cannot actually detect with drone data and also because you have all the time serious at the end and also with the drone you use a different technology lighter it's true no then probably no we didn't use lighter which that's a problem because we just choose the DMs from the flight which brings a lot of error as well because obviously you have all the artifacts from like yeah wind and all the external kind of things that you have to deal with and obviously you try to do as equal as possible one flight to the other one but there I think that's something also really interesting that you have I think it shows those differences but the error is bigger in the drone data than inside data okay that's very interesting it's very good to know yeah and it would be really sorry it would be really interesting actually to use lighter and to see that but the predaginates like lighter in different times as well and that's really difficult that's another project yeah anyone has another question two bits of it no well I think we are a little bit over time then I am just going to end the webinar thank you so much for both speakers presentation and I'm just going to say to record the first part and I hope it does goes well but just free feel free to go if you have any plans or you can stay for just five minutes of the recording but yeah thank you so much for participating in this webinar because you have something to say actually Veronica maybe you can do the first part for recording that will be good well I thank you all of you first of all actually for organization of this webinar Veronica it was a big if for thank you very much thank you Will thank you with Sabir for very interesting presentations and work you are doing I wish very good luck with your work and I hope you can find some collaboration work all of us to do some stuff together in future and thank you again


Hey Bolo buddies. Thanks for watching. All right, you guys I've got 32 bread and butter Bolo's come in your way items that I have picked up at thrift stores garage sales estate sales YouTube auctions Facebook marketplace items that I bought low that I sell for a profit These are typically easier to find items items that are going to sell for $35 or less So I'm going to tell you where I got it what I paid for it and what I sold it for my name is Courtney Also known as Bolo buddies. I have two other channels. Those are linked down below So be sure to check those out and we're gonna get started here. We're gonna dig into some Bolo items All right, so the first item I sold are these Hallmark itty bitty plush It's a little mermaid and wedding day aerial and these did take a little well to sell They were more of a long tail item. I don't even remember where I got them I think I got them in like a plush lot And I sold these for $10 and 79 cents and the buyer was all in for $15 and 69 cents These items were cross-posted to Mercari and Poshmark and these items sold faster on eBay If you want to learn how I cross post I do not do it manually. I use an extension called list perfectly There's a demo video down in the description and if you check it out and you decide you want to try it You can get 30% off your first month with referral code Bolo buddies all one word I'm Highly recommend it. I've been using it almost since they became a company. I'm manually manually Cross-posted like 3000 items from eBay to Poshmark when I first started Poshmark because there wasn't a cross-posting service available So I have done it both ways and I am so thankful for list perfectly So check out that video in the description. All right, you guys these little baseball They're by Norcrest and they are made in Japan and they are so so cute. Let me show you the bottoms They've got this little sticker on them and I couldn't find any others like it. So I went ahead and priced them a kind of on the high end Somebody offered me 3490 and I only had about five bucks and all three from a thrift store So I went ahead and took the 3490 and the buyer was all in for $47 and 86 cents The next item I just love it. It's from 1981 you guys. It's a little teeny tiny lion. It is so cute I saw this and I was like that is adorable I wasn't even sure if it would be worth anything or not But I paid a buck for it at a garage sale I ended up taking a best offer of $24 which I'm really happy with and the buyer was all in for $30 and 34 cents Look how cute it is you guys. I Cannot stand it. It's so cute. All right the next item I sold are these Pee Wee dolls and clone dolls. So these are made in Hong Kong and It I think some are Pee Wee. I think some are unbranded. It's like a mixed lot I got these in an estate sale for four bucks. I sold them for $24 the buyer was all in for 29 65 This came out of a mystery box I had a big old mr. Potato head and had all this potato head stuff in it and I saw these animals and I'm like I'm gonna part those out. I Think it's a dog and a cat. I don't know it kind of looks like two dogs But when I looked it up one of them said it was a cat. So I just put dog and cat Anyway, I sold these for best offer of 12 bucks and the buyer was all in for 1940 for these and they are vintage from 1995 This is a Sarah Coventry Costume bracelet. I got this at a garage sale for $1. I'm sorry I sold it for 1550 and the buyer was all in for 21 90 The next item I sold is this Jim Shore Santa now I did pay up for him because I know that Jim Shore typically does good I actually saw that chosen goods reseller Carissa over on her channel. She did a what sold and she had sold an ornament a Smaller ornament that was similar to this and I want to say it was like in the 30 range or something like that. So when I saw this I was like I should definitely get that and I did pay seven for it I ended up selling it on best offer for 35 and the buyer was all in for 49 27 I do believe that I could have gotten more for this but I Just went ahead and took the offer Sometimes if I'm having a slow day, I'll just I'm more likely to take an offer The next item are these cute little mice I got these at a garage sale for like a quarter and I sold them for $25 you guys and they're missing an ear Each one is missing an ear and I still sold them for 25 bucks. The buyer was all in for 2980 The next item is this little teeny tiny poly pocket dog and it has paint loss you guys It's not even in great condition, but it was a poly pocket and I just wanted to see if I could sell it I did I sold it for seven dollars and ninety two cents and the buyer was all in for eleven dollars and ninety two cents And I got this in a thrift store mystery box The next item I sold is this vintage Fiji or fi gi. I'm not sure it's inside art glass And it is a bluebird on a dogwood from 1999 and I sold this for $17.50 the buyer was all in for 24 43 and I got this at a thrift store for a quarter All right, these are caterpillar buildable interchangeable toys and I believe these came in a mystery box I sold them for 1799 and the buyer was all in for 29 21 The next item I sold is this sesame Street Oscar the Grouch squeak toy from 1979 I got this in a mystery toy box from auctions for you If you would like to see the entire unboxing of everything that I got in that box, which was amazing You can check that out on my channel I get great stuff from auctions for you and Donna Telebot Alina. They do YouTube selling events So check them out down in the description This little guy ended up selling for ten dollars on best offer. It went internationally The buyer was all in to me for 1550, but it was global shipping So it was probably much like I don't know with the fees and the import taxes They paid quite a bit for this One of my favorite things to sell this is just such a fantastic bread and butter And a lot of people don't know about them unless they watch me and this one even had some damage where excuse me the Staple had rusted a little bit and it had writing in it But these are little miniature ornaments you guys and this one has Christmas carols in it Some of them have stories, but I always do super well on these they always sell for me Some of them I've sold for 25 bucks each this one I ended up selling for a best offer of 15 and the buyer was all in for 1990 The next item I sold is this vintage Corgi classic Clipper blue. It's a little car What do you call them? Is it diecast? I think it's diecast, but this is from 1997. It's new old stock This also came out of a mystery box from auctions for you. This box was a new old stock box from big Buyout she just did a vintage toy. So I Also have that unboxing on my channel this item sold for $21 the buyer was all in for 31 89 The next item is this vintage 3d house needle point and I sold this for $30 on best offer The buyer was all in for 4125 and I got this at the Goodwill for three dollars and fifty cents So the story behind this was I picked up Okay, I went to a garage sale and there was a whole bunch of these and I wanted them But they had them priced like really high it would have been like 70 or 80 bucks for all of them I think I think 15 or 20 bucks a house and I'm like no way and I should have negotiated I thought about going back and negotiating, but I didn't and I found them at the Goodwill like the next week and I want to say it was like 20 bucks for all of them But it ended up I parted them all out and it ended up being three dollars and fifty cents a listing So I was super excited when I saw him at the Goodwill. It was like, oh my gosh, I can't believe it They're here. So it was really an exciting moment. Has that ever happened to you? Vintage Halloween trick-or-treat cardboard cone hats I got these out of a Halloween mystery box from a thrift store sold these for 16 on best offer and the buyer was all in for 2399 These I got at a garage sale in the free box. There was a free section. They had a ton of these It was a retired school teacher.  I brought them home laid them out. I've sold I think two or three sets. I still have quite a few left I thought they were going to do better than they have done But that's okay. They were free. I sold these for 24 best offer the buyer was all in for 34 12 The next item I sold is this chicken squeak toy. Okay, you guys I got this at a thrift store for 50 cents sold it for 12 24 and the buyer was all in for 19 37 I put a video over on youtube And it is called the most annoying video ever blue chicken squeeze it how it works And then I also posted it here in the video but somebody over on my youtube channel said I just went and bought this item And the person that bought it had zero feedback. So they found this item from my youtube channel came and bought it from me there did end up being an issue and It wasn't delivered on time and it just it took forever It actually I think it ended up showing delivered, but they didn't get it So I walked them through what to do. I said call your post office. It ended up all working out. They got their annoying chicken But just a cool story that shows that putting your items on youtube could potentially drive business into your uh ebay store The next item I sold is this vintage tambourine. I got this in a mystery box and it did have a little bit of Condition issues. It wasn't perfect. Um And I sold this for 28 80 And the buyer was all in for 46 23 The next item I sold is this boyd's bears the shoebox bears flutterby and I got this out of a mystery box from a thrift store And I think I should have priced this higher because it sold really quick And I think that this would have done better on mercari So it seems like the boyd's bears that are resin do better on mercari So if you're not selling on mercari definitely check out that platform things that are saturated on ebay tend to do better on mercari and poshmark for me. Um, I do have uh links down in the description If you guys want to sign up for mercari or poshmark, you'll get a coupon and then I get a coupon The next item I sold is This disney trading pin and this was actually sold to pat d's the traveling picker and he was doing a A show and what he was doing is he was going into um different youtubers Uh ebay stores and he was talking about their stores and while he was talking about my store he went ahead and um Made me an offer on this pin and he was making offers on other people's stuff and buying stuff and I So he made me an out offer and I counter offered lower because I knew it was him And um, I sent him an offer of five dollars on this So he went ahead and took my offer and he was all in for nine dollars and seventy cents for this trading pin And he really likes disney stuff. So I was happy to give him a good deal on that So shout out to pat d's the traveling picker go check him out on youtube The next item is this vintage coro clip on earrings. This was actually um from a jewelry lot that my husband picked up and um His cost of goods is probably not much But I don't know if I under sold these or not. I think I did an auction first and they didn't sell But as soon as I put them up, um, the sale had not kicked in yet So they were at 22 somebody offered me 20 And I went ahead and took that um, but it's showing the wrong sold price because it's reverting to the um Sale price, but I accepted the 20 and the buyer was all in for 25 20 So if anybody knows anything about these did I give it away? Did I mess up? But you can see here They are signed coro on the back and they needed cleaned up and stuff. So I just I don't know I I'm happy with that. I think my husband's happy with that. So The next item is this marvel. Um, I probably got this in a toy lot or something So probably not much in it. I sold it for 12 dollars and 96 cents and the buyer was all in for 17 91 It is the marvel legends spider-man harry osborne action figure The next item I sold is this. Um, it's a locket. It's a triple photo locket. You guys this thing is so cool It opens on three sides and you can put little photos in it. The brand is 1928 I've done pretty well with that brand and I actually I did sell similar and I forgot to take out a state find I feel super bad because I did not get this in an estate Um, I got this at a thrift store I guess it could have come from an estate. Maybe somebody donated it from an estate, but That was totally just a mistake. I should have removed that and I missed it. So, um Kind of stinks, but that's what happens sometimes when you click sell similar Uh, I sold it for 30 and the buyer was all in for 35 20 I don't think somebody was buying this because it said a state find so I think it's okay But you know, just I wanted to be completely honest with you guys about that The next item is this boys figurine. Um, it is moose treat ding dong moose troop He's from 2004 and he sold fast. I got him out of a halloween mystery box Ended up taking a best offer of $34 and the buyer was all in for 42 13 and he sold fast Um I want to talk to you about this real quick. This is a resin figure Most of the boys bear resin figures are very very long tail If they are halloween themed and stuff like that They They do better but some of just the normal ones they do take a long time to sell and they don't sell for much So do your research before you pick up a bunch of resin boys bears This is a simpsons kid rock action figure still in the packaging I got this at an estate sale for $1 took a best offer of 15 and the buyer was all in for 22 95 This is a little keepsake hallmark ornament. I love picking up hallmark ornaments Some of them can go for a hundred dollars. Uh, you just never know So you want to scan that barcode or look them up this one was a series It's 12th in the series and I sold this for $25 you guys I got it at a garage sale for 50 cents and the buyer was all in for $32 and 76 cents with tax shipping um And uh, yeah tax and shipping sales tax This is a calico critters furniture loft bed with the two hangers So I usually pick up the houses and then I part out the furniture Sometimes I sell them together, but I usually part it out got this at a garage sale for 25 cents The buyer was all in for 19 37 and it did sell for the asking price here of 12 96 The next item came out of a mystery box from auctions for you This is just a little fraggle rock figure. It's from 1983 Um, it is a major bread and butter, but people are looking for these items and when they find them They're like so happy. So I do list bread and butter. My cost of goods was not much for this Um, I sold it for seven dollars and the buyer was all in for 12 62 So again, that was a mystery toy box from auctions for you This I got at a garage sale for two bucks and I sold it for a best offer of 20 The buyer was all in for 32 65.  Um, it's the replacement french doors And I sold these for a best offer of 13 My cost of goods was about 50 cents when I divided it out and parted out this and it I'm sorry. This the buyer was all in for 19 24 The next item are these nylon cartridge ribbons and um You guys this took forever to sell. I don't even know if I recommend you getting this Um The ink was still wet when I listed them But my concern was what if they've dried up since I listed them But now that I'm thinking about it like they were already vintage and weren't dried up. So it was probably fine I didn't get a return or anything. So I'm guessing everything was fine But you know how you've had something so long and you start second guessing it after it sells That's what I did here. Um, I sold this for a best offer of nine dollars and 50 cents and the buyer was all in for 16 58 I got this at a garage sale for a dollar and I was so happy to see it go It was so long tail you guys. I'm like get out of here. Um But yeah, so, um this little necklace I'm going to be listing this in my ebay store It is from the 344 pounds of jewelry. It's just costume, but it's got like glitter in it And it's super super cute my ring light is really making it like bling I don't know if it'll be that blingy without a ring light, but it's really cute It's got like silver flaked glitter again. It'll you know, it's just costume, but um I will be unboxing more of the 344 pounds of jewelry over on my sourcing with bolo buddies channel in the near future So be sure to subscribe over there and if you um By the time you see this video, I may have some footage already there so you can check out the replays also All right, you guys thanks for being here. Thanks for watching There's going to be some videos popping up here and here a subscribe button and another video down here So check out another video and be sure to subscribe if you hit that bell You will get alerts when I post new videos. Also, if you're new here, um over on my bolo buddies facebook group I have um post where I will invite members of my facebook group to Share a bolo and then I feature those bolos and videos So if you would like to be a part of that go join my group check under the announcements featured section It is completely free. It's just something fun to do and um, I hope to see your bolo. All right, you guys Thanks for watching


What's up guys, welcome to today's vlog. So on the vlog today we're gonna be going over a layered haircut for super thick hair. I've got some fun techniques packed in there for you guys. We're gonna do it with a razor as well. Also to make things even better, I got a live model for today's video. So I'm sure you guys will like that as well. Now, last video I promised I was gonna give away the Neuro Angle Iron from Paul Mitchell. So today I'm giving that away to somebody that subscribed to the channel and also made a comment in the comments below saying that they would really like the iron. The winner for today is right here. It's Lion, L-I-O-N-N 101. He says, I would love to win this iron because as a new stylist your videos are a help and the top tools also help with creating the best for my clients. So congratulations to you. Make sure you email contest at freesaloneducation.com and say that you're the winner of the Neuro Angle Iron. And leave your address, your phone number, and your full name so that we can get that iron out to you. Now as we move into today's video, I'm gonna start another giveaway because I just like giving things away and I got a lot of stuff. If you can't see all those boxes back there are full of hair tools and products. So I don't want them anymore. I need to give them away. So here we go. So today we're gonna start a giveaway. Make sure you subscribe to the channel and also comment below that you would like to win this vibra straight iron. It's a really cool iron. We're gonna use it in today's video to finish up the style. So no need to extend this any longer. Let's get started with this step by step. Here we go. All right guys, so this is my model for the day. This is Amy. What we're going to do on her hair, she's got really, really thick hair. She also has a shaved underneath which I'm gonna show you guys in a second. That is not something I did. It's something that she does even on her own. So what I wanted to do is just clean that up. But I also like the fact that she has that cause it does take out a lot of weight from the underneath. I probably wouldn't go up as high as it is but because it's already there, it is what it is. So for the sectioning, I create a horseshoe section all the way around parietal ridge back around a little bit lower on the crown.  I'm gonna cut those layers and then the rest of the hair is gonna hang over top of it. So we're gonna be creating a disconnection. And my goal with that is to make sure that I have that hair that falls over it so I don't have short layers everywhere. So right there, you can see I kind of exposed that underneath section. That again, she doesn't go to a salon to get that done. They just kind of shave it up. So I wouldn't go quite as high up on the occipital bone but that's just my personal preference. And it actually does work pretty well for her hair. I'm using the Andis Envy. This is a cordless clipper. I'm using a one guard on it. Just to go in and again, clean it up. I'm not really trying to do anything fancy underneath here. I just want it to look nice. So take the one guard and then I go through. I go a little bit, a half step down.  And then I go in with my Andis T-Trimmer just to finish it up, create some lines in the back. And that is how we do the bottom of the haircut. Now I let down the interior part of the haircut. And what I'm gonna be using, the tool of my choice for this cut is the Donald Scott Swivel Twist Razor. The reason I like this tool is because I can use any comb that I want. Using the YS Park 339 comb to get a nice tight tension and I like a smaller comb. I also spray all over the head the Donald Scott Prepare which is a, it's a liquid tool glide for the razor. It also is pure coconut and sunflower oil so it gives a nice slip to the hair. It helps keep the hair protected when using it for razor cutting. So the thing I like about the twist is you'll see I'm not carving into the hair, I slide through it. So I pick where I want to cut and I bring everything straight out from the head and I'm working in basically pie shaped sections all the way around the underneath of the hair starting at the mid shaft and working my way to the end, sliding that razor down. What that's gonna do is remove weight for me but keep her length. If you're afraid of cutting into your guest's length then what I would do is just drop that length out and then do your slide cutting. For me, I keep the length right at the tip of my finger so I know exactly where it's at. I'm gonna continue these pie shaped sections around the head to the right as well. The only difference is now my fingers are pointing down and we've talked about this in many videos but as I work to the right hand side of the head you never wanna comb your guide into the new hair. So I'm always combing the new hair towards the guideline. So on the left hand side, I comb the hair towards the middle and then on the right hand side, I'm still combing the hair towards the middle so I make a shift in my hand position which keeps me consistent, keeps my sectioning there and my guideline there as well. So just working my way all the way to the front hairline and then sliding from mid shaft to the ends keeping the length but removing the weight. Now, this is a pretty simple haircut because all I'm gonna do is drop down one half of that U shape and I'm gonna do the same thing, sliding. What I wanted to do here was over direct everything to the very front, create a stationary guide, create layering around the face but then pushing that weight to the back. So what we did was we created the layers underneath, we took out a ton of weight but now I'm pushing that hair over top of it. So at the back of the U shape, if you think about it, that's gonna be the longest point. The shortest point is right in the front of the face. So we're creating layers and then the longest point is falling over top of those layers that we created in the back. Also, what you'll notice about this technique is we're creating a fringe and using the Donald Scott Twist, it has one side of it cuts 100% which is the side I'm using right now and then what I do is I go through at the very end and I use the 25%, you can see it right there, 25% cutting side which takes out 25% of the hair to just remove some bulk. So I comb it around the face, 25% carve it a little bit below the mid-shaft and just work my way through it and that lightens it up so it's not so heavy. Now I'm gonna go in with the Bercato Conditioning Moose, it's cloud nine. I'm a moose, I really love moose so I'm always on the search for my favorite moose. This one I love because it's really fluffy and thick feeling but it doesn't feel heavy on the hair, it's conditioning, it helps repair the hair as well. So nice, nice thick moose and then also the root fix, it's a root lifter moose that I spray right at the base because I'm gonna go in and do a round brush blow-dry, I wanted to build up some volume. The blow-dry I'm choosing today, this is the Minerva blow-dry, I've been given away on the podcast quite a bit so if you guys listen to our podcast live on Wednesday nights you have a chance to win one of these blow-dryers from Minerva. This thing is super powerful so I go in, I do a power blow-dry and now I'm gonna section off the Mohawk section of the hair and start in with my Ergo round brush. This is the number 43 brush so it's not the largest one that we have, it's a little bit, it's more like a medium size. I like using that because it gives me a little more volume. People think that the bigger the round brush, the bigger the volume, but it's actually the complete opposite so I like to use a medium brush when I'm looking for some volume in the hair. Notice how when I'm blow-drying I work that section back and forth. I also use the nozzle on the blow-dryer to keep the hair nice and smooth and I allow it to pass over the hair to keep that cuticle down. Shiny hair is when the cuticle is laying down so that's what you wanna be creating, you don't wanna be shaking the blow-dryer back and forth all over the place. Now I'm gonna speed it up because I do the same thing all the way around the head, just basically blow-drying and wrapping it back off of her face is my goal with this. Now as I move into the top portion I'm gonna take horizontal partings across the top, let's call it horizontal because it's right in front of me and I'm gonna section it, I'm blow-drying round brushing back off of the face until I get to the midpoint of the head and then I'm gonna round brush everything forward to create extra volume.  I didn't need maximum volume in the very back of the head because of the fact that she's got so much thick hair back there but she likes to wear her hair kinda tossed to either side in the front, that's why we cut it to almost appear like we're gonna cut it for a center parting but I wanted to keep it more symmetrical because she likes to toss it back and forth on both sides. Now to finish it up, this is that vibrostrate iron, this is the giveaway that we're doing so if you wanna win this iron, it is an inch and a half iron that vibrates as you smooth it, you can curl with it, smooth, straighten, do whatever you want. The vibration separates the hair as it goes through so it really allows you to not have as many passes over the hair so it creates less damage. This one has two separate settings so it's really good, it's a good consumer-based iron, they also have a professional version as well. Then we're gonna finish it off with the Bracato Maximum Hold hairspray. This is basically one of those sprays that will stop a bird in flight, we used to say. It's super firm hold so I just spray it a little bit just to hold it in place and you can see all those layers, all the movement that we created in this cut, it looks nice and light but also still thick and full as well, it's much easier for her to manage. Check out the bottom where that shave part is. It's nice and airy, it's got a nice textured look to it so hope you like it, let me know in the comments below. Thanks for watching. All right guys, and like always if you liked the video then make sure you hit the like button below. Subscribe to the channel if you want a chance to win this Vibra straight iron. Also remember if you want any of the tools that you saw in this video we have them all for sale on freesaloneducation.com and plus we have a ton more free education videos on our website so go check it out. Thank you guys so much for watching. I'll see you guys on the next video. Thanks.


Karena léks de Ṣāna Ṭhāgabhān veda bhyāsyan ka Ṣāna dhyo pañi Ṣāne dhyo ruasi Ṣāna dhyo paṁ Ṣāna dhyo ka ri thle.  Yaubashara rei joghi mandiro pa rikramā kari pa pa rei puja rikama ka ri thle. Ṣāna dhyo paṁ Ṣāne dhyo pa rikramā ka rei puja rikama ka ri thle. ,  Analysis. I have demonstrated this first, to all of us . I have showed this also to all of our teachers. Clap your hands. Yes, I speak M być Murshli. With your very constant fortification, to the entire world,


is the strain for activity of C4s. So among these three neurons, you can see two neurons which were not involved in associative learning and one neuron which participated in the acquisition of this associative memory. Since you can see the dendrites and spines of this neuron, you can start comparing what happens in the neuron which was involved in conditioning with, for example, a number of spines compared to the neurons which can neutralize this dendrites.


Hi everyone, and welcome to this lesson on derivatives of exponential functions. We'll be walking through a couple examples as well as an application problem at the end, but we're going to begin with a calculator exploration. So if you grab your calculators, we're going to start out by really discovering what the derivative of the function y equals e to the x equals. So under y equals, if you just set that up to graph e to the x, and we'll just do a standard graph, zoom six, and of course we all know that's what it looks like. So in order to discover what the derivative of e to the x is, we're going to take note of a couple different values, function values at various x values, and then we're going to use the calculators built in functions to calculate the derivative at those same x values. So let's start with determining the function value when x equals two. So when x equals two, the function value is approximately 7.389. So let's determine next the derivative, the slope of the tangent line, at that same x value. Remember you can do that graphically with the built-in features down at number six, dy dx, and simply type in two and enter, and notice how the derivative at that point is that same 7.389. Interesting. So let's try another one. Let's try negative one. So let's begin with the function value at negative one, and we get approximately 0.368, and now we're going to figure out the slope of the curve, the derivative at that same point. Down to number six again, and simply type in negative one and enter, and there is that same 0.368. So we're two for two. Let's try another one. Let's try when x equals a half, and we'll begin again with the function value. The function value when x equals a half is 1.649. So let's now do the derivative at that same x value, and once again we get 1.649. We could sit here all day and do this with different x values, and you will notice that the derivative value of e to the x is always equal to the function value at e to the x for that same x value, which really leads to the rule. The derivative of e to the x is simply going to be e to the x. It's the only function which is its own derivative, kind of interesting. Now of course you are going to run into situations with the chain rule. With the chain rule, as we learned with other functions, it's going to be the same base function, so you'd have e raised to the exponent, but then you simply need to multiply by the derivative of that exponent. So we'll be walking through some of those as we do our examples today. So next let's address the derivative of a to the x. So we're going to assume that a is a positive real number, not equal to 1, obviously because if we had 1 to the x, that's simply going to be 1. The derivative rule is that it's a to the x multiplied by the natural log of a. Now if you think about this rule and then apply it to the one we just looked at with e to the x because e is a positive real number, if you go back to this rule and apply that a to the x rule to here, remember what we would have is e to the x, but then you're multiplying by natural log of e, which we just know to equal 1 anyway. So the rule does actually bear out. The proof for this is generally straightforward. It involves taking the derivative of both sides of the equal sign after you rewrite once you have it as y equals a to the x. It actually does work out. Let me see if I can give you a mini proof over here. So if we have y equals a to the x, if we just take the natural log of both sides, something you learned to do when you solved exponential equations, of course we can pop the x out in front, so it looks like that. By doing the derivative of both sides, derivative of natural log of y is going to be 1 over y. You might not have learned that yet. Multiply by y prime. You have to remember this natural log of a is a number, which is really the coefficient of the x. So the derivative over here is simply natural log of a. When you go to solve for y prime and multiply both sides by y, well y is equal to a to the x. So it's a relatively straightforward proof. Once again, though, you'll run into occasions with the chain rule. You'll have the same basic rule, so you'll have a to the u times natural log of a, but then you just need to multiply by the derivative of the x prime. So let's take a look at a few examples. And all we're doing is doing straightforward derivatives. So with the first one, of course, we're going to rewrite that square root function as x to the 1 half and think of it that way. So our derivative of the square root function, you've already learned to do that with the power rule. Now with the other one, remember the three stays put for now. Now we have to do the derivative of e to the 4x. So that's going to be e to the 4x. But then we have to multiply by the derivative of the exponent, which is just 4. So in the end, our answer is 1 half x to the negative 1 half minus 12 e to the 4x. So the next one involves the other rule. So to start, the 4 will stay put. When we go to do the derivative of this, it's going to be 10 to the x. Derivative of x is, of course, just 1, so no need to write that down, times natural log of 10 minus, and of course this is just a power rule situation. So you could leave it like that. Sometimes you'll see the 4 and the natural log of 10 kind of brought together as a coefficient of that 10 to the x. Otherwise that would really be all you could do. Now in this one, remember that e and pi are both numbers. So when we go to take the derivative of e to the pi, that really is just a number. That derivative is going to be 0. The derivative of pi raised to the 2x, that's really the rule for a to the x, or a to the u, because it would be a chain rule problem. Pi, of course, is a positive real number. So we're going to have pi raised to the 2x times the natural log of pi, but then we need to multiply by the derivative of the exponent, which is just 2. So you could maybe see it rewritten as 2 natural log of pi. That's sort of like a coefficient of sorts times pi to the 2x. Now in the next one, you do need to remember, again, natural log of 3 and natural log of 4 are both numerical values. So when we go to do the derivative of natural log of 3 times x squared, that's really just a power rule problem. So we'll have 2 natural log of 3, and that's just times x, plus we'll keep the natural log of 4 there for now, and now we're doing the derivative of that. So that's that e to the u rule. So we'll have e to the negative 9x times negative 9. So we can rewrite it a little bit. Add 2 natural log of 3 times x. We can make this minus 9 natural log of 4, perhaps, e to the negative 9x. A couple more before we get to our application problem. So for this one, let's think of it as e raised to the x to the 1 half, kind of a super chain rule problem. So think of it as the rule for e to the u. It is a chain rule. So we'll have to keep the function as it is. So e, if you prefer to write it at this point, is just square root of x. That's fine, because we're essentially keeping the function. But now remember we need to multiply by the derivative of that exponent. So that's where we're taking the derivative now of the x to the 1 half. So it's perfectly fine to leave it like that. If you want to rewrite it, we could have e to the square root of x over 2 square root of x like that.  And number 6 will be another souped up chain rule problem. So think of it as just doing the derivative of 3 to the u. So we'll keep the function as is. Multiply by natural log of the base. Now we need to multiply by the derivative of the exponent. So that'll be 2x minus x. Not a whole lot you can do to rewrite... Oh, minus 1. So let's take a look at an application problem. And I'm sure you've studied exponential functions and application problems. So this is one of those. So the value of a car purchased back in 1997 can be approximated by the function v of t equals 25 times 0.85 raised to the t. Where t is the time in years from the date of purchase and v is the value of the car in thousands of dollars. So the first thing we're asked to do is simply to evaluate and interpret v of 4. So if we want v of 4, we can simply do that on our calculators. I'll give you a second to do that. So when you go ahead and do that, you should come out with about 13.050. So that's what v of 4 is equal to. In terms of the interpretation, remember this is going to represent the value, but it's in thousands of dollars. So if you multiply this point 13.050 times 1,000 and maybe even preserve more decimals that you might have had there on your calculator. What we find is that the car's value four years after purchase, approximately 13,050. And check the decimals on your calculator. It should be about 16 cents. So that would be the interpretation. So I'm sure you've done that kind of problem before. So let's get more into the calculus then. The next thing we're going to do is find an expression for v prime of t and include what the units of measure would be. So if you go back to the function, what it was, I can rewrite it here for us. So remember v of t was given to be 25 times 0.85 to the t. So when we find v prime of t, the 25 is the coefficient simply remains. Then the 0.85 raised to the t, that's like doing the derivative of a to the t, a to the x. So we're going to keep the 0.85 to the t. And we need to multiply by the natural log of 0.85. And of course the derivative of t is simply 1, so we don't really need that. Now in terms of the units of measure, remember that a derivative is a rate of change. And we know a rate of change is going to be change in y over change in x, like any slope would be. And if you think about what the y's, the v's stand for in this case, it's the cost of the car, which of course we're going to measure in dollars. And it's over the number of years from the date of purchase. So that's going to be years in the denominator. So the units for this would be dollars per year. So simply think of it as a rate of change, a slope, in order to determine what the units of measure would be. So finally let's end by interpreting and finding v prime of 4. So we have 25, you can do this of course on your calculator. So if you go ahead and do that, you should get that it's a negative approximately 2.121. So first of all, the negative, the fact that this is negative, and remember what the derivative represents in this case, it's the amount per year that the car's value is changing. So the fact that it's negative tells us the car's value is decreasing, which makes sense. The older the car gets, it's worth less money. And this is really a measure of how much it's decreasing each year. So at 4 years from the date of purchase, the car's value is decreasing. That's because it's negative, approximately. And remember this is in thousands. So again if you multiply this by a thousand and maybe even go back to your calculator value with the extra decimals, perhaps take a peek at that and it should be about $2,120.90 per year. Alright, notice how the negative is not included there. We have the negative accounted for in the fact that it's decreasing. So hopefully this has provided a good base for you in how to do derivatives of exponential functions, including an example of how you might see them with an application.