## 3 Results
### Forest Monitoring Results  
Figure 5 gives the NDVI values in forest land of Fuling District from 1999 to 2013. As NDVI is the indicator of greenness, we can use its value to predict forest land in the study area. It can be seen that the forest land has kept a generally upward trend during the study period. From 2000 to 2008, the forest increased steadily, and in 2009, it experienced an abrupt growth. The following two events may explain the above phenomena. In 2002, the policy of returning farmland to forests was fully implemented in the Three Gorges reservoir area in order to protect the ecological environment of the reservoir area and prevent soil erosion [31]. In 2008, Chongqing Municipality launched the Chongqing Forest Project [32; 33]. Figure 6 displays the geographic distribution of the forest land change in the study area. The forest land range in the study area is based on the interpretation results of the GF-1 satellite image acquired in 2014 and the SPOT data of the Three Gorges Reservoir Area in 2009. According to the statistical results, there are about 121,297 hectares of woodland in growth, about 8969 hectares in decay, and about 451.68 hectares without apparent changes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Band**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Mean Value**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{1}{c}{**Root Mean Square**} \\  & & & & & **Deviation** & **Difference (RMSE)** \\ \hline \multirow{3}{*}{**HJ-1 B**} & 1 & 95 & 163 & 108.576 & 5.055 & 0.330 \\  & 2 & 39 & 83 & 47.459 & 3.312 & 0.530 \\
**20090930** & 3 & 36 & 107 & 49.337 & 5.775 & 0.536 \\  & 4 & 32 & 112 & 62.556 & 7.901 & 0.637 \\ \hline \multirow{3}{*}{**Landsat 7**} & 1 & 76 & 155 & 98.643 & 7.185 & - \\  & 2 & 54 & 146 & 76.332 & 8.121 & - \\
**20090825** & 3 & 40 & 191 & 70.826 & 13.830 & - \\  & 4 & 25 & 126 & 63.228 & 14.684 & - \\ \hline \multirow{3}{*}{**Fused image**} & 1 & 76.002 & 172.125 & 98.251 & 7.367 & 0.156 \\  & 2 & 55.489 & 182.686 & 75.652 & 9.456 & 0.339 \\ \cline{1-1}  & 3 & 36.738 & 235.146 & 69.876 & 13.564 & 0.188 \\ \cline{1-1}  & 4 & 22.840 & 128.591 & 62.035 & 13.562 & 0.396 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical values before and after fusion in a sample region. Figure 4: Comparison of remote sensing images (display in standard false color) before and after fusion: (**a**) HJ-1 B (2009-09-30); (**b**) Landsat 7 (2009-08-25); (**c**) Fused image. Table 3 lists the percentages of forest land area with different growth trends. It can be seen that the forest with strong growth possesses the highest percentage, accounting for 60.08% of the total forest area. Figure 5: Trend of normalized difference vegetation index (NDVI) changes in Fuling District, Chongqing, 1999–2013. Figure 6: Geographic distribution of woodland growth trend in Fuling District, Chongqing, 1999–2013. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \begin{tabular}{c} **Strong** \\ **Recession** \\ \end{tabular} & \begin{tabular}{c} **Micro-Recession** \\ \end{tabular} & \begin{tabular}{c} **Unchanged** \\ \end{tabular} & \
\begin{tabular}{c} **Weak Growth** \\ \end{tabular} &
\begin{tabular}{c} **Strong Growth** \\ \end{tabular} \\ \hline
**Percentage (\%)** & 2.15 & 4.72 & 0.35 & 31.98 & 60.08 \\ \hline \end{tabular}
\end{table} 
Table 3: Percentages of forest land area with different growth trend. The remote sensing image trend detection and analysis can not only detect the long-term growth trend of the forest land, but also can set several thresholds and compare them with the calculated statistics \(Z\) to obtain the degree of growth of a certain area. Therefore, it is possible to separate the region with a greater degree of change from the less variable area. From the management point of view, policy support can be given priority to the areas with greater reduction of regional growth.