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4.4.2. Accuracy Assessment
Accuracy assessment was done comparing the prediction as the result of logistic regression as being identified any value closed by
1 or deforestation occurrence from calculating logistic regression equation above. Any value less tha 0.5 is identified as 0 or stable
condition. This study itself was done in 2005-2006, so it can not be
ground checked directly to the field, unless using high resolution image such as IKONOS or QuickBird. Since the limitation of budget
this ground truth method was not be done. Validation to the field was done December 2005, just to compare the spectral characteristic to
define the classification, especially by visual classification. Figure 4.18 is showing the prediction of deforestation and
actual deforestation in 2001 as comparison result. One of the most common means of expressing classification
accuracy is a classification error matrix confusion matrixcontingency table. In this study, error matrix compare on a
category by category basis, the relationship between result of logistic regression calculation and result of automated classification by
ERDAS and See5 program. Overall accuracy is computed by dividing the total number of
correctly classified pixels 0 – 0 7,552 and 1 -1 381 by the total number of study area 11,014. This overall accuracy now is 72.03
. Producer’s category result is from dividing the number of
correctly classified pixels in each category 0 7,552 and 1 381 by the number of logistic regression model result in column total 9,265
and 1,749. This figure indicates how well regression logistic model of the given value 0 and 1 are classified.
Prediction 1990 -1997 Actual 1997 - 2001
Figure 4.18 . Comparison between prediction deforestation and actual
deforestation in 2001
.
Table 4.11. Error matrix resulting from classifying logistic regression
model
Logistic Regression Model 0 1
Total
7552 1368 8920
Classification 1
1713 381 2094
Total 9265 1749
11014
Producers Accuracy Users Accuracy
Unchanged Area 69
Unchanged Area 85
Deforestation 3 Deforestation
18
Overall Accuracy 72.03
User’s accuracy are computed by the dividing the number of correctly classified pixels in each category by the total number of
pixels that were classified in the category the row total 8,920 and 2,094. This figure is a measure of commission error and indicates
the probability that pixels classified into a given category actually represents that category on the model.
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V. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
Predictor Altitude or elevation of ≥ 250 meters is non-significant
variable to be entered further to the model. Since the study area is relatively flat, with undulating area and with the ratio less or greater 250 meters, it must
not be influenced to deforestation or stable occurrence. The accuracy of prediction is 56.8 is actually too small for being
used the model for predicting deforestation rate at the same location and different time 1997 – 2001 according to available data. This less overall
accuracy was caused by there are many independent variables contributed deforestation driving force that being assumed are able to increase
deforestation occurrence. Wald test as one of the important test accepts null hypothesis and
means that all variables of road, river, distance from population center, distance form coast line, slope, and aspect can be used to predict deforestation.
LogR_COM or independent variables of aspect and LogR_Slp Slope have higher Sig. value. Aspect and slope have contributed more cells that
being assumed for deforestation occurrence. Odd ratio, in this study is showing that distance less than 1,000 km
tends to deforested occurrence 3.097 times than distance greater or equal 1 km from existing road. .
Rapid assessment of image classification result of deforestation Period 1990 – 1997 and Period 1997 – 2001 can be said that this classification has the
accuracy non spatial of 91.63 Forest area in Period 1990 – 1997 was 6,077 ha, but had changed to be
5,262 ha in polygon vector cells. Comparison between raster and vector process showed the wide changing between both processes. This phenomenon
showed, Assigning data by location spatial join was not able to
Kategori