Spatiotemporal Analysis of Land Cover Changes in the Chemoga Basin, Ethiopia, Using Landsat and Google Earth Images

Round 1

Reviewer 1 Report

Dear Authors,

The manuscript entitled “Spatiotemporal Analysis of Land Cover Changes in the Chemoga Basin, Ethiopia, using Landsat and Google Earth Images” exploits the advantages derived from the combination of different Landsat satellite imagery data and Google Earth-based data for detecting and mapping the land cover changes over a period of 30 years (1987-2017) in a basin of Ethiopia. In order to produce the multi-temporal land cover maps by this combination, a remote sensing-based supervised classification method was applied. The produced land cover maps were comparatively evaluated in terms of accuracy metrics.

General remarks

The manuscript deals with a topic of high interest which makes it suitable for publication. Its output results are certainly valuable. As a result, the manuscript can be considered as suitable for publication in the “Sustainability” journal.  In general, the structure and content of the manuscript are acceptable. However, some comments and suggestions as minor revisions are provided below so that the authors take them under consideration.

Comments and suggestions

Lines 62-64: This sentence cannot be easily understood due to the fact that it refers to two different issues. The first is the reason of population’s concentration in the northwestern highlands. The other one is the high risk of highlands in land cover changes. Thus, reprase this sentence or split it into two separate.

Lines 67-68: Land use and land cover are two different terms. I think that your study focuses more on land cover and less on land use. Delete the term of “land use”. Consider for that in the whole text of the manuscript.

Figure 1: What (c) sub-figure represents? Relative information are not provided. Furthermore, a legend is needed for it.

Line 97: Which is the source of used DEM? Provide information and/or a relative reference.

Line 111: More information about the algorithm that you selected to use for supervised classification is needed.

Line 119: “218 unchanged polygons overlain on each land cover type”...I would like to see the distribution of these data, either as their original format (polygons) or by their location (as points). It would be useful in order to realize if this dataset covers in a satisfactory extent the whole study area.

Figure 2: Be careful! In the flowchart the different satellite Landsat sensors are presented to be incorrectly matched to the corresponding classification years, according to all you mention in lines 89-90. Correct them.

Line 180: Delete “morphological”. Land cover changes mostly refer to biophysical properties of land surface.

Line 190: Plural is needed  for “two index”. The correct is “two indices”.

Lines 233-234: Clarify that totally, including both periods of 15 years, “this increase was the largest shift”. Considering the first period (1987-2002), the decrease of woodlands was the largest shift. Otherwise, clarify between positive and negative shifts.

Lines 236-237: According to Figure 4, it is not observed a reverse pattern in the second period of 15 years for PF. It presents increase in both the two periods. Correct this mention or the figure.

Discussion: Support this part of your manuscript by the output results.

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

The study is interesting and relevant as regards the identification of land cover conditions and changes through the mixed use of Landsat and Google Earth images. Validation of land covers through field observations was satisfactory with reference to a case study concerning the Chemoga Basin located in the Northwestern highlands, about 300 km to the northwest of Addis Ababa (Ethiopia).

In my opinion, the submitted manuscript is not ready to be published, since a number of points need to be appropriately addressed.

In a revised version of the study, the following questions should be carefully considered.

i. Subsection 2.1 “Study Area.” Although it is conceivable that the choice of the Chemoga Basin was driven by data and information availability and, perhaps, by the familiarity of the authors as regards this spatial context, a comparison of this spatial context and other international contexts should be implemented, in order to make the reader aware of the reasons which make the submitted manuscript interesting for the vast scientific and technical public of the readers of Sustainability.

ii. Section 4 “Discussion.” The current Discussion section focuses on the link between the land cover conditions and dynamics, detected through the implementation of the proposed methodology into the Chemoga Basin, and the behavioral attitudes and expectations of the local societies. I would recommend the authors be much more specific in providing statements concerning the implications of their results as regards Ethiopian public planning policy. E.g. the authors claim that “Land conversion in the upper catchment reflects the preference of local communities. Agricultural lands in high and intermediate altitude regions were shifted into Eucalyptus tree plantations. The fast-growing Eucalyptus can directly benefit residents as used for food, fuel, and household goods [24, 40]. Urbanization and road development are other major drivers for plantation forest expansion. Most Eucalyptus plantations were situated near villages and roadsides” (lines 313-319). In my view, it would be very important and interesting to describe not only the results’ implications in terms of the preferences of the local societies, but also as regards the role played by the national and local public administrations in designing public policies which may have supported or hindered the targeted land cover transitions. A thorough discussion concerning this point would be particularly helpful to highlight the usefulness of the proposed and implemented methodology for the definition and development of Ethiopian public planning policies.

iii. Section 4 “Discussion.” I would recommend the authors extend the Discussion Section in order to analyze the results in the light of the available studies concerning land cover identification and transitions. Moreover, I would recommend the authors analytically discuss the advancements implied by their study as compared to the current literature, in order to make the reader aware of the value added of the submitted manuscript. In the Introduction section a number of studies available in the literature are quoted and it is quite amazing that the authors do not feel worth comparing those studies with their results.

iv. Section 5 “Conclusions.” I would recommend the authors discuss the exportability of their methodological approach to other international contexts different from the Ethiopian context. This would imply the identification of the reasons the study implemented with reference to the Chemoga Basin is likely to be helpful in addressing similar issues in other locations.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The revised version of the manuscript shows important improvement and appropriately addresses the recommendations I raised in the first place except for the last one.

As a consequence, I would recommend Sustainability accept the study for publication, provided that the authors address the following point.

The authors should discuss the exportability of their methodological approach to other international contexts different from the Ethiopian context. This would imply the identification of the reasons the study implemented with reference to the Chemoga Basin  is likely to be helpful in addressing similar issues in other locations.

The discussion should be a detailed and referenced discussion, much wider than the discussion proposed in the present version of the paper. The discussion should include qualified references to other International contexts.

Author Response

Dear Editors and Reviewer

We would like to thank again the editors and reviewer for kindly reviewing this manuscript. The comments and suggestion can improve the quality of manuscript in order to meet the requirement of Sustainability editorial process. We have been able to incorporate changes to reflect the suggestions provided by the reviewer. We have highlighted the changes within the manuscript.

In detail, we rephrased the discussion section by adding the below (L 377-394)

"Several studies have been conducted to monitor land cover change over the world by using space-borne imagery [6, 9]. Remote sensing techniques have long been recognized as an essential and powerful tool for land cover mapping. Despite land cover change is occurring rapidly in developing or less developed countries, remotely-sensed observation on land features for a long-term time horizon is still limited in these countries [43], due to lack of accurate reference data for times long past. To overcome this obstacle, GE images were used in this study to extract pseudo-ground truth data. GE allows scientists and researchers to access vast amount of high resolution images with free of charge and apply different processing procedures in remote sensing studies.

One powerful approach in this study was the integration of Landsat and GE images for land cover analysis. The scarcity of ground truth for various land features is a major obstacle in employing remote sensing technique for accurate land classification. GE is well implemented to synthesize pseudo-ground truth data used to represent land features at a suitable scale. The advantages of using GE provides historical imagery, allowing the multi-temporal analysis for landscape change over time [44] when ground truthing is not possible. The further identification of land features is also possible with help of GE images. Higher spatial resolution of GE images allows for accurate identification of each land cover at different temporal and spatial scales. Generally, GE enabled us to identify relatively diverse features, including shapes, colors, and textures of each land feature. This helped us to build high confidence training datasets for further land classification"

 

And, we also added one reference.

 

Thanks