Pattern 2: Rajang River Delta in Sarawak, Malaysia

I’m getting ready to present this project in a seminar on Monday. In preparation for this, I wanted to start a new pattern so I could walk everyone through Chart Minder and show them my process. A couple of days ago, the Landsat Facebook page posted a photo of the Rajang River Delta in Sarawak, Malaysia. It’s quite striking and squiggly, which means it’s not going to be easy to turn into a knitting pattern. Of course, that makes it the perfect example.

Malaysian Rivers Landsat Original
Image description: A picture of the Rajang River Delta in Sarawak, Malaysia. This image was taken by Landsat 8 on June 16, 2016. There are labels on the rivers as well. The river in the top lefthand corner of the photo is the Batang Paloh River, and it connects to the Hulu Seredeng River on its right. The Rajang River sits at the bottom of the photo. The picture’s color palette consists of greens, browns, yellows, and even some brick red and muted turquoise. This NASA Earth Observatory image is by Mike Taylor. You can find the original image here.

I won’t include an in-depth description of the science behind the image in this particular entry. But if you’d like to learn more about it, you can read about it here.

Instead, I’ll tell you about Chart Minder, and about the process I use to turn an image like this one into a Fair Isle pattern.

First, I scan the image into Chart Minder, like so:

Chart Minder 1
The Chart Minder interface. There are options to change the color palette, the chart size (or the number of stitches there are in width and length), the stitch size (which is like the project’s resolution, or the number of stitches per image), and the opacity of the stitches over the original image. There is also a zoom feature and two options for the Chart Minder algorithm’s settings: it can choose stitch colors by center (“centre,” because it’s British) point and average color (“colour”). I’ll explain these features in more detail in another post. At this point, the image is just a quite opaque (at 80% opacity) grid of stitches in black and white, lying on top of the original image.

Then, I pick my colors. As you might recall from my very first post, Fair Isle patterns traditionally contain two to five colors of yarn. Chart Minder allows you a maximum of five colors — at least, that’s what its algorithm lets you have. You can add more colors in manually after Chart Minder creates your image. But we’ll get to that later.

Color Palette 3
Here’s a view of my Adobe Illustrator window. The original Landsat image is here. Next to it is a color palette of what I’ve deemed to be the five most important colors in the image. There’s a buttery beige, a forest green, a muted brick red, a black with touches of grey and green, and a jade hue.

I use the dropper tool in Adobe Illustrator to choose the five colors that I see as most prominent in the image. I then take those colors’ hex codes and input them into Chart Minder. WordPress wouldn’t let me caption this slideshow, so I’ll include the caption in this text: there’s a view of the Chart Minder interface. There are five different pictures, one for each time that I added in one of the five colors described above. The image becomes more detailed with each color I add.


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Then, I make the stitches smaller. This is the knitting equivalent of increasing the project’s resolution:

Chart Minder 10
Here’s our good ol’ Chart Minder interface again. By making the stitches smaller, I’ve given the project the equivalent of a higher resolution.

Now, here’s the cool part: Chart Minder officially produces the pattern:

Here’s the Fair Isle pattern that the Chart Minder algorithm created. Each row and column are numbered as well, like a graph. The pattern looks like a “pixellated” version of the original image. However, it’s not nearly as detailed. This is partially because the original image has a lot of delicate squiggles that the chunkier stitches whose detail this “lower-resolution” knitting project simply can’t accomplish. Additionally, the original image has far more than five colors in it. Therefore, the algorithm’s ability to map the five colors onto the image misses the detail from the original. There’s a lot of work to do here…

This image is sixty-five by fifty stitches, which means it won’t be very large at all: it will be about the size of a baby blanket, or maybe even smaller, depending on the thickness of the yarn and the size of the knitting needles. I’ve done this on purpose, because I wanted to challenge myself to make a very squiggly pattern out of a grid with few stitches.

Now, here’s the extra fun part: I go in and manually paint in stitches and colors that the Chart Minder algorithm missed. In the case of this pattern, it’s quite a bit of work. I would say that it took me a couple of hours to get the image to a point where I was pleased with it. I added four more colors that I thought were necessary to make the image work and, as you can see, I had to improvise a lot of the squiggly river parts (and leave some that were just too small and squiggly out).

So, here’s my final color palette:

Malaysian Rivers and final color palette
Once again, the original image in illustrator, now sitting beside a slightly more detailed color palette. I’ve added a deep seafoam, a yellow-y olive, a kelly green, and a color that I think I can only describe as a cross between tan and lime green. I actually think it’s really pretty, but the descriptor doesn’t quite do it justice.

And here’s the final image, after a couple of hours of manual stitch-“painting”:

Malaysian Rivers Landsat Pattern
A far more detailed Fair Isle pattern with all nine chosen colors. This pattern is also the feature image for this post. I don’t want to claim that it looks just like the original image (it doesn’t). But it’s semi-squiggly.



Why Knit the World? Revisited

After writing my first blog post and contemplating the project some more, I have some more clarified thoughts on the ethos of this project.

A couple of years ago, stories were circulating about Finland’s decision to scrap disciplinary learning for a more phenomena-based approach. This might look something like this: instead of having separate engineering, physics, forestry, history, philosophy, and policy courses where we address issues related to earth remote sensing satellites and earth data, we have one course that incorporates all of these subjects. Furthermore, the course wouldn’t be seated within any one particular discipline. Rather, this class would involve a holistic discussion of earth remote sensing. It might include everything from orbital mechanics to the ethics of environmental conservation and natural resource management and all nuanced subject matters in between. The idea is to break down entrenched disciplinary boundaries to explore learning phenomena in deeper ways.

Virginia Tech is already doing this, to some extent. We have 14 Interdisciplinary Graduate Education Programs (or IGEPs), all of which have broad themes (like Remote Sensing and Regenerative Medicine). And they each comprise disciplines from multiple colleges across the university, in order to address those times. All 14 IGEPs are largely STEM-based (STEM is an acronym we frequently use when we speak of any Science, Technology, Engineering, or Math-based field). That is, they begin with topics in science and engineering fields, and add folks from the humanities and the social sciences later on. Not all IGEPs have curricula, but the ones that do have a selection of courses mostly based in the STEM fields. This can make it difficult for students and faculty from non-STEM fields to participate in a way that’s fair and that speaks to the rigor of their disciplines. Many folks in the IGEPs — across all disciplines — are aware of this issue and are working hard to incorporate the humanities and the social sciences in ways that give them a seat at the table. It is difficult work. It is worthwhile work.

All IGEPs have faculty and graduate students — from STEM fields, the humanities, the social sciences, and the arts — who are both dedicated and overextended. In other words, they do this interdisciplinary work on top of all of the other grant proposals and papers and syllabi that their home disciplines already require of them. And while both students and faculty in these IGEPs work hard to create and/or participate in truly interdisciplinary discussion, we oftentimes find ourselves without a map. It is easy to promise ourselves that we will try harder to think beyond the disciplinary structures in which we are brought up; it is much more difficult to actually break the existing structures and create new ones in their place. It is particularly difficult when one’s career is dependent on one following those old structures.

This does not mean that creating truly interdisciplinary work is an insurmountable task. It can’t be. It is also not a task that is only relevant to Virginia Tech or to the institution of academia. No, interdisciplinary is a far broader issue. Just look around you: the problems that our collective futures hold for us are inherently interdisciplinary problems. We can’t address climate change or international conflicts or epidemics or broken educational systems with a single mode of thought. But we should acknowledge our predicament with honesty: we are without an interdisciplinary map, in an unyielding academic structure that favors STEM fields and quantitative social sciences over the humanities and the arts; that favors hard grants over soft funding; that favors projects chock full of results over projects filled with questions. And furthermore, I will argue that these structures, however angering, exist, at least in part, for a reason. We ought to have clear standards about what is fundable and what isn’t; we ought to be concerned with the impacts that academic research will have in the world; we ought to demand that questions ultimately beget some kind of recommendation that we can put into action and utilize in ways that we deem best (see: virtue ethics). However, we ought to think through these issues of impact and practicality differently than we do now. And we ought not assume that those in the humanities and the arts are not equipped to think through these issues in practical ways.

And so I am knitting.

Just kidding. I mean, I am knitting. But I wasn’t about to argue that knitting itself is practical.

Or was I?

I’ll pose a question to you: is knitting practical? And then I’ll pose another question: how do we go about deciding whether or not knitting is practical? First, we have to define our terms, and the conditions under which they exist. It’s impossible for me to tell you whether or not knitting is always, has always been, or will always be practical. For instance, if you are in search of an affordable blanket but big box stores don’t exist anymore in your post-apocalyptic dystopia, knitting a blanket is utterly practical. Or maybe you’re a graduate student that wants a beautiful handmade pair of gloves but can’t afford to buy a pair that are of the same quality as the ones you could make. However, if you’re looking for a way to travel from Baltimore to Kansas in in under a week, knitting a blanket or a pair of gloves won’t be of much help to you. So let’s narrow our scope a bit: Is knitting practical within the context of this dissertation side project I’ve set out to do? That’s a more reasonable question. But of course, we can’t answer that until we decide what knitting and practical mean.

I’ll lay out those terms for you, in ways that I hope will convince you of my definitions. Practical, in the instance of this project, should require that the project do something in the world, or at least within the fields in which I participate. If the project is truly practical, it will, by way of its existence and an audience’s interactions with it, change some minds and help its audiences think through problems in new and interesting ways. (Should we define new and interesting? Maybe, but I think it’s turtles all the way down. So let’s just remain on this turtle for now.) Furthermore, the project is situated within the fields of Remote Sensing and Science, Technology, and Society (STS), and Fiber Art. And so, I will put forth the notion that, if this project creates places for these fields to talk to each other in ways they could not before (in STS, we call these places trading zones), then we can accept this project as practical. I also think these tenets of practicality are relatively achievable within the scope of this project. Don’t you agree?

I would also like to point out that the definition of practical that fits this project is not the same kind of practical that fits all projects. For instance, if I apply this working definition of practical to a Boeing 737, it wouldn’t be a good situation. I don’t know about you, but I don’t want to sit on a Boeing 737 that is more concerned with creating places for fiber artists and aerospace engineers to find common ground than it is with, you know, flying me to my destination in one piece. Does this mean that people who design and fly Boeing 737’s should never be able to comment on my knitting projects, and that I should never be able to comment on the fly-ability of their places? Of course not. Sign me up for any knitted airplane that I can take apart and put back together, or for a knitting pattern that teaches me about algorithms that control plane hydraulics. Sign me up for a knitting that helps us think through issues of aerospace engineering, and an aerospace engineering that helps us to think through the technicalities of knitting. But sign me up for these projects under the right circumstances. Just as my project’s working definition of practicality shouldn’t apply to all situations, I urge those who are reading this and are questioning its use or practicality to ask yourselves: what is your definition of practicality, anyway? Is it one that is situated within this project, or one that originates from somewhere else? And if it originated somewhere else, why should we equivocate it with the working definition in this project?

I think I am trying to find a new language for the ways in which we might decide where to put boundaries around issues of interdisciplinary, and where we want disciplines to interfere (in the diffractive and productive sense, rather than the destructive and oppositional sense) with each other. We should carefully consider our definitions and contexts of practicality. That way, we don’t find ourselves sitting on a Boeing 737 filled with yarn-covered aerospace engineers, spiraling into oblivion, or receiving blankets made of bracing wire as gifts, forcing us to pretend they are snuggly and warm when they are actually injuring us. This practicality should allow us to diffract aerospace engineering and fiber arts through each other in the best possible configurations.

Which brings me to another question, my very first question: why knit the world? I’ve outlined a couple of reasons in this post: (1) to put seemingly disparate disciplines into conversation with each other in new ways, and to explore new configurations of interdisciplinary thought; (2) to experiment with phenomena-based thinking and learning in a fun and low-stakes project that doesn’t involve grant money or overextended faculty and graduate students (except for this overextended graduate student and some guidance from her overextended colleagues and mentors); (3) to show you that even something as simple as a knit square can involve a multitude of theories, techniques, and legacies that span the interdisciplinary spectrum (by way of reasons 1 and 2). These reasonings don’t provide a complete answer to the question, of course. But what ever does?


Pattern One: Infrared Bands. The Electromagnetic Spectrum. Postphenomenology. Fair Isle.

I’ve been delaying my first post a bit, mostly because I’ve been perplexed re: how to begin writing about this project. Initially, I thought I might start out by explaining the history of code within the context of the history of the Jacquard loom, and situating this project within that context (note: this project is situated within that context). But then I spent some hours scouring the library website and Google Scholar for suitable histories of weaving and the loom, and realized this explanation was going to be more involved than I thought. I currently have a folder entitled “Loom Readings,” that is, well, looming over me. Anyway, this is just about how every project I embark on begins: some trepidation about where to begin, a lot of scouring, more trepidation, then an epiphany about how involved it will be, and then an acceptance that I have to begin somewhere and nothing is ever perfect. And with weaving, quilting, knitting, etc., it is tradition to slip a mistake into every piece, because only deities are perfect. So, here we are.

Save the looming Loom Readings, I’ve been progressing on this project in other ways. So today, I’ll tell you a little bit about my first Landsat knitting pattern, which will inevitably involve a short discussion of the electromagnetic spectrum, infrared light, postphenomenology, and Fair Isle knitting techniques. Inevitably. And if you don’t know what any of those things are, that’s perfectly ok: at one point in time, I didn’t either. And for that reason, I’ll try to explain everything as clearly as I can.

I started with this Landsat 8 image of Houston, TX, taken on May 13th, 2013.

Image description: An image of Houston from above. The image appears in bright and deep reads, silvery blues, and deep and inky turquoises. There’s a particularly large turquoise, inky blob at the bottom of the photograph. That’s Trinity Bay, on the Southeast side of Houston.

It’s an infrared image, which means that it uses Landsat 8 bands 5, 4, and 3 (typically used for viewing vegetation. Let’s put a pin in the concept of infrared light — I’ll explain that in a moment. First, though, you might be asking: what are band combinations? Heck, what are bands? To understand what bands and band combinations are, you must first have a basic understanding of the electromagnetic spectrum. You might not think about the electromagnetic spectrum very much, but it’s in everything you see and do. Every color you perceive is really just light reflecting off of a material in a certain way. For instance, it’s technically incorrect to say that healthy trees are green. Rather, their leaves reflect light in a particular way that makes them appear green, and they absorb all other kinds of wavelengths. Similarly, ocean water sometimes appears blue or turquoise to us because it reflects electromagnetic wavelengths in the form of blue light, and absorbs all other kinds of wavelengths. Color is an illusion. Note that I am using the terms light and wavelength interchangeably. However, I should also note that there is probably some nuance that could disrupt my interchangeable use, much of which is beyond the scope of this blog and the scope of my expertise.

However, all of the light I’ve talked about up until this point is from the visible portion of the electromagnetic spectrum. UV, or ultraviolet, light — the kinds of wavelengths that damage our eyes and skin — are actually invisible to us. They are comprised of waves that are closer together than those within the visible part of the electromagnetic spectrum. Remote sensing satellites don’t just capture wavelengths that we can see with our eyes: they can “see” larger portions of the electromagnetic spectrum. Hence, the image of Houston pictured above.

However, satellites don’t just capture any and all wavelengths at all times. Rather, they’re equipped with a variety of sensing instruments that capture light in portions of the spectrum that we deem most valuable in a given instance. We refer to these chosen portions of the electromagnetic spectrum as a satellite’s bands. Think of it this way: the electromagnetic spectrum is a sliding scale, and teams of scientists and engineers that design satellites select particular points on the spectrum they’d like to see reflected from a target material (in this instance, a particular feature on earth, like trees or the ocean). Landsat 8, for example, measures wavelengths by way of 11 different spectral bands. There are all kinds of band combinations that allow you to view a single scene within many different contexts. For instance, on Landsat 8, bands 4, 3, and 2 allow you to view an image as a true color approximation. Here’s a complete list of those bands and band combinations and their technical specs, if you’d like to see them. Now, let’s contextualize this within the image of Houston: it shows us light from bands 5, 4, and 3. Band 3 measures green light in the visible spectrum; band 4 measures red light within the visible spectrum; band 5 measures near-infrared (NIR) light.

So, what is infrared light, and why do we care about it? Healthy vegetation reflects light in the near-infrared portion of the spectrum, wavelengths that are slightly too long for us to observe with our eyes. Different disciplines define the infrared portion of the spectrum differently, and the boundaries between NIR, short-wave infrared wavelengths (or SWIR), mid-range infrared wavelengths (MWIR), and far-range infrared wavelengths (FWIR) are fuzzier than one might think. But here’s what I feel confident explaining to you: near-infrared (NIR) and short-wave infrared (SWIR) come to us in the form of reflected light. We detect mid-range (MWIR) and far-range (FWIR) in the form of heat: they are thermal. While NIR and SWIR wavelengths are longer than the ones we can observe with our corporeal vessels, we can view these wavelengths with the same kinds of instruments we would use to detect visible light (i.e. the light we can see with our eyes). NIR and WIR wavelengths, however, are not as long as MWIR and FWIR wavelengths. And because MWIR and FWIR wavelengths are quite long, we use different instruments to detect them (i.e. radiometers).

But back to the image. Check out the slider on this page to view this image through infrared color bands and as a true color approximation. You’ll see that everything that looks red through infrared color bands is something that looks green and in a true color approximation (and that would look green to us): so, trees, grass, etc. The light blue and silver portions are infrastructure: roads, buildings, etc. And the giant turquoise blob is Trinity Bay, reaching out to Houston by way of smaller bays and bayous.

I love Landsat imagery because it opens up space for us to think about how even our own bodies mediate and confine the ways in which we see and experience life — and that there are many other possible ways to see and experience a scene (Houston, in this case). The idea that experience is always mediated through some kind of instrument — even if that instrument is our own eyesight — comprises a subfield of philosophy called postphenomenology. I’ll talk more about postphenomenological theory in future posts. But for now, I wanted to point out that we can think through Landsat imagery in both physical and philosophical terms. In fact, I’d be willing to bet that philosophers reading this likely conceptualized my explanations of the electromagnetic spectrum and color bands as both a philosophical project and a scientific exploration.

For now, though, I’d like to think through this Landsat 8 image in terms of fiber art. Recently, I discovered a fun and free online software called Chart Minder. Chart Minder lets you create your own Fair Isle knitting patterns. Fair Isle is a knitting technique that I’ll sum up for you as knitting pretty and colorful patterns. Fair Isle patterns typically involve five or less colors of yarn, and only knitting and purling stitches (the most basic of stitches). Fair Isle also involves some techniques that help you carry the strands of yarn that are dormant in your pattern for a given stitch, series of stitches, or rows. Knitters call this catching floats. Catching floats is like the knitting equivalent of packing a lunch in the morning and carrying it to school with you, so it’s there for you a couple of hours later. We carry strands of yarn that aren’t presenting in the pattern along with us at the back of the project for a similar reason: so they are present when we need them.

You can make almost any pattern into a knitting pattern. If it’s really loopy and detailed, it may appear a bit pixellated (much like an old digital rendering of PacMan, who we know is all cute and round, but has some jagged edges because of pixellation). Chart Minder takes this idea and runs with it, inviting you to scan in an image of your choosing. It uses an algorithm (likely some kind of image recognition algorithm) to turn your images into discrete, knit-able squares. Each square represents a single stitch. And so, this infrared band image of Houston…

Image description: the same image of Houston as earlier, for comparison’s sake.

…becomes this infrared band image of Houston:

Infrared Houston on 5_13_2013 via Landsat 8
Image description: the same image of Houston, but now in the form of a knitting pattern. The 5-color limit of the Fair Isle pattern generator has taken away some of the depth and diversity of color from the image. However, there is still a deep red and a bright red (likely vegetation), a muted coral (perhaps land), a silvery blue (infrastructure), and a deep turquoise (water). Each square represents a stitch.

where each square represents a single stitch.

As you can see, it’s far from a perfect rendering. Unfortunately, Chart Minder did not want to make my image 300 x 300 stitches, which would have been about the size of a very large blanket. This would have allowed for much more detail. However, Chart Minder kept crashing on me whenever I tried to adjust the image settings to allow for this kind of detail. So I settled for a pattern that’s about 140 stitches wide and 90 stitches tall. This will make a small blanket (maybe a blanket for a cat).

I also discovered that, limited by traditional Fair Isle specifications, Chart Minder only lets you select five colors for the algorithm to automatically match to the image. I later found out that you can go in after the rendering, choose additional colors manually, and paint them in with a paintbrush tool. I’m currently working on another image where I’m employing this manual technique heavily. I’ll reveal this image in time. 🙂

Note: I’d like to extend a special thank-you to my colleague in the Department of Forestry and the Remote Sensing Interdisciplinary Graduate Education Program (IGEP), Jill Derwin. Thanks, Jill, For checking over my explanation of the electromagnetic spectrum, color bands, and infrared light.