Quantifying Understory Complexity in Unmanaged Forests Using TLS and Identifying Some of Its Major Drivers

Round 1

Reviewer 1 Report

Include some brief information on what and why TLS for this study?

Please include a figure to show the location/extent of the study sites. 
 
Another figure to show the sample plot locations (sampling scheme) at the sites will also be helpful. And, how did you chose the number and location of the sample plots within each site?

Include a methodolody flowchart or guide. 

Figure 1 - Can you include the RGB point cloud data instead of the grayscale 2D representation. Also, a legend showing the heights can be included. 

Under Section 1, all sub-sections are named as 1.1. And, all sections are numbered as 1 - does this need to be corrected?

Line 211 - http://worldclim.org/version2 is getting redirected - is this the right URL? 

Abbreviate MAT in figure 2. 

Table 2 headings in the x and y axes have to be formatted. 

Author Response

please see attached file

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript under review focuses on an interesting topic, as is attempting to describe the understorey layer of a forest by means of terrestrial lidar cloud points, and then try to relate the degree of complexity with other stand and environmental attributes, through a geographical gradient in Chile. While logically designed, the study presents some flaws that should be solved before recommending its publication.

My first general concern is related to the main aim of the manuscript. Since submitted to a journal as “Remote Sensing” I’d expect deep emphasis on the methods for data acquisition and data processing. On the contrary, these issues are scarcely presented in the methods section. A deeper description of UCI index (even including charts describing the method) are required, and the potentialities of this index with respect to others should be discussed and – if possible – contrasted.  In addition, why UCI index and canopy openness are the unique strand attribures derived from LIDAR cloud point? I strongly recommend that other interesting attributes related with crown density, stand height, … should be derived and entered into the GAM analysis.

Another main criticism is related with the way UCI (as response variable) and sampling point level explanatory variables (basal area, canopy openness) are processed, as shown in figures 4 and 5. According to the text, 123 sample points from 27 sample plots were installed, and from each sampling point UCI, basal area and canopy openness was recorded/derived. However, in this figures it seems as if the authors had averaged the values of the attributes to have a single value per plot, which is then used in the analysis. While this commonly mean a better fit, is a clear error by ignoring the within-plot variability. In addition, by reducing the sample (from n=123 to n=27) the authors are reduing the power of the statistical analysis, and this may lead to biased results. The authors should clarify this issue.

The third main concern is related with the use of climate variables as potential explanatory covariates for the complexity index. Given the sampling design, climate differences are merely representing between site differences. Within site climate differences are – logically – much smaller than the differences between sites, so authors are facing a close to pseudoreplication problem. I mean, are the differences between sites due to climate, or to other factors (e.g. soil, lithology)? As climate is not a randomized treatment between sites (i.e. we don’t have different sites with similar climate to check these hypothesis) it is not possible to disentangle the effect of climate from the effect of site, even if using SEM. Due to this, I’m not very confident on the results and interpretations from structural equations models, and do not recommend to enter climate in this analysis. Instead of this, I suggest to discuss the observed difference between sites (figure 3) in terms of climate-environmental differences.

Summarizing these major concerns, I suggest to focus the manuscript on the remote sensing issue, include more terrestrial LIDAR attributes into the GAM analysis, carry out the analysis at sampling point scale, and do not include climate data as a potential explanatory covariate but only as an issue to discuss.

MINOR COMMENTS

• Through the whole text: please check section and subsection numbering (is always 1.1)
• Line 27 and through the whole text: I’d rather the use of “understorey layer” instead of “shrub layer”, since not only shrubs are included in the analysed layer
• Lines 132-140: how was plot location selected within each site?
• Line 175-176: technical limitations linked to the tripod height may affect the lower limit of the layer, but not the upper one. WIllims et al 2019 justify this decssion in order toa void points from tree crown canopy, is the same in this case?
• Have the authors made any restriction based on the distance from the scanner to homogeneize cloud point and prevent shadowing by other trees?
• Lines 233-235: irrespective of my awareness of using climate data, the authors should indicate why they have selected these and not other climate attributes
• Figure 2 does not reflect correlation between basal area and UCI. Ina ddition, figure 2 and table 2 are redundant, so one of them could be removed
• Figure 3. It is not clear which analysis have the authors carried out. ANOVA? Wilcoxon test? Please clarify
• Figure 6. The symbols should be divided among sites, to be consistent with previous figures

Author Response

please see attached file

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Include some brief information on what and why TLS for this study? - This has not been covered or addressed in the manuscript. Authors explain about TLS device specifics only in chapter 2.2. Information on why terrestrial LiDAR, benefits, how it works can briefly be included in the Introduction. 

Abbreviate MAT in figure 2 - Authors mentioned that they don't understand -  Not sure about this. MAT should be abbreviated as 'Mean Annual Temperature' and that was missing in the caption or the figure in your original submission. But, it has been added in the revised version now, so looks good. 

Figure 1 - Please revise this figure and include all basic map elements like grid/graticule, map scale (this is very important), legend (blue triangles) and the study area at different scales (the one you have can be the detailed one). But another scale showing the location of Chile will be helpful. 

The names of the sites in figure 1 and table 1 don't match which is confusing to the readers. Please check and revise all the names. For example, figure 1 says S. P. de Tregua, and table 1 says San Pablo de Tregua (abbreviation as San Pablo). Make sure the study site names are consistent throughout the manuscript.  Even in figure 7, it's mentioned as San P. d. Tregua. 

What is the significance of figure 3? 

Figure 4 - Include a legend (showing the above ground level height breaks). 

Section 2.4 - Seems like there is an hyphen after 'world' in https://www.worldclim.org/data/world-clim21.html and the link is still getting redirected.

Figure 5 - Thanks for adding the flowchart, but please improve/simplify this figure - the arrows look very confusing. The quality of the flowchart also has to be improved. 

In the contributions of the co-authors, can you please elaborate or rephrase 'Software' - because that can refer to building the software or doing the processing/analysis using another software. 

Author Response

see attached file for detailed response

Author Response File: Author Response.pdf

Reviewer 2 Report

This is my second review on the manuscript and now I consider that the authors have matched - or at least soundly rebutted - my previous concerns on the text, thus I recommend its publications. 

Author Response

There for no further remarks.

Thank you for your constructive comments!