A Method for Estimating Forest Aboveground Biomass at the Plot Scale Combining the Horizontal Distribution Model of Biomass and Sampling Technique
, Hui Xu 1,2, Jialong Zhang 1,2, Aiyun Wang 4, Heng Wu 5, Rui Bao 5 and Guanglong Ou 1,2,*
Luis Cutz
Round 1
Reviewer 1 Report
The article is interesting due to developing a plot-scale methodology for estimating aboveground biomass through models to improve efficiency, reduce costs and provide the reliability of estimation for biomass. The article is very well structured, organized, explicit, and outlined with the exception of two sentences that I detail in the comments. When I initially checked the structure of the article, I thought the discussion would be too short but in the end, this was compensated as the results were presented as they were explained simultaneously.
I recommend the following changes based on text and format
Line 33: please replace “; The STS-” by “; and lastly the stratified sampling method (STS)-HDM-6” because is the first time that is mentioned.
Line 39: please replace “model;” by “model (CPM);”.
Line 51: please replace “[11-13]. Where” by “[11-13] where”.
Line 53: please replace “rapidity” by “velocity”.
Line 76: please replace “And biomass” by “Biomass”.
Line 78: please replace “(LDM) in this study” by “(LDM). In this study,”.
Line 94: please replace “methods, and assess the potential of this method.” by “methods to assess their potential.”.
Lines 99 and 336: please replace “m×60” by “m × 60”.
Lines 115, 262-363, and 351-355: I consider that this information is not necessary because is already been specified before. However, this only will depend on the authors' interest or journal.
Line 120: please replace “canopy profile model” by “canopy profile model (CPM)”.
Lines 145, 148, 162, 167, 180, 261, 265, 268, 381, 383, 385, 399, and 406: please replace “canopy profile model” by “CPM”.
Table 2: in this table are represented some values with three decimals. Please replace “2.767” by “2.77”, and “1.428” by “1.43”.
Line 164: please replace “(RNDIC)” by “(RDNIC)”.
Line 181: please replace “crown radius (CR)” by “CR”. Already mentioned previously in line 163.
Line 182: please replace “relative depth into the crown (RDINC)” by “RDNIC”. Already mentioned previously in line 164.
Line 225: please replace “variation; E” by “variation; and E”.
Line 228: please replace “cost.In” by “cost. In”.
Line 236: please replace “Pidenotes” by “Pi denotes”.
Line 238: please replace “AGB; ν” by “AGB; and ν”.
Line 239: please replace “ith sampling” by “i-th sampling”.
Lines 247-248: the sentence “The parameter estimation and model evaluation results are shown in Table 3” have to be deleted because is already reported to follow.
Line 256: please replace “are more effective than the three models.” by “are the more effective of the three models.”.
Line 258: please replace “RNDIC” by “RDNIC”.
Line 266: please replace “crown; CR” by “crown; and CR”.
Line 271: please replace “density; RCR” by “density; and RCR”.
Line 286: please replace “figure” by “Figure”.
Line 302: please replace “HDM; (b)” by “HDM; and (b)”.
Lines 315-318, and 327-330: I don´t understand the sentence between these lines. Please explain better.
Line 316: please replace “HDB” by “HDM”.
Line 321: please replace “size; (b)” by “size; and (b)”.
Line 326: please replace “STS-HDM” by “STS-LDM”.
Line 333: please replace “ratio; (b)” by “ratio; and (b)”..
Line 343: please replace “7 m and 6 m” by “6 m and 7 m”.
Line 387: please replace “and so on. These” by “and so on, these”.
Line 394: please replace "UAV" by “unmanned aerial vehicles (UAV)".
Author Response
Open Review
(x) I would not like to sign my review report
( ) I would like to sign my review report
English language and style
( ) Extensive editing of English language and style required
( ) Moderate English changes required
( ) English language and style are fine/minor spell check required
(x) I don't feel qualified to judge about the English language and style
Response: We have checked the English language and style of the manuscript again and have revised it in light of the comments and suggestions you have given.
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Does the introduction provide sufficient background and include all relevant references? |
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Are all the cited references relevant to the research? |
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Is the research design appropriate? |
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Are the methods adequately described? |
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Are the results clearly presented? |
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Are the conclusions supported by the results? |
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Response: We have revised the manuscript according to the comments you have given which has led to a further improvement in the quality of the manuscript.
Comments and Suggestions for Authors
The article is interesting due to developing a plot-scale methodology for estimating aboveground biomass through models to improve efficiency, reduce costs and provide the reliability of estimation for biomass. The article is very well structured, organized, explicit, and outlined with the exception of two sentences that I detail in the comments. When I initially checked the structure of the article, I thought the discussion would be too short but in the end, this was compensated as the results were presented as they were explained simultaneously.
Response: We are very grateful for your approval and recognition of our research and manuscript. We also greatly admire your strict and careful working attitude. We have done our best to respond to your comments and suggestions individually, and we hope that the revised content will meet with your approval.
I recommend the following changes based on text and format
Line 33: please replace “; The STS-” by “; and lastly the stratified sampling method (STS)-HDM-6” because is the first time that is mentioned.
Response: We are very sorry for our negligence of the rule that “Things mentioned for the first time should not be abbreviated”.
“; The STS-” has been replaced by “; and lastly the stratified sampling method (STS)-HDM-6”.
Line 39: please replace “model;” by “model (CPM);”.
Response: “model;” has been replaced by “model (CPM);”.
Line 51: please replace “[11-13]. Where” by “[11-13] where”.
Response: “[11-13]. Where” has been replaced by “[11-13] where”.
Line 53: please replace “rapidity” by “velocity”.
Response: “rapidity” has been replaced by “velocity”.
Line 76: please replace “And biomass” by “Biomass”.
Response: “And biomass” has been replaced by “Biomass”.
Line 78: please replace “(LDM) in this study” by “(LDM). In this study,”.
Response: “(LDM) in this study. It” has been replaced by “(LDM). In this study, it”.
Line 94: please replace “methods, and assess the potential of this method.” by “methods to assess their potential.”.
Response: “methods, and assess the potential of this method.” has been replaced by “methods to assess their potential.”.
Lines 99 and 336: please replace “m×60” by “m × 60”.
Response: “m×60” has been replaced by “m × 60”.
Lines 115, 262-363, and 351-355: I consider that this information is not necessary because is already been specified before. However, this only will depend on the authors' interest or journal.
Response: As you suggested, this information has been specified before. We have therefore removed this duplicate information.
Line 120: please replace “canopy profile model” by “canopy profile model (CPM)”.
Response: “canopy profile model” has been replaced by “canopy profile model (CPM)”.
Lines 145, 148, 162, 167, 180, 261, 265, 268, 381, 383, 385, 399, and 406: please replace “canopy profile model” by “CPM”.
Response: “canopy profile model” has been replaced by “CPM”.
Table 2: in this table are represented some values with three decimals. Please replace “2.767” by “2.77”, and “1.428” by “1.43”.
Response: We are sorry for not carefully checking the data format of Table 2.
“2.767” has been replaced by “2.77”, and “1.428” has been replaced by “1.43”.
Line 164: please replace “(RNDIC)” by “(RDNIC)”.
Response: “(RNDIC)” has been replaced by “(RDINC)”.
Line 181: please replace “crown radius (CR)” by “CR”. Already mentioned previously in Line 163.
Response: “crown radius (CR)” has been replaced by “CR”.
Line 182: please replace “relative depth into the crown (RDINC)” by “RDNIC”. Already mentioned previously in Line 164.
Response: “relative depth into the crown (RDINC)” has been replaced by “RDINC”.
Line 225: please replace “variation; E” by “variation; and E”.
Response: “variation; E” has been replaced by “variation; and E”.
Line 228: please replace “cost.In” by “cost. In”.
Response: “cost.In” has been replaced by “cost. In”.
Line 236: please replace “Pidenotes” by “Pi denotes”.
Response: “Pidenotes” has been replaced by “Pi denotes”.
Line 238: please replace “AGB; ν” by “AGB; and ν”.
Response: “AGB; ν” has been replaced by “AGB; and ν”.
Line 239: please replace “ith sampling” by “i-th sampling”.
Response: “ith sampling” has been replaced by “i-th sampling”.
Lines 247-248: the sentence “The parameter estimation and model evaluation results are shown in Table 3” have to be deleted because is already reported to follow.
Response: Thank you for your careful review, this is indeed our negligence.
This sentence “The parameter estimation and model evaluation results are shown in Table 3” has been deleted.
Line 256: please replace “are more effective than the three models.” by “are the more effective of the three models.”.
Response: “are more effective than the three models.” has been replaced by “are the more effective of the three models.”.
Line 258: please replace “RNDIC” by “RDNIC”.
Response: “RNDIC” has been replaced by “RDINC”.
Line 266: please replace “crown; CR” by “crown; and CR”.
Response: “crown; CR” has been replaced by “crown; and CR”.
Line 271: please replace “density; RCR” by “density; and RCR”.
Response: “density; RCR” has been replaced by “density; and RCR”.
Line 286: please replace “figure” by “Figure”.
Response: “figure” has been replaced by “Figure”.
Line 302: please replace “HDM; (b)” by “HDM; and (b)”.
Response: “HDM; (b)” has been replaced by “HDM; and (b)”.
Lines 315-318, and 327-330: I don´t understand the sentence between these Lines. Please explain better.
Response: We are very sorry for not making these two paragraphs clear. In this study, to highlight the HDM method, we have therefore used LDM as a comparison. The savings are all for the HDM method relative to the LDM method. The analysis revealed that the schemes with the HDM distribution method generally had smaller sample sizes and sampling ratios for the same sampling method and sampling cell size. This suggests that modelling the distribution of biomass using the HDM method can be effective in improving sampling efficiency. We have rewritten these two paragraphs as follows.
“Combining with Figure 7b, it can be seen that under the two sampling methods, compared with the LDM method, the HDB method reduces the sample capacity saving rate as the sample unit size increases, and the maximum is 89.24%. The HDM method can save the sample size to a certain extent in both sampling methods.” has been replaced by “Combined with Figure 7 (b), we can see that when the sampling method and the scale of the sampling unit are the same, the sample size of the scheme with HDM distribution is generally smaller than that of LDM. Moreover, as the sample unit scale decreases, the saving ratio of sample size ((sample size of LDM - sample size of HDM)/ sample size of LDM) increases for HDM versus LDM, up to a maximum of 89.24%. It is evident that under different sampling designs, the biomass distribution method using the HDM method can be used for a better saving of sample size.”
“Combining with Figure 8b, it can be seen that among the plans with the same sampling method, the scheme's sampling ratio, whose biomass distribution mode is HDM, is always more minor, and the maximum sampling ratio difference can reach 0.56. Therefore, the biomass sampling estimation schemes based on the HDM method have a lower sampling ratio.” has been replaced by “Combined with Figure 8 (b), it can be seen that the sampling ratio of the scheme with distribution method HDM is always smaller than that of LDM with the same sampling method and sampling unit size, and the maximum sampling ratio difference can be up to 0.56. Therefore, under different sampling designs, the biomass distribution method with HDM can effectively reduce the sampling ratio.”
Line 316: please replace “HDB” by “HDM”.
Response: “HDB” has been replaced by “HDM”.
Line 321: please replace “size; (b)” by “size; and (b)”.
Response: “size; (b)” has been replaced by “size; and (b)”.
Line 326: please replace “STS-HDM” by “STS-LDM”.
Response: “STS-HDM” has been replaced by “STS-LDM”.
Line 333: please replace “ratio; (b)” by “ratio; and (b)”..
Response: “ratio; (b)” has been replaced by “ratio; and (b)”.
Line 343: please replace “7 m and 6 m” by “6 m and 7 m”.
Response: “7 m and 6 m” has been replaced by “6 m and 7 m”.
Line 387: please replace “and so on. These” by “and so on, these”.
Response: “and so on. These” has been replaced by “and so on, these”.
Line 394: please replace “UAV” by “unmanned aerial vehicles (UAV)”.
Response: “UAV” has been replaced by “unmanned aerial vehicles (UAV)”.
Reviewer 2 Report
The choice of the case study is justifiable and relevant, considering that inaccurate estimation of the aboveground biomass (AGB) could lead to a butterfly effect when this data/models are used in more complex data. Several models are analysed for AGB estimation but authors fail to provide a comparison between these models and more advanced tools so it can properly provide a contribution to the scientific community. The significance of the analysis raises a number of questions as some statistics are missing.
I provide some general and detailed comments that can help the authors in further developing the research topic.
Specific comments:
What are the benefits and potential applications of AGB models with higher accuracy. Please read.
· Wirasatriya, A.; Pribadi, R.; Iryanthony, S.B.; Maslukah, L.; Sugianto, D.N.; Helmi, M.; Ananta, R.R.; Adi, N.S.; Kepel, T.L.; Ati, R.N.A.; Kusumaningtyas, M.A.; Suwa, R.; Ray, R.; Nakamura, T.; Nadaoka, K. Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery. Sustainability 2022, 14, 706. https://doi.org/10.3390/su14020706
· Geospatial supply-demand modelling of biomass residues for co-firing in European coal power plants. GCB Bioenergy 10 (11), 786-803. O. Cintas, G. Berndes, O. Englund, L. Cutz, F. Johnsson.
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Line 72
Authors clearly indicate the need for an HD model, although fail to indicate which gap in the state-of –art they are trying to fill. Is this the first HD model? If not, please include previous work and indicate the specific contribution.
Line 166
Authors indicate that the power function, quadratic parabolic function, and logarithmic function are simple in model form, flexible, and easy to apply. But what is the error of these models? Since one of the drives of this research is to provide a better estimation of the AGB.
Line 220
Please expand the definition of the reliability indicator, as it will probably be not clear to every reader. Also please specify the units of each of the terms, if any.
Line 261
Although the fitting is poor, authors claim that there is a significant relationship between model and data. But what about the significance? Please report p-value.
Line 301
No comparison with more advanced models? Remote sensing models? Machine-learning models?
Line 409
Authors indicate “The AGB distribution of the sample plot simulated by HDM is more uniform and continuous than that of LDM”. Bu this only applies when the sample size is small, right? Please check
Author Response
Open Review
( ) I would not like to sign my review report
(x) I would like to sign my review report
English language and style
( ) Extensive editing of English language and style required
( ) Moderate English changes required
(x) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
Response: Thank you for your careful review of our manuscript. We have further enhanced the English language and style of the manuscript following your suggestions.
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Can be improved |
Must be improved |
Not applicable |
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Does the introduction provide sufficient background and include all relevant references? |
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Are all the cited references relevant to the research? |
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Is the research design appropriate? |
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Are the methods adequately described? |
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Are the results clearly presented? |
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Are the conclusions supported by the results? |
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Response: Thank you for your objective comments on our manuscript. We have revised and added to the manuscript's content based on your comments. We hope that the revised manuscript will meet with your approval.
Comments and Suggestions for Authors
The choice of the case study is justifiable and relevant, considering that inaccurate estimation of the aboveground biomass (AGB) could lead to a butterfly effect when this data/models are used in more complex data. Several models are analysed for AGB estimation but authors fail to provide a comparison between these models and more advanced tools so it can properly provide a contribution to the scientific community. The significance of the analysis raises a number of questions as some statistics are missing.
Response: Thank you for recognizing our research and for your valuable comments on the study. Model comparisons were not carried out, firstly, because there are few existing models of biomass horizontal distribution; secondly, because these modeling data would require destructive measurements; and finally, because the complexity of the model form could not be better applied to this study. Therefore, the comparison of estimation effects among existing biomass horizontal distribution models was not addressed in this study. On the other hand, your suggestion that advanced tools be used in this study is consistent with our idea that our next step should be to use remote sensing tools like multispectral UAVs, terrestrial laser scanning, and airborne laser scanning to collect data on individual trees and stands for biomass horizontal distribution modeling. Furthermore, your proposal of applying machine learning methods to modeling is something we have not considered, and we will seriously consider and adopt your suggestion. This study was initially designed to explore a new biomass horizontal distribution model and apply it to biomass sampling estimation, so no advanced tools were applied.
I provide some general and detailed comments that can help the authors in further developing the research topic.
Specific comments:
What are the benefits and potential applications of AGB models with higher accuracy. Please read.
- Wirasatriya, A.; Pribadi, R.; Iryanthony, S.B.; Maslukah, L.; Sugianto, D.N.; Helmi, M.; Ananta, R.R.; Adi, N.S.; Kepel, T.L.; Ati, R.N.A.; Kusumaningtyas, M.A.; Suwa, R.; Ray, R.; Nakamura, T.; Nadaoka, K. Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery. Sustainability 2022, 14, 706. https://doi.org/10.3390/su14020706
- Geospatial supply-demand modelling of biomass residues for co-firing in European coal power plants. GCB Bioenergy 10 (11), 786-803. O. Cintas, G. Berndes, O. Englund, L. Cutz, F. Johnsson.
Response: Thank you for recommending these two papers to us, from which we have gained some helpful information and insights that will be useful in enhancing the quality of our manuscript and facilitating the next step of our research. We have cited some of the content of these two papers.
The specific changes are as follows.
A new paper is cited.
- Wirasatriya, A.; Pribadi, R.; Iryanthony, S.B.; Maslukah, L.; Sugianto, D.N.; Helmi, M.; Ananta, R.R.; Adi, N.S.; Kepel, T.L.; Ati, R.N.A.; Kusumaningtyas, M.A.; Suwa, R.; Ray, R.; Nakamura, T.; Nadaoka, K. Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery. Sustainability, 2022, 14,706.
Line 72
Authors clearly indicate the need for an HD model, although fail to indicate which gap in the state-of –art they are trying to fill. Is this the first HD model? If not, please include previous work and indicate the specific contribution.
Response: Thanks for your pertinent suggestions.
1) We explore this biomass estimation method by combining the biomass horizontal distribution model and sampling technique to achieve an efficient, accurate, low-cost, and reliable estimation of small-scale forest biomass. From then on, we will use the sampling technique combined with remote sensing to expand the estimation scale and finally realize the efficient, accurate, low-cost, and reliable estimation of forest biomass at a large scale.
The specific changes are as follows.
Original:
Therefore, it is necessary to explore a biomass horizontal distribution model (HDM) that fits the AGB distribution at the sample site scale and then develop a fast, accurate, and reliable method for estimating AGB in combination with sampling techniques.
Revised:
Therefore, it is necessary to explore a biomass horizontal distribution model (HDM) consistent with the actual distribution of AGB at the sample plot scale and combine it with sampling techniques to develop an AGB estimation method. Based on this method, we intend to achieve an efficient, accurate, and reliable estimation of forest biomass at the sample plot scale and provide technical support and methodological reference for expanding the scale of biomass estimation in the next stage.
2) The model in this study is not the first biomass horizontal distribution model. Several existing studies of biomass horizontal distribution have their own characteristics, but their results do not meet the needs of this study, so they were not previously expressed in the manuscript. We have now added this aspect.
We have added a paragraph to the introduction, as follows.
There are few studies on models of biomass horizontal distribution, Kershaw et al. [22] and Xu et al. [23] studied the horizontal distribution of leaf biomass, Nielsen et al. [24] and Fehrmann et al. [25] analyzed the distribution of tree roots. However, none of these studies developed an exact model of biomass horizontal distribution. Mascaro et al. [26] assumed that biomass is uniformly distributed within the canopy projection, which is simple but not realistic. It was not until Kleinn et al. [27] and Pérez-Cruzado et al. [28] constructed the first models for the horizontal distribution of leaf, stem, and branch biomass that the construction process was so complex. Therefore, we need to find ways to simplify the modeling process and make it easy to use.
We have added several references.
- Kershaw, J.A.; Maguire, D.A. Crown Structure in Western Hemlock, Douglas-Fir, and Grand Fir in Western Washington: Trends in Branch-Level Mass and Leaf Area. Canadian Journal of Forest Research1996, 25, 1897-1912.
- Xu, M.; Harrington, T.B. Foliage Biomass Distribution of Loblolly Pine as Affected by Tree Dominance, Crown Size, and Stand Characteristics. Canadian Journal of Forest Research1998, 28, 887-892.
- Nielsen, C.C.N.; Mackenthum, G. Die horizontale Varia-tion der Feinwurzelintensität in Waldböden in Abhängigkeit vonder Bestockungsdichte. Einerechnerische Methode zur Bestimmung der “Wurzelintensitätsglocke” an Einzelbäumen. Allgemeine Forst Und Jagdzeitung1991, 162, 112-119.
- Fehrmann, L.; Kuhr, M.; von Gadow, K. Zur Analyse Der Grobwurzelsysteme Großer Waldbäume and Fichte [Picea abies (L.) Karst.] Und Buche [Fagus sylvatica L.] (In German:“Analyis of the Coarse Root Systems of Large Trees at Spruce [Picea abies (L.) Karst.] and Beech [Fagus sylvatica L.] ”). Forstarchiv2003, 74, 96-102.
- Mascaro, J.; Detto, M.; Asner, G.P.; Muller-Landau, H.C. Evaluating Uncertainty in Mapping Forest Carbon with Airborne LiDAR. Remote Sensing of Environment2011, 115, 3770-3774.
Line 166
Authors indicate that the power function, quadratic parabolic function, and logarithmic function are simple in model form, flexible, and easy to apply. But what is the error of these models? Since one of the drives of this research is to provide a better estimation of the AGB.
Response: These are mentioned at the end of Section 2.2.2, but they may not be clear and perfect. Therefore, we have made some modifications to this part.
The specific changes are as follows.
Original:
The model evaluation used the coefficient of determination (R2), and the root mean square error (RMSE) to evaluate the effectiveness of the model fit and the mean absolute error (MAE) and the prediction accuracy (P) to test the independence of the model.
Revised:
The biomass horizontal distribution model established in this study has two aspects of error: the model fitting error on the one hand and the model prediction error on the other. Coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model fitting error, and mean absolute error (MAE) and prediction accuracy (P) were used to evaluate the prediction error.
Line 220
Please expand the definition of the reliability indicator, as it will probably be not clear to every reader. Also please specify the units of each of the terms, if any.
Response: It was an oversight on our part to not consider the full picture. We have refined our definition of reliability indicator.
The specific changes are as follows.
Original:
���(���, df) denotes the reliability indicator; α is the critical value; df is the degree of freedom;
Revised:
���(���, df) denotes the reliability indicator, also known as probabilistic assurance, which is determined from the reliability index by which confidence intervals can be calculated with probability 1- ���; ��� is the critical value, which is 0.05 in this study; df is the degree of freedom, which is equal to n-1;
Line 261
Although the fitting is poor, authors claim that there is a significant relationship between model and data. But what about the significance? Please report p-value.
Response: Thank you for your suggestion. Adding column p-values to Table 3 would indeed make the model fitting more convincing.
The specific changes are as follows.
Original:
Table 3. The parameter estimation and model evaluation for three canopy profile models.
|
Model |
Parameter Estimation |
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Model Fitting |
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Test of Independence |
||||
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a |
b |
c |
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R2 |
RMSE |
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MAE |
P (%) |
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Power Function |
3.996 |
0.710 |
— |
|
0.631 |
0.627 |
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0.508 |
66.36 |
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Quadratic Parabolic Function |
0.288 |
5.010 |
–1.338 |
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0.628 |
0.630 |
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0.509 |
68.72 |
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Logarithmic Function |
2.858 |
0.825 |
— |
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0.541 |
0.700 |
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0.651 |
47.29 |
Revised:
Table 3. The parameter estimation and model evaluation for three CPMs.
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Model |
Parameter Estimation |
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Model Fitting |
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Test of Independence |
|||||
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a |
b |
c |
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R2 |
p-value |
RMSE |
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MAE |
P (%) |
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Power Function |
3.996 |
0.710 |
— |
|
0.631 |
<0.001 |
0.627 |
|
0.508 |
66.36 |
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Quadratic Parabolic Function |
0.288 |
5.010 |
–1.338 |
|
0.628 |
<0.001 |
0.630 |
|
0.509 |
68.72 |
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Logarithmic Function |
2.858 |
0.825 |
— |
|
0.541 |
<0.001 |
0.700 |
|
0.651 |
47.29 |
Line 301
No comparison with more advanced models? Remote sensing models? Machine-learning models?
Response: We regret that our manuscript does not compare our approach with more advanced models. We have added relevant content to the discussion section and hope that the additions will meet with your approval.
We have added a segment at the end of the discussion section, as follows.
In terms of modeling data acquisition, constructing a CPM model requires destructive measurements of a certain number of trees to obtain branch resolution data. In contrast, the remote sensing modeling method uses remote sensing means, such as airborne multispectral, terrestrial laser scanning, and airborne laser scanning [28], to acquire data, which can effectively avoid destroying trees and significantly improve efficiency and accuracy with tremendous potential for application [50] but may also bring problems such as rising costs. As for the type of models, this study chose parametric models for the convenience of the research. These models may only be adapted to the region and are not very generalizable. In order to improve the estimation capability of the model, machine learning methods such as Support Vector Machine , Random Forest and Gradient Boosted Regression Tree [51-53] can be applied to the construction of biomass horizontal distribution models as a next step to further improve the robustness of the models.
We have added several references.
- Wirasatriya, A.; Pribadi, R.; Iryanthony, S.B.; Maslukah, L.; Sugianto, D.N.; Helmi, M.; Ananta, R.R.; Adi, N.S.; Kepel, T.L.; Ati, R.N.A.; Kusumaningtyas, M.A.; Suwa, R.; Ray, R.; Nakamura, T.; Nadaoka, K. Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery. Sustainability, 2022, 14, 706.
- López-Serrano, P.M.; Domínguez, J.L.C.; Corral-Rivas, J.J.; Jiménez, E.; López-Sánchez, C.A.; Vega-Nieva, D.J. Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests. Forests, 2020, 11, 11.
- Chen, L.; Wang, Y.; Ren, C.; Zhang, B.; Wang, Z. Assessment of Multi-Wavelength SAR and Multispectral Instrument Data for Forest Aboveground Biomass Mapping Using Random Forest Kriging. Forest Ecology and Management2019, 447, 12-25.
- Zhang, J.; Lu, C.; Xu, H; Wang, G. Estimating Aboveground Biomass of Pinus densata-Dominated Forests Using Landsat Time Series and Permanent Sample Plot Data. Journal of Forestry Research 2019, 30,1689–1706.
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Authors indicate “The AGB distribution of the sample plot simulated by HDM is more uniform and continuous than that of LDM”. Bu this only applies when the sample size is small, right? Please check
Response: We carefully verified that this conclusion is not related to the sample size but the scale of the sample units.
When the HDM method is used to simulate the horizontal distribution of biomass, the smaller the scale of the designed sample units, the finer the partitioning of the sample area, and the more continuous the variation of biomass value in a single sample unit and the biomass values in the surrounding sample units. However, when the LDM method was used, only the biomass values were available in the sample units at the location of the individual trees, while the biomass values in the other units were zero. When the scale of the designed sample units was larger, the biomass distribution in the sample units was closer to that of the HDM method.
