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identifying coffee plantations from remotely sensed data of tropical forestry. The<br />
main focus of the study was to identify a means of separating the areas of coffee<br />
from similar data (in terms of wavelength and spectral values) representing<br />
tropical forest. This problem is made even more difficult due to the fact that the<br />
coffee plantations are often set under forestry (due to the shelter the cover<br />
provides for the crop). In addition the terrain is often mountainous so the authors<br />
had the additional problems of variety in terms of elevation, associated mist/ cloud<br />
cover and shade to overcome. While these problem is not something that was a<br />
large factor in the low flown aerial photography of Irish suburban landscape used<br />
in this thesis, the methods the authors employ to deal with cloud and haze are<br />
relevant. It was advantageous to the thesis to be able to reduce the impact of any<br />
of the areas of shading that existed.<br />
The authors took rectified Landsat imagery of a large tract of land in central Costa<br />
Rica, the Central Valley surrounding San Jose. This imagery had been captured<br />
during the rainy season. They broke the study down into a series of steps, starting<br />
with classifying three different waveband combinations in the imagery. They then<br />
developed what they termed a “Coffee Environmental Stratification Model”<br />
(Coredo-Sancho & Ader, P.1581) before comparing this with supervised results<br />
(from known data). They then set out to identify which waveband combination<br />
best matched coffee crops.<br />
The identification of a control section of water which the authors used to reduce<br />
haze in their imagery helped with the development of the image key for this thesis.<br />
The method the authors used in the study was to find an area of deep water to<br />
identify a minimum reflectance value and subtract this from each of the non<br />
thermal bands. The next step the authors took was to remove clouds from the<br />
imagery, they did this by creating a binary mask using a classification of arbitrary<br />
clusters they had developed. This allowed them to recode areas which it identified<br />
as being contaminated by cloud or shadow. When this was applied the clusters<br />
were recoded to a zero value, they also digitized “isolated” (Cordero-Sancho &<br />
Ader, P.1582) clusters on a case by case basis. It is very beneficial to smooth areas<br />
of shade within a polygon. This might be done by estimating a percentage of<br />
shade that should be present in the polygon (based on time of day and vector code<br />
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