Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China
and Qiyong Qin 1,2,3
Round 1
Reviewer 1 Report
The paper focuses on constructing the vegetation ecology quality index using six remote sensing indicators to China between 2000-2017. The manuscript is well-structured and provides a coherent background about the general issues, moreover a detailed description of the selected methodology. The results are clear and well usable for further studies since VEQI analysis is completed with a climate-related assessment. The parallel analysis of changing climatic patterns and land use aspects reveals important correlations. The paper can be accepted in its present form.
Author Response
We would like to express our sincere gratitude to your comments on the manuscript.
Reviewer 2 Report
The authors presented a paper that introduces vegetation ecology quality metric and anlyses its' spatiotemporal pattern. The general direction of the paper is OK, however it has major flaws that cannot be overlooked.
My main concern is the calculation of the vegetation ecology quality (VEQ or VEQI). The calculation is perfomed using PCA, which is not an appropriate method for calculation of the metric. PCA is used to perform a change of basis of the data and not to calculate a metric in this way.
Some other minor issues:
In the abstract you wrote:
"It’s crucial to research the spatio-temporal characteristics of VEQ and its response to climate change in China"
Why is it crucial to research the spatiotemporal characteristics of VEQ? The reader does not know what VEQ is at this point.
Since you already have given a map of VEQ, I don't understand what is the point of Figure 4?
In 3.2.2, you have given information on how VEQ changes within different altitudes, but haven't provided any reasons why it occurs.
Author Response
Dear editorial teacher\reviewer!
Thank you very much for your review comments on our paper "Spatiotemporal pattern of vegetation ecology quality and its response to climate change between 2000-2017 in China". Now, we reply to the review comments as follows.
- My main concern is the calculation of the vegetation ecology quality (VEQ or VEQI). The calculation is perfomed using PCA, which is not an appropriate method for calculation of the metric. PCA is used to perform a change of basis of the data and not to calculate a metric in this way.
Description on the modification: In this study, VEQI was constructed based on principal component analysis. The principal component analysis we used was based on the research results of many scholars. Similarly, Liu et al constructed the Remote-Sensing Ecological Index based on principal component analysis to measure the ecological status of Bayinbruck[1]; Xu et al constructed the Remote-Sensing Ecological Index based on principal component analysis to quantify the ecological status of Fujian Province[2]; Yue et al used principal component analysis to study the ecological quality of 35 major cities in China[3]. Therefore, the use of principal component analysis in this study has some scientific basis. Although the principal component analysis method used may not be the most appropriate, it does not contain any errors.
References
[1]Liu Q, Yang Z P, Han F, et al. Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke. Sustainability,2019,11(22): 6385.
[2]Xu H, Wang Y F, Guan H D, et al. Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis. Remote Sensing,2019,11(20). 2345.
[3]Yue H, Liu Y, Li Y, et al. Eco-environmental quality assessment in China's 35 major cities based on remote sensing ecological index. IEEE Access, 2019, 7(1):51295-51311.
- Some other minor issues:
In the abstract you wrote: "It’s crucial to research the spatio-temporal characteristics of VEQ and its response to climate change in China" Why is it crucial to research the spatiotemporal characteristics of VEQ? The reader does not know what VEQ is at this point.
Description on the modification: Appropriate change has been made to the background and significance of the study section of the abstract. The specific modification is the addition of the ecological role of VEQ. It is not so abrupt in this way. This is shown below(Red font is the modified part):
Vegetation ecology quality (VEQ) is an important indicator for evaluating environmental quality and ecosystem balance. The VEQ in China has changed significantly with global warming and gradual intensification of human activities. It’s crucial to research the spatiotemporal characteristics of VEQ and its response to climate change in China. However, most previous studies had used a single indicator to reflect VEQ in China, which needs to combine the effects of multiple indicators to reveal its variation characteristics.
- Since you already have given a map of VEQ, I don't understand what is the point of Figure 4?
Description on the modification: The main expression in Figure 4 is the distribution and trend characteristics of VEQ in longitude and latitude. Although the distribution map of VEQ has been given in Figure 3, it is very limited in what it provides. To quantitatively analyze the characteristics related to VEQ in longitude and latitude, it is necessary to draw and add Figure 4, otherwise it will not be convincing.
- In 3.2.2, you have given information on how VEQ changes within different altitudes, but haven't provided any reasons why it occurs.
Description on the modification: The main purpose of subsection 3.2.2 is to characterize the distribution and trend of VEQ at different altitudes. Therefore, we did not perform an imputation analysis. In order to make the study more comprehensive, we added the following attribution analysis in the discussion section of subsection 4.2:
Overall, the average VEQI values of natural vegetation ecosystems were not significantly different below 4 km. When the altitude is greater than 4 km, the average value of VEQI decreases with the increase of altitude. This is because the zones with altitude greater than 4 km in China are mainly located in the alpine regions such as the Qinghai-Tibet Plateau and the Tianshan Mountains. Seriously affected by the topographic height, the water and heat conditions in these areas are poor, which is not conducive to the healthy growth of vegetation. And the higher the altitude, the worse the ecological quality of vegetation. The improvement of VEQ in low elevation areas throughout the study period may be mainly influenced by climate and human activities. The increase of precipitation in the low elevation area during the vegetation growth season in the last 18 years has promoted the healthy growth of vegetation. Human activities (e.g., afforestation, grazing ban, reforestation, etc.) had also greatly contributed to the improvement of VEQ.
Reviewer 3 Report
My comments are as follow:
- In the Keywords section, I suggest to replace the words “China”, “VEQ” or “spatio-temporal variation”; by “germplasm”, “fruit yield”, or “yield components”, by “hurst exponent”, “principal component analysis” or other keywords, to improve the scope of your research in the scientific websites databases and to increase the scope of your research.
- What about the statistical analysis of your manuscript? The principal component analysis (PCA), what software did you use?
- What about the future work in your research?
Author Response
Dear editorial teacher\reviewer!
Thank you very much for your review comments on our paper "Spatiotemporal pattern of vegetation ecology quality and its response to climate change between 2000-2017 in China". Now, we reply to the review comments as follows.
- “In the Keywords section, I suggest to replace the words “China”, “VEQ” or “spatio-temporal variation”; by “germplasm”, “fruit yield”, or “yield components”, by “hurst exponent”, “principal component analysis” or other keywords, to improve the scope of your research in the scientific websites databases and to increase the scope of your research.”
Description on the modification: The original keywords were changed to "vegetation ecology quality; principal component analysis; SEN+Mann-Kendall; climatic factor; China". The new keywords used include the research object, method and study area, which are more comprehensive. In addition, the key words "germplasm", "fruit yield", or "yield components" mentioned by the reviewer are not applicable to this paper. therefore, they were not adopted.
- What about the statistical analysis of your manuscript? The principal component analysis (PCA), what software did you use?
Description on the modification: (1) we could not understand what the reviewer said about the statistical analysis of the manuscript because the reviewer may not have expressed it clearly enough. (2) In the principal component analysis, we mainly used IBM SPSS Statistics 25 software. The raster data points were first converted to TXT numerical format in arcgis 10.4 software, and then the principal component analysis was performed using IBM SPSS Statistics 25.
- What about the future work in your research?
Description on the modification: We have described future research work in the original article. Editors and reviewers can refer to subsection 4.3 of the article.
Reviewer 4 Report
line 217: Could the authors clarify if the VEQIi is the result of eq.10?
Fig. 3b: Is it VEQ or VEQI?
Fig 4b and 4d: These figures present the latitudinal and longitudinal trends of VEQI. From a and c figs reader can notice that VEQI values increase and decrease along both directions. Why in b and d there is no negative value for trend? It seems to me that something is wrong or misunderstood here.
lines 432,433: Is it VEQ or VEQI?
Author Response
Dear editorial teacher\reviewer!
Thank you very much for your review comments on our paper "Spatiotemporal pattern of vegetation ecology quality and its response to climate change between 2000-2017 in China". Now, we reply to the review comments as follows.
- line 217: Could the authors clarify if the VEQIi is the result of eq.10?
Description on the modification: We have modified and illustrated equation (11) as shown below.
VEQIN=[ VEQIi-min(VEQIi)]/[ max(VEQIi)-min(VEQIi)] (11)
where VEQIN is the standardized pixel value of VEQIi, and VEQIi is the unnormalized VEQI pixel value calculated by Equation (10). For details, please refer to the article revision.
- Fig. 3b: Is it VEQ or VEQI?
Description on the modification: The text in Fig. 3b is VEQ, which is not a problem here.
- Fig 4b and 4d: These figures present the latitudinal and longitudinal trends of VEQI. From a and c figs reader can notice that VEQI values increase and decrease along both directions. Why in b and d there is no negative value for trend? It seems to me that something is wrong or misunderstood here.
Description on the modification: Fig. 4a and Fig. 4c show the distribution of veqi mean values along the latitude and longitude. The increase and decrease of average VEQI values along the latitude and longitude do not have any correlation with the trend values of Fig. 4b and Fig. 4d. Therefore, there is no error here.
- lines 432,433: Is it VEQ or VEQI?
Description on the modification: Lines 432,433 is VEQ. It was checked by us to be correct.
Round 2
Reviewer 2 Report
The authors should then add some citations of solid works where PCA has already been used for spatial index calculations in this way before.
Author Response
Dear reviewer!
Thank you very much for your review comments on our paper "Spatiotemporal pattern of vegetation ecology quality and its response to climate change between 2000-2017 in China". Now, we reply to the review comments as follows.
- The authors should then add some citations of solid works where PCA has already been used for spatial index calculations in this way before.
Description on the modification: We have added some citations of solid works where PCA had already been used for spatial index calculations in sub-section 2.3.2 (i.e. Construction of VEQI) of the article. And the references were updated in real time. The details of the modification are shown below (Red font is the modified part).
2.3.2. Construction of VEQI
The proposed VEQI should integrate the characteristics of the above six indicators. Principal component analysis (PCA) can reduce data redundancy and reflect the information of the original variables as much as possible. It’s also objective and can avoid human error [25]. It had already been used for spatial index calculations in the researches of many scholars. Similarly, Liu et al constructed the Remote-Sensing Ecological Index based on PCA to measure the ecological status of Bayinbruck [35]. Xu et al constructed the Re-mote-Sensing Ecological Index based on PCA to quantify the ecological status of Fujian Province [30]. Yue et al used PCA to research the ecological quality of 35 major cities in China [36]. Consequently, PCA was utilized to perform the analysis of dimensions reduced on six indicators. Firstly, the original data were standardized to eliminate the effects of disparate scales and dimensions [37], and the sample correlation coefficient matrix was calculated. Subsequently, the eigenvalues and eigenvectors of the sample correlation coefficient matrix were acquired, and momentous principal components were also selected. Finally, the VEQI was obtained by the weighted summation formula. The standardized and weighted summation formulas are shown below, respectively [37].
Author Response File: 
Author Response.doc
