Earlier this year Bruce Blair kindly loaned us a copy of an important body of work in the field, C3: Nuclear Command, Control, Cooperation, by Valery E. Yarynich. We digitized it (thanks to two graduate research assistants) and have put the book in its entirety up on Scribd. It’s even OCR’ed!
Much to my dismay, I find that I’m still catching up on reading from the summer. In August, Hui Zhang published a new report on China’s uranium enrichment capacity, and he makes a wonderful identification of a new civilian centrifuge facility associated with Plant 814 in Sichuan province. While on the subject of Plant 814, this report’s publication gives me the perfect opportunity to say a few words about the somewhat mysterious gaseous diffusion facility also associated with Plant 814.
The Plant 814 gaseous diffusion facility—commonly referred to as the Heping GDP because of it its proximity to the Heping Yizu (和平彝族), a third-level administrative township of the Yi minority—is also known as the Jinkouhe GDP as it is located near Jinkouhe (金口河), in the Leshan City prefecture of Sichuan province. It is the second of China’s gaseous diffusion plants (the first is in Lanzhou).
The Plant’s history remains somewhat opaque, but the facility was built as part of the third line industries in the mid-1960s and likely began producing enriched uranium in 1970. It has been difficult to determine the current status of Jinkouhe. China reportedly stopped producing HEU for weapons in 1987, but China does not openly discuss the Jinkouhe GDP. Some analysts have assumed the plant is no longer operational, but as Zhang notes, local press accounts suggest the plant is still operational. China may still enrich uranium for military purposes, including fuel for naval reactors. Indeed, the district remains closed to foreigners, lending credence to the idea that the facility still has a sensitive military purpose:
I’m inclined to agree that the plant remains operational, both for the reasons Zhang cites in his most recent publication and because of something I can see in satellite imagery.
My colleagues at CNS and I have been exploring the idea of using Landsat 8 satellite imagery from the US Geological Survey to detect changes at nuclear facilities. Landsat 8 images, in addition to being free, contain thermal infrared data from thermal infrared sensors (TIRS) on board the satellite. As NASA explains, the TIRS sensors can detect land temperature by using
Quantum Well Infrared Photodetectors (QWIPs) to detect long wavelengths of light emitted by the Earth whose intensity depends on surface temperature. These wavelengths, called thermal infrared, are well beyond the range of human vision.
Most nuclear facilities are not “hot” enough for observable temperature differences to appear, but gaseous diffusion plants are more observable as they are very hot. Unfortunately, the resolution of the thermal infrared images is quite low, only 100 meters. As it happens, the Jinkouhe GDP is enormous, large enough that it is possible to observe a difference in temperature between the gaseous diffusion hall buildings and the surrounding environment at 100m resolution.
At first blush, Landsat 8 imagery appears to confirm that plant remains operational:
Some Chinese accounts suggest that China has converted all uranium enrichment to centrifuge technology, but there is little evidence to confirm that the Jinkouhe facility has been converted, nor are signs of conversion like construction or increased vehicle activity visible on available satellite imagery. Moreover, centrifuge facilities use less energy and are far “cooler” than a gaseous diffusion plant. One would not expect to see a centrifuge plant on a Landsat 8 thermal image.
How hot is the Jinkouhe GDP? This is a question I’m still trying to figure out how to answer. Just this year we were able at CNS to obtain licenses for ArcGIS and ERDAS Imagine, and we are now teaching ourselves how best to process and analyze Landsat 8 imagery. What I can observe at the moment is that the Jinkouhe GDP is hot.
Courtesy of Jeffrey, here is a short clip (from here) to help our readers visualize the “hot” part:
As we move into autumn (not that seasonal change means much here in temperate and beautiful Northern California), I am trying to move into a regular posting schedule. Up first is a rather short post adding to the hot takes on last week’s 70th WWII Anniversary Parade in China.
- Image: Zhang Siyang/GT
Let’s talk a bit about the Dong Feng missiles that were present and at attention.
The Second Artillery Force comprises six missile bases, numbered 51-56. An ongoing task of ours at CNS is to match missile deployments to specific bases (and launch brigades when possible). In fact, Jeffrey and I created a notional order of battle for the Second Artillery in Jeffrey’s latest book. (With thanks to Mark Stokes and Henry Boyd.)
During the parade, a major general from each base accompanied a missile that his base ostensibly deploys:
- Major General Li Jun (李军) | DF-21D
- Major General Chen Guoqiang (沈国强) | DF-15B and DF-16
- Major General Li Yuchao (李玉超) | DF-10A
- Major General Xue Jinfeng (薛今峰) | DF-26
- Major General Zhang Mingguo (张明国) | DF-5B
- Major General Wang Dingfang (王定放) | DF-31A
Identifications of these generals from Chinese news articles over the past few years appear to confirm some guesses from the notional battle about unit deployments and help inform others. I usually cross-reference information on personnel assignments from Chinese sources with the Directory of PRC Military Personalities. In this case, I do not have the 2015 version. Looking at the 2014 edition though, there appear to have been quite a few shifts in the leadership of the bases.
Base 51 | Major General Li Jun (李军) | DF-21D
The association with Major General Li appears to confirm that Base 51 deploys the DF-21D. It is possible that the some of the base’s older DF-3 and DF-21 launch brigades have been converted to the DF-21D.
Base 52 | Major General Chen Guoqiang (沈国强) | DF-15B and DF-16
The association with Major General Chen appears to confirm that Base 52 is primarily for conventional SRBMs. Given that the base’s nine launch brigades are scattered along the Southeastern coast of China, this is no surprise.
Base 53 | Major General Li Yuchao (李玉超) | DF-10A
The association with Major General Li confirms that at least one launch brigade under Base 53—which appears to deploy conventional SRBMs like Bases 51 and 52—deploys the DF-10A.
Base 54 | Major General Xue Jinfeng (薛今峰) | DF-26
Parade announcers specifically referred to the new DF-26 as capable of carrying both conventional and nuclear warheads, which as many analysts have noted may be problematic from a command and control perspective. Major General Xue appears to have been promoted last year and moved from the Second Artillery Testing Base 22, where he was the Chief of Staff according to the 2014 Directory.
Base 55 | Major General Zhang Mingguo (张明国) | DF-5B
The association with Major General Zhang appears to confirm that a launch brigade under Base 55 that deployed the DF-5A has been converted to the DF-5B ICBM.
Base 56 | Major General Wang Dingfang (王定放) | DF-31A
Major General Wang is the only base leader to also appear in the 2014 Directory of PRC Military Personalities in the same position. The association confirms that at least one of the four launch brigades under Base 56 deploys the DF-31A ICBM.
As always, I’m happy to hear any other thoughts in the comments from interested readers.
CNS made some beautiful 3D models of Iran’s IR-1, IR-2M, and IR-4 centrifuges for the NTI website. These weren’t easy to make. Fortunately, we have some smart, innovative, hardworking students. Bo Kim’s just finished her first year as a Nonproliferation and Terrorism Studies student at MIIS and is dividing her summer on campus between a CNS and a Cyber internship. Watch out future employers, she’s fluent in Korean too! Without further ado, Bo will explain how she took the measurements to make the IR-1.
Author: Bo Kim
Centrifuges, like the rest of us aspiring Instagram models, suffer from bad photo angles.
If you look closely, (or accurately measure as I will shortly) the ends of the centrifuges are smaller (i.e. further away) than the middle of the centrifuge, where the camera is positioned. There are several great open source tools that correct pincushion or barrel distortion, such as Hugin or Gimp 2.8. However, I found that Photoshop’s Perspective Warp function was more user friendly with a smaller margin of error.
Photoshop’s Perspective Warp Tool
1. Drop image into Photoshop and create a new layer via copy. (Precautionary measure.) Layer > New > Layer Via Copy
A menu of options will appear in the navigation bar. Click ‘Layout’ to see a box pop up inside the image. (shown in red below).
3. From here, move the four corner pins to match the face of the centrifuge.
You’ll already see here that the edges of the centrifuge are not perfectly linear. If you look closely, the holes of the bisected coils start out in line with the red perspective box, and moves off the line as the line progresses.
Then, hit ‘Warp,’ and the inner grid will disappear.
From here, click the ‘Vertical/Horizontal’ alignment button, and the centrifuge is now rectilinear. You can see the warp on the side.
To reduce the margin of error, I photo warped both of the IR-1s in this image, plus as many IR-1s as I could find, and averaged the measurements. After several nights of wanting to gouge out my eyes, I finally came up with numbers that added up. Skepticism forced me to compare the measurements of the centrifuges with and without the perspective warp, and while no photo is ever going to be a 100% accurate, I’ve noticed that when building the 3D models, the measurements from the perspective warped photos seemed to be truer to the original images.
Web Plot Digitizer
Now on to WebPlotDigitizer to measure the freshly warped centrifuges! File > Load Image….
For measuring images within photographs, I always go with Plot Type: Image. Then ‘Align Axes.’
Then, at the top, ‘Analyze’ to ‘Measure Distances.’ From here, I hit ‘Add Point’ on the right every time I want to add a new measurement.
You’ll also see here on the right, that there is a zoom window. You can use the ‘+’ or ‘-‘ buttons to zoom in or out. The zoom window will move when you mouse over the image. The intersecting lines in the zoom window helps pinpoint the starting measurement.
The first measurement I made was a horizontal line to define the floor. The camera angle of this photo plus the color of the floor make it very difficult to define a flat surface from where we can start measuring. Luckily, we can see in this photo the shadows of where two opposing ends of the centrifuge touch the floor. I drew a line here from edge to edge to define my horizontal axis, as well as the hypotenuse of my base. Simple math (including diagrams) coming up.
I then measured the height using Project Alpha’s calculation.
I measured from the top of the centrifuge to the defined floor.
Now for the simple math.
Project Alpha says our total height is 210 cm.
Webplotdigitizer gives a distance of 460.92 units.
So setting up my calculations: known = (unknown) (constant)
210 cm = (460.92) (C)
C = 0.456
This constant, C = 0.456 must be multiplied to all of the measurements in this image. Like a numerical scale.
One last tiny bit of math.
The base distance is 77.72 units in Webplotdigitizer. So multiply that with the constant and I know the length is 35.44 cm.
If you loved geometry (anybody?) then you’ll remember the hypotenuse of an isosceles triangle with sides x is √ 2 X.
So, that gives us:
√ 2 X = 35.44 cm
X = 25.06 cm
Seems about right.
Now for the rest of the measurements, I’ll just give you a quick snapshot of my final marked-up product:
Yes, I really did measure every nook and cranny on these centrifuges -for several images, of several centrifuges, several times. I found it useful to do two separate images in Webplotdigitizer- one for horizontal measurements, and one for vertical. If some of the measurements are ever hidden, or if you’d like to keep track of the data, you can hit ‘View Data.’
Once you have all your measurements, you can then move on to 3D modeling!
And if you start feeling nostalgia for the math, you can move on to next level centrifuging- counting the number of centrifuges in Natanz, geolocating the facilities, and then lovingly placing each centrifuge into a cascade. 50,000 centrifuges will guarantee you a hat tip from the Grand Guru herself, Melissa Hanham. (Which will likely be immediately followed by requests to recolor, rearrange, recount, remake, and rerecord everything.)[<--MH Edit: By Grand Czar Jeffrey Lewis]
I just got back from London, where Nick Gillard and I tried to stump the very smart participants of RUSI‘s UK PONI conference with a pub quiz. Despite a few technical glitches and a few fire alarms, it came down to the sudden death tie-breaker question. Many thanks to Andrea Berger and the whole RUSI team for indulging our madness with iPads and pub food for the teams!
See if you can do it too! Remember they only had wifi enabled iPads, and these questions were projected on a screen. The winning team enjoyed a 50£ tab at a local pub thanks to Nick! I can only give you ACW fame… Enjoy!
Answer this question in 2 minutes or less!
Answer this question in 5 minutes or less.
Answer this question in 5 minutes or less. (We had to clarify that offshore meant water not air)
Answer this question in 2 minutes or less!
See if you can answer this in 5 minutes or less.
First correct answer won the game, see if you can do it in less than 5 minutes.
Sadly, there is no textbook for imagery analysis. I would love to contribute to one someday, but until that becomes an option, here’s the March 1996 Unclassified Photo Interpretation Student Handbook. I picked it up from from a rather unusual meeting on a DC trip and several students lovingly/begrudgingly scanned it for our benefit. It’s an excellent resource for identifying everything from transportation to military installations. You’re welcome!
Searching for silos?
Sizing up subs?
Still not enough Photo Interpretation? Then check out the 1944 vintage stored at the University of Nebraska. It’ll help you verify vessels!
A lot of people are asking how to take measurements and make 3D models from 2D images. If conditions are juuusst right, SketchUp’s Match Photo technique is the way to go. Unfortunately, in our world, conditions are almost never just right, unless you happen to be able to take the photo yourself. Here’s an alternative method using Web Plot Digitizer, which is still free and can scale for as much (or as little) information as you have (keeping in mind a greater margin of error).
A few days ago the DPRK’s state news agency, KCNA released the first images of North Korea’s new submarine launched ballistic missile (SLBM). These photos we’re great, but with just sky and water, there wasn’t much context about the size of the missile.
That is until one of our eagle-eyed research assistants, Dave Schmerler, spotted this:
BOOM! and, we were off running (well, typing furiously on a JFK-> SFO flight). Surprise! That carefully crafted message of Kim Jong Un hanging on his yacht smoking a cigarette and watching a missile launch from a submarine? Not so much. Turns out they start off launching from an underwater platform just like the rest of us.
So, to the measurements:
1) Geolocate the ship!
Dave already took care for that for us. It’s in Sinpo! (40.026008°, 128.166174°) Using the measurement tool in Google Earth, we can see that from stern to cabin, the ship is about ~10.03 meters.
2) Load the screen shot from YouTube into Web Plot Digitizer
Load in the very best screen shot you can muster from the YouTube video which shows both the missile and the ship. Next, choose the “Map With Scale Bar” option, and hit “Align Axes.” So… we don’t actually have a map with a scale bar, but we are going to simulate one. And, because the entire ship isn’t in the image, we are going to use the distance from the stern to the cabin as our scale.
Zoom in as much as is comfortable, and use the window in the top right to place your points. You can adjust them as needed. I measured along the top of the water to keep things level. When ready, click “Complete!” and enter the units. I measured 10.03 meters on Google Earth, so that’s what I put in.
Under the Analyze tab you will find the “Measure Distances” option. Select it and make sure “Add a Pair (A)” is highlighted. Again, zoom in as much as is comfortable, and use the window in the top right to place your points. If you want to start over you can “Delete a Pair” or “Clear All.”
I did this several times, and got slightly different measurements, but they were all around 9 meters long with 1.5 diameter. There is a kind of faint halo that forms around the missile, and I tended to discount it in the measurements. I’d be interested to see what measurements others get, so that maybe we could average them. This rough estimate makes it seem like they are working from an R-27.
Using a higher resolution image of the missile (which, unfortunately, doesn’t have the ship for scale) I got a ratio of 90.64 to 14.95 pixels.
Hey! Dave even made a great 3D model for NTI! Check it out:
4) The Fine Print
Like it says in the title, these are just back of the envelope calculations. Please don’t set your ballistic missile defense to it ;)
First and foremost, these aren’t very high res images. And, taking a screenshot of a YouTube video of a still image, is kind of scraping the bottom of the barrel. A pixel here and a pixel there on a low resolution image can add up. On top of that, we don’t know the distance or heading of either the ship or the missile relative to the camera or each other, which causes a greater margin of error.
Another fantastic research assistant, Bo Kim, will write up a more detailed account of how to use this process to measure Iranian centrifuges taking into account their angle relative to the camera. STAY TUNED!
One of the easiest and most useful methods for an open source analyst is to extract metadata from imagery.
Metadata is data that is often included with an image, such as the time it was taken, the type of camera that was used, and yes, if you are lucky — GPS coordinates. This data is useful for photographers, those who like to stalk cats, and people like us: geolocators and myth-busters.
There are many different types of metatadata, some generated by cameras or phones, and others generated by editing software like Photoshop. Extracting metatadata can not only give you clues about when and where the image was taken, but also about whether it was altered. Last, if you are trying to generate a 3D model from a 2D image, it can give you some clues about the distance the object is from the camera.
Last week I posted an image to ArmsControWonk.com, challenging fellow wonks to geolocate the image. There were some grapevines and California poppies in the picture, but if you hadn’t been there before, it would have taken some time to find the location. That’s why your first step should always be to check for metadata.
Photoshop is a good tool for extracting metadata, but if you are looking for free options, there are plenty. Try copying the above image’s URL and pasting it into one of the below:
Jeffrey’s Exif Viewer (No relation, I presume)
This site has some nice extras, though they are not always accurate. It will try to give you an approximate address, a color histogram, and a circle of confusion if possible. You can see that the geolocation is close, but not quite accurate. Also, the viewer shows the photographer facing the wrong way.
This site is cool, because in addition to the regular metadata it will give you an Error Level Analysis (ELA). ELA lets you see when the compression rate of the image changes. So if something is “airbrushed” or pasted, stretched or cropped, you can see a difference.
No obvious alterations here, but you can see a faint grid pattern (cross your eyes like you are looking at a stereogram) showing that the jpeg was likely resaved.
Fortunately for us, nothing has replaced the wonk yet! As helpful as the metadata from the geo quiz photo was, it wasn’t 100% accurate. It will put you within a few meters of where the photographer was standing, but it’s still best to go to Google Earth to fine tune the coordinates and the direction of the camera.
Ah yes, now imagine a glass of chardonnay in your hand!
Back by popular demand: a geo quiz! This is a teaser for a how-to I will write next week.
Q1: Where is the photographer standing?
Q2: What is the address of the nearest building shown in the picture?
Put your answers in the comments below, and NO PEEKING!
Many people ask where they can get satellite imagery. Working at a nonprofit, my preferences tend towards the free, but there are some great resources on the cheap too. Why check more than one map? Because: Volkel.
Here’s a list of my favorites:
It’s free. Even Pro is free. It has a time slider, allowing you to look at change over time easily. You can also overlay images (or maps!). There are a ton of KMZs available for download. Some of these may be curated by experts, others are wikis, you can even make your own! Last, it’s 3D. You can import your own 3D models, calculate elevation changes, even build a missile flyout!
Google Maps has some of its own advantages — it’s easier to embed being the one. You still need to use Google Earth to see the date of the image. You can also set alerts for imagery updates, and there’s even 3D data!
Bing is getting better — for example, there’s road data for South Korea now (yikes, that took a long time). The greatest advantage over the maps below is this little tool, which tells you the date of the imagery.
Try Yandex and Baidu. Many draw from the same DigitalGlobe catalog, but may be from a different date. If you are interested in South Korea, try Daum and Naver, but they won’t help you much north of the DMZ.
This isn’t the high resolution imagery you associate with the above, but depending on your needs, it might be better. The USGS’ Earth Explorer tool lets you search an enormous database of free or low cost imagery, including declassified images and Landsat’s thermal infrared bands.
Corona Atlas of Arkansas
Digital Globe Foundation
Still don’t have what you want? If you are affiliated with a university, you can apply for an image grant.