## 4 Results and Discussion  
### Variation of \(L\)ulc and NPP  
#### 4.1.1 LULC Classification and Uncertainty Processing  
The overall accuracies of 11 types classification were 82.5% (Kappa = 0.80), 81.4% (Kappa = 0.78) and 83.3% (Kappa = 0.81) for 2001, 2003 and 2006, respectively, which are, in general, sufficient to meet the monitoring accuracy demands of LULC change in Guangzhou. The classification accuracy is higher than that of MODIS 12Q1 by about 80% [45]. According to the classification error distributions, two steps are used to reduce the effects of both errors and image noise in the NPP analysis.  
1. Considering a classification accuracy of \(x\), the pixels remained within a range of \((1-x)/2\) to \((1+x)/2\), of which 95% of pixels are regarded as significantly normal pixels with a high accuracy.
2. The unsatisfactory pixels that are out of the range are identified, further validated, and then rejected in the succeeding analysis process.  
As a result, the comparison of NPP mean values for the two groups of pixels for the same covers led to two findings: (1) the NPP mean values of unsatisfactory pixels are greater than the normal pixels due to the misclassification; (2) the difference between the two groups is within the range of 2-10 g-C\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), and the maximum difference ratio is within 3.0% of the corresponding mean value. It is demonstrated that the classification errors have impacts and were eliminated first. At last, the classification results were subsequently re-sampled to 1 km resolution to cooperate with the NPP. The resample accuracy was 91.1% suggesting a good guarantee of NPP.  
As shown in Figure 2, the SAM decision tree classifier map presents a more robust depiction of the vegetation-cover mosaic, and the small croplands and open shrublands throughout the woodland grasses and forests are more evident. By comparing the conversion areas from the origin cover to other areas in Figure 3, the following distinct LULC changes are obtained. (1) Evergreen coniferous forest was reduced and transformed to mixed forest and evergreen broadleaf forest during the first period and to shrublands during the latter period. (2) Evergreen broadleaf forest was mainly converted to mixed forest and woody savannas during the first and second periods, respectively. (3) Shrublands, woody savannas, and grasslands were clearly converted to croplands and cropland/vegetation mosaics during the first period. Thus, the trend of forest conversion to shrubs and woody grass was high, and a portion of the shrub and woody grass areas were subsequently reclaimed as croplands and cropland/vegetation mosaics. It is clearly evident that areas of cropland and cropland/vegetation located in suburbs surrounding the Huadu and Baiyun districts were increased by the occupation of original grasslands in 2003 and were decreased during urban expansion such that conversion to grasslands occurred again in 2006. In 2006, large areas of forests decreased in size, and certain town areas were associated with urban landscapes. There was a high conversion of forest and woody grass cover into large areas of cropland and cropland/vegetation mosaics, as shown in yellow, in the suburban areas near the Conghua, Luogang and Zengcheng districts. These changes during the initial period corresponded with the implementation of the urban development strategy in 2000 in Luogang, Conghua and Zencheng, and changes during the latter period correlated with the integration of the Nansha district into the mega-city of greater Guangzhou.