In such situations, when and where there are technical inefficiencies, it would be important to critically scrutinize and choose the most feasible methods and technologies [23]. Past and current practices of forest cover assessment as well as land-use and land-cover (LULC) mapping in Ethiopia, digital image classification, and mapping methods are practiced, such as supervised, unsupervised, and object-based classification (e.g., [13; 14]). However, digital image classification and mapping methods in general require a high level of technical skill, and in most cases, technical software [25; 26]. Furthermore, the spectral similarity of land-cover classes is also challenging. As a result, the wider application and use of such methodologies for large-scale activities such as nationwide assessment could be problematic. For instance, Ethiopia's historical data on LULC changes between 2000-2013 for forest reference level (FRL) submission to the United Nations Framework Convention on Climate Change (UNFCCC) were generated through digital image classification and mapping using Landsat data. Nevertheless, the report underlined the need for the further reduction of uncertainties of the estimates [27]. In response to the drawbacks of digital image classification and mapping mentioned above, some platforms of free and open source software such as Geo-Wiki, VIEW-IT, and Sky Truth are developed by different bodies, including academic institutions [28; 26]. The software support visual satellite image interpretation and LULC map validation by non-remote sensing experts, while some of the programs can also be used for other advanced applications by professional users [26; 29]. The application of these software packages is associated with the use of very high-resolution images, which are often known to have a small geographic scope and an irregular time interval of acquisition, in turn limiting its utility for large areas. On the other hand, using Landsat images will provide a global coverage with bi-monthly acquisitions; however, interpretation is limited by its low spatial resolution. FAO designed the software system Collect Earth as an open source tool that helps to collect, analyze, and compile reports on LULC through visual image interpretation based on freely available satellite images mainly with the Google Earth platform [25]. The tool has been adopted, and many countries have used it around the world for activities such as national forest inventory, LULC mapping, and the estimation of activity data for REDD+ [25]. The mentioned software supporting visual satellite image interpretation are efficient tools for gathering reference data. Such reference data gathered using interpretation of satellite imagery with different spatial resolutions under different forest conditions has been used to provide forest area and forest cover change estimates [30; 31]. However, forest conditions and properties of the imagery, such as spatial resolution and revisit times, could potentially influence estimates. Hence, it is important to evaluate different types of satellite images under different forest conditions in order to provide guidelines for using such tools at a national level. Thus, the aim of this study was to identify and evaluate different and alternative types of satellite images to minimize the level of uncertainties of the estimation of forest area and canopy cover in different forest conditions in Ethiopia. The specific objectives of the study were to: (1) evaluate the use of PlanetScope, RapidEye, and Sentinel-2 satellite images for forest area and canopy cover estimation through visual interpretation; and (2) assess the influence of sample sizes on the estimates. Accordingly, efforts were made to see if there is variation in the pattern of the estimates and the uncertainty of estimates from the three types of satellite images over the different areas of interest (sites of the study), which are characterized by having different biomes.