February this year USGS has sent another satellite up into orbit to continue observing the earth. This satellite, named Landsat 8, provides the same spatial resolution in all bands as Landsat 7. That is one pixel equals 15m pan-chromatic and 30m for all other bands (excluding thermal). In addition, it has a couple extra bands for water, cloud and surface temperature (this link explains the other similarities and differences in more detail).
Here is a sample that I have pan-sharpened using the open source Orfeo Toolbox. Bands displayed are 6, 5 & 2, which is comparable to 5, 4 & 1 in Landsat 5 & 7. This image was capture 27th of May 2013. Most of the bright green paddocks are canola and you can see some of the earlier cereals coming through.
More info: As suggested in comments, Spectral Transformer for Landsat-8 imagery by GeoSage is another free option for pan-sharpening Landsat 8 imagery. I have tried it and it works well if you don’t mind following some simple command line instructions. Check it out at this link.
Even more info: To get an idea of the best Landsat 8 band combinations and comparison with Landsat 7 bands check out this blog article from ESRI.
Our winter plant operation has finished for another year. We came into May with good sub-soil moisture but dry in the top 10cm. The wheat and barley was planted shallow into the dry soil. A 12mm fall of rain mid-May was enough to wet up the soil and germinate the seed. Following the rain we planted chickpeas into moisture. After finishing planting the chickpeas we had another 10mm.
We are trialling Spitfire and Suntop wheat this winter, alongside our usual Sunvale. Below are some comparisons so far.
And a couple pictures of our chickpeas and malt barley.
As most GIS users will know the Landsat archive of satellite images is available for free download from USGS. This is a valuable resourse of historic and recent course resolution images covering most of the earth. I find the USGS Global Visualization Viewer the best way to sort through and download images. You need to add images to the basket. Often you can download them directly from the basket, but sometimes they need processing time which can take a day or two. Note that images from L7 2003 onwards have lines of no data caused by a faulty sensor. Up until recently L5 was still working producing quality images but is now out of service. Shortly we will have Landsat 8 – it has been launched and more free imagery is not far off!
Once the image file is downloaded you will have a compressed .tar.gz file which can extracted using built in Windows functions or 7Zip. Once extracted you have a TIFF (.tif) file for each of the bands of the image. For L7, that is 9 TIFF files about 60mb each except band 8 which is higher resolution band for pan-sharpening usually 200mb+.
The next step is to composite these bands to make a coloured image to load into your GIS. If you are a Windows or Mac user I suggest you try out MultiSpec. Download and extract to a folder of you choice. I keep it at C:MultiSpec for simplicity.
A comprehensive guide on how to composite images has been written up by the creators of MultiSpec and is available here. The process I give below is a simplified explanation based on the MultiSpec tutorial.
1. Launch MultiSpec.
2. Go to File > Open and select all bands you want to composite into one image.
3. For the ‘Set Display Specifications’ dialogue press OK (Try setting Bands to Red: 5, Green: 4 and Blue: 1. This is not critical for this exercise but is useful to visually inspect the image inside MultiSpec).
4. Press Ctrl + R to open ‘Set Image File Format Change Specifications’
5. You can select Channels and reorder by choosing ‘Subset…’ (I usually include bands 1-5) and also select file format in the ‘Header’ drop box (I always stick with GeoTIFF format). Press OK and choose where you want your composite image to reside and give it a useful name (do not forget to add the .tif extension).
6. Load up your favorite GIS desktop software, QGIS for me, and add your new TIFF file.
7. Edit layer properties and try a few different band combinations and contrast enhancements.
For good visibility of vegetation try Red: Band 5, Green: Band 4 and Blue: Band 1.
For a more natural look try Red: Band 3, Green: Band 2 and Blue: Band 1.
I hope this helps make Landsat imagery more accessible to everyone.
We are now well into April and you can start to feel a chill in the air. I was updating my phone this morning which means backing up all my photos which I have taken over the last few months. Its been an interesting summer with I’ll attempt to cover briefly with my poor quality phone photos. We also grew some sorghum this summer, but that is covered in other posts.
The 2012 winter crop harvest went well with good standing crops and little rain during the operation. We were left with big stubble loads and very dry, cracked soil.
We did not seem to get much of a break after harvest and we were straight into our summer weed control spraying program. This particular night the bugs were bad.
The spraying continued. Each storm that came through even if only a few mm would bring up more weeds. Thankfully we are able to cover large areas with a boom width of 36m.
This year we applied more paraquat than ever to combat our rising glyphosate resistance issues. Combining several modes of action we were able to control most problem weeds and prevent them from seeding.
This photo shows where we had lightly disced a headland to to level it out. Unfortunately, due to burying surface seed and disturbing our residual chemical, this promoted an extremely high germination of barnyard grass.
We had a couple wild storms this summer. They did not bring a lot of rain but managed to do plenty of damage.
It took a cyclone off the east coast, but eventually we did get the rain to fill our soil profile and set us up for a 2013 winter crop.
By February we had started applying urea for our winter crop. We used a disc seeder so there was minimal soil disturbance and low fuel consumption.
Then it rained again – we did more spraying…
I am looking forward to the 2013 winter growing season. Good luck to everyone and God bless!
Lizmap is Open Source software that has the capacity to display GIS data on the web with minimal effort once it has been configured correctly. This software sets itself apart by making use of the QGIS Server in a unique and user friendly way. In essence this allows the user to configure a map in QGIS desktop software and simply run a basic plugin to publish their data on the web with a Google Maps satellite or street layer as a base map.
I have setup Lizmap software on my server to demo its capabilities. I left the original demo map of transport in Montpellier, France and added my own with data from Lindon – one of our properties. I hope to demo its mobile device capabilities soon.
Yield data is collected from most modern harvesters. This data can be used to analyse a crops individual performance, compare with previous crops and combined with several years of data to generate statistical maps. In this post I show you three individual years yield maps for one paddock: wheat, sorghum and chickpeas. Using this three seasons of data I then ‘stack’ them on top of each other to produce maximum, minimum and mean. These statistics can then be used as a starting point to develop paddock zones to apply variable rate fertiliser or seed.
What Google is doing with the earth’s satellite imagery is really quite amazing. Check out Google’s Earth Engine. On the landing page you get some great videos. I recommend Amazon Deforestation: Timelapse and Drying of the Aral Seas: Timelapse.
Once you have had enough of these visit their data catalog where you can load any of the common earth observing satellites into a Google Workspace which is the Google Map environment with additional features allowing you to display the satellite images.
Google say they have developed their Earth Engine mainly for monitoring deforestation which is valuable but there are several ways this technology is useful in agriculture. For example load the MODIS Daily EVI into the Workspace. Once in the workspace go to a farming area you know well and you can monitor how much (healthy) crop is established. Although MODIS imagery is free to download, Google take out all the processing time and make it easy to keep an eye on the district.