## 4 Results and Discussion
### Preprocessing Stage  
Much time and effort were dedicated to the preprocessing stage in early studies. With the developments in specified algorithms for Landsat data and release of Collection 1 and 2, today the preprocessing stage is almost eliminated. As seen in Table 3, we highlighted the trend in preprocessing steps of Landsat change detection studies by categorizing them into automated and user-based strategies. Geometric correction is one of the key requirements for time series analysis. Small miss-registrations can result in noise, which may be incorrectly attributed as a meaningful change in different algorithms. Additionally, the geometric accuracy of results is required for optimal usage in GIS systems. Geometric corrections have been carried out by users in early applications of Landsat data with a few images. In particular, coregistration was usually done using other maps or images with a reliable geometric accuracy. If ground control points were available, polynomial fitting was done with programming or with the help of commercial software. However, the availability of L1 TP Landsat data with less than \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Strategy** & **Method** \\ \hline \multirow{4}{*}{**Geometric**} & Automated & L1 TP standard data \\ \cline{2-3}  & & \begin{tabular}{c} Coregistration \\ Polynomial fitting \\ Commercial software \\ \end{tabular} \\ \hline \multirow{4}{*}{**Atmospheric**} & & \begin{tabular}{c} Surface reflectance data \\ LEDAPS \\ LaSRC \\ L8 SR \\ \end{tabular} \\ \cline{2-3}  & & \begin{tabular}{c} MODTRAN \\ LOWTRAN \\ FLAASH \\ DOS \\ COST \\ ATCOR \\ 6S \\ \end{tabular} \\ \hline \multirow{4}{*}{**Cloud/Cloud shadow**} & & \
\begin{tabular}{c} Fmask \\ CFmask \\ Tmask \\ Cloud score \\ QA band \\ \end{tabular} \\ \cline{2-3}  & &
\begin{tabular}{c} Manual mask \\ Thresholding \\ Cloud-free image selection \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different strategies and methods for preprocessing steps of Landsat change detection studies. 12 m RMSE almost eliminated the need for geometric preprocessing of Landsat imagery and as a result, studies using terrain corrected data have increased (see Figure 16). Atmospheric correction is usually needed in Landsat change detection research due to the influence of different atmospheric conditions across time and space. Early studies used physical radiative transfer models for atmospheric correction, which required information about the atmospheric condition at the time, including humidity, pressure, and sun angle. When this information was not available, relative methods with less accuracy were used. The development of automated atmospheric correction methods for Landsat sensors and processing of Landsat surface reflectance data eliminated the need for atmospheric preprocessing and ensured more reliable change results. As shown in Figure 16, recent studies prefer to use automated atmospheric correction methods or analysis ready data (ARD). From studies that used all available data, 79% applied automated atmospheric correction methods to ensure radiometric consistency through time series. Early studies usually made efforts to select Landsat images with no or very little cloud coverage, and when necessary, masked cloud/cloud-shadows using manual digitizing or thresholding. However, accessing to annual cloud-free images in some geographical locations can be challenging and even impossible due to nearly permanent cloud cover conditions in some geographic regions. Figure 16 shows an increasing number of studies used automated cloud/cloud-shadow mask algorithms, as studies are increasingly using more frequent-date data. Amongst studies using high-frequency datasets, 76% used automated cloud masking methods. Some studies require annual or seasonal images, and to ensure per pixel quality of data, a composite image must be created from images available within a restricted period of time (e.g., available images in one season).