Analyses of MODIS Land Cover/Use and Wildfires in Italian Regions Since 2001

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

Thank you for the authors’ thoughtful revisions following the first-round review. The manuscript has improved overall, but several key issues still require attention before it can be considered for publication:

 

Although the manuscript focuses on administrative regions rather than national-level analysis, the distinction from prior studies remains insufficiently described. Please strengthen the discussion (preferably in the Introduction or Discussion section) by highlighting how this study fills specific gaps in the literature and offering a clear comparison with previous research.

 

FireCCI51 is a satellite-derived product whose accuracy may vary depending on topography and regional conditions. It should be evaluated for its applicability in Italy, or relevant validation studies demonstrating the product's regional accuracy in this study area should be cited.

 

In Table 1, the term “Date” should be clarified: does it represent the full period of data availability or the period actually selected for analysis? In addition, in a wildfire-focused study, why was the 2001–2023 period chosen specifically for land cover analysis?

 

Some of the subgraphs in Figure 4 are not clear and legible even when enlarged.

 

In Figure 6, it is recommended to use distinct colors to represent burned areas for different years or time intervals.

Author Response

Reviewer #1,

Thank you for the authors’ thoughtful revisions following the first-round review. The manuscript has improved overall, but several key issues still require attention before it can be considered for publication:

Dear reviewer,

We would like to thank you very much for your time and insightful comments that helped us improve the presentation of our manuscript further. We have carefully addressed your comments in the revised version. Please see below our point-by-point response to your comments where the changes are highlighted in the revised version.

Although the manuscript focuses on administrative regions rather than national-level analysis, the distinction from prior studies remains insufficiently described. Please strengthen the discussion (preferably in the Introduction or Discussion section) by highlighting how this study fills specific gaps in the literature and offering a clear comparison with previous research.

Response: Thank you for your insightful comment. We searched the literature and discussed some further related study to highlight the importance of our study. We added the following paragraph in the discussion section:

“Several studies have investigated the potential impact of climate change on wildfires and ecosystems in Italy. For example, Lozano et al. [44] simulated wildfires using the minimum travel time fire spread model and projected an increase in burn probability and fire size for the period 2041–2070, which may significantly impact Mediterranean ecosystems. In another study, Ferrara et al. [45] examined 174 indicators and found significant relationships between socioeconomic contexts and wildfire regimes on a municipal scale in Italy from 2001 to 2007. They also suggested that specific wildfire protection plans are required for rural areas. Michetti and Pinar [46] analyzed monthly burned areas for Italian regions during 2000–2011 and used climate change projections for the period 2016–2035 to project burned areas across Italy. They also highlighted the role of education and the suppression of fraudulent activity in controlling the fire regime. The present study provides further elaboration on wildfire events and their influential factors for Italian administrative regions over a two-decade period (2001–2020).”

FireCCI51 is a satellite-derived product whose accuracy may vary depending on topography and regional conditions. It should be evaluated for its applicability in Italy, or relevant validation studies demonstrating the product's regional accuracy in this study area should be cited.

Response. The MODIS product (FireCCI51) has been used and verified in many studies and has a good accuracy for regional studies although for local studies it may not be very useful. Some related references are added. In Section 4.3, we added:

Likewise, MODIS burned area product performs well for detecting large size fires during summer season as it is less affected by cloud contamination, but this product is less likely to detect small size burned areas. For instance, Fusco et al. [48] showed that in the western United States fire events can be well detected in summer season for tree and herb land cover classes; however, small fire events on shrublands may be undetected and so ground-based data or higher resolution satellite images are useful. Nevertheless, the FireCCI51 product has been validated in various regions, including the Mediterranean, Latin America, and the Caribbean, by numerous researchers, demonstrating its effectiveness in detecting burned areas [49, 50]. 

[48] Fusco, E.J.; Finn, J.T.; Abatzoglou, J.T.; Balch, J.K.; Dadashi, S.; Bradley, B.A. Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States, Remote Sens. Environ., 2019, 220, 30–40  https://doi.org/10.1016/j.rse.2018.10.028

[49] Katagis, T.; Gitas, I.Z. Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems. Remote Sens., 2022, 14, 602. https://doi.org/10.3390/rs14030602

[50] Gonzalez-Ibarzabal, J.; Franquesa, M.; Rodriguez-Montellano, A.; Bastarrika, A. Sentinel-2 Reference Fire Perimeters for the Assessment of Burned Area Products over Latin America and the Caribbean for the Year 2019. Remote Sens. 2024, 16, 1166. https://doi.org/10.3390/rs16071166

In Table 1, the term “Date” should be clarified: does it represent the full period of data availability or the period actually selected for analysis? In addition, in a wildfire-focused study, why was the 2001–2023 period chosen specifically for land cover analysis?

Response: Thank you for your insightful comment. We updated the caption of Table 1 and added:  The column “Date” shows to the period when specific data were available at the time when this study was conducted.  The period for wildfire available data was 2001–2020 but for land cover/use was 2001–2023.

Some of the subgraphs in Figure 4 are not clear and legible even when enlarged.

Response. Thank you for your comment. In the final version, we provide high-resolution (300 dpi) figure so that readers can better visualize it.

In Figure 6, it is recommended to use distinct colors to represent burned areas for different years or time intervals.

Response. Thank you for your insightful comment. We have tried using different colors for different years but because of the compactness of the burned pixels, the use of different colors can create confusion due to potential color mixing and overlapping. However, we provided the data for interested users to visualize the burned areas in open-source software like QGIS.

We hope the changes made are satisfactory

Thank you!

Best regards,

Ebrahim Ghaderpour, PhD

Reviewer 2 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

Each of the observations made was answered, observing a substantial improvement in the manuscript, which is why I consider it suitable for publication once the minor observations are addressed.

Comments for author File: Comments.pdf

Author Response

Reviewer #2,

Each of the observations made was answered, observing a substantial improvement in the manuscript, which is why I consider it suitable for publication once the minor observations are addressed.

Dear reviewer,

We would like to thank you very much for your time and insightful comments that helped us improve the presentation of our manuscript further. We checked the annotated pdf and addressed your comments: we included the compass rose in Figure 1 and verified the coordinates using QGIS and online tools. We also carefully proofread the manuscript and checked the grammar, figures, tables, etc.

We hope the changes made are satisfactory

Respectfully yours,

Ebrahim Ghaderpour, PhD

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript utilizes multi-source MODIS remote sensing data to analyze land cover/use changes and the spatiotemporal characteristics of wildfire occurrences across Italy's administrative regions during the period 2001–2023, and further explores the relationship between precipitation and fire activity. The research topic is of practical relevance, with a long temporal span, and the methodology incorporates the Mann-Kendall trend test, Sen’s slope estimation, and Pearson correlation analysis. However, the manuscript still exhibits notable shortcomings in data selection and the depth of analysis, which require further enhancement.

 

Although the study applies standard statistical methods such as the Mann-Kendall trend test and Pearson correlation, the overall analytical depth is limited. There is a lack of detailed discussion on the drivers behind land cover/use changes, and little attempt is made to explain spatial heterogeneity across regions (which cannot be simply mentioned only in the discussion section). The analysis of wildfire-prone areas is also superficial, without consideration of key explanatory variables such as topography, population density, or land management practices. Please consider incorporating more robust analytical methods (e.g., spatial regression or time series clustering) and enhancing the interpretative discussion with relevant ecological or socio-economic theories.

 

The MODIS land cover dataset used (MCD12Q1) has a spatial resolution of 500 meters, whereas the burned area data (FireCCI51) has a finer resolution of 250 meters. This mismatch may introduce spatial uncertainty, particularly in identifying the specific land cover types most affected by wildfires. Moreover, higher-resolution global land cover products are now available and could improve the accuracy of land classification. It is strongly recommended to adopt higher-resolution land cover datasets or, at a minimum, to include a sensitivity or uncertainty analysis addressing the implications of spatial resolution mismatch.

 

In Introduction, the authors should engage more thoroughly with the existing literature, highlighting both consistencies and discrepancies, and clearly positioning the current study’s contributions in terms of temporal coverage, spatial scale, or methodological improvements.

 

Most of the current visualizations are limited to boxplots and time series graphs. Although the study includes a cumulative map of total burned areas, it lacks in-depth spatial analysis to effectively investigate the geographic patterns and underlying drivers of wildfire distribution.

 

The presentation of Figure 2, the study flowchart, could be improved for better clarity and visual appeal.

Author Response

Reviewer #1,

The manuscript utilizes multi-source MODIS remote sensing data to analyze land cover/use changes and the spatiotemporal characteristics of wildfire occurrences across Italy's administrative regions during the period 2001–2023, and further explores the relationship between precipitation and fire activity. The research topic is of practical relevance, with a long temporal span, and the methodology incorporates the Mann-Kendall trend test, Sen’s slope estimation, and Pearson correlation analysis. However, the manuscript still exhibits notable shortcomings in data selection and the depth of analysis, which require further enhancement.

Dear reviewer,

We would like to thank you very much for your time and insightful comments that helped us improve the presentation of our manuscript further. We have carefully addressed your comments in the revised version. Please see below our point-by-point response to your comments where the changes are highlighted in the revised version.

Reviewer's Comment: Although the study applies standard statistical methods such as the Mann-Kendall trend test and Pearson correlation, the overall analytical depth is limited. There is a lack of detailed discussion on the drivers behind land cover/use changes, and little attempt is made to explain spatial heterogeneity across regions (which cannot be simply mentioned only in the discussion section). The analysis of wildfire-prone areas is also superficial, without consideration of key explanatory variables such as topography, population density, or land management practices. Please consider incorporating more robust analytical methods (e.g., spatial regression or time series clustering) and enhancing the interpretative discussion with relevant ecological or socio-economic theories.

Response. Thank you for your insightful comments. The purpose of this study is to provide an overview of how land use/cover change for each Italian Administrative Regions and how these changes may play a role in wildfire patterns. In addition, how climate interacts with wildfires pattern and extends of burned areas within each region. Following your suggestions, we extended our analysis and included the following contributions additionally:

• Classifying and depicting the burned areas based on elevation ranges for each region.
• Demonstrating correlation results between vegetation and land surface temperature and between vegetation and precipitation for Italian regions during 2001–2020

The ecological or socio-economic theories and human-made activities causing wildfires in the regions are also mentioned in the discussion section, including references.

Reviewer's Comment: The MODIS land cover dataset used (MCD12Q1) has a spatial resolution of 500 meters, whereas the burned area data (FireCCI51) has a finer resolution of 250 meters. This mismatch may introduce spatial uncertainty, particularly in identifying the specific land cover types most affected by wildfires. Moreover, higher-resolution global land cover products are now available and could improve the accuracy of land classification. It is strongly recommended to adopt higher-resolution land cover datasets or, at a minimum, to include a sensitivity or uncertainty analysis addressing the implications of spatial resolution mismatch.

Response. Thank you for your insightful comments. In this research, the aim is to provide a statistical summary for each administrative region. Thus, average of precipitation data at 11 km resolution within each region, average of Land cover/use at 500 m within each region and burned areas within each region are considered for each region. Our aim is not local study. For local scale study using, for example Sentinel-2 and World View images, we added the following paragraph in the Introduction, referring to our recent article in Ecological Informatics:

“Dadkhah et al. [13] employed MODIS FireCCI to study wildfire patterns in the provinces of Campania, Italy, during the period 2001-2020 and observed relatively higher burned areas in 2007 and 2017. They also employed high-resolution Sentinel-2 and Dynamic World data and calculated the differenced normalized burn ratio (dNBR) to quantify burn severity in Ischia Island. They observed that MODIS FireCCI could not detect burned areas on the island due to the wildfire that occurred in August 2017, primarily because of its coarser spatial resolution. However, MODIS products are excellent for regional studies where the purpose is to gain a general overview of environmental and climate dynamics at the regional scale, which is the primary purpose of the present study.

Reviewer's Comment: In Introduction, the authors should engage more thoroughly with the existing literature, highlighting both consistencies and discrepancies, and clearly positioning the current study’s contributions in terms of temporal coverage, spatial scale, or methodological improvements.

Response. Thank you for your insightful comment. We added some paragraphs and expanded our main contributions and summarized them at the end of the Introduction. Please see the highlights.

Reviewer's Comment: Most of the current visualizations are limited to boxplots and time series graphs. Although the study includes a cumulative map of total burned areas, it lacks in-depth spatial analysis to effectively investigate the geographic patterns and underlying drivers of wildfire distribution.

Response. Thank you for your insightful comment. We added the classification results of burned areas based on topography (please see Figure 9) and also added geospatial maps of correlation between NDVI and land surface temperature and NDVI and precipitation (please see Figure 10) and summarized the results for each region in Table 5.

Reviewer's Comment: The presentation of Figure 2, the study flowchart, could be improved for better clarity and visual appeal.

Response. Thank you for your comment. We reproduced the Flowchart and added more details.

We hope the changes made are satisfactory

Thank you

Best regards,

Ebrahim Ghaderpour, PhD

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript explores the assessment of landscape diversity and its implications for spatial planning, engaging with an important and methodologically relevant topic in landscape ecology and territorial analysis. The integration of spatial metrics to evaluate landscape heterogeneity is appropriate; however, several critical revisions are required to enhance the scientific rigor, reproducibility, and interpretive strength of the study.

1. Conceptual Framing and Literature Review:
The introduction provides a broad overview of landscape-related challenges, but it lacks a clearly defined research gap and an explicit rationale for the study. The theoretical framing should be sharpened by articulating what specific dimension of landscape diversity is being advanced, and how this study builds on or diverges from existing literature. Incorporating recent and high-impact studies on landscape metrics, especially those applying them in planning contexts or similar geographic regions, would strengthen the conceptual foundation and underscore the study’s novelty.

2. Methodological Transparency:
The methods section requires significant expansion to meet the standards of reproducibility. While the general analytical framework is sound, key elements are insufficiently described. Specifically:

• The criteria and rationale for selecting the landscape indicators (e.g., Shannon's Diversity Index, Patch Richness, etc.) should be justified in relation to ecological or spatial planning objectives.

• The spatial resolution and extent of the analysis units (e.g., grid size, administrative boundaries) must be clearly defined, including how scale effects were addressed or mitigated.

• The source, classification process, and resolution of input land use/land cover data should be stated in detail. Were data harmonized across time periods?

• Any preprocessing, including rasterization, reclassification, or projection alignment, should be explicitly described.

• Indicate the tools or software used for metrics calculation (e.g., FRAGSTATS, QGIS/GRASS, R/landscapemetrics), including relevant parameters or scripts if custom processing was applied.

3. Results Interpretation and Analytical Depth:
The results section presents landscape metrics across different spatial or temporal conditions, yet the interpretation remains largely descriptive. To increase analytical rigor:

• Provide comparative metrics with statistical summaries (e.g., mean, standard deviation, confidence intervals) to quantify spatial or temporal variability.

• Incorporate spatial autocorrelation or clustering diagnostics (e.g., Moran’s I, Getis-Ord Gi*) to support claims of landscape pattern changes.

• Discuss the ecological and planning implications of metric trends more critically—e.g., what does an increase in patch fragmentation or edge density suggest for land use policy or ecosystem service provisioning?

Figures 1 and 3 look a little deformed, you have to check. 

Also review the general text formatting between captions and image proportions.

Comments on the Quality of English Language

The manuscript is written in generally understandable English, but the quality of language could be improved to enhance clarity, precision, and academic tone. Several sections contain grammatical errors, awkward phrasing, or overly complex sentence structures that may hinder readability. In particular, transitions between ideas and explanations of technical terms could be made smoother and more concise.

Author Response

Reviewer #2,

This manuscript explores the assessment of landscape diversity and its implications for spatial planning, engaging with an important and methodologically relevant topic in landscape ecology and territorial analysis. The integration of spatial metrics to evaluate landscape heterogeneity is appropriate; however, several critical revisions are required to enhance the scientific rigor, reproducibility, and interpretive strength of the study.

Dear reviewer,

We would like to thank you very much for your time and insightful comments that helped us improve the presentation of our manuscript further. We have carefully addressed your comments in the revised version. Please see below our point-by-point response to your comments where the changes are highlighted in the revised version.

1. Conceptual Framing and Literature Review:

The introduction provides a broad overview of landscape-related challenges, but it lacks a clearly defined research gap and an explicit rationale for the study. The theoretical framing should be sharpened by articulating what specific dimension of landscape diversity is being advanced, and how this study builds on or diverges from existing literature. Incorporating recent and high-impact studies on landscape metrics, especially those applying them in planning contexts or similar geographic regions, would strengthen the conceptual foundation and underscore the study’s novelty.

Response. Thank you for your insightful comments. The purpose of this study is to provide an overview of how land use/cover change for each Italian Administrative Regions and how these changes may play a role in wildfire patterns. In addition, how climate interacts with wildfires pattern and extends of burned areas within each region. Following your suggestions, we improved the literature and highlighted the main contributions and research gaps.

“Therefore, the present work focuses on the Italian administrative regions, not Italian ecoregions. In addition, MODIS burned area images for 2001–2020 are employed to study the wildfire occurrences across Italian regions and their potential triggering fac-tors, such as vegetation (fuel), precipitation, and land surface temperature. The main contributions of the present study are:

• Demonstrating the distribution of each land cover/use type within each Italian region by boxplots, utilizing MOD12Q1 images for 2001–2023.
• Estimating linear trend and its statistical significance for each land cover/use type within each Italian administrative region during 2001–2023.
• Illustrating the monthly MODIS burned area and GPM precipitation time series for each Italian region in 2001–2020 and estimating the correlation between them.
• Classifying and depicting the burned areas based on elevation ranges for each region.
• Demonstrating correlation results between vegetation and land surface temperature and between vegetation and precipitation for Italian regions during 2001–2020.
• Comparing the results with the results of other studies and discussing the potential impact of land cover/use change on ecosystems.

The present research aims at filling the research gap for a comprehensive region-wise analysis of land cover/use change, wildfire occurrences, and their correlation with climate and topography.”

 

2. Methodological Transparency:

The methods section requires significant expansion to meet the standards of reproducibility. While the general analytical framework is sound, key elements are insufficiently described. Specifically:

• The criteria and rationale for selecting the landscape indicators (e.g., Shannon's Diversity Index, Patch Richness, etc.) should be justified in relation to ecological or spatial planning objectives.
• The spatial resolution and extent of the analysis units (e.g., grid size, administrative boundaries) must be clearly defined, including how scale effects were addressed or mitigated.
• The source, classification process, and resolution of input land use/land cover data should be stated in detail. Were data harmonized across time periods?
• Any preprocessing, including rasterization, reclassification, or projection alignment, should be explicitly described.
• Indicate the tools or software used for metrics calculation (e.g., FRAGSTATS, QGIS/GRASS, R/landscapemetrics), including relevant parameters or scripts if custom processing was applied.

Response. Thank you for your insightful comments. Following your suggestion, we improved the methodology section, we mentioned all the details about data resampling, harmonizing, and projection in the updated flowchart. All calculations, including image subsetting, spatial resampling, and image alignment, are performed in Python using gdal.ReprojectImage() command with gdalconst.GRA_Med, and geospatial maps are generated using QGIS software.  We also added:

“All calculations, including image subsetting, spatial resampling, and image alignment, are performed in Python, and geospatial maps are generated using QGIS software. For correlation maps, images of higher resolution are downsampled and aligned with those of lower resolution using a median approach. To match the temporal resolution of NDVI to LST and precipitation, a weighted method is used to bring 16-day intervals to monthly intervals [5]. Then, for NDVI–LST, the images were resampled to ~5.5 km, and for NDVI–Precipitation, the images were resampled to a resolution of GPM, i.e., ~11 km, using a median approach [5].”   

“In the present research, the number of burned pixels within each region is calculated to generate a monthly time series for temporal analysis and estimating the correlation between the number of burned pixels and monthly GPM precipitation for each region.”

3. Results Interpretation and Analytical Depth:

The results section presents landscape metrics across different spatial or temporal conditions, yet the interpretation remains largely descriptive. To increase analytical rigor:

• Provide comparative metrics with statistical summaries (e.g., mean, standard deviation, confidence intervals) to quantify spatial or temporal variability.
• Incorporate spatial autocorrelation or clustering diagnostics (e.g., Moran’s I, Getis-Ord Gi*) to support claims of landscape pattern changes.
• Discuss the ecological and planning implications of metric trends more critically—e.g., what does an increase in patch fragmentation or edge density suggest for land use policy or ecosystem service provisioning?

Figures 1 and 3 look a little deformed, you have to check. 

Response. Thank you for your insightful comments. Figure 4 and Table 2 provide the basic statistics such as median, quartiles, slopes and intercepts. Table 3 also provides the pixel counts before and after applying confidence intervals. We extended the classification results of burned areas based on topography (please see Figure 9) and also added geospatial maps of correlation between NDVI and land surface temperature and NDVI and precipitation (please see Figure 10) and summarized the results for each region in Table 5. Please also note that the focus of the study is providing statistical results for the land cover/use change and burned areas on the region-scale. We also expanded the discussion section and added the following subsection:

4.2. Ecological and Planning Implications  

Hidalgo et al. [42] studied fragmented landscapes in Mediterranean regions and observed a high rate of transformation in fragmented landscapes in vulnerable areas, such as Apulia, where species migration is complex. An increase in patch fragmentation or density can reduce connectivity and the number of habitat patches, which in turn can decrease the ecosystem's potential to provide essential services, such as clean water, pollination, and carbon sequestration, thereby negatively affecting the value of ecosystem services [43]. Therefore, regional sustainability planning and proper land use management have become crucial.  

Also review the general text formatting between captions and image proportions.

Response. Thank you. Done!

Comments on the Quality of English Language

The manuscript is written in generally understandable English, but the quality of language could be improved to enhance clarity, precision, and academic tone. Several sections contain grammatical errors, awkward phrasing, or overly complex sentence structures that may hinder readability. In particular, transitions between ideas and explanations of technical terms could be made smoother and more concise.

Response. Grammar is checked carefully with Grammarly software and a score of above 95% is achieved.

We hope the changes made are satisfactory

Respectfully yours,

Ebrahim Ghaderpour, PhD

Reviewer 3 Report

Comments and Suggestions for Authors

It is recommended not to repeat keywords already included in the manuscript title. Keywords in a scientific article are essential for its visibility and discovery by the scientific community. They help readers find the relevant article, facilitate cataloging and indexing in databases, and contribute to the dissemination of research.

 

The spatial resolution used in this analysis is considered very low, as better resolution images are available. For example, Landsat images have a resolution of 30 m/pixel and Sentinel images have a resolution of 10 m/pixel.

 

There are Normalized Burning Indices (NBR), which allow analysis of the severity of fires, showing values ​​of healthy vegetation, bare soil, as well as the impact of fires on ecosystems. These analyses combine the use of near infrared (NIR) and short wave infrared (SWIR) wavelengths, for which Landsat (30 m/pixel) and Sentinel (10 m/pixel) type images are used, which means that the precession of the information is better.

 

Bioclimatic variables representing annual, seasonal, and monthly averages and extremes of temperature and precipitation are freely available and have been widely used for ecological modeling and in broader biogeographic and climate change impact studies.

 

Due to the level of resolution of the images used, it is likely that there is a great bias in the information obtained, therefore it is important to determine the Kappa correlation indices to measure the degree of agreement between two or more evaluators who wish to replicate the study.

Comments for author File: Comments.pdf

Author Response

Reviewer #3,

Dear reviewer,

We would like to thank you very much for your time and insightful comments that helped us improve the presentation of our manuscript further. We have carefully addressed your comments in the revised version. Please see below our point-by-point response to your comments where the changes are highlighted in the revised version.

Comment: It is recommended not to repeat keywords already included in the manuscript title. Keywords in a scientific article are essential for its visibility and discovery by the scientific community. They help readers find the relevant article, facilitate cataloging and indexing in databases, and contribute to the dissemination of research.

Response. Thank you for your insightful comment. The keywords are updated.

Comment: The spatial resolution used in this analysis is considered very low, as better resolution images are available. For example, Landsat images have a resolution of 30 m/pixel and Sentinel images have a resolution of 10 m/pixel.

Response. Thank you for your insightful comment. The purpose of this study is to provide statistical analysis of land cover/use at regional scale not local. MODIS products are commonly used for such purposes by many researchers, and we emphasized in in the Introduction section and extended our contributions.

Comment: There are Normalized Burning Indices (NBR), which allow analysis of the severity of fires, showing values ​​of healthy vegetation, bare soil, as well as the impact of fires on ecosystems. These analyses combine the use of near infrared (NIR) and short wave infrared (SWIR) wavelengths, for which Landsat (30 m/pixel) and Sentinel (10 m/pixel) type images are used, which means that the precession of the information is better.

Response. Thank you for your insightful comment. We employed Sentinel images (10 m/pixel) with dNBR in one of our recent publications for a local study (Ischia Island). We added the following paragraph in the Introduction:

“Dadkhah et al. [13] employed MODIS FireCCI to study wildfire patterns in the provinces of Campania, Italy, during the period 2001-2020 and observed relatively higher burned areas in 2007 and 2017. They also employed high-resolution Sentinel-2 and Dynamic World data and calculated the differenced normalized burn ratio (dNBR) to quantify burn severity in Ischia Island. They observed that MODIS FireCCI could not detect burned areas on the island due to the wildfire that occurred in August 2017, primarily because of its coarser spatial resolution. However, MODIS products are excellent for regional studies where the purpose is to gain a general overview of environmental and climate dynamics at the regional scale, which is the primary purpose of the present study.”

Comment: Bioclimatic variables representing annual, seasonal, and monthly averages and extremes of temperature and precipitation are freely available and have been widely used for ecological modeling and in broader biogeographic and climate change impact studies.

Response. Thank you for your insightful comment. We calculated and illustrated the correlation maps between NDVI and Precipitation and NDVI and LST in the discussion section. We added the classification results of burned areas based on topography (please see Figure 9) and also added geospatial maps of correlation between NDVI and land surface temperature and NDVI and precipitation (please see Figure 10) and summarized the results for each region in Table 5.

Comment: Due to the level of resolution of the images used, it is likely that there is a great bias in the information obtained, therefore it is important to determine the Kappa correlation indices to measure the degree of agreement between two or more evaluators who wish to replicate the study.

Response. Thank you for your insightful comment. The MODIS products are already preprocessed. The flowchart in Figure 2 now shows the preprocessing steps including subsetting, resampling, and image alignment. We added some related sentences in the datasets and method sections. Please also note that this study is mainly region-wise where the average values of the pixels within each region are calculated.

We hope the changes made are satisfactory

Thank you!

Best regards,

Ebrahim Ghaderpour, PhD