## 5 Discussion
### Why Use GLASS LAI Data? There are many remote sensing products that can be used as input parameters for the detection of disturbances, such as NDVI, enhanced vegetation index (EVI), and LAI. GLASS LAI [33] was selected as the input data for the following reasons: (1) the time series of GLASS LAI products have good continuity; (2) GLASS LAI in the vegetation cycle is relatively smooth. If the time series data has large fluctuations, it needs to be smoothed before use; In addition, (3) the LAI is more sensitive to detecting disturbances in forested land, whereas NDVI is prone to moderate to high saturation. After extracting the fire area coordinates in the Jinhe large forest fire, a time series curve from 2001 to 2005 was drawn from burn-related data using MODIS NDVI. In 2003, NDVI data showed a rapid rise after a brief decline; compared to the previous data with no fire, the NDVI changes were not obvious. One of the consequences of fire is that a burning forest will leave a wealth of organic matter. Fire is necessary to cycle nutrients, especially on sites with deep organic soils. After a fire, adequate nutrients play a significant role in weed and understory growth [38]. The satellite observes forest land from a high altitude; therefore, both the canopy and understorey will be reflected in the results of NDVI. LAI can be regarded as a three-dimensional characteristic parameter of forests, which is superior to NDVI as an input parameter. The time series of MODIS LAI data of the same burned pixel were plotted. Compared with NDVI, the LAI appears to be significantly reduced after May 2003, but MODIS LAI in the time series has a very large jump; the occurrence of jump points will affect the modeling parameters so that the detection results will be in error. If we use MODIS LAI data to model, we need to smooth the LAI in accordance with the growth pattern and weaken the detection error caused by the jump point. As can be seen from Figure 9, the curves using GLASS LAI data are smoother than those using MODIS NDVI and MODIS LAI and are, therefore, more suitable for season-trend modeling and the detection of disturbances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Landsat**} & \multicolumn{2}{c}{**MODIS**} & \multirow{2}{*}{**Producer’s**} & \multicolumn{2}{c}{**GLASS TSM**} & \multirow{2}{*}{**Producer’s**} \\ \cline{2-2} \cline{5-6}  & **Burned** & & & **Unburned** & & **Accuracy** \\ \hline Jinhe (\(k=0.758\)) & & & & \(k\) **= 0.776** & & \\ Burned & 473 & 107 & 81.5\% & 521 & 59 & 89.8\% \\ Unburned & 150 & 3955 & 96.3\% & 103 & 4002 & 97.5\% \\ User’s accuracy & 75.9\% & 97.4\% & & 83.5\% & 98.5\% & \\ \hline Shibazhan (\(k=0.634\)) & & & & \(k\) **= 0.838** & & \\ Burned & 1407 & 265 & 94.7\% & 1579 & 93 & 94.4\% \\ Unburned & 1115 & 17377 & 97.3\% & 321 & 18171 & 98.3\% \\ User’s accuracy & 80.8\% & 99.3\% & & 83.1\% & 99.5\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regional confusion matrices and kappa coefficient (\(k\)) from geographic accuracy assessment. MODIS = Moderate Resolution Imaging Spectroradiometer; TSM = time-stepping modeling. As with any remote sensing method, our developed algorithm has a limit and cannot distinguish some non-fire types of forest disturbances. In a future analysis we will use auxiliary data to achieve a more detailed classification of the results in order to enhance the versatility of the algorithm and distinguish the different types of forest disturbances. The LAI of forests will be at a lower level in winter and spring, the variations of the model parameters that occurred during this period were not as sensitive as that of the growing season. Although we can use subsequent data to detect the occurrence of disturbances by changes in LAI time series, the time and range of the disturbance in the detection results will be affected. We will improve the LAI's simulation method to allow it to detect those secondary disturbance too. As it can be seen in Figure 3, the season-trend model can detect forest recovery.