General remarks

The paper is well written and comprehensible, the data sets listed in the paper are accessible online and complete. However, in its current form the paper requires major revisions in order to accommodate the standards of the ESSD journal.

The uniqueness of the paper lies in fact that historic aerial photographs for Ethiopia have been processed by modern methods of digital photogrammetry and thus, made available to a wide user community. For other parts of the world the same approach has been applied in manifold ways meaning that well established standard methods have been used in this study.

The motivation of the study presented in the introduction mainly focuses on thematic aspects, such as ecosystem, biodiversity, land use and climate change research in Ethiopia and the need for historic high-resolution spatial data (DEM and surface cover) for change and impact analysis. However, a profound assessment of globally available high-resolution satellite remote sensing data is missing. Such data include the KH-1 to KH-9 Corona (1960-1986), IRS (since 1995) and Digital Globe (since 1999) programs among others. The authors need to embed and compare their two snapshots in time (in)to a bigger time line and thus, demonstrate that their data spatially align to the globally available datasets in order to ensure data continuity in space and time which is listed as the first priority in using remote sensing data for biodiversity and related purposes (Tuner et al., 2015).

Moreover, the paper is missing a relevant case study demonstrating the value of the datasets for one of the significant purposes mentioned in the introduction. Just picking one case – volume difference at one section of a gravel road – is not sufficient for that. Such a case study also needs to include the comprehensible demonstration of alignment with other globally available high-resolution data sets.

Overall, the paper is written rather ‘minimalistic’ whereas most of the content is dedicated to standard technical descriptions (sections 2 and 3). The lack of more in-depth evaluation of the data is also reflected in the content and the quality of the figures as well as the results and discussions parts of the paper.

Specific remarks

2.2 Data: From the description is unclear which data are used for accuracy assessment. The listed DEM data sets cannot be used for accuracy assessment since they are of much coarser spatial resolution. Later in the paper, the authors state the use of independent GCP’s for accuracy assessment. If this is the case, this information needs to be mentioned already in this section. The authors also need to consider using TanDEM-X DEM information (12,5 m resolution) in their study.

2.3.4 Ground control points: Have these GCP’s only be used for reference purposes or has a subset of them also be used for independent accuracy assessment of the resulting DEM’s? Such an approach using control information of significantly higher accuracy would be needed for a state-of-the-art accuracy assessment.

4 Results and discussion: Do the achieved accuracies represent internal accuracies or do they relate to external reference systems too? At this point it becomes evident that the methodological section 3 of the paper lacks a subsection describing the approach used for accuracy assessment in this study which would need to include the internal and the external accuracies as well as the alignment with other high-resolution data sets as already mentioned above. So far, there is only a very brief methodological description at beginning of section 4.2. The thematic discussion in section 4.1 is very minimal and needs to be extended (see comment under general remarks).

The quality assessment part proofs the high quality of the positional accuracy of the derived data sets. However, analysis of height accuracy seems to be missing and should be easy to perform using the available GCP’s. The further analysis of the quality of readily available DEM’s based on the same GCP’s is interesting but not an accuracy assessment related to the data sets derived in this study. However, these data sets could partly be used in order to analyze the alignment with external data. The current discussion between the different external DEM’s lacks clarity, the authors need to describe methodologically sound which parameters have been used in order to asses the accuracy of their data sets and the different external DEM’s. Moreover, RMS-errors of around 10 meters are not huge for DEM’s derived from medium resolution satellite data.

This article presents an intriguing approach to constructing a digital elevation model of the Bale Mountains in Ethiopia using high-resolution historical images. However, for the manuscript to be considered for acceptance, there is significant room for improvement in various aspects, including overall structure, abstract, introduction, materials, results, and discussion. I offer the following suggestions in hopes of providing constructive feedback.

I am concerned about the paper's originality, as a related article has already been published: Nyssen et al., 2022, Online Digital Archive of Aerial Photographs (1935–1941) of Ethiopia. I would appreciate clarification on the innovative aspects of this manuscript compared to the paper. Please provide a detailed explanation of the data and reconstruction algorithm employed for the digital elevation model.

The abstract is generally well-written, but it should emphasize the innovative aspect of this study, specifically the reconstruction of a detailed digital elevation model, as well as the method utilized. Since this paper offers a public dataset, please include validation methods and accuracy measures.

The introduction section could be further strengthened by emphasizing the importance of a detailed digital elevation model and addressing the limitations of previous research. It is worth mentioning that three-dimensional modeling has been a relatively mature technology dating back to the 1960s. What methods were employed before then to construct these models? Additionally, provide a brief introduction to how the technology used in this study handles image data. While the structure of this section is clear, further clarification is needed regarding the importance and significance of the research area and research questions. 

The second and third sections can be combined into one, and I highly recommend using a flowchart to clearly explain the entire reconstruction process, including data preprocessing, algorithm modules, and so on. The materials and methods section needs substantial revision. Although the paper mention numerous data preprocessing methods and digital elevation model reconstruction algorithms, many aspects require further elaboration. For instance, consider providing more detail on calibrated focal length, principal points, fiducial marks, and ground control points, accompanied by corresponding images. The Structure-from-Motion Multi View Stereo Photogrammetry algorithm mentioned in the paper should be described in detail, including its processing specifics and core formulas. Highlight the method's specific advantages in your research, such as efficiency, accuracy, and other aspects of the reconstruction process. Overall, these two sections lack detail, and providing more information can significantly enhance the paper's readability.

The results and discussion section also need substantial revision. The paper does not highlight many points of interest, such as the study area's topography, vegetation, etc. Consider providing Google Earth images of the study area. From a validation standpoint, the dataset appears to have produced good results. I am curious about the specific validation method, as the elevation datasets being compared differ in time and spatial resolution. When performing comparative validation, zoom in on local maps to emphasize the differences between various DEM datasets. Additionally, discuss the influence of vegetation and terrain on mapping accuracy during the elevation model creation process. This section needs significant expansion; otherwise, it will be difficult to attract attention. Lastly, please rewrite the conclusion section.

Other suggestions include:

Adjust the font size and style of all figures and tables for easier reading.

Figure 2 may be challenging for readers to interpret due to its grayscale appearance. Please provide appropriate satellite images.

Correct the latitude and longitude information corresponding to Figure 3 and Figure 4.

Improve Figure 5's visual appeal.

Unify the paper's font style, such as the red font at line 222.