Transcript:

BRAD NEWBOLD 0:09
Hello, everyone, and welcome to the Expert Guide to Leaf Area Index: Theory Methods and Application. Today’s presentation will be about 45 minutes, followed by about 10 minutes of Q&A with our presenter Jeff Ritter, whom I will introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive, so we encourage you to submit any and all questions in the questions pane. And we’ll be keeping track of these for the Q&A session towards the end. Second, if you want us to go back or repeat something you missed, don’t worry. We will be sending around a recording of the webinar via email within the next three to five business days. All right, with all of that out of the way, let’s get started. Today we’ll hear from METER plant physiologist Jeff Ritter, who will discuss the practical application of leaf area index, and how this measurement can transform research. Jeff is the product manager for plant canopy and atmospheric monitoring instrumentation here at METER. He earned his master’s degree in plant physiology from Washington State University, where his research focused on leaf level gas exchange and the impact of plant biochemistry on the measurement of the global carbon cycle. Prior to- prior to working at METER, he held the research faculty position at Washington State University in the Department of Crop and Soil Sciences. So without further ado, I’ll hand it over to Jeff to get us started.

Jeff Ritter 1:29
Thanks, Brad, and thanks for the introduction. As Brad mentioned, today, we’re gonna be talking about what we were calling the complete guide to Leaf Area Index, or as I’ve subtitled it here is “How I Learned to Stop Worrying and Love the Trees”. So we’re going to talk about what leaf area index is and why you might want to measure it. And then we’re going to dive into some of the methods that you would use to measure it. But before that, I wanted to take a step back and kind of talk about some of my experiences in plant science and specifically in this particular topic with leaf area index and talking about canopy productivity. So I wanted to go back not to my background in in measuring photosynthesis, but actually back to what got me interested in this in, in the first place. So if you will forgive me, I want to tell a brief story about my childhood, as I as I think there’s some relevant information there. I grew up with my backyard being adjacent to a fairly large forest in the Pacific Northwest region of Washington State. And, you know, the forest to my backyard looked not too terribly dissimilar from from this one that you’re looking at now, although there were probably more ferns in the understory and more Douglas firs and less cedar trees. And my brothers and I used to spend countless hours out there in the woods. And we got to know that there are even though we refer to it as one forest, there are distinct regions of that forest with different aspects to it. So we knew were the the oldest trees in the forest were that survived the clear cutting of the early 1900s. We knew the points where the canopy grew thin. We needed to look out for stinging nettle, that’s where the stinging nettles would grow and the paper wasps would build their nests in there. And we knew regions of the forest that that had distinct ecosystems one of my favorite parts of those woods, we’re about halfway in there, there’s a little clearing where you get enough sunlight for clovers and wildflowers to grow. And I used to like to go back there as a kid and and lay on my back and look up at the canopy above me as the light would be coming in through the through the leaves. So I remember fairly distinctly one day would have been in the summer at some point I was looking up at this alder tree and I started thinking about all these distinct regions of what we called one forest and and what goes into allowing certain plants to grow in one area and not others. And so I started thinking about how the canopy itself played a role in that. So in my mind as a child, I thought, well I wonder if it has to do with the the number of leaves in the canopy above me. So maybe if I can count the leaves in the canopy here in the clearing, I can get a sense of what makes this different. So that was my my question was trying to understand if different independent ecosystems within this forest and to do that I laid on my back and I looked straight up at this alder tree and I started trying to account leaves in a line directly between me and the sky. And so as you look at this picture, try and do that for yourself, try and and look at a tree like this and try counting tree counting leaves. What I realized quickly as I’m sure you would too, and you probably didn’t need me to tell you this beforehand is that that’s a very difficult thing to do, it’s very difficult to differentiate leaves between trees. And it’s very difficult to actually pick out individual leaves and not count the same leaf twice. So I didn’t get very far in that before I looked around. And I realized that even if I was able to do that, on this alder tree, right over there is a is a Douglas fir. And over there is a giant grand fir. So how am I going to differentiate between these two, look around further I saw that, I’m going to have to also account for the salal berries that are growing on the edge of this clearing and the stinging nettles that are growing over there. So with that complexity in mind, I was quickly overwhelmed. And I gave up the affair entirely. And the reason I’m telling you this story is because while my attempt at that point may have not succeeded, I still think it was a worthwhile question to ask. And knowing what I know now, there are several methods I could have used to better address that question. So what I was ultimately trying to understand is something about how the canopy architecture impacts ecosystems in and around them, and the overall productivity of those ecosystems. So instead of just asking about the total number of leaves and try to count the number of leaves in the forest, I probably should have been asking question about leaf area index. I didn’t know what Leaf Area Index was at the time. And so I’m going to walk briefly through it here to today, just just for the sake of my 12 year old self. But it is not honestly not a super complex thing to understand. Rather than simply counting leaves, we need to have some measure of the leaf area. And we want to reference that to a certain amount of ground area. So in this example, that you’re seeing, if we take a giant leaf that’s one meter by one meter, so we have one square ,meter of leaf, we want to reference that to one square meter of ground beneath it. So we then have a leaf area to ground area ratio of one to one, so our LAI value would be one. So that’s how we would scale this up. If you have more of these giant square leaves stacked on top of each other, are going to count the leaf area above a one square meter of ground area. So in this example, we have three leaves, so we would have three square meters of leaf referencing that to one square meter of ground gives us a three to one leaf area to ground area ratio, or LAI would be three. Nature is hardly ever so simple as that. And if it was, I probably could have succeeded in simply counting the leaves above me. A canopy even in a rather simple, idealized plant like this is is much more complex. We can’t just count the leaves in here and get some direct measurement of the leaf area that’s involved there. So once we start adding in other plants of different growth phases, other species, a canopy can become very complex indeed. And so we need some methods order to measure that. But before we get too far into how we measure that I want to talk briefly about why we want to measure this. So I hinted at this a little bit in my story, some some reasons, you might want to measure this. And I’m sure that if you’re watching this, you probably already have some questions in mind that you think Leaf Area Index, or LAI could help you address it could help you answer. So I’m not going to belabor this too much of why you might measure Leaf Area Index. But I do want to talk about this parameter in general, and then some things you can do with it. So leaf area index is extremely ubiquitous. And it’s used in a lot of research, because it’s highly descriptive. It allows you to to quantify parameters of canopy productivity, biomass accumulation, etcetera, into a single value. So if we look at this map that I have up on the screen now that’s it’s showing you how LAI is dispersed across the globe in every continent, aside from Antarctica, I guess, because that wouldn’t be extremely interesting to look at. And on this map, just to orient you, if you look at very light areas on the map that is extremely low LAI and as we’ll talk about further, that means that there’s very little leaf material there. Areas that are darker on the map refer to areas with higher LAI which means they’ve got thicker canopies, which means that those are more productive regions. So if you take a look at right around the equator, that equatorial zone has very high LAI values because those are very dense canopies. Those are very highly productive canopies as well as you move North and South into regions that have deserts. Again your LAI can drop to zero in places where there’s no plants growing and then again as you move up into the the temperate zones, you see LAI increases once again. This is related to different paths earns variables such as water availability, light regime, seasonality, these all play a role in that. But you can quantify and unify that with a measurement like LAI. So if you are looking at any sort of, ask questions about how much light is being harvested by your canopy, whether you’re talking about in terms of productivity, specifically, things like biomass accumulation, crop growth or yield, those can be addressed with LAI as it is descriptive of both of those. If you were asking phenological questions, which is how your ecosystem changes with with the seasons, tracking LAI over the course of those, those seasons can be extremely powerful. If you have the leaf area from the time of leaf emergence, all the way through senescence, you can really get a sense of when your canopy is at its peak productivity and when that starts to die back off questions of canopy structure and how that impacts ecosystems in and around them like I was referring to in my story can be answered with LAI as we discussed.

With transpiration, any any amount of increase in your canopy productivity is also going to increase the amount of water that your canopy is using. So, you can use things like LAI if you are looking at evapo transpiration. For example, if you use something like Penman Monteith, evapotranspiration, you are probably familiar with the concept of crop coefficients. And you can use LAI to directly get at crop coefficients, you can use LAI, to know when your canopy is covered, or is completely closed. So that you can more readily apply things like reference etc, to your canopy. Also, LAI is very useful for scaling leaf level processes to beyond the plant or even the plot, but up to sometimes the ecosystem or the global scale. What I mean is that if you are measuring something at a local level, on your plot, how do you know how it applies to the rest of of your of your field or your forest without knowing how much leaf area is in the rest of the forest, and so LAI can help you scale those processes. Okay, I want to talk about for a while how we would measure LAI. But I want to preface this by saying that there’s no absolute correct way to measure LAI. We’re going to talk about some both direct and indirect measurements today. But there’s no one truth when it comes to measuring LAI there are some that we consider to be more accurate. Typically, we considered the direct measurements to be a little bit more accurate, but they’re also far more time consuming. And we consider the indirect measurements are largely much easier to scale and to take more samples with and have a greater sampling size. But they have some drawbacks as well. So I want to make sure that we understand that every method we’re talking about has advantages and disadvantages, and there’s caveats and considerations for all of them. Also, this is not an exhaustive list. There are other methods that I’m not going to be discussing today. That doesn’t mean that I don’t think they’re worthwhile, they don’t have their place. It’s just we can’t talk about all of them. For example, I’m not going to talk about one of the older methods to the plumb line method of calculating leaf area index, I’m also only going to be talking about one form of a part transmittance technique today. There are other methods out there, though, and there are more methods being developed currently. There I’ve seen publications just within the last year or two that are still coming up with other methods to measure LAI. So this is a example of some of the methods that we get a lot of questions on. So these are the ones I want to talk about. So I want to start by talking about the direct methods of measuring LAI. These tend to be some of the older methods. And these direct methods also tend to serve as very good reference points for a lot of the indirect methods that we that we use. The first one that I want to talk about his destructive harvesting, which you can probably tell what it is just from the name this is actually when you are collecting the leaves from your canopy to count and measure later. So this can be as extreme as cutting down a tree as you see in this picture on the left, collecting all the leaves and analyzing them back in your lab. In many cases, this is probably not what I would recommend if you are in a tall forest canopy, there’s probably easier, less time consuming ways you are removing a good amount of material from your canopy by doing this and so that it has effects as well. There are some cases though, like this picture in the middle of this desert ecosystem that there might not be any other way to get at LAI other than destructive harvesting so they have to go out there with a plot marker, they throw it on the ground, and they harvest all of the material that’s within that plot marker, and then we take it back to their field to back to their lab to analyze it, you might be able to get it this with something like a reflectance technique that we’ll talk about near the end. But even then, you need some sort of a reference to get from that reflectance technique to an actual LAI value. So, in some cases, destructive harvesting is your only option. Another option that’s been used, going back decades, are litter traps, where, if you have a canopy that’s big enough to put these underneath, these can work well where there’s no real standard for for litter traps, these, you know, you build them out of wood, or PVC, and then you put a net between them. And these will catch any sort of falling senescing leaves. And you go out at a time period and collect those leaves and again, manually analyze them to get a leaf area index. So these can again work very well so long as they can fit under your canopy. And you have the you have the labor required to do this, because it’s a very labor intensive thing going out there all the time and collecting these leaves, you then also need quite a bit of effort to analyze these leaves, you need to count and measure them. Now unless you are very old school, you’re probably not doing this by hand, you’d probably use something like this leaf scanner is an example of this is a licor example of a leaf scanner, the LA 3100 that you would scan that through and would give you the measurements of your leaves. Now these direct measurements also have one other advantage that the indirect measurement dont’t. These are the only ones that are going to give you what’s called your species specific LAI. So if you are in any candidate that has more than one species, you’re not going to know the contributions to LAI. From those individual species with the indirect measurements, the only way to get at that is through some sort of direct sampling. So if you’re needing species specific LAI, just keep in mind that you can maybe get an integrated signal with your indirect method, but you’re going to have to do some amount of sampling to get out the species specific contributions. So those direct methods, like I said, they can serve as a good reference for indirect methods. I’ve seen publications where they’re comparing different indirect methods, and then they see how close it gets to one of the direct methods and using that as a reference. And largely when we’re talking about indirect methods, it’s all referring to how light is interacting with the canopy. So when light reaches the top of the canopy, it has three different fates. And we can use these different fates as different measurement techniques. Now, the one that we are really ultimately concerned about is how much light is absorbed by the leaf because that’s what drives photosynthesis. That’s what drives yield and overall productivity. That’s not one that is easy to directly measure. Instead, we can measure the light that is either transmitted through the canopy and makes it down to the ground or light that’s reflected off the top of the canopy as long as we can get a sense of it up above. So the first of these methods I want to talk about and we’re gonna start with some below canopy methods are is hemispherical photography, which is a method where you’re actually taking pictures and it requires a camera with what’s called a fisheye lens and a leveling deck and a tripod, just like you would see this set up here. So this is a technique that has been around for a long time. And it continues to be used. And that’s because it is a very robust way of making this measurement. You set the your your camera set up on the ground, and you get an image like this one. And all the other measurements we’re talking about today will give you you know, singular values. This one though, it gives you this archival picture that you can save and store. And you can pull other information out of this well beyond, as well beyond just leaf area index. For example, you can get leaf area index, but you might also be interested in how sunflex impact ecosystem below and you might have some species that that fire up their their photosynthetic processes only during sunflecks. And so you need to know when those sunflecks are going to happen, and how long they’re going to happen. Well, with a picture like this, you can actually track where the sun is going to be and estimate when those sunflecks will happen and how long they’re going to be there. So there’s a lot of data in a picture like this. And you can see that typically you will take this different pictures over the course of a growing season. You could generate a series like this. So this is essentially a time lapse picture taken over the course of several months. This is going from earliest spring to mid summer. So each image in here is going to have a different LAI. Based on how much the canopy has filled in what’s not quite as easy as just taking this picture and spitting out a value, you then do some work for that you need to take that image and analyze it in software. And that involves a process called thresholding, where you need to decide what are the lighter parts of your picture? And what are the darker parts that are going to refer to leaves. In the past, this is a manual thing where you’d manually manually choose which is the sky and what are the leaves. And from there, you can find all the all the black areas of your photo and calculate that as leaf area. This is kind of where you could run into some issues with hemispherical photography. Because there’s some subjectivity in determining that threshold, especially if you’re doing it manually. There are a lot of algorithms and programs out there that can help you do it. But depending on what you choose to use, it’s going to impact your ultimate result.

One of the benefits as I said of hemispherical photography is that you have these images saved. And so you can, you can apply multiple techniques as there are techniques that arrive later, you can use those, but in the moment, you need to be able to determine which of these techniques works for you. And understand that there could be some noise there if other researchers are using other thresholding techniques. Some other considerations with hemispherical photography isn’t that it doesn’t work for everybody in all cases. So there are some times when you definitely should be careful, taking me spherical photographs. If we take this picture as an example, the first thing that I see here is that the sun is visible in this picture, so that solar disk is going to make determining your threshold very difficult, because it’s going to be the brightest thing in the picture. The other thing that I see are some very harsh shadows down at the bottom. Meaning that if you are running an algorithm, the woody tissue of half of that tree might be counted as a leaf and the deep shade of that might not or the inverse might be true, where the woody tissue might be counted as sky depending on what you’re applying. So regardless, it can cause an error in your thresholding. And thirdly, having variable clouds in the sky like this can cause a big issue, you see how bright that cloud is relative to the sky behind it. So that sky is gonna be much darker than that cloud, again, causing issues. So all of this combined is why we typically recommend that if you’re gonna do hemispherical photography, you do it in uniformly overcast conditions as much as possible. Where you would use hemispherical photography also can be a little limiting. You need a canopy that’s big enough for you to get your camera set up fully underneath. This top right picture. Those verdant rolling hills, kind of where we are here at METER headquarters, it would be very difficult to get a camera set up underneath and to take a picture of the full canopy. And so hemispherical photography would probably not work very well for that canopy, but the one beneath it with the tall forest, that would be an ideal circumstance for it. So long as again, the the sky conditions are, are adequate. So keep that in mind with the questions you’re asking. The hemispherical photography, it can be time consuming, and there’s some post processing to take into account but you can get a very data rich measurement that you have that you can reprocess over and over again, so long as it works for you and your conditions. Okay, so hemispheric photography is an example of a below canopy measurement that’s using transmitted light. But I want to talk specifically about another method that you use below canopy that rather than looking at an image is is using a measurement of the transmitted light directly. So we’re going to talk about this technique called par transmittance. So I will kind of interchangeably say, light and par and visible light in this presentation. Know that in the context of LAI I’m always referring to the band of light that plants are using to dry photosynthesis, which is what par is referring to. So that’s photosynthetically active radiation. So if I’m talking about light, or visible light, I’m talking about that that band of photosynthetically active radiation is visible light and par very closely overlap. So just to kind of orient ourselves, again with what we’re talking about with transmitted light in LAI. If you have a low LSI value, that means you got a very thin canopy. You don’t have a lot of leaf material in your canopy, which means less light is going to be absorbed or reflected by that canopy. So most of it’s going to be transmitted so low LAI means high light transmittance. Well, when you have a fuller canopy, you’re going to have high li a lot of leaf material in the canopy. And that means less light is going to be transmitted. So for the purposes of this method, I want you to think about the canopy as a light attenuating medium. Okay, and the reason for that is we are going to kind of dive into a model that discusses the canopy as such as a medium that attenuates light. So the way that we can take a measurement of the below canopy light conditions and relate that to leaf area actually stems from Beer’s law. So you might be more familiar with Beer’s law in the context of something like a photo spectrometer, where you’re shining a light through cuvette in order to measure the concentration of of some molecule that’s in solution. Well, in this context, rather than shining light through a cuvette, we have the sun is shining through your canopy. So instead of the cuvette, attenuating, your light we have the canopy attenuating your light, and rather than some some molecule of interest, you have leaves. So we can use beers law in this context for LAI in the same way. You might look at this equation, you say, Okay, I see we have the transmitted light. So that’s PARt, that’s the light on the below canopy. And that’s equal to how much light you have at the top of the canopy PARi times some extinction coefficient and the path link some some information about the canopy itself, I don’t see any information there specific to how we get from light level to leaves. And so that’s where we have to rely on a model that is based on Beer’s law. So this is the model that I really want to get into today. A little bit to this is the Campbell and Norman model for leaf area index that is derived from Beer’s law. And we’re not going to walk through the derivation of this model today. But I do want to talk about some of the parameters in here. If you are familiar with Beer’s law, you probably understand the concept of an extinction coefficient. This is the magnitude of that that light is attenuated when it moves through a given medium. In this case, that medium is our canopy. And this is going to play a key role in how we understand light loss moving to the canopy how that relates to leaf area index. So I want to tease apart this extinction coefficient variable. Not that I expect anybody to remember these equations exactly. But at least to understand that there are some variables in these models that we make assumptions about. And most of the time, those assumptions are perfectly valid. But some of you are going to be going out there making measurements in cases where you need to tweak these values a little bit because you’re working in some some weird ecosystems or some extreme cases. And so without knowing what these parameters are, it’s hard to know when to when to tweak them. So we’re going to talk about, there’s two other variables that go into the extinction coefficient, you can see the Greek letter chi there, that’s our leaf angle distribution parameter, which we’re going to get into, I also want to briefly talk about the solar zenith angle that’s represented by the theta there in the numerator. Now, while this one is not one that you necessarily need to worry about a ton, it’s not one that you need to measure, it does play a role in your extinction coefficient. So we need to talk about a little bit just to make sure that we understand what it is. So your zenith angle is nothing more more complicated than just the position of the sun relative to your position on Earth. So if the or if the sun is directly overhead, you have a zenith angle of zero. If the sun is on the horizon, the zenith angle is 90. This is, again, not something you have to measure. As long as you know the time of day when the measurement was taken, and your geographic location, you can plug latitude and longitude into a couple of equations and you can get zenith angle with a high degree of accuracy. But it’s important to understand that zenith angle is going to impact how light interacts with your canopy. And that’s going to happen as a function of what we call the leaf angle distribution parameter, that chi value that I talked about earlier. So this one takes a little bit more explanation to understand how we get from leaf angles down to one value that goes from zero to infinity. As you can see, the vertical canopy below says chi value of zero and the horizontal canopy says chi value for infinity. So what does that mean? And we’re not going to get into this too terribly much. But essentially, if you take a leaf from your canopy, at a at the angles that it exists at in your canopy, and you project that onto some surface, you can get a measurement of both the horizontal and the vertical components of that projection. This chi value is then simply the ratio of If your horizontal components to your vertical components, so then it makes sense why a perfectly vertical canopy would have a chi value of zero, as you would have zero horizontal components with as the ratio to some nonzero vertical component. Obviously, there’s no such thing as a perfectly vertical canopy in nature. And so something like onions have a chi value of less than one, maybe, .6 or .5. On the other side, looking at that horizontal canopy on the right, you have some as horizontal component increases relative to your vertical component. And that vertical component approaches zero, your chi value within approach infinity. Again, there’s no such thing as a perfectly horizontal canopy. So something that is highly horizontal, like strawberries is going to have a chi value of three.

And lastly, before we talk about why this matters, I want to just draw your attention to the picture in the middle, where it says a spherical canopy chi equals one, that doesn’t mean the canopy shape is spherical, it just means the angle angular distribution is said to be spherical, that means you have a perfectly even distribution of horizontal to vertical components of your leaf angle. Most canopies have a chi value of one. And if you don’t know the chi value of your canopy, it is typically safe to assume a value of one. And we’ll talk about here in another few slides how even if that’s not quite true, you’re not actually going to be making that much of an error by doing so. Before we get to that, I do want to talk about how, how and why zenith angle and leaf angle distribution play a role in light attenuation and how that impacts this measurement. So if we take, for example, this onion canopy, we think about when the sun is just barely creeping up over the horizon, the rays from that sun are going to hit that canopy dead on and a lot of that light is going to be absorbed or reflected. As the sun continues to move through the sky, however, and your zenith angle becomes closer to zero, much more of that light is going to transmit straight through that canopy and less is going to be absorbed. So that is going to highly impact our extinction coefficient, and therefore our recalculated LAI. And so there are a few reasons why we need to be aware of this. So I want to start off talking about this graph on the left where we have zenith angle on the x axis in degrees. And again, a zenith angle of zero means the sun is directly overhead. And that’s showing extinction coefficient on the Y axis. And all these lines represent canopies with different chi with different chi values. So let’s just focus on the extremes here. So this graph this line on the bottom of chi value of zero, that again, will be for a perfectly vertical canopy. You can see how sensitive this relationship between zenith angle and its extinction coefficient is for vertical canopy, how small changes in zenith angle cause extinction coefficient to quickly rise. So again, when zenith angle is zero, this is when the sun is directly overhead, you can see that extinction coefficient of zero means all the light is being transmitted. So if you look over here on the graph on the right, you can see it’s also zenith angle on the x axis, and the fraction of transmitted light is on the y axis. So for this vertical canopy, that’s exactly what you see.100% of the light in a vertical canopy will be transmitted through. That quickly decreases as the sun angle gets closer to the horizon. And that’s exactly what we talked about before that sun is just over the horizon, more of that light is interacting with canopy. As you increase how horizontal a canopy is, though, these, this relationship becomes less sensitive to zenith angle. So you see, when you get to a perfectly horizontal canopy, there’s actually no change at all in extinction coefficient, because of the way the light is always striking those leaves. The main takeaway from this that I hope you take away is that while these values are important to consider, especially if you’re working in something with a chi value that is, you know, not one, if you’re working in strawberries, or you’re working in something where that chi value is highly vertical. Maybe like onions, you need to take this into account, but most of the time, we can assume a value of one. And even if the actual chi value is something other than one, you don’t introduce a lot of error into your measurements. These extinction coefficients are actually very close to each other at any zenith angle above 20 or 30 degrees, and you can see that how much light is transmitted as well. It’s it Even though they can have, you know, chi values other than one, the amount of error you’re introducing to the measurement is fairly minimal. So keep that in mind, you need to understand a little bit about the canopy structure of what you’re measuring. But it might not matter that much if you aren’t measuring in something extreme. Okay, I want to talk about just briefly a few other parameters in this equation, not ones that we need to dive into too much, because they’re not things that you need to worry about on a day to day basis as far as measurement but something you should be aware of. The first one is this f sub b parameter. So this is the fraction of direct beam radiation, meaning how much of the radiation that is reaching your canopy comes from a single point is it comes directly from the sun, and what proportion of that comes from diffuse radiation. So even on a perfectly clear day, you’re going to have some diffuse radiation as the sun is bouncing, the sun’s rays are bouncing off of particles in the atmosphere, and diffuse radiation is coming from all angles. Direct beam radiation is coming just from one angle, as we discussed this coming, you can measure that based on the zenith angle. Once the sky is overcast, diffuse radiation will dominate. And all of a sudden light is coming from all angles. And so this is going to impact how how light interacts with your canopy, how it can penetrate through your canopy, and what how your canopy architecture matters. Think about this kind of like how shadows are formed on a clear day where you can get very strong shadows, directional with the sun. Whereas on overcast days, you might not get any shadows at all, because you’ve got light bouncing around in all direction, the same sort of thing happens with your canopy. So we take that into account. But it’s not something that you need to necessarily calculate or impact on a application specific level. I also want to briefly mention this A parameter in the denominator. And this is one that again, for the most most applications, you can ignore this, you can simply assume a value of .9. And this is the fraction of absorbed photosynthetically active radiation by leaves. So that leaf par absorptance value we can assume to be point nine. And that’s true for most canopies. But not all canopies. There are some cases where this is going to be lower. In cases where you are measuring in very young leaves or more commonly, if you’re interested in measuring in very old leaves in in leaves that are senescent leaves that are started moving nitrogen and pigmentation out of their leaves. When chlorophyll content goes down, you can actually see par absorptance go down. Now I can’t tell you how much it’s gonna go down in your particular application that’s going to be species specific that’s going to be dependent on how senescent those leaves are. I’ve seen some reports that measuring senescent leaves can lower this by 20, or 25%. So it’s something to keep in mind if that’s your question of interest. But most of the time, we can just leave this as .9. So you might be looking at this so far and say I mean, these are all just calculated or modeled, where’s the actual measurement here, you said, we’re going to be making a blow canopy measurement. And we haven’t talked about that yet. And that’s what this very last parameter is that tau that we’re going to talk about now. So eagle eyed, viewers might have seen that in beers law, we have this exponential where we multiply light incident the top of the canopy by the exponential of the extinction coefficient and pathlength. And all of a sudden here in this model we’re using we have the a natural log. And so you would be right in thinking that that is a function of the derivation of this model, where that the natural log of tau, it simply relates to the ratio of light that’s transmitted through the canopy to light instant at the top of the canopy. So that’s where that comes from. But you might look at that and say, well, that’s two measurements then right? Well, you’ve got to have the ratio of the transmitted light, comparing that to the ratio of the instant light above the canopy, and you’d be right. This is a technique that we need both the measurement at the top and bottom of the canopy in order to make it work. So let’s move away from the theoretical and look at the practical a little bit and how you actually make this measurement. So going back to our idealized plant canopy here, as the sun’s rays are moving through that canopy and being transmitted out of the below canopy space. We need something there that can measure that transmitted light. In this case, I’m showing you that with what’s called a scepter ometer, which has a giant sensing wand that has a whole bunch of par sensors on it. So this particular version has at different par sensors on it to give you an integrated signal.

You also need something to measure the above canopy light. And so I’ve shown that here with a single par sensor. And then again, the ratio of those two will give you your tau value, which you can plug into the model. Something like the SEP tonometer is great for discrete measurements, you can go out to your your forest canopy and set a line and walk down that line taking measurements every 10 meters, you can go out into an agricultural field and sample different plots at different intervals. But in some cases, you might not want discrete sampling. And you might want continuous monitoring of one plot, in which case you would need to get more of these par sensors and put them up under the canopy. I want to talk about why I’m showing several par sensors under the canopy and why something like that septometer that LP80 has so many par sensors but but first I want to make one other statement here. This method, as I said, requires both above and below canopy measurement, which means you need to have a plan of how you’re going to simultaneously get both. Sometimes it’s okay to not have them be absolutely simultaneous, if like conditions aren’t changing rapidly, which would be the case if if there’s no clouds out, if you have a clear sky, you can take that septometer and hike out to a clearing, take what we would call an above canopy reading and then hike back under the canopy and make measurements for for 15 or 20 minutes. But if it’s cloudy, those above canopy light conditions are changing very rapidly. And so if you are if you have 20 minutes in between your above canopy and you’re below canopy all of a sudden, those measurements aren’t valid. So if you’re working in a very tall canopy, make sure you have a plan of how you’re going to get both above and below canopy measurements that can be matched up in time. But back to this and why I’m showing you multiple par sensors that are under the canopy is because I want to take a bit to talk about the spatial heterogeneity of canopies. So say you find yourself measuring Leaf Area Index out in a forest that looks like this. And let’s say you already have some way to get your above canopy measurement. Where are you going to go to measure the leaf area index of this canopy? Do you put your PAR Sensor here? Where the sun is shining through? If you do, you’re going to measure a much higher proportion of transmitted light, and therefore you’re going to get a lower calculated LAI. Or do you put it over here in the shadows, where it’s much darker? If you put it there? Conversely, you’re gonna get a much higher LAI because you’re less light is being transmitted through to that area? Or do you go out with something like a scepter ometer, which can integrate a larger signal? But then again, do you measure it there in the light? Do you measure multiple places? And the answer is that it really depends on what your question is. If you are looking for one integrated value for LAI over this whole forest, you’re going to need sensors in in multiple locations, you’re going to need to take samples throughout the forest, get a sense of variability and measurements and scale your samples accordingly. There’s some interesting research done by a researcher named Steve Garrity who he took about 30 individual par sensors and set them out in a forest like this to look at their individual response. And we don’t need to walk through the specifics of of what these values mean. But just looking at the trends over the course of this year, where you had extremely different results from individual par sensors. And that’s not that some par sensors are right and some par sensors were wrong. It’s just related to where in the canopy they are because this speaks to how heterogeneous the light environment under a canopy can be. Remember plants, they grow with certain set patterns. They will branch off at certain nodes, but it’s also in response to environmental stimuli. It’s in response to how close your neighbors are growing to them. You are going to encounter huge amounts of leaf clumping or amounts where there’s sparse canopies. There’s all sorts of things that can impact this. So understanding how heterogeneous your canopy is is important to understand how much sampling you need to do. So that kind of plays into the next technique where we can use reflectance techniques to start scaling up a little bit. So we’re going to move out of the below canopy space now and move up above the canopy. So these reflectance techniques, rather than just measuring a total amount of light, they tend to use the ratio of different wavelengths of light. And we can do that because leaves want to absorb visible or photosynthetically active radiation. And they don’t want to absorb near infrared. So if we look at the ratio, and of those two have par to near infrared and how that changes over the course of the season, we can get some information over how that canopy is developing. So we’re going to talk specifically about one of these banded methods, that’s called normalized difference vegetation, or vegetative index or NDVI. I’m not going to dive too much into the theory behind this, we have a lot of information on our website that goes into NDVI. We’ve got some great webinars that dive more fully into that, just know that it is looking at different bands of light, it’s looking at red light, which plants want to absorb a near infrared, which plants do not want to absorb and how that changes over the course of the season. Now, with all of these reflectance methods, you need to have a way to get a sensor up above the canopy aim down at your canopy itself, we can use something like NDVI to get at LAI though, so NDVI might not be what you want to measure directly you want LAI and there are ways to get from one to the other. You can see this, this relationship and the graph over here NDVI is on the x axis and LAI is on the on the Y axis. And you see this relationship and he said okay, so if I just measure NDVI, I can then calculate my LAI. And if you are working in this particular canopy for which this relationship was developed, yes, you could do that. Unfortunately, though, every canopy has a unique and NDVI to LAI relationship that you need to establish for your canopy. So this isn’t something you can just go out and do in an afternoon or even in a week. This has to be done over the course of a growing season, where you would aim some sort of a spectral reflectance sensor something like this for measuring NDVI, and correlate that to simultaneous measurements of leaf area index that you take something probably like an on the ground measurement with a septometer and then generate this relationship. If you do that over the course of one growing season, you then have this relationship. So in the future, you can simply take out your NDVI sensors, point them at your canopy and get simultaneous measurements of NDVI and LAI. This can be very powerful for continuous monitoring. But sometimes it’s just not possible to develop that relationship between NDVI and LAI in the timeframe that you want. So even if you can’t though, these reflectance based techniques can be used simply as proxies of LAI and canopy photosynthesis. So there was some interesting work done by Youngryel Ryu back in 2010, out of UC Berkeley, where they were looking at the correlation between NDVI and canopy photosynthesis. So canopy photosynthesis is a very direct way to measure canopy productivity. But it can be very time consuming if you’re doing this on a leaf level. Or if you’re doing this with something like Eddy Covariance tower, it’s not exactly highly mobile, you can’t take this around plot to plot. Instead, if we can show that NDVI and canopy photosynthesis are tightly correlated, then we can simply measure NDVI and use that as the trend of canopy productivity no one productivity is is peaking. But we are not interested necessarily just in NDVI we want to correlate that back to LAI. And so we can also show that NDVI is correlated to LAI. This is an example that was taken over the course of multiple growing seasons, showing the trends and NDVI and how they correlate to leaf area index. And so you can see an NDVI plateaus, that also is showing us a plateau and leaf area index. So the kind of takeaway here is that things like NDVI and certain other reflectance measurements, they might not get you directly at LAI. But sometimes that’s okay if you don’t need absolute LAI. Okay, the last thing I want to talk about is scaling and how you can use these reflectance measurements to scale up and how that can give you a lot of power in your measurements. Let’s take first this example of this image down in the bottom left that’s an aerial image of some different management practices in in agricultural fields. And you can see that there’s some color differences in in some of the light green and the dark green But these reflectance based methods allow you to take an image like that, and convert it to quantify values give it to LAI. That’s what we see over here on the right. So this allows that researcher to rather than going in and taking individual measurements in each field, to scale up and take a measurement at at a much bigger level. So if you are dealing with a lot of spatial heterogeneity, a lot of leaf clumping, and sample size is a problem for you one way to increase that sample size is to scale up in this way. So a lot of these are are based on satellite data. Now as well, that image I showed you from the beginning, on that map is is based on satellite data, it’s how we get LAI over the over the entire globe.

But I don’t want you to go away thinking, then that that’s kind of the end all and be all of LAI, what’s the point of any other technique, because even with the satellite methods, you need to use them in conjunction with some sort of a ground truthing method. Typically, after you use one of these satellite products, you need some ground truth measurement of LAI. To see how close that product is, so you can then apply some confidence to the rest of your satellite data. So these can be, again, phenomenal for looking at trends. And they can be great for even at getting the absolute values, so long as you have some way of referencing those values. So I want to just wrap up with asking you a couple of questions that I would like you to think about when you are when you’re asking these questions. Starting with why are you measuring LAI, is LAI actually the parameter that you are interested in? Sometimes people will want to measure LAI because they want to get at transmitted light or fractional interceptance. So make sure you understand what you’re asking because some of these techniques will work for you for that, and some will not. Are you using a are you working in a taller or short canopy? That’s that’s greatly going to change which techniques will work for you. And do you need species specific LAI? Keep in mind that you might still be able to use these indirect techniques, but there might be some sub sampling involved. So make sure you have a plan of of how often when you’re going to be needing to do that. Are you, are you needing continuous monitoring? Or discrete sampling? And if so, there are there’s different instrumentation? And there’s different methods that work better for you. And how much do you need to scale? How much heterogeneity Are you? Are you dealing with? Are you is your question, something that ends at the plot level, you need to scale it up beyond that to your ecosystem to the globe? That’s going to change the way that you need to measure these. And lastly, are you needing absolute LAI? Or can you deal with just trends in the data? And that really depends on what questions you’re asking. So most of what we’ve talked about today can be found here in the we have a a page on our website called The Complete Guide to leaf area index that you can find at this link. If you’re watching this later, you can use that QR code to scan and it’ll take you directly there. I just wanted to wrap up by kind of circling back to when we were laying laying on the ground in the in the forest looking up at the trees. Cause that’s how I like to spend my time when I can. And just one takeaway from this is that, yes, the canopy can be an extremely complex place. And there are questions out there that that need to be answered. But I hope you don’t get too overwhelmed as I did with the complexity of that canopy. And I hope that you take away the sense that there are methods for every application, and that can help you answer your questions. And I hope that kind of helps you stop worrying and just learn to love the trees. So I think I’ve talked long enough, so I will take any questions you guys might have.

BRAD NEWBOLD 54:16
Alright, Thanks, Jeff. So I think we’re going to use the next few minutes. We’ve got a few minutes left in the hour to take some questions from the audience. Thank you to everybody who sent in questions already. There’s still time to submit questions if you’d like. And we’ll try to get to a few of these before we finish today. If we don’t get to your question here live, don’t worry about it. We do have them recorded. And Jeff or one of our other METER environment experts will respond directly to your question with the email that you registered with. So we’ll take a couple of these here. This first one is asking is it appropriate to use an open canopy measurement of par transmittance to substitute for an above canopy measurement?

Jeff Ritter 54:59
Yeah, so by by open canopy, so long as you’re out fully in a clearing, you just need to make sure that there’s no impact from the canopy around you on the measurement that you’re making. So even if you can’t get above the canopy, that that’s okay, it, it’s not that the height or the you know, any of that will actually play a role. It just matters that you’re not getting any shading from any obstructions around you. So yeah, a clearing can work as long as it’s it’s adequate for avoiding obstructions. That’s tends to be the way that a lot of this forest work has to be done. You know, if you’ve got to hike into the forest for for, you know, an hour, you can’t hike all the way back out every time you need to take an above canopy measurement. So you need to find some sort of a clearing or something to set your above canopy reference.

BRAD NEWBOLD 55:55
All right. This one actually just came in, and we’ve gotten a few other ones similar to it. In general, what time of day should be best for taking LAI or what other factors should they consider and taking LAI in a big field?

Jeff Ritter 56:11
Yeah, generally. Generally, it’s recommended to do it some time around solar noon, when you’ve got good strong light conditions, and your zenith angle is huge, the sun is more directly overhead. Keep in mind, though, as we talked about, that can intrude that can increase the error involved with a bad chi value. As we showed, the relationship between extinction coefficient and zenith angle is more sensitive at low zenith angles. So when the sun is directly overhead, if your chi value isn’t correct, you’re going to introduce more error into your measurement. Again, it doesn’t really impact a lot of canopies if if you are assuming a value of one and your canopy is close to that. So so typically around I would say 10:00 to 2:00 in the afternoon or ideal conditions.

BRAD NEWBOLD 57:06
All right. You talked earlier about hemispherical photography. And this person is wondering about doing that with small shrubs or at least shorter canopies. They’re talking in general about, you know, half meter in height is, you had mentioned that that as long as you can get your, your hemispherical camera underneath that you should be able to work with that kind of photography, do you have any insight for using smaller canopies?

Jeff Ritter 57:35
I would avoid it, it really is going to depend on the quality of the image that you get. So and again, it’s gonna be your application specific depending on on what your canopy looks like. Most of the time, if you’re dealing with a canopy like that, one of these other techniques is going to be much better for you. Something like the par transmittance technique technique works very well for a situation like that. So depending on on the size of the shrubs and the size of the canopy, you might be able to do it, but it’s probably not worth the effort for a lot of those.

BRAD NEWBOLD 58:11
Alright, I think this is going to be our final question for today. Again, thank you for all your questions, we’ve got several that we have not been able to get to we will get to those via email. And so please keep asking your questions. And this this final one is kind of a combo question. I’m cheating here a little bit. But a couple of people were asking about about taking LAI on plants with complex leaf structure with small leaves, even climbing plants, different things like that. Any insights or or tips for those folks?

Jeff Ritter 58:43
Yeah. So depending on the technique, a lot of these the actual structure or the size of the leaves don’t matter too much because that’s that’s ultimately what we’re trying to get at. So something again, like a reflectance technique or a par transmittance technique, that’s just looking at the light conditions so long as you can get a sensor under the canopy. Actual leaf structure doesn’t matter so much other than again, that chi value that we talked about making sure that you have that that set properly. For climbing plants, though that can pose a unique challenge for some of these techniques. Obviously something like hemispherical photography is not going to work. Even a par transmittant technique is going to be difficult because a lot of those sensors the assumption is that they are placed horizontal on the surface and so all of a sudden you’re trying to position these on a wall or something else at the plant is climbing on. So your options are some sort of direct technique direct harvesting could could work in in all cases. But that obviously is not ideal. For as far as how much time it takes and destructive sampling a plant might not be an option at all. So your best bet might be something like a The reflectance measurement because that can be aimed at whatever canopy you have. And you can get get values from that, regardless of the growth form the canopy angle relative to the ground or anything of that sort. So that might be the best way for you to approach that is some sort of a reflectance based technique, something like an NDVI sensor.

BRAD NEWBOLD 1:00:24
All right, I think that’s gonna wrap it up for us today. Thank you again, everybody for joining us. We hope that you enjoyed this discussion. Thank you again, for all your great questions. Please consider answering the short survey that will appear after the webinar is finished, just to let us know what types of webinars you’d like to see in the future. And for more information on what you’ve seen today, or if you’d like to chat with an expert on this topic, please visit us at metergroup.com. And finally, look for the recording of today’s presentation in your email. And stay tuned for future METER webinars. Thanks again, stay safe. Have a great day.