Discrimination of Bark Beetle-Damaged Forest Stands Using Vegetation Indices Derived from Landsat 8
, Gonca Ece Özcan 1, Korhan Enez 1, Fatmir Laçej 2, Leonidha Peri 2 and Ilir Myteberi 2Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe study aimed to analyze the discriminatory efficacy of vegetation indices derived from Landsat 8 OLI data at the stand level in distinguishing between forest stands affected and unaffected by Ips sexdentatus damage. The study is well designed, the results support the study’s conclusions, and it falls within the scope of Forests. However, there are several issues that must be addressed before the manuscript can be considered for publication. The issues to be addressed primarily concern the methods and results. Below are my comments on the manuscript.
L2: Remove “Statistical” from the title.
Abstract
L22-23: Are there 8 or 40? Please clarify.
Introduction
L80-84: Clearly define the study’s objective, which must be linked to an appropriate research question.
Materials and Methods
L90: Scientific names should be in italics; review throughout the manuscript.
L99-101: Explain quantitatively what is meant by “as homogeneous as possible in terms of dominant species composition, age class, and canopy closure.”
L105 (Figure 1): Locate the study area within the study region. Additionally, improve the resolution of the maps and the text size.
Results
In the Results section, present only the interpretations of the results. Do not repeat or explain the methods used. These should be explained in depth in the Materials and Methods section.
L165-175: Move to Methods
L176 (Table 3): The p-value cannot be zero. Change to < 0.001
L179: Improve the legend; the map should have a transparent background to indicate the vegetation index level.
L220 (Figure 3): Explain in detail the methods used to generate the PCA. Additionally, explain how the ellipses were generated, how autocorrelation of the variables was handled, etc.
Discussion
Add the limitations of the study and the future outlook
Author Response
Dear Dr. Lana Li
Assistant Editor
MDPI, Forests
On behalf of co-authors, we thank you very much for giving us an opportunity to revise our manuscript; we appreciate editors and reviewers for their positive and constructive comments and helpful suggestions on our manuscript. We have studied the reviewer’s comments carefully and have made revisions. We have tried our best to revise our manuscript according to the comments. We hope that all these changes fulfill the requirements to make the manuscript acceptable for publication in the Forests. The detailed response to the reviewers' comment is sent as supplemental material. The corrections and revised sentences we made to the manuscript are shown in red color.
Thank you very much for your kind consideration.
Yours sincerely…
Prof. Dr. Fatih SİVRİKAYA
Response to the Reviewers' Comments:
Reviewer: 1
General Comments: The study aimed to analyze the discriminatory efficacy of vegetation indices derived from Landsat 8 OLI data at the stand level in distinguishing between forest stands affected and unaffected by Ips sexdentatus damage. The study is well designed, the results support the study’s conclusions, and it falls within the scope of Forests. However, there are several issues that must be addressed before the manuscript can be considered for publication.
Response. We thank Reviewer #1 for his/her positive and valuable comments on the manuscript.
Comment 1. L2: Remove “Statistical” from the title.
Response 1. Thank you for your valuable comments. In line with the Reviewer' Comment, we removed the “Statistical” from the title.
Comment 2. L22-23: Are there 8 or 40? Please clarify.
Response 2. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the sentence as follow:
“Eighty forest stands (40 forest stands, both with and without Ips sexdentatus damage) selected through fieldwork in the Araç Forest Directorate, Kastamonu, were studied.”
Comment 3. L80-84: Clearly define the study’s objective, which must be linked to an appropriate research question.
Response 3. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the sentence as follow:
“Consequently, this study examines the subsequent research question: Can vegetation indices obtained from Landsat 8 OLI images effectively discriminate between forest stands with and without Ips sexdentatus damage at the stand level? This study assesses the discriminatory efficacy of specific vegetation indices (NDVI, NDMI, MSI, TCW, and RGI) by (i) quantifying the spectral index value disparities between damaged and undamaged stands and (ii) evaluating the statistical significance and multivariate separability of these differences through nonparametric and multivariate statistical methods.”
Comment 4. L90: Scientific names should be in italics; review throughout the manuscript.
Response 4. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Scientific names as italics throughout the manuscript.
Comment 5. L99-101: Explain quantitatively what is meant by “as homogeneous as possible in terms of dominant species composition, age class, and canopy closure.”
Response 5. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the sentence as follow:
“To minimize potential confounding effects, stands were selected to be as homogeneous as possible in the same tree species (Pinus nigra), age class (mature), and canopy closure (40-70%)”
Comment 6. L105 (Figure 1): Locate the study area within the study region. Additionally, improve the resolution of the maps and the text size.
Response 6. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Figure 1.
Comment 7. L165-175: Move to Methods
Response 7. Thank you for your valuable comments. In line with the Reviewer' Comment, we moved L165-175 to Methods.
Comment 8. L176 (Table 3): The p-value cannot be zero. Change to < 0.001.
Response 8. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Table 3.
Comment 9. L179: Improve the legend; the map should have a transparent background to indicate the vegetation index level.
Response 9. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Figure 2.
Comment 10. L220 (Figure 3): Explain in detail the methods used to generate the PCA. Additionally, explain how the ellipses were generated, how autocorrelation of the variables was handled, etc.
Response 10. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised and added related information as follow:
“The importance of the group effect was assessed utilizing the Pillai Trace statistic. Principal Component Analysis (PCA) was utilized to investigate the correlations among the variables in the dataset. The FactoMineR package in R was used to perform PCA on all 15 numeric variables, including the minimum, maximum, and mean values of the five vegetation indices (NDVI, NDMI, MSI, TCW, and RGI). Standardization was employed to ensure that each variable contributed equally to the analysis, regardless of its magnitude, since vegetation indices operate on distinct numerical scales. The correlation matrix of the standardized variables was eigendecomposed to transform intercorrelated vegetation index variables into orthogonal principal components and mitigate multicollinearity. Group membership (with and without beetle damage) was utilized to color-code observations following the projection of stand scores into the two-dimensional space defined by the first two principal components. Using the within-group covariance structure of the principal component scores, 95% concentration ellipses were constructed for each group to assess their separation visually. All statistical analyses were conducted in R.”
Comment 11. Discussion Section Add the limitations of the study and the future outlook.
Response 11. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised and added related information as follow:
“This research utilized a single satellite image (Landsat 8) from a certain date. This constrains the capacity to clarify the temporal dynamics of bark beetle infestations. More-over, using average vegetation index values across stands may diminish sensitivity to beetle damage across varied environments, particularly because these averages can ob-scure the specific conditions that contribute to beetle infestations and their impacts on dif-ferent vegetation types. Future research should focus on using multi-temporal, high-er-resolution spatial data to detect beetle damage. The application of machine learning algorithms alongside vegetation indices for damage identification warrants evaluation.”
Reviewer 2 Report
Comments and Suggestions for AuthorsThe scope of general information is partially sufficient. The introduction presents the basic background of the problem, i.e.: the importance of the intensive occurrence of bark beetles for forest ecosystems, the role of remote sensing in monitoring forest health (including that shaped by harmful insects) and the possibility of using vegetation indicators to monitor the health and advancement of the phenomenon of tree dieback under the influence of harmful insects (NDVI, NDMI, MSI, etc.).
The narration remains persistently superficial and lacks substantive synthesis. This section fails to address the physiological mechanisms that affect spectral signals and does not adequately highlight research gaps in observational methods and result interpretation, such as comparisons between classification and statistical discrimination in spectral analyses.
The information is conceptually weak and poorly scoped. There is a striking absence of references to contemporary data analysis methods (e.g., machine learning, deep learning), relevant comparison studies of analytical effectiveness, and recent work on time-series analysis, despite its established importance in the field.
There is no substantive critical analysis of the literature; summaries dominate over synthesis. The literature review is incomplete and lacks critical evaluation. To improve the introduction, strengthen the synthetic component of the review, directly contrast statistical and ML methods, and precisely articulate the research gap. The current introduction is barely sufficient and falls short of MDPI standards without significant revision.
Evaluation of the research methodology proposed by the authors of the text:
The method's description is moderately accurate but falls short of the transparency and repeatability standards required in scientific journals such as MDPI. Identification procedures for damaged stands are well established. However, there is a clear absence of precise coordinates or boundaries for the study area, explicit criteria for 'damaged vs. undamaged,' and details regarding the accuracy and validation of field data. These deficiencies must be addressed to ensure the methodology meets publication standards.
The description of satellite data processing methods specifies the data source (Landsat 8, level 2) and the basics, such as atmospheric correction and cloud masking. However, critical implementation details are missing: the tools used, processing parameters, georeferencing accuracy, cloud-masking methods, noise-filtering processes, and the justification for the selected observation date. These omissions undermine methodological transparency and must be clarified.
In the section defining vegetation indicators, the formulas for the indices and the justification for their selection are provided, but there are also deficiencies, including terminological ambiguities and a lack of information on possible data normalization or transformation.
In the description of statistical analyses, there is no information about: software version and R packages; Assumption verification procedures (e.g., MANOVA), the standardization method (apart from the general statement); lack of description of how to deal with multiple tests; lack of measures of effect and confidence intervals; lack of PCA details (e.g., extraction method, component selection criterion).
To meet MDPI journal standards, authors should: provide full data processing details (tools, parameters); detail statistical procedures (assumptions, R-packages); clarify field classification criteria; add information about data and code availability; and consistently use technical terminology.
The results presentation lacks clarity and fails to facilitate effective interpretation. Despite appropriate statistics, the structure and detail are insufficient, impeding the reader's understanding.
The sequence—statistical description, tests, interpretation—is preserved, yet marked by several issues: unclear separation between results and interpretation, repeated content, and a missing synthetic summary of key results. Table 3 is illegible and inconsistent; variable and statistic identification is difficult; measures of effect and confidence intervals are missing; an excessive amount of data is presented without adequate synthesis. "p" values and basic statistics are present, and result significance is indicated, but reporting of test statistics is incomplete, effect size measures are absent, and corrections for multiple comparisons are not applied.
Drawings are insufficiently described, lacking detailed legends, scales, and interpretive guidance. Their analytical value is limited and primarily illustrative. Comparison charts, such as boxplots and violin plots, are absent and would substantially enhance readability.
The interpretation suffers from redundancy, triviality, and insufficient linkage to statistics. There is no discussion of effect size or critical evaluation. Enhance clarity by redesigning tables and adding comparison charts, such as boxplots and distribution charts.
The justification for the conclusions is superficial. While statistical differences and discriminatory abilities are acknowledged, the practical effectiveness and adequacy of generalizations are poorly substantiated.
When revising, authors should: restrict conclusions to statistical analyses (differences, not classifications); avoid using terms like 'reliable' without validation; supplement analyses with classification metrics (e.g., ROC, accuracy); discuss constraints (e.g., single date, lack of variable control); and distinguish statistical significance from practical usefulness.
Author Response
Dear Dr. Lana Li
Assistant Editor
MDPI, Forests
On behalf of co-authors, we thank you very much for giving us an opportunity to revise our manuscript; we appreciate editors and reviewers for their positive and constructive comments and helpful suggestions on our manuscript. We have studied the reviewer’s comments carefully and have made revisions. We have tried our best to revise our manuscript according to the comments. We hope that all these changes fulfill the requirements to make the manuscript acceptable for publication in the Forests. The detailed response to the reviewers' comment is sent as supplemental material. The corrections and revised sentences we made to the manuscript are shown in red color.
Thank you very much for your kind consideration.
Yours sincerely…
Prof. Dr. Fatih SİVRİKAYA
Response to the Reviewers' Comments:
Reviewer: 2
Comment 1. The scope of general information is partially sufficient. The introduction presents the basic background of the problem, i.e.: the importance of the intensive occurrence of bark beetles for forest ecosystems, the role of remote sensing in monitoring forest health (including that shaped by harmful insects) and the possibility of using vegetation indicators to monitor the health and advancement of the phenomenon of tree dieback under the influence of harmful insects (NDVI, NDMI, MSI, etc.). The narration remains persistently superficial and lacks substantive synthesis. This section fails to address the physiological mechanisms that affect spectral signals and does not adequately highlight research gaps in observational methods and result interpretation, such as comparisons between classification and statistical discrimination in spectral analyses. The information is conceptually weak and poorly scoped. There is a striking absence of references to contemporary data analysis methods (e.g., machine learning, deep learning), relevant comparison studies of analytical effectiveness, and recent work on time-series analysis, despite its established importance in the field. There is no substantive critical analysis of the literature; summaries dominate over synthesis. The literature review is incomplete and lacks critical evaluation. To improve the introduction, strengthen the synthetic component of the review, directly contrast statistical and ML methods, and precisely articulate the research gap. The current introduction is barely sufficient and falls short of MDPI standards without significant revision.
Response 1. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Introduction section. The corrections we made are shown in red in the manuscript.
Comment 2. Evaluation of the research methodology proposed by the authors of the text:
The method's description is moderately accurate but falls short of the transparency and repeatability standards required in scientific journals such as MDPI. Identification procedures for damaged stands are well established. However, there is a clear absence of precise coordinates or boundaries for the study area, explicit criteria for 'damaged vs. undamaged,' and details regarding the accuracy and validation of field data. These deficiencies must be addressed to ensure the methodology meets publication standards. The description of satellite data processing methods specifies the data source (Landsat 8, level 2) and the basics, such as atmospheric correction and cloud masking. However, critical implementation details are missing: the tools used, processing parameters, georeferencing accuracy, cloud-masking methods, noise-filtering processes, and the justification for the selected observation date. These omissions undermine methodological transparency and must be clarified. In the section defining vegetation indicators, the formulas for the indices and the justification for their selection are provided, but there are also deficiencies, including terminological ambiguities and a lack of information on possible data normalization or transformation.In the description of statistical analyses, there is no information about: software version and R packages; Assumption verification procedures (e.g., MANOVA), the standardization method (apart from the general statement); lack of description of how to deal with multiple tests; lack of measures of effect and confidence intervals; lack of PCA details (e.g., extraction method, component selection criterion). To meet MDPI journal standards, authors should: provide full data processing details (tools, parameters); detail statistical procedures (assumptions, R-packages); clarify field classification criteria; add information about data and code availability; and consistently use technical terminology.
Response 2. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Materials and Methods section and sub-section. The corrections we made are shown in red in the manuscript.
Comment 3. The results presentation lacks clarity and fails to facilitate effective interpretation. Despite appropriate statistics, the structure and detail are insufficient, impeding the reader's understanding. The sequence—statistical description, tests, interpretation—is preserved, yet marked by several issues: unclear separation between results and interpretation, repeated content, and a missing synthetic summary of key results. Table 3 is illegible and inconsistent; variable and statistic identification is difficult; measures of effect and confidence intervals are missing; an excessive amount of data is presented without adequate synthesis. "p" values and basic statistics are present, and result significance is indicated, but reporting of test statistics is incomplete, effect size measures are absent, and corrections for multiple comparisons are not applied. Drawings are insufficiently described, lacking detailed legends, scales, and interpretive guidance. Their analytical value is limited and primarily illustrative. Comparison charts, such as boxplots and violin plots, are absent and would substantially enhance readability. The interpretation suffers from redundancy, triviality, and insufficient linkage to statistics. There is no discussion of effect size or critical evaluation. Enhance clarity by redesigning tables and adding comparison charts, such as boxplots and distribution charts.
Response 3. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Results section. Statistical explanations and necessary interpretations have been made, and the figures have been revised. The corrections we made are shown in red in the manuscript.
Comment 4. The justification for the conclusions is superficial. While statistical differences and discriminatory abilities are acknowledged, the practical effectiveness and adequacy of generalizations are poorly substantiated.
Response 4. Thank you for your valuable comments. In line with the Reviewer' Comment, we revised the Conclusion section. The corrections we made are shown in red in the manuscript.
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThe revised version shows significant advances in scientific precision, structure, and data completeness.
Below is a summary of the changes introduced:
Initially, a discrepancy appeared regarding the number of forest stands examined ('Eight forest stands (40 forest stands...)'). The revised version clarifies this by specifying 'Eighty forest stands' (80 tree stands), divided equally into 40 damaged and 40 healthy stands.
The introduction in the revised version has been significantly expanded, with a physiological context added. Information has been added about the mechanisms of tree dieback, including phloem drying (the loss of function in the plant tissue responsible for transporting nutrients), chlorophyll degradation (the breakdown of the pigment essential for photosynthesis), and how these changes affect reflection in the NIR (near-infrared) and SWIR (shortwave infrared) bands.
The authors now more clearly articulate the scarcity of prior studies that address the statistical differentiation of indicators at the forest stand level, rather than focusing solely on classification accuracy.
The revised version specifies the geographic coordinates of the study area and confirms that field assessments were conducted by two seasoned forestry engineers. The GPS device utilized (Garmin GPSMAP 66i, ±3 m accuracy) is explicitly noted.
Criteria for selecting tree stands in the revised version are delineated in greater detail, including a defined age class and crown compactness range (40–70%).
The revised version presents the results in a more structured manner, following the MDPI journal template, making it easier to interpret the statistical significance (p < 0.001) of individual indicators, such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), and MSI (Moisture Stress Index).
The revised manuscript features an updated and expanded reference list (up to 56 entries), incorporating recent publications (including previews/pre-prints from 2025), thus demonstrating improved alignment with current scientific discourse.
In summary, the revised manuscript represents a markedly more mature publication, effectively addressing the substantive and technical issues identified in its initial iteration.
Author Response
Dear Dr. Lana Li Assistant Editor MDPI, Forests
On behalf of co-authors, we thank you very much for giving us an opportunity to revise our manuscript; we appreciate editors and reviewers for their positive and constructive comments and helpful suggestions on our manuscript. We have studied the reviewer’s comments carefully and have made revisions. We have tried our best to revise our manuscript according to the comments. We hope that all these changes fulfill the requirements to make the manuscript acceptable for publication in the Forests. The detailed response to the reviewers' comment is sent as supplemental material. The corrections and revised sentences we made to the manuscript are shown in red color.
Thank you very much for your kind consideration. Yours sincerely…
Prof. Dr. Fatih SİVRİKAYA
Response to the Reviewers' Comments:
Reviewer: 2
General Comments: The following three specific changes should be required before acceptance.
Response. We thank Reviewer #2 for his/her positive and valuable comments on the manuscript.
Comment 1. Add comparative distribution figures (boxplots or violin plots) for all five vegetation indices, showing group separation between damaged and undamaged stands. These should replace or supplement the current thematic maps in Figure 2, or be added as a new figure.
Response 1. Thank you for your valuable comments. In line with the Reviewer' Comment, we added comparative distribution figures to manuscripts as follow:
“Figure 3 displays violin and box plots for all five vegetation indices (NDMI, MSI, RGI, TCW, NDVI), demonstrating distributional differences between damaged and undamaged stands. In all five indices, the two groups exhibit distinctly non-overlapping central tendencies, corroborated by the statistical test results. An r value for NDVI over 1.0 signifies near-perfect rank separation and should be considered as indicative of a substantial effect.”
Figure 3. Comparative violin–boxplot distributions of vegetation indices in stands with and without beetle damage
Comment 2. Report effect sizes for all Mann-Whitney U tests (r = Z/√N) in Table 3, and for the MANOVA result (partial η²). This is a standard reporting requirement in quantitative ecology journals.
Response 2. Thank you for your valuable comments. In line with the Reviewer' Comment, we report the effect sizes for all Mann-Whitney U tests (r = Z/√N) and MANOVA results (partial η²) in Table 3 as follow:
Table 3. Pairwise comparisons of NDVI, NDMI, MSI, TCW, and RGI values in stands with and without beetle damage using the Mann-Whitney U test
|
NDVI |
Stands |
N |
Mean |
Min. |
Max. |
Mean rank |
Rank sum |
P* |
FDR** |
|
NDVI(min.)
|
With beetle damage |
40 |
0.156 |
0.032 |
0.248 |
21.86 |
874.50 |
< 0.001 |
0.892 |
|
Without beetle damage |
40 |
0.240 |
0.175 |
0.283 |
59.14 |
2365.50 |
|||
|
NDVI(max.)
|
With beetle damage |
40 |
0.248 |
0.212 |
0.336 |
21.98 |
879.00 |
< 0.001 |
0.886 |
|
Without beetle damage |
40 |
0.321 |
0.281 |
0.396 |
59.03 |
2361.00 |
|||
|
NDVI(mean)
|
With beetle damage |
40 |
0.207 |
0.148 |
0.259 |
20.50 |
820.00 |
< 0.001 |
1.064 |
|
Without beetle damage |
40 |
0.279 |
0.264 |
0.316 |
60.50 |
2420.00 |
|||
|
NDMI(min.)
|
With beetle damage |
40 |
0.069 |
-0.074 |
1.344 |
25.95 |
1038.00 |
< 0.001 |
0.678 |
|
Without beetle damage |
40 |
0.097 |
0.025 |
0.144 |
55.05 |
2202.00 |
|||
|
NDMI(max.)
|
With beetle damage |
40 |
0.126 |
0.068 |
0.172 |
22.65 |
906.00 |
< 0.001 |
2.022 |
|
Without beetle damage |
40 |
0.169 |
0.127 |
0.208 |
58.35 |
2334.00 |
|||
|
NDMI(mean)
|
With beetle damage |
40 |
0.089 |
0.0208 |
0.136 |
21.30 |
852.00 |
< 0.001 |
0.945 |
|
Without beetle damage |
40 |
0.138 |
0.113 |
0.162 |
59.70 |
2388.00 |
|||
|
MSI(min.)
|
With beetle damage |
40 |
0.777 |
0.707 |
0.873 |
58.35 |
2334.00 |
< 0.001 |
0.850 |
|
Without beetle damage |
40 |
0.712 |
0.656 |
0.774 |
22.65 |
906.00 |
|||
|
MSI(max.)
|
With beetle damage |
40 |
0.935 |
0.778 |
1.159 |
56.05 |
2242.00 |
< 0.001 |
0.678 |
|
Without beetle damage |
40 |
0.825 |
0.747 |
0.951 |
24.95 |
998.00 |
|||
|
MSI(mean)
|
With beetle damage |
40 |
0.837 |
0.7608 |
0.961 |
59.65 |
2386.00 |
< 0.001 |
0.940 |
|
Without beetle damage |
40 |
0.758 |
0.721 |
0.798 |
21.35 |
854.00 |
|||
|
RGI(min.)
|
With beetle damage |
40 |
0.972 |
0.9028 |
0.996 |
56.10 |
2244.00 |
< 0.001 |
0.717 |
|
Without beetle damage |
40 |
0.960 |
0.950 |
0.981 |
24.90 |
996.00 |
|||
|
RGI(max.)
|
With beetle damage |
40 |
1.021 |
0.979 |
1.096 |
54.33 |
2173.00 |
< 0.001 |
0.579 |
|
Without beetle damage |
40 |
0.990 |
0.969 |
1.030 |
26.68 |
1067.00 |
|||
|
RGI(mean)
|
With beetle damage |
40 |
0.989 |
0.972 |
1.026 |
58.16 |
2326.50 |
< 0.001 |
0.740 |
|
Without beetle damage |
40 |
0.972 |
0.962 |
0.987 |
22.84 |
913.50 |
|||
|
TCW(min.)
|
With beetle damage |
40 |
20082.012 |
16779.328 |
24466.629 |
29.40 |
1176.00 |
< 0.001 |
1.000 |
|
Without beetle damage |
40 |
21548.119 |
19011.262 |
23878.527 |
51.60 |
2064.00 |
|||
|
TCW(max.)
|
With beetle damage |
40 |
24870.382 |
21189.240 |
30334.213 |
37.40 |
1496.00 |
0.233 |
0.310 |
|
Without beetle damage |
40 |
25200.552 |
22196.122 |
28828.914 |
43.60 |
1744.00 |
|||
|
TCW(mean)
|
With beetle damage |
40 |
21814.634 |
19492.918 |
25526.531 |
29.30 |
1172.00 |
< 0.001 |
0.475 |
|
Without beetle damage |
40 |
23087.825 |
21699.934 |
24769.214 |
51.70 |
2068.00 |
*p<0,05 **Effect size r = Z/√N (where N = 80); values ≥0.1, ≥0.3, and ≥0.5 indicate small, medium, and large effects, respectively.
“Effect sizes for Mann-Whitney U tests were computed. The effect sizes for all five vegetation indices varied from r = 0.475 (TCW) to r = 1.064 (NDVI), signifying substantial to exceedingly substantial practical differences between groups. The MANOVA yielded a partial η² of 0.295 (Pillai's Trace = 0.870, F(16,63) = 26.35, p < 0.001), indicating a substantial multivariate effect (η² > 0.14).”
Comment 3. Address multiple comparison correction explicitly: either apply a correction (Bonferroni or Benjamini-Hochberg FDR) to the 14 pairwise tests or provide a methodological justification for why correction is considered unnecessary in this specific analytical context (e.g., confirmatory rather than exploratory framing, pre-registered hypotheses).
Response 3. Thank you for your valuable comments. In line with the Reviewer' Comment, we explicitly addressed the multiple comparison correction and applied a correction (Benjamini-Hochberg FDR) to pairwise tests as follow:
“False discovery rate (FDR) approach was employed to regulate the family-wise Type I error rate over the 15 pairwise comparisons for all p-values [48].”
Comment 4. Additional comment-Section 2.3 Statistical Analysis: missing methodological references
The Statistical Analysis section describes several analytical procedures-the Kolmogorov-Smirnov normality test, the Mann-Whitney U test, MANOVA with Pillai's Trace, and Principal Component Analysis-but provides no bibliographic references to support the choice or implementation of these methods.
Response 4. Thank you for your valuable comments. In line with the Reviewer' Comment, we provide bibliographic references as follow:
“The normality assumption was evaluated using the Kolmogorov–Smirnov test [50]. Group comparisons were conducted using the Mann–Whitney U test [51]. MANOVA assessed multivariate group separation with Pillai’s Trace statistic [52,53].”
Comment 5. In addition, the section does not report the version numbers of the software used for statistical analyses (R) and spatial data processing (ArcGIS, QGIS), which are required for reproducibility.
Response 5. Thank you for your valuable comments. In line with the Reviewer' Comment, the version numbers of the software used for statistical analysis (R) and spatial data processing (ArcGIS, QGIS) are given as follow:
“All statistical analyses were conducted in R (version 4.5.3). Spatial data pre-processing was performed in QGIS (version 3.44) and ArcGIS (version 10.8)”
Comment 6. The Introduction and Conclusion improvements, while incomplete, are acceptable at the minor revision stage provided the above three mandatory changes are made.
Response 6. We thank Reviewer for his/her positive and valuable comments.
Comment 7. Line 303: "afflicted" should be replaced with "infested" (or "affected") to conform to standard entomological and forestry terminology.
Response 7. Thank you for your valuable comments. In line with the Reviewer' Comment, we used "infested" instead of "afflicted".
Author Response File: 
Author Response.pdf
