Seven steps for turning camera trap photos into useful datasets: manual processing workflow

Seven magical steps into a dataframe.

By Nargol Ghazian

This is a summary of the protocol I have been using for that past few months to process all the amazing camera trap photos from the Mojave National Preserve and the Carrizo National Monument. After reading a few papers on cam trap processing and exploring the CamtrapR package, the best approach would be to create your datasets manually as no other program is able to automatically detect animals for you. This method also ensures that you obtain the best dataset for the statistical analysis you wish to perform. This seven step guide should give you a quick rundown on how to get started with processing and maintaining a good workflow.


  1. Start by naming your columns as below. These heading are best suited for this project but yours can include more columns.
1.Year We are working on the 2017 images
2.Region MNP is Mojave and CNM is Carrizo
3.Site Mojave or Carrizo
4.Calendar date The date the picture was taken in dd-mm-yr. I like to do the pictures belonging to the same date for each photo rep in order. If the date is wrong, don’t worry too much, just do it all as one for the last date of the particular week you are working on
5.Microsite Carrizo is shrub or open, 3 weeks for each. Mojave is Buckhorn or Larea, also 3 weeks for each.
6.Day This goes in a 1,2,3..n order
7.Rep This refers to the camera trap station. There are 10 stations per microsite. For example you might have four pictures for the same day in station #2 of open, so you would write 2 four times: 2,2,2,2 each corresponding to an image
8.Photo Rep A continuous number starting at 1 and continuing until you’ve finished processing all your pictures for the particular site
9.Animal The animal in a hit photo. The most common are rat, rabbit, squirrel, fox, lizard and sometimes bird. There are times where you might have to guess. If it’s really hard then write ‘unidentifiable’. If it’s a false hit leave it blank.
10.Animal. Capture binary 0 = false hit, 1 = animal present
11.Time block Look at the timestamp. Is the photo taken at night, noon, afternoon or in the morning. If the timestamp is wrong, guess based on the darkness or lightness.
12.Night. Day Based on whether it’s dark or light.
13.Actual time Actual time written on the photo. Let’s hope it’s the correct timestamp J
14.Observations If you see absolutely anything interesting in the photo, note it! Otherwise leave this column blank. I usually write ‘x2’ or ‘x3’ if there is more than one animal in the photo. Sometimes I write ‘eyes visible’ if it’s dark and you can only tell the presence of the animal from its shining eyes (rats usually)
15.Temp of positive This is noted on the picture in Fahrenheit or Celsius. Whatever unit is shown, note it in your meta-data. If you’ve been working with one unit, and a certain photo rep has a different one, just use a converter to convert to the units you’ve been using for that particular photo rep.
16.Week This is either 1, 2 or 3 since there are only 3 weeks per microsite. This column is super important because sometimes the datestamps are wrong but at least the week of sampling is correct

*Note: The only time we actually fill in anything for columns 9 and 11-15 is when we have a “hit” and there is an actual animal.

  1. Since each row corresponds to a particular image, I liked to start with at least 100,000 just so I don’t have to go back and paste more rows. You can delete the extra rows when you’re done processing. For year, region, site and microsite write the correct label in the cell right below your column heading. Select the cell, then using control+C input the number of cells you want by using a colon in the area below the clipboard in Excel. For examples if you are in row B, it would be written as B2:B100000 then click enter.
  2. You can use the above method for the date as well. I input the date as I go along. For example if the photos start on 2017-05-22 I paste 100 thousand of that date. Obviously this is far too many, so once all the images for that particular date is finished, I control+C from the last cell and enter the new day for up to 100 thousand and so on until you are finished for the week and can delete the extra cells.
  3. Days work exactly like calendar dates.
  4. When it comes to rep, select all the image belonging to a particular date in your particular station of cam trap files and click properties. This tells you how many images you have for that date, in that station. Use the above method discussed to paste as many cells needed for that station. Keep a calculator handy because you would need to determine your ending cell by adding the number of images to the current cell you’re on.
  5. Photo rep is just a continuous number, as already mentioned in the chart. It’s basically the total of all the images processed for either Carrizo or Mojave. Use the ‘fill’ button in excel for numbers in a series to do this step.
  6. If you come across a cool photo, make sure to copy and paste it in a different folder. This includes two animals together, animals fighting, clear photos of animals etc. (use your judgement). Do not cut and paste!



Data logger discoveries

The first micronet study is now complete. Cuyama Valley was instrumented using Onset micro-stations with temperature and soil moisture sensors for two full seasons. I made some very important discoveries in wrapping it up this autumn.


  1. Cables are heavy.
  2. Animals are much more active in the summer.
  3. Cables are yummy to animals.
  4. Deploying loggers with sensors is a magnet for new burrows.
  5. Heat cooks batteries.

Here is hoping the data recovered are just as fascinating. Honestly, I am tempted to do a wire-addition experiment. Observation suggests that there is a very real magnet effect of wires.


Posted in fun

Ecoblender hosting a workshop: An Introduction to R and Generalized Linear Models

Full details are provided here.

General Information

The purpose of this workshop is to provide tools for a new/novice analyst to more effectively and efficiently analyse their data in R. This hands-on workshop will introduce the basic concepts of R and use of generalized linear models in R to describe patterns. Participants will be encouraged to help one another and to apply what they have learned to their own problems.

Who: The course is aimed at R beginners and novice to intermediate analysts. You do not need to have any previous knowledge of the tools that will be presented at the workshop.

Where: 88 Pond Road, York University. Room 2114 DB (TEL). Google maps

Requirements: Participants should bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) with administrative privileges. If you want to work along during tutorial, you must have R studio installed on your own computer. However, you are still welcome to attend because all examples will be presented via a projector in the classroom. Coffees and cookies provided for free.