Post-Logging Canopy Gap Dynamics and Forest Regeneration Assessed Using Airborne LiDAR Time Series in the Brazilian Amazon with Attribution to Gap Types and Origins

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is review of the manuscript “Post-logging canopy gap dynamics and forest regeneration assessed using airborne LiDAR time series in the Brazilian Amazon with attribution to gap types and origin” submitted for publication in remote sensing. This MS is demonstrated the capacity of airborne LiDAR multi-temporal data in characterizing the impacts of forest degradation and subsequent recovery. Some of the results and discussion need to be revised. I recommend to minor revision the MS at this stage.

 

Lines 263-264: What are the reasons for the increase?　Please describe the factors forming the natural gap and the probability of its occurrence.

 

Line 289: Where in Table 5 should the average rate of closure of 16.66% be read?

 

Line 291: Figure 6 is missing.

 

Lines 314-315: Does tree growth match actual observations?

 

Line 334: An explanation of how to see Figure 5 is needed in the text.

 

Line 337: As it is an important figure, I would suggest that it should be included in the text, not in a supplement.

 

Line 345: Figure5(F) cannot be read to show a spatial relationship that differs from random. Figure5(D)-(F) can be read as spatially random over a period of time, so further analysis of the results should be done.

 

Lines 403-404: Please specify the important knowledge gaps created by previous studies.

Author Response

Please see in attachment the response for Reviewer 1, and also Reviewer 2 for reference.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article uses different time frames of Lidar to study forested gaps
in the rain forest and how the gaps fill over time.  The article is
very well written though quite long winded as the observations being
made fall in the 100's.
There is one significant missing link that needs to be addressed.  Lidar
is being treated as a black box not a measurement tool that has imperfections.
First does this Lidar include multiple responses and if yes how are
the multiple responses used.  If no multiple responses has does this limit
the analysis.  This is no mention of classification and how this process
occurred.  Most importantly there is no analysis of the accuracy of the
Lidar most specifically the GPS-IMU that controlled it.  Plus no mention
of ground coordinate checks.  Discussing spatial accuracy in detail allows
one to build confidence in the ensuing statements that are made.
The following need to be addressed in the article and should not be simply a
statement made.
Line 120-121 - why two estimates of average rainfall?
Line 151 - define CHM
Line 153-155 - what process was used for classification?
Line 156-158 - why was growth not considered in this "shift: process and
what was the amount of shift as it indicates error in GPS-IMU used in
Lidar acquisition.  Why is horizontal shift not considered?
Line 173 - How do you get quality GPS in field in a rainforest?
Line 246 - Explain wht 1% is used
Line 262-263 .01% - is this using too many digits they are not significant
Table 3 - .01% - can you really state to that precison?
Line 323 - with an error larger than the value can you really say anything
of significance
Figure 4 - it shows the randomness of the data does not justify the
showed line there is not enough data quality
Line 415 - why do you say 0.15 m - each Lidar acquisition has different error?
Line 479 - your model suggests that annual changes in weather variability
do not need to be considered in the model

Author Response

Please see in attachment the response for Reviewer 2, and also Reviewer 1 for reference.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

There are still too many numbers past the decimal place in many places and it is not consistent as corrections were made in places based on comments.  It seems % should be to nearest 1 percent or perhaps .1 percent everywhere and numerical results should consistently be to 0.1 meters but I feel that is optimistic and nearest meter is more appropriate.  Thanks for addressing the constructive critique of the review.

Author Response

Reviewer #2

There are still too many numbers past the decimal place in many places and it is not consistent as corrections were made in places based on comments.  It seems % should be to nearest 1 percent or perhaps .1 percent everywhere and numerical results should consistently be to 0.1 meters but I feel that is optimistic and nearest meter is more appropriate.  Thanks for addressing the constructive critique of the review.

Addressed in revision: We thank the reviewer for catching this. We had fixed some of the too precise decimals on the previous version following the reviewer’s suggestion, but some remained. We agree that .1 is at the limit of the precision of the data but if we round to the nearest integer some of the nuances of the differences between growth, for example, in between gap vs non-gap would have been lost and results would be the same between the two - which would make no sense ecologically, thus we expect 0.1 to be reasonable. Therefore, we went through all the text and rounded all results to 0.1 decimal. We thank you again for your time on helping us improve the manuscript.