Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Abstract:
Please write your hypothesis on the relationship between CST and the infestation problem. Please write the number of samples (training/validation) for the used models.
Introduction:
The first paragraph should be extended, and the problem should be comprehensively explained.
Line 48-49: Please state for which regions those timelines are valid.
The color of the leaves can be changing as a result of natural seasonal cycle. Please better explain how the natural and pest related leaf color change should be understood.
Lines 55-56: You should better explain how the water content changes with the pest infestation.
You should better explain the literature gap that you are filling.
Figure 2 needs revision.
Please write the spectral range of the thermal sensor.
Feature selection strategy for LIDAR data should be better explained.
Authors should comprehensively analyze the recent literature and compare their findings with those in the literature by clearly explaining pros and cons.
Conclusion is repeating the results. Authors should deliver a solid scientific message and some future insights in this section.
Comments on the Quality of English Language
Minor editing of English language required
Author Response
Dear Editor and Reviewers:
Thank you for the letter and valuable comments concerning our manuscript. The comments greatly helped us improve the quality of our manuscript. Considering all your comments, we made major revisions as follows:
(1) Additional details have been mentioned in the Introduction section to explain the pest infestation’s problem and the leaves’ color change. Furthermore, we have modified the description of the pest life-cycles and water content changes within pest infestation stages.
(2) The list of 30 LiDAR metrics is provided in appendix A, and the metrics selection strategy of LiDAR metrics has been corrected in Section 2.3 and appendix B.
(3) The conclusion and discussion were corrected with considerable modifications.
(4) Some minor issues such as inappropriate images, tables, and description have been corrected.
(5) The language for our manuscript has been comprehensively proofread word by word from the peer experts.
Please find below our point-to-point responses to the reviewers’ comments. We hope that our revised paper has addressed all the reviewers’ concerns and is acceptable for publication in Forests.
Reviewer 1:
- Please write your hypothesis on the relationship between CST and the infestation problem.
[Response]: Thank you for the constructive suggestions. The relationship between the CST and infestation has been added in the revised manuscript. The modifications have highlighted in the revised manuscript (Line 71-75).
Details are as follows:
However, due to the disruption of water transport tissues caused by pest boring, the water content of needles undergoes significant change during the green attack. The disturbance of water content affects the temperature through leaf evapotranspiration, which induces small but detectable changes probed by thermal infrared (TIR) remote sensing.
- Please write the number of samples (training/validation) for the used models.
[Response]: Thanks for your comments. The number of samples (training/validation) was added to the images of each used model (Figure 5, Figure 7, Figure 8, and Figure 10).
- The first paragraph should be extended, and the problem should be comprehensively explained.
[Response]: We appreciate your comment. Accordingly, the first paragraph was extended to describe the problem in detail (Line 38-Line 46).
Details are as follows:
The fast-spread characteristic of pest infestation severely threatens forest health status, thereby decreasing of their vitality and carbon sequestration over large areas. Moreover, changes in climatic conditions, which have decreased forest resistance, have intensified outbreak opportunities for pest infestation [5, 6]. The commonly used artificial ground survey exhibits great limitations and high costs for detecting pest infestation in a large region [7].
- Line 48-49: Please state for which regions those timelines are valid. The color of the leaves can be changing as a result of natural seasonal cycle. Please better explain how the natural and pest related leaf color change should be understood.
[Response]: We have rephrased the original text in the revised manuscript to describe the beetles’ life cycles (Line 53- Line 69). The pest infestation occurs from May to November, the discoloration of foliage has visible change to human eyes in 4-5 weeks after shoot attack; Moreover, and the discoloration will be celebrated due to the higher evapotranspiration in July. For the healthy shoot of Yunnan pine, the needles’ color remains green for one or two years until new needles grow.
- Lines 55-56: You should better explain how the water content changes with the pest infestation. You should better explain the literature gap that you are filling.
[Response]: Thanks for your great suggestion. We have incorporated necessary changes in the revised manuscript (Line 58- Line 60).
Details are as follows:
As time passes, the discoloration of the foliage occurs due to the decrease of water and nutrients, which transportation was hindered by infestation, rendering them inaccessible.
- Figure 2 needs revision.
[Response]: Figure 2 has been modified in the revised manuscript.
- Please write the spectral range of the thermal sensor.
[Response]: Thanks for your comments. The spectral range of the thermal sensor was added in Section 2.2.2 (Line 151).
- Feature selection strategy for LIDAR data should be better explained.
[Response]: Thank you for your helpful comments. The metrics selection for LiDAR data was modified (Line 214-Line 219). The detailed selection strategy of LiDAR metrics was added in Appendix B.
- Authors should comprehensively analyze the recent literature and compare their findings with those in the literature by clearly explaining pros and cons.
[Response]: We have added recent literatures and compared the study results with their findings to describe the limitation of TIR and LiDAR data (Line 344-Line 353, Line 384-Line 388).
Details are as follow:
Following individual tree segmentation, we filtered the segmentation with tree height above 4m; then, we divided the crown into eight directions and gridded its vertical profiles of each direction with 1-m resolution. The 30 LiDAR metrics (Table A2) containing the structure and intensity information of tree canopies were generated from point cloud data using the MATLAB software (MathWorks. Inc., USA). The 8-segmenting method and formulas of those metrics were thoroughly described in previous report [45].
- Conclusion is repeating the results. Authors should deliver a solid scientific message and some future insights in this section.
[Response]: Thanks for your constructive suggestions. We have modified the Conclusion section to better show the results and future insights (Line 398-Line 403, Line412-Line 415).
Details are as follow:
The canopy temperature has been used in the previous literatures for monitoring disease or pest infestation. Furthermore, it is a good indicator for moderate and severe stage, due to a decrease of transpiration and a rise in temperature with the chlorotic and necrotic foliage [24,53]. However, the CST with insignificant increase at early stage is insufficient for pest infestation monitoring. This result shown in Figure 5(b) is in agreement with a previous report [24]. This may be caused by high uncertainties in TIR-based canopy temperature values [54]. The CST is liable to influence directly by various environmental factors, such as ground surface radiation, especially when the canopy volume or leaf area index (LAI) is low [27,55]. This particular condition aggravates the mixed radiation of tree crowns measured by TIR sensors, weakening the correlation between SDR and CST [27].
Some studies have also indicated that LiDAR metrics only distinguished the moderate infestation with a classification accuracy of 66% [38]. However, the researchers pointed out that LiDAR intensity still had the potential in assessments of infestation severity [10,38].
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
The authors of ‘Individual tree‐level monitoring of pest infestation combining 2 airborne thermal imagery and LiDAR’ provide a concise and thoughtful investigation on the use of a fusion between UAV- thermal infrared and UAV-lidar data for the detection of pine shoot beetle infestation severity. Their analysis and discussion are straightforward and provide sufficient clarity to their readers. Only minor recommendations are provided below.
1. Lines 22-23, it should be clarified what type of LiDAR is used in this study (i.e., UAV-lidar).
2. Lines 48-57, this explanation exceptional.
3. Figure 1, a coordinate grid (i.e., grid lines) should be added to both map extents.
4. Section 2.2.3, lines 161-165, what software and specific processing settings were used for this segmentation? These options can greatly impact the results.
5. Table 2, this table is a result from your analysis and should be moved to that section.
6. Lines 188-189, how were these LiDAR metrics derived? If they are not all listed, a clear description of the methods should be given.
7. Table 3, this is also the results of an analysis. The methods would be clearer if there were a more distinct division between procedures and findings.
8. Figure 6 caption, line 273, each acronym only needs to be defined the first time that it is used in the abstract, main text, or caption.
9. The discussion could be improved if a connection was made to other studies which have used thermal remote sensing for vegetation health and/or pest monitoring.
Comments on the Quality of English Language
Only minor revisions are needed.
Author Response
Dear Editor and Reviewers:
Thank you for the letter and valuable comments concerning our manuscript. The comments greatly helped us improve the quality of our manuscript. Considering all your comments, we made major revisions as follows:
(1) Additional details have been mentioned in the Introduction section to explain the pest infestation’s problem and the leaves’ color change. Furthermore, we have modified the description of the pest life-cycles and water content changes within pest infestation stages.
(2) The list of 30 LiDAR metrics is provided in appendix A, and the metrics selection strategy of LiDAR metrics has been corrected in Section 2.3 and appendix B.
(3) The conclusion and discussion were corrected with considerable modifications.
(4) Some minor issues such as inappropriate images, tables, and description have been corrected.
(5) The language for our manuscript has been comprehensively proofread word by word from the peer experts.
Please find below our point-to-point responses to the reviewers’ comments. We hope that our revised paper has addressed all the reviewers’ concerns and is acceptable for publication in Forests.
Reviewer2:
- Lines 22-23, it should be clarified what type of LiDAR is used in this study (i.e., UAV-lidar).
[Response]: Thanks for your comments. This problem has been corrected.
- Lines 48-57, this explanation exceptional.
[Response]: We have reorganized the description of the beetle life cycles, and the modifications have been highlighted in the revised version of manuscript (Line 53-Line 68).
- Figure 1, a coordinate grid (i.e., grid lines) should be added to both map extents
[Response]: The coordinated has been added in Figure 1.
- Section 2.2.3, lines 161-165, what software and specific processing settings were used for this segmentation? These options can greatly impact the results.
[Response]: We have added the information of software and processing setting in the revised manuscripts (Line 181-Line 191).
Details are as follow:
Therefore, we used the LiDAR point clouds to extract the individual tree crowns sepa-rated by the point cloud segmentation (PCS) algorithm embedded in the Lidar 360 software with default parameters. Before point cloud segmentation, the LiDAR data was processed by denoising, filtering, ground point classification, DEM, and digital surface model (DSM) in Lidar 360 software. The elevation of point cloud data was normalized by DEM. The CHM was generated from DEM and DSM, which was used to derive seed points with a watershed algorithm. Subsequently, the seed points and the normalized LiDAR point data were imported into Lidar 360 software for individual tree segmentation used by PCS algorithm. In Lidar 360 software, the seed points could be edited by manual for improving the accuracy of tree segmentation. Following the segmentation of all trees, the crown boundary, height, and crown volume were generated from individual tree point clouds. The LiDAR segmentation results are shown in Table A1.
- Table 2, this table is a result from your analysis and should be moved to that section.
[Response]: Table 2 was moved to the Appendix A renumbered as Table A1.
- Lines 188-189, how were these LiDAR metrics derived? If they are not all listed, a clear description of the methods should be given.
[Response]: The detailed strategy for LiDAR metrics has been added in Appendix B; furthermore, 30 LiDAR metrics was listed in Appendix A (Table A2).
- Table 3, this is also the results of an analysis. The methods would be clearer if there were a more distinct division between procedures and findings.
[Response]: Table 3 is moved to the Appendix B as Table B1.
- Figure 6 caption, line 273, each acronym only needs to be defined the first time that it is used in the abstract, main text, or caption.
[Response]: We have deleted the abbreviation “SDR” in the caption of Figure 6.
- The discussion could be improved if a connection was made to other studies which have used thermal remote sensing for vegetation health and/or pest monitoring.
[Response]: Thanks for your helpful suggestions. We have modified the discussion and added recent literatures to compare with other findings. The modifications have highlighted in the revised manuscript.
Details are as follows:
The canopy temperature has been used in the literature for monitoring disease or pest infestation. Furthermore, it is a good indicator for moderate and severe stage pest infestation due to a decrease in transpiration and a rise in temperature with the chlorotic and necrotic foliage [24,53]. However, CST with no considerable increase at early stages is insufficient for pest infestation monitoring. This result shown in Figure 5(b) is in agreement with a previous report [24]. This may be caused by high uncertainties in TIR-based canopy temperature values [54].
Author Response File: Author Response.pdf
