Thailand/Miller, Nualchawee and Tom 
The assumption that future changes in forest cover can be measured in terms of those which 
occurred in the past allows a simple projection of the fate of the future forest cover within 
the site. Cross-tabulation of each pair of consecutive forest cover data planes available in the 
landscape model (e.g., 1968 with 1972) provides a probability transition matrix. This matrix 
is applied in a Markov process to the distribution of forest cover recorded in the second or 
more recent data plane to project future forest cover trends in the test site (Fig. 7.3)*. Only 
a few years of validity may be assumed for these projections, as the process controlling the 
observed forest cover changes during the training period (i.e., 1968 to 1972) can only be as- 
sumed to persist for a few years into the future (i.e., beyond 1972). A much more complex 
model has been developed which is capable of projecting the future spatial behavior of the 
forest cover of the site based upon the controlling landscape features represented in the over- 
laying data planes. Essentially, the spatial projection model takes all the specific changes in 
forest cover as tabulated between two consecutive forest cover data planes, groups them to- 
gether into like changes, and correlates their occurrence with the corresponding landscape 
parameters also available in the landscape model (i.e., slope, distance to roads and trails, etc.). 
Details on this approach and its results can be found in the list of references provided. 
The Landsat imagery existing for the site represented five consecutive anniversary dates which 
were geometrically rectified and resampled to the one hectare square cells. Difference images 
were formed between like spectral bands for consecutive pairs of dates. Displays of these 
easily obtained image analysis products provide a mechanism for quick reconnaissance-level 
detection of areas of change between consecutive years. These areas of change represent both 
shifting cultivation (Fig. 7.5) and annual shifting crop-type patterns where permanent agri- 
culture is practiced (Fig. 7.6). 
Landsat land cover classification in areas of significant relief is limited in accuracy by that 
portion of the radiance variation created by the terrain which is independent of the surface 
cover type. Overlaying slope, aspect and elevation data planes (Fig. 7.7a) in the landscape 
model provided a basis for several approaches designed to minimize this impact. Overlays of 
cultural features (Fig. 7.7b) as data planes were also evaluated to determine their contribu- 
tion to the accuracy of the classification of the 8 cover types noted aboveT. 
The value of combining these various types of ancillary landscape data planes with the Land- 
sat MSS bands was tested using stepwise linear discriminant analysis as the classifier (Table 
7.1). Three types of training sets were evaluated. These consisted of rectangular representa- 
tions of each cover type, rectangular areas selected to represent sun-facing and opposite 
aspects, and grid sampled training sets assembled for a 1/9 sample of the test site consisting of 
every third row and column of one hectare image cells. Tests were also completed to assess 
the value of having apriori knowledge of the composition of the area to be mapped (propor- 
tional probabilities), in comparison to having no knowledge of the composition of the area 
(equal probabilities). 
  
*Land cover trend models have also been computed for the following pairs of dates: 1954 with 1972, 1975 
with 1966, 1966 with 1968 and 1968 with 1972. 
TTeak was omitted in the Landsat classification as its area occupied only 40 ha. 
    
    
   
    
    
     
   
    
     
   
    
    
   
   
  
  
  
  
   
  
  
  
  
  
  
  
  
  
    
    
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