multivariate poisson hidden markov models for analysis of spatial ...

at each of the 150 grid locations (Figure 4.5), with one quadrat sampled at a distance 1
meter north, south, east and west of the grid locations. Then take the total of the four
quadrats as the weed count by species at that grid location. It is obvious that the
observations recorded are spatially dependent. Also, one main goal of this study is to
find out how many different clusters (states or distributions) are present in this field
(Figure 4.6). The different clusters are formed due to factors such as the soil type,
location and soil moisture or any other factor. Since only counts are recorded, the
number of different clusters is unknown (i.e. hidden). Here, it can be assumed that this
data structure follows a hidden Markov random field (HMRF).
Row index
Column index
Figure 4.4: Distribution of weed counts in field #1
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