Analysis and Prediction of Gap Dynamics in a Secondary Deciduous Broadleaf Forest of Central Japan Using Airborne Multi-LiDAR Observations

Round 1

Reviewer 1 Report

Dear authors, first of all congratulations on your very interesting work. After minor corrections I will recommend this article for publication.

 

• Firstly "gaps", they may differ in different forests around the world but what is far more important they may have different definitions. Gap in Japan may have completely different meaning comparing to US, Brazil or Europe. This term must be precisely clarified. I’m not thinking about size/area only, I’m wondering what gap means also from horizontal perspective. We may have gaps in canopy layer and does not always mean that "empty spaces" are down to the ground. In conclusion it should be clarified what gap means precisely especially if shrubs or lower tress were not included (?) Were the lower layers removed from DHCM or not?
• Lidar technology is well known (or have been popularized) at least from the late nineties. To be honest I don't see any reason to clarify one more time how it works especially in Remote Sensing.
• Line 344-347 what understory vegetation means precisely, what is the height of such trees to be classified as understory vegetation, what about density of canopy layer etc.? All this definitions must be clarified.

Author Response

Dear Dr. Reviewer-1,                                                                                   

Thank you very much for helpful comments on our manuscript to improve it. We changed the manuscript as follows based on your comments. Please check the revised manuscript. We hope that we understand your comments correctly and revised properly.

Please check the manuscript and attached document files.

Best wishes.

Yours Sincerely,

Author Response File: Author Response.doc

Reviewer 2 Report

1. Line 111-114: The standard deviations (SDs) were 1.05, 3.08, and 1.64 in the LiDAR-2005, LiDAR-2011, and LiDAR-2016 datasets, respectively. The LiDAR-2011 data, captured by a helicopter borne platform instead of a fixed-wing platform, showed the highest SD. Why? Helicopter borne platform could also get good quality data.
2. Line 254-256: Numerous tiny openings were extracted from LiDAR-2011, this dataset showed the smallest number of gaps among the three LiDAR DSMs. It is obvious that pulse density differed among the three DSMs and it had little effect on gap extraction. So, is it suitable using the LiDAR-2011 data?
3. Line 434-435: Mask layer processing showed that 15%, 28%, and 20% of the gaps present in 2005, 2011, and 2016, respectively. It is interesting that gaps in 2011 are highest. What is the most factor for causing more gaps in 2011?
4. Line 457-458: Gaps with longer duration tended to expand via disturbances, perhaps because they experienced a greater number of disturbances. What kind of disturbance is most popular in the study area? Is it typhoon?
5. This manuscript provides a method for generating gap maps that show gap distribution, size, and lifespan. It is very helpful to provide reference information for monitoring gap dynamics.

Author Response

Dear Dr. Reviewer-2,   

Thank you very much for your helpful comments on our manuscript to improve it. We changed the manuscript as follows based on your comments. Please check the revised manuscript. We hope that we understand your comments correctly and revised properly.

Please check the attached manuscript and document files.

Best wishes.

Yours sincerely,

Author Response File: Author Response.doc