Assessment of the State of the Landscaping System in the City of Aktobe, the Republic of Kazakhstan, under Conditions of Man-Made Load Using Remote Sensing

Round 1

Reviewer 1 Report

This study is aimed to assess the ecological condition of the green zone in the city of Aktobe using remote sensing. Although the topic is interesting, I’m quite confused by the data and objectives. I don’t think  the spatial features of the separate classes of the total phytomass of green plants and the spatial features of the territorial zones of the city can be determined only by calculating NDVI. Anyway, several other comments are listed below for reference.

1.      Instruction: pls clarify the research gaps of previous studies and the objectives of the manuscript.

2.      The manuscript is not well-structured. For example, in the Section Research methodology, I can’t see the information on the remote sensing images until he last part. It’s unfriendly to readers. I was also confused by the relationships between NDVI and species composition.

3.      The labelled texts in Figure 2 are too small.

4.      In my opinion, the Results should be prior to Discussion.

5.      What is the theoretical basis of Figure8 ? Based on the analysis in the discussion, I can’t derive the figure.

Author Response

We would like to express our sincere gratitude to you for your important research comments, which helped to enrich and improve the content of our work. Their value lies in the fact that they helped us deepen our understanding of the subject of study, identify additional aspects of the problem, and also suggest additional methods for data analysis. This made our research more comprehensive and informative. We wish you all the best and creative success.

This study is aimed to assess the ecological condition of the green zone in the city of Aktobe using remote sensing. Although the topic is interesting, I’m quite confused by the data and objectives. I don’t think  the spatial features of the separate classes of the total phytomass of green plants and the spatial features of the territorial zones of the city can be determined only by calculating NDVI. Anyway, several other comments are listed below for reference.

1. Instruction: pls clarify the research gaps of previous studies and the objectives of the manuscript.

Previous studies had limited coverage in assessing the state of the city's greening system under conditions of man-made load. Some aspects were missed, such as the impact of technogenic factors on the health of residents, the environmental consequences of technogenic activity and the effectiveness of green spaces in mitigating negative consequences [27-29].

The purpose of the study is to conduct a comprehensive assessment of the state of the city's greening system under conditions of technogenic load using earth remote sensing (ERS). This includes the following aspects:

• the use of remote sensing data allows obtaining information about the state of green areas and plantings in the city, such as parks, squares, alleys, forest parks, etc.;
• using remote sensing data, it is possible to identify changes in the structure and density of green spaces in the city over different periods of time, which makes it possible to assess the dynamics of changes and identify problem areas of the city;
• the data obtained can be used to develop strategies for planning and managing green areas of the city, including optimizing the distribution of green spaces, their care and development.

2. The manuscript is not well-structured. For example, in the Section Research methodology, I can’t see the information on the remote sensing images until he last part. It’s unfriendly to readers. I was also confused by the relationships between NDVI and species composition.

The Normalized difference vegetation index (NDVI) is widely used to assess the condition of vegetation on the Earth's surface based on data obtained from land remote sensing. It provides information about the health and density of plant cover. A relationship between NDVI and vegetation species composition is generally present, although it can be ambiguous and depends on various factors including climate, soil type, hydrological conditions and anthropogenic impacts. Here are some key points linking NDVI and vegetation species composition:

• Higher NDVI values generally indicate healthier, denser vegetation. Different plant species may have different responses to changes in canopy density and composition, which may affect NDVI values;
• Different types of vegetation have different NDVI characteristics. Wooded areas may have higher NDVI values compared to grassy or arid areas;
• Vegetation species composition can influence temporal changes in NDVI throughout the year. Different species have different periods of active growth and development, which may be reflected in changes in NDVI at different times of the year;
• Vegetation species composition can also be altered by human impacts such as deforestation, soil and water pollution, and the introduction of invasive species. These changes may affect NDVI.

3. The labelled texts in Figure 2 are too small.

Figure 3 has been modified at your suggestion.

4. In my opinion, the Results should be prior to Discussion.

Combined results and discussion

5. What is the theoretical basis of Figure8 ? Based on the analysis in the discussion, I can’t derive the figure.

Removed figure 8

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments about the Paper: “Assessment of the state of the landscaping system in the city of Aktobe, the Republic of Kazakhstan under conditions of man-made load using remote sensing”.

 The paper aims to analyze the ecological conditions of Aktobe city, in Kazakhstan by, performing statistical data analysis, processing of satellite images (calculation of NDVI index) for green areas, etc. The results are promising, concerning the identification of environmental conditions of the green zones as well as the suggestions about the formation of green space system in the city of Aktobe.

However, some sections of the manuscript need reformation, as listed below.

-          2.2 Research methodology:  Τhe methodology needs restructuring. A flowchart about the research methodology should be applied in order to be clear the methodological steps. Moreover, you need to justify why did you get EO data in June 2010, 2016, 2023. 

-          Material section is missing.

-          3. Discussion: I believe refers to the results section and should be merged with the section 4. Results (line 495)

-           A discussion section is missing and must be added in comparison with similar studies conducted by RS specialists in the ecological condition of city the green zone.  What did your method contribute to enhance capacities in monitoring the state of greenness in urban areas.  Are there still weaknesses / needs for further development?  How accurate is the proposed methodology?  How could the methods be transferred / generalized?

All in all, my recommendation is to accept the paper for publication, subject to major revision. Please find in the attached file the amendments which I believe are required prior to accepting the paper.

Comments for author File: Comments.pdf

Author Response

We would like to express our sincere gratitude to you for your important research comments, which helped to enrich and improve the content of our work. Their value lies in the fact that they helped us deepen our understanding of the subject of study, identify additional aspects of the problem, and also suggest additional methods for data analysis. This made our research more comprehensive and informative. We wish you all the best and creative success.

However, some sections of the manuscript need reformation, as listed below.

-          2.2 Research methodology:  Τhe methodology needs restructuring. A flowchart about the research methodology should be applied in order to be clear the methodological steps. Moreover, you need to justify why did you get EO data in June 2010, 2016, 2023. 

-          Material section is missing.

-          3. Discussion: I believe refers to the results section and should be merged with the section 4. Results (line 495)

Combined results and discussion

-           A discussion section is missing and must be added in comparison with similar studies conducted by RS specialists in the ecological condition of city the green zone.  What did your method contribute to enhance capacities in monitoring the state of greenness in urban areas.  Are there still weaknesses / needs for further development?  How accurate is the proposed methodology?  How could the methods be transferred / generalized?

We made a lot of changes to the article based on your questions.

NDVI calculation is based on the two most stable (independent of other factors) sections of the spectral reflectance curve of vascular plants. In the red region of the spectrum (0.6-0.7 μm) lies the maximum absorption of solar radiation by chlorophyll of higher vascular plants, and in the infrared region (0.7-1.0 μm) is the region of maximum reflection of the cellular structures of the leaf. That is, high photosynthetic activity (usually associated with dense vegetation) leads to less reflection in the red region of the spectrum and more in the infrared. The relationship of these indicators to each other allows us to clearly separate and analyze plant objects from other natural objects. Using not a simple ratio, but a normalized difference between the minimum and maximum of reflections increases the accuracy of the measurement and makes it possible to reduce the influence of such phenomena as differences in image illumination, cloudiness, haze, absorption of radiation by the atmosphere, etc.

NDVI can be calculated based on any high, medium or low resolution images that have spectral channels in the red (0.55-0.75 µm) and infrared range (0.75-1.0 µm). The NDVI calculation algorithm is built into almost all common software packages related to the processing of remote sensing data (Arc View Image Analysis, ERDAS Imagine, ENVI, Ermapper, Scanex MODIS Processor, ScanView, etc.).

Due to all these features, NDVI maps are often used as one of the intermediate additional layers for more complex types of analysis. The results of which can be maps of forest and agricultural land productivity, maps of landscape types, vegetation and natural zones, soil, arid, phyto-hydrological and other ecological and climatic maps. Also, on its basis, it is possible to obtain numerical data for use in calculations for assessing and forecasting crop yields and productivity, biological diversity, the degree of disturbance and damage from various natural and man-made disasters, accidents, etc.

In general, the main advantage of NDVI is the ease of obtaining it: to calculate the index, no additional data or techniques are required other than the satellite imagery itself and knowledge of its parameters.

Data from Landsat and Sentinel-2B satellites are comparable when calculating NDVI. This is due to the fact that the spectral characteristics of these satellites are similar. The main condition that must be observed to assess the state of vegetation using satellite data is to use images with the second level of processing (Level 2), at which geometric and radiometric correction was carried out and reflection values from the lower layers of the atmosphere were obtained [56].

To analyze the nature and density of vegetation, the work used remote sensing data, namely, satellite images in the red and near-infrared range. Remote sensing missions Sentinel 2 and Landsat 8/9 were used. For Aktobe, 2 images from the Landsat 8/9 mission and 13 satellite images from the Sentinel 2 mission, dating from 2010 to 2023, were found and processed. The images taken were taken during the growing season (from May to June). The reason for selecting satellite images from May and June is that during this period the growing season of vegetation is active. Thanks to this, we can clearly see areas with dense vegetation and carry out assessment work:

• The methods make it possible to use remote sensing data to monitor the greening of urban areas. This includes Normalized difference vegetation index (NDVI) analysis to assess the health and density of vegetation on the ground's surface;
• The methods can help in identifying different types of vegetation in urban areas and classifying them. This allows us to study in more detail the structure and composition of landscaping and evaluate its diversity;
• Methods can be used to evaluate the effectiveness of various measures for greening urban areas. The impact of planting new trees or creating green spaces on improving environmental quality can be assessed;
• Methods can be integrated with other data sources, such as air pollution data, climate data and soil quality data, to provide a more complete picture of the state of greenery and its impact on the environment.

Overall, the methods provide opportunities for more effective and detailed monitoring of the state of green spaces in urban areas, which can help city authorities and public organizations make more informed decisions on the management and development of green areas.

In the development of monitoring the state of greening in urban areas, there is always room for improvements and further development. Some of the potential weaknesses and needs in this area include:

• To enable the identification and analysis of smaller items and plant structures, a higher spatial resolution of the data is required for a more thorough monitoring of the status of landscaping;
• For a more accurate assessment of the state of landscaping, it is necessary to take into account seasonal changes and phenological characteristics of vegetation, as they can affect the values of the vegetation cover index (NDVI);
• It is important to take into account the features of the urban environment, such as development, transport routes, infrastructure and other factors that may affect the condition and effectiveness of landscaping;
• To develop greening monitoring, it is necessary to take into account the needs and interests of various stakeholders, including city authorities, the population, public organizations, scientific researchers and the business sector.

Addressing these weaknesses and meeting needs will create a more effective and comprehensively informed system for monitoring urban greening, promoting sustainable urban development and improving the quality of life and the environment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Review of the manuscript entitled Assessment of the state of the landscaping system in the city of Aktobe, the Republic of Kazakhstan under conditions of man-made load using remote sensing, the authors assessed the ecological condition of the green zone in the city of Aktobe by using NDVI. overall, this manuscript is well-designed with description and results analysis. However, I have a few concerns and questions about this manuscript as listed below:

1. In the Abstract, the authors need to add more quantitative analysis results from this manuscript.

2. there is no data introduction, I think the authors need to supply more information on the data used for calculating NDVI. In addition, since the Landsat and Sentinel-1/2 have different spatial resolutions, how did the authors assess these data?

3. NDVI was based on the green conditions of vegetation, could the authors explain which month of these remote sensing data were used for calculating NDVI?

4. From Figure 2, I cannot get more information on NDVI, the average NDVI value of different years (2010, 2016, and 2023), what is the changing trend from 2010 to 2023? The authors did not analysis these remote sensing data deeply.

5. Basically, there is no discussion section. The Discussion and Conclusion Sections failed to engage with the wider readership of this international and interdisciplinary journal. The novelty and originality should be justified because the manuscript contains sufficient contributions to the new body of knowledge from the international perspective.

Author Response

We would like to express our sincere gratitude to you for your important research comments, which helped to enrich and improve the content of our work. Their value lies in the fact that they helped us deepen our understanding of the subject of study, identify additional aspects of the problem, and also suggest additional methods for data analysis. This made our research more comprehensive and informative. We wish you all the best and creative success.

1. In the Abstract, the authors need to add more quantitative analysis results from this manuscript.

The growth of the city causes a complex of problems related to the increase in the pollution of the urban environment and the shortcomings in its improvement. The territory of the modern city is characterized by the highest man-made loads on the natural environment. The main problems are the low level of green areas, as well as the reduction of trees in many areas, which does not allow the city residents to live comfortably. Currently, Earth remote sensing methods using vegetation index (NDVI) are one of the dominant means of assessing the condition. In this regard, the purpose of the study is to assess the ecological condition of the green zone in the city of Aktobe. To solve this problem, complex assessment was carried out, including statistical data analysis, processing of satellite images by calculation of NDVI index for green areas and their mapping. The article analysis lays in the field of development and landscaping of the urban environment of Aktobe. The description of the current state of the system of green areas of the city was provided. On the basis of the data of remote sensing of the earth, the spatial features of the separate classes of the total phytomass of green plants within the city of Aktobe and the spatial features of the territorial zones of the city were determined during the differentiation of green plantings. A study of the dynamics of changes in the vegetation cover index (NDVI) during 2010, 2016, 2023 allowed us to identify trends in the development of green spaces and their changes over time due to city growth and other factors. The data obtained as a result of the research can be used in the justification of urban planning decisions, landscape planning of the ecological infrastructure of the city, optimization of landscaping systems.

2. there is no data introduction, I think the authors need to supply more information on the data used for calculating NDVI. In addition, since the Landsat and Sentinel-1/2 have different spatial resolutions, how did the authors assess these data?

NDVI calculation is based on the two most stable (independent of other factors) sections of the spectral reflectance curve of vascular plants. In the red region of the spectrum (0.6-0.7 μm) lies the maximum absorption of solar radiation by chlorophyll of higher vascular plants, and in the infrared region (0.7-1.0 μm) is the region of maximum reflection of the cellular structures of the leaf. That is, high photosynthetic activity (usually associated with dense vegetation) leads to less reflection in the red region of the spectrum and more in the infrared. The relationship of these indicators to each other allows us to clearly separate and analyze plant objects from other natural objects. Using not a simple ratio, but a normalized difference between the minimum and maximum of reflections increases the accuracy of the measurement and makes it possible to reduce the influence of such phenomena as differences in image illumination, cloudiness, haze, absorption of radiation by the atmosphere, etc.

NDVI can be calculated based on any high, medium or low resolution images that have spectral channels in the red (0.55-0.75 µm) and infrared range (0.75-1.0 µm). The NDVI calculation algorithm is built into almost all common software packages related to the processing of remote sensing data (Arc View Image Analysis, ERDAS Imagine, ENVI, Ermapper, Scanex MODIS Processor, ScanView, etc.).

Due to all these features, NDVI maps are often used as one of the intermediate additional layers for more complex types of analysis. The results of which can be maps of forest and agricultural land productivity, maps of landscape types, vegetation and natural zones, soil, arid, phyto-hydrological and other ecological and climatic maps. Also, on its basis, it is possible to obtain numerical data for use in calculations for assessing and forecasting crop yields and productivity, biological diversity, the degree of disturbance and damage from various natural and man-made disasters, accidents, etc.

In general, the main advantage of NDVI is the ease of obtaining it: to calculate the index, no additional data or techniques are required other than the satellite imagery itself and knowledge of its parameters.

Data from Landsat and Sentinel-2B satellites are comparable when calculating NDVI. This is due to the fact that the spectral characteristics of these satellites are similar. The main condition that must be observed to assess the state of vegetation using satellite data is to use images with the second level of processing (Level 2), at which geometric and radiometric correction was carried out and reflection values from the lower layers of the atmosphere were obtained [56].

3. NDVI was based on the green conditions of vegetation, could the authors explain which month of these remote sensing data were used for calculating NDVI?

To analyze the nature and density of vegetation, the work used remote sensing data, namely, satellite images in the red and near-infrared range. Remote sensing missions Sentinel 2 and Landsat 8/9 were used. For Aktobe, 2 images from the Landsat 8/9 mission and 13 satellite images from the Sentinel 2 mission, dating from 2010 to 2023, were found and processed. The images taken were taken during the growing season (from May to June). The reason for selecting satellite images from May and June is that during this period the growing season of vegetation is active. Thanks to this, we can clearly see areas with dense vegetation and carry out assessment work:

• The methods make it possible to use remote sensing data to monitor the greening of urban areas. This includes Normalized difference vegetation index (NDVI) analysis to assess the health and density of vegetation on the ground's surface;
• The methods can help in identifying different types of vegetation in urban areas and classifying them. This allows us to study in more detail the structure and composition of landscaping and evaluate its diversity;
• Methods can be used to evaluate the effectiveness of various measures for greening urban areas. The impact of planting new trees or creating green spaces on improving environmental quality can be assessed;
• Methods can be integrated with other data sources, such as air pollution data, climate data and soil quality data, to provide a more complete picture of the state of greenery and its impact on the environment.

Overall, the methods provide opportunities for more effective and detailed monitoring of the state of green spaces in urban areas, which can help city authorities and public organizations make more informed decisions on the management and development of green areas.

4. From Figure 2, I cannot get more information on NDVI, the average NDVI value of different years (2010, 2016, and 2023), what is the changing trend from 2010 to 2023? The authors did not analysis these remote sensing data deeply.

In Table 2, the objects that have undergone major changes are: Jasyl Tobe Forest Park, The Park of the First President, Kargaly Forest Park, Batys Forest Park, Tabigat Forest Park.

Table 2. Table of average indicator of vegetation index NDVI of greenery of the city in 2010, 2016,2023.

The decline of the Jasyl Tobe Forest Park can be characterized by the expansion of the territories of the Aktobe Ferroalloys Plant and Aktobe Chromium Compounds Plant and the strong impact of man-made impacts.

In the period from 2010 to 2014, specialists and a private company were involved in timely maintenance of the landscaping of the First President Park. In 2015, changes in the city administration led to the closure of this institution, as a result of which green spaces in the park area were not maintained, and in addition, sanitary pruning was carried out untimely.

The reduction in landscaping in the Kargaly Forest Park is due to the expansion of the Kargaly River bed without a plan or special regulations. In 2017-2018, flood prevention problems were solved by expanding river channels in the city. As a result, the thickets located along the banks of the river led to massive cutting down and destruction of ponds.

5. Basically, there is no discussion section. The Discussion and Conclusion Sections failed to engage with the wider readership of this international and interdisciplinary journal. The novelty and originality should be justified because the manuscript contains sufficient contributions to the new body of knowledge from the international perspective.

We made a lot of changes to the article based on your questions.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

As the manuscript was improved, I have no further comments.

Reviewer 2 Report

I  believe the manuscript has been sufficiently improved. The authors have been take into consideration all the comments.

Pay attention to the maps. Their legends should be legible

All in all, my recommendation is to accept the paper for publication. 

Reviewer 3 Report

I have no further comments.