## 3 Results and Discussion
### Validation of the Land-Use Maps Using the Ground Truth Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & **Reference** & **Classified** & **Number** & **Producer’s** & **User’s** \\  & **Totals** & **Totals** & **Correct** & **Accuracy** & **Accuracy** \\ \hline \hline
**ENVISAT 2005** & & & & & \\ \hline
**Water** & 4 & 7 & 4 & 100.00\% & 57.14\% \\
**All rice** & 24 & 29 & 23 & 95.83\% & 79.31\% \\
**Aquaculture** & 20 & 22 & 15 & 75.00\% & 68.18\% \\
**Rainy season rice and shrimp culture** & 4 & 1 & 0 & 0.00\% & 0.00\% \\
**Settled areas, coconut, sugar cane and orchard** & 19 & 12 & 12 & 63.16\% & 100.00\% \\
**Urban** & 4 & 4 & 4 & 100.00\% & 100.00\% \\ \hline
**2005 Totals** & _75_ & _75_ & _60_ & & \\ \hline
**ENVISAT 2009** & & & & & \\ \hline
**Water** & 4 & 6 & 4 & 100.00\% & 66.67\% \\
**All rice** & 24 & 30 & 24 & 100.00\% & 80.00\% \\
**Aquaculture** & 20 & 18 & 14 & 70.00\% & 77.78\% \\
**Rainy season rice and shrimp culture** & 4 & 3 & 2 & 50.00\% & 66.67\% \\
**Settled areas, coconut, sugar cane and orchard** & 19 & 14 & 14 & 73.68\% & 100.00\% \\
**Urban** & 4 & 4 & 4 & 100.00\% & 100.00\% \\ \hline
**2009 Totals** & _75_ & _75_ & _62_ & & \\ \hline
**ENVISAT 2011** & & & & & \\ \hline
**Water** & 4 & 5 & 4 & 100.00\% & 80.00\% \\
**All rice** & 24 & 24 & 21 & 87.50\% & 87.50\% \\
**Aquaculture** & 20 & 25 & 17 & 85.00\% & 68.00\% \\
**Rainy season rice and shrimp culture** & 4 & 2 & 0 & 0.00\% & 0.00\% \\
**Settled areas, coconut, sugar cane and orchard** & 19 & 15 & 14 & 73.68\% & 93.33\% \\
**Urban** & 4 & 4 & 4 & 100.00\% & 100.00\% \\ \hline
**2011 Totals** & _75_ & _75_ & _60_ & & \\ \hline
**SPOT 2005** & & & & & \\ \hline
**Water** & 3 & 3 & 3 & 100.00\% & 100.00\% \\
**All rice** & 24 & 19 & 19 & 79.17\% & 100.00\% \\
**Aquaculture2** & 23 & 26 & 22 & 95.65\% & 84.62\% \\
**Settled areas, coconut, sugar cane and orchard** & 18 & 20 & 16 & 88.89\% & 80.00\% \\ \hline _SPOT 2005 Totals_ & _68_ & _68_ & _60_ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of the results. likely due to the similar land cover characteristics. Low user's accuracies for \"Water\" are likely caused by flooded areas or aquaculture paddies misclassified as water bodies. The trends in rice land-use changes based on the optical data appear to be supported by the interviews with the local people. However, it should be emphasized that the accuracy of the optical results before 2005 is uncertain as the data are single date data and because the only reference data available before 2005 was the interviews. According to the interviews in 2005, three rice fields changed to aquaculture, one changed from rice to aquaculture in 1995 and a rice field changed to a banana plantation in 2008. The EO data appears to support the assumption that the rice cultivated areas have decreased during the time period under investigation. At the same time, the aquaculture and coconut/orchard acreages have increased in the area. The result is in line with the information acquired from the farmers during the field survey campaign. According to the land-use maps based on the SAR imagery, _ca._ 19,000 ha of rice has changed to other land use between 2005 and 2011. Biggest relative changes have taken place in the inland districts of Ben Tre and Tra Vinh provinces. Moreover, the results from the optical imagery (Landsat 1979 and SPOT 2005) indicated that the land-use changes in coastal areas have occurred before the year 2005, which also was verified by the farmers in the target area. Also, [PERSON] _et al._[19] reported rapid increase in shrimp acreage in Cai Nuoc (Ca Mau province) around the year 2000 when a government decision allowed farmers to transform coastal saline rice fields into shrimp farms. It should be noted that supervised methods or more advanced image classification algorithms, such as object based techniques or methods designed for SAR, might have improved the classification accuracies for single datasets. However, here we have focused on change detection and the aim was not to develop classification algorithms.