## 4 Results and Discussion
### Landsat Change Detection Methods  
As suggested in a previous review paper [41], Landsat change detection methods can be placed into five categorical approaches. The most common approach is differencing, wherein images at different times are compared to each other across spectral bands or indices, land cover types from post-classification, or endmembers from spectral mixture analysis (SMA; see Figure 17). Post-classification, which is easily implemented and used in 217 studies, is the most used Landsat change detection method and provides \"from-to\" maps. However, the accuracy of such methods greatly depends on the quality of the final classifications and the availability of substantial amounts of training and testing data needed through the time series. Thresholding applies a user-defined or estimated threshold to identify changes of a single land cover type. A threshold is usually applied to images transformed into other dimensions sensitive to specific land cover types, such as integrated forest Z-score (IFZ). Trajectory methods identify changes based on prior knowledge of changes and the behavior of trajectories in different change situations. Multidates classification methods are also included in this category. Statistical, segmentation, and regression methods require larger amounts of data. Regression methods assume linear or nonlinear relationships between time and spectral bands or indices. Segmentation methods use data from the same period in different years and identify changes by breaking a time series into straight lines. Statistical methods are capable of processing all available data and assume that the time series observations  
Figure 17: Trends in different Landsat change detection method categories over time. follow a statistical model. These approaches apply classification after identifying changes and use harmonic coefficients as input for classification. Trends in different Landsat change detection categories are shown in Figure 17. Differencing methods (mostly post-classification) are the most commonly used approach given their simplicity. Although the accuracy of change detection methods depends on several factors, such as number of classes, class types, complementary data, study area, and applications, different methods achieve a range of different accuracies (see Figure 18). Statistical methods such as CCDC [60] and BFAST [91] achieved high accuracies using all available data. Post-classification methods have a range of accuracies depending on use of different classification methods. Some methods that used only for a single land cover type, such as the Vegetation Change Tracker (VCT) [92] and LandTrendr [93], used for forest change detection, have demonstrated science and application relevant results. Although pixel-based algorithms are the most common approaches and have less computational complexity, the number of object-based and subpixel based studies have increased in recent years, which added different levels of information to Landsat change detection results. As demonstrated in Figure 19, a pixel-based approach is preferred for change detection studies because a standard processing chain can be easily defined at the pixel level but not for other processing units, such as objects. Figure 18: A scatter plot showing the different Landsat change detection methods and overall accuracies over time. The accuracy of many Landsat change detection products (mostly from post-classification methods) relies on the quality of classification results. Early studies used unsupervised classifiers, wherein the number of classes should be determined before applying some of them, or supervised classifiers, wherein some assumptions about the distribution of data should hold. Although early classifiers were easy to implement, they had less reliable results and required a high amount of training data, which limited the spatial extent of study areas.