Kanya the Remote Sensor

Well since it is lunch time and I feel like making my work sound more exciting than it really is…I thought I would write a blog about it.

So I am a remote sensor. Eek well remote sensing is the collection of information about objects/features without physically touching them. We carry out remote sensing every time we take a picture of someone….our camera captures the response of us based on what we reflect. Yes so you are also a remote sensors too.

But my type of remote sensing is a bit more complicated because instead of photographs I work with images generated from satellites. You are correct, satellites are those things that float around in space. There are a number of satellites that capture different types of information about our earth at different times. Tracker uses satellites to track where your car is a, a GPS uses satellites to provide us with location based information. Some satellites focus on climate others specialise in vegetation and so on. The really great satellites capture great detail spatially (measurement of a pixel – the smaller the pixel size the better) and spectrally (# of bands that the satellite captures information about – the more bands the better). These are the really expensive satellites and are required for exceptional precision and detail….we talking hyperspectral images where you can map what is beneath the ground.

I am currently using Landsat Satellite imagery to produce a basic Landcover map for a couple of African river basins. Landsat spatial resolution is 30m which isn’t the greatest because that is as zoomed in as you can get…30m. However Landsat has a pretty decent spectral resolution of 7 bands which allows you to capture more spectral information like types of vegetation. The other benefit of using Landsat is its affordability as it is freely available on the internet. However for projects such as mine I needed to purchase the imagery because all the images needed to be taken during the same time of the year and I needed them to be in the best condition as possible.
___________________________________________________________________















_________________________________________________________________
This job isn’t always easy. It requires good software to process the images, such as Erdas or Envi, which can be very expensive. Also a number of processes need to be achieved before your image can be classified. Now that I have gone through all the stages – like removing the atmospheric effects (Haze and clouds), geometrically correcting each image (so that they actually fit accurately on a map)…ect...I am now classifying the images.

Classification is probably the best part of my job. It would have been ideal if I had been in the field before I had started mapping. But due to budget constrains and the purpose of this map field studies prior to classification wasn’t needed. This is where Google earth becomes very handy as I am able to zoom into areas and confirm features/landcover types much quicker than I would have done in the field. I feel like I am flying around the world to places I have never been, observing cultivated fields, wetlands, rivers, lakes ect. I can imagine what it would be like to be on the ground some areas are just so remote I wonder if people have even touched the land there.
___________________________________________________________________

















________________________________________________________________
The benefits of being in my organisation is that I am able to confirm with in-house wetland experts and ecologist about features and landscape patterns that may not be familiar with to me. I need to make sure that the map I produce for the field visit is as accurate as I can get it. Later on this month our field experts will be going into the field to confirm if I got my classes correct. If not …it would be back to the drawing board.
So thats Kanya the remote sensor in a nut shell .


(The images in this blog show a river flowing over a different band of rock type which caused the river to braid). One image is from landsat(top) and the other is google earth (bottom). Notice the differences in resolution. ...google being more detailed.