## 4 Results and Discussion
### Accuracy Assessment  
As computational capabilities and algorithms designed for change detection improve over time, an increased need can be seen for the quantity and quality of reference datasets to train models and evaluate the final product. Ideally, change detection accuracy is measured by the juxtaposition of the output map with an isolated set of assessment data. We categorize methods for collecting reference datasets into two main techniques fieldwork- and office-based. Fieldwork often provides high quality and high accuracy training data; however, it is costly and laborious, especially for large-scale studies. Other issues include difficulties associated with accessing to private properties or unreachable terrain. These challenges can be addressed by limiting the sampling area, which reduces flexibility, or by using pre-existing field datasets that rarely match the spatial and temporal scope of the projects.  
On the other hand, office-based techniques for collecting ground truth data are an alternative solution to mitigate limitations associated with field campaign. High-resolution satellite or aerial images can be interpreted by a skilled analyst to create validation data. In addition, paper and digital land cover maps from non-remote sensing sources can be used, either to aid interpretation or for use as a validation dataset [94]. Amongst reviewed studies, 131 studies did not report overall accuracy, due to the lack of appropriate reference data, or used other evaluation methods, such as the area under the receiver-operating characteristic curve (AUC) or F-measure. Of those studies that report overall accuracy, 339 studies (77%) used office techniques and 102 studies (23%) used fieldwork to collect assessment datasets (see Figure 21).  
Figure 20: Recent trends in Landsat change detection studies.  
Although office-based image interpretation is subjected to some level of uncertainty, these techniques provide the opportunity to view a wider landscape and offer a viewpoint similar to that of the satellite imagery. Office-based image interpretation techniques rely upon the availability of high-resolution images before and after a change event. Google Earth, which was used for validation in 96 studies, provides high-resolution images of world since 2000. Other sources of high-resolution images, such as satellites with high spatial resolution, historical archives of aerial images, and historical land cover maps, can be used for ground truth data collection before that time. However, even with the availability of such data, their quality and quantity over time may not provide enough support for rigorous evaluation protocols [5].  
Importantly, visual detection of subtle change targets in high-resolution images, especially in the case of complex change processes, could be challenging. Similarly, it is impossible to detect changes occurring in areas obscured from view, such as under the forest canopy. Although field data are considered a more reliable source of validation data, it can only examine current conditions. Furthermore, a delay between a disturbance and field visitation may cause additional errors. In this review, we compared the overall accuracies derived from these two techniques. Overall result agreement between field- and office-based validation is expected, yet office techniques reported higher overall accuracies. However, questions about the reliability and uncertainty of office-based results remain.