## 4 Results and Discussion
### Spatial and Temporal Characteristics  
Applications of change detection are expected to increase spatially and temporally over time given the increasing availability of data and computational power. Among the reviewed papers, 99 used all available data, 85 used more than one image per year, 58 used an image for each year, and 248 studies used less than one image per year. As shown in Figure 14, studies using one or more images per year are increasing. Early studies often used pairs of images or a single image for only a limited number of years (e.g., one image for every 5 years), but today, annual or interannual changes such as vegetation phenology  
Figure 13: Usage of other EO data along with Landsat data in the reviewed studies. are easily monitored. High-frequency Landsat change detection using all available data and dense time series are recently achievable with a cloud computing platform and robust change detection methods [60; 88; 89]. The use of more frequent data is possible only if the data have a high spatial and radiometric standard, and if preprocessing steps are done using automated methods. Radiometric consistency and the use of surface reflectance data as a physical value are key to avoiding noise in a time series. Increasing the frequency of data applied in research helps to improve the overall accuracy results of Landsat change detection as well (Figure 15). Using more data helps to better distinguish temporary trends, such as separating seasonal changes from permanent changes. When monitoring change over long periods, the Landsat historical archive is the only option. Figure 14 shows an increasing trend towards studies examining change over longer periods, and multidecade cross-sensor radiometric calibrated data studies have increased during the past years. Amongst the reviewed papers, 14% used MSS data gathered as early as the 1970s, 41% as early as the 1980s, 16% used TM data collected as early as the 1990s, and 29% used other sensors since 2000s in their change analysis. As such, most long-term studies reviewed in this study performed their change detection analysis by data collected since the 1980s. In particular, some studies begin their change analysis in 1982, using data gathered by the Thematic Mapper sensor onboard Landsat-4. However, due to the limited global coverage of Landsat-4, a large number of studies carried out their analysis by data captured since 1984 when Landsat-5 was launched. Landsat-5 is a key point for long-term change detection studies, given its nearly 29-year life span and 30 m spatial resolution, which ensures spatial and spectral compatibility with later Landsat missions. When both  
Figure 14: Frequency, time period, and size of Landsat change detection studies over time. One of the most important challenges involved for studies using time series data over a long time is the accessibility to high quality validation data for evaluating models with early historical records. However, the availability of Landsat-1-Landsat-8 surface reflectance data is useful for this purpose, making long-period studies reliable. In addition, with cross-calibration and Landsat data as a radiometric base point, other sensors can be easily integrated into long-term studies [90]. Improvements to data storage capacity and computational power have led to increasing application of change detection over geographically large study areas (see Figure 14). A study area with a large size allows for a more comprehensive understanding of patterns in land cover within and between regions. Additionally, some applications, such as climate change, are not limited to regional or national scales, but rather are relevant at the scale of the globe. However, there are still many studies covering small areas due to limitations in the amount and quality of reference data, as well as desired accuracy. Notably, of investigated studies over large area only 18% used field-collected reference data, as collecting reference data over large areas is time- and cost-inefficient.