Vegetation Dynamics and Driving Mechanisms Considering Time-Lag and Accumulation Effects: A Case Study of Hubao–Egyu Urban Agglomeration

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors examined spatio-temporal dynamic of vegetation and its response to climatic factors and land use types using remote sensing data. The topic of the study falls into the scope of the journal. The data are plentiful. However, it is a pity that the manuscript is not well structured. The wording is bad and this makes readers very confused.

 

Major concerns:

Looking through the manuscript, I am still confused of what ‘lag effect’ and ‘accumulation effect’ are. The authors should give a clear definition of two terms. In addition, the authors should give a description or a formula to evaluate ‘lag effect’ and ‘accumulation effect’.

In the abstract, the authors did not show the patterns of vegetation dynamic response, but the spatio-temporal patterns of vegetation dynamic. How did the authors evaluate the ‘independent impact’? What is the temporal effect?

In the method part, I strongly recommend adding a workflow chart of this study. The sub-titles should be replaced with the aim of this study, like vegetation response to land use types and climatic factors, lag effect and accumulation effect, not the names of statistical methods. Since the focus of this study is not to discuss the methodology.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper aims to presents a novel approach to understanding the complex dynamics of vegetation in the environmentally sensitive Hubao-Egyu Urban Agglomeration in northern China. The study aims to not only chart out the spatial and temporal trends of vegetation cover but also delve into the factors governing vegetation response to climatic elements and enhance the residual analysis model by integrating temporal effects. This unique methodology promises to provide a more accurate attribution of shifts in vegetation to climate and human activities.The research utilizes techniques such as adapting the Normalized Difference Vegetation Index (kNDVI) to monitor vegetation dynamics, which provides a more precise assessment of vegetation conditions. The data, ranging from 2000 to 2022, were derived from the MOD13Q1 dataset, and various statistical methods, such as Theil-Sen trend analysis and Mann-Kendall tests, were applied to assess vegetation trends. Partial correlation analysis determined the lag and accumulation effects of climatic variables such as temperature, precipitation, and potential evapotranspiration (PET) on vegetation.The results revealed noteworthy regional greening, with a kNDVI slope of 0.0163 per decade, primarily influenced by precipitation. The study discovered that vegetation's response to climate varied across different land types, with the most evident lag effect of temperature observed in cropland and grassland and the most pronounced accumulation effect of precipitation in grassland. The incorporation of lag and accumulation effects augmented the explanatory power of climate on vegetation dynamics by 6.95%, rectifying the underestimation of climate contributions by conventional models. To sum up, the study reiterates the significance of accounting for time-lag and accumulation effects when scrutinizing vegetation dynamics in the context of climate change. The findings, with their significant implications, offer valuable insights for regional ecological management and strategies to address climate change. They underscore the necessity for more sophisticated models that incorporate temporal effects to comprehensively comprehend and address vegetation changes in environmentally vulnerable areas..

I have the following comments and suggestions for including information and improvements:

Abstract

It would be greatly appreciated if the word limit in this section could be adjusted to adhere to the author's guidelines, as the current abstract exceeds the recommended 200-word limit. For further details, please refer to the following link: https://www.mdpi.com/journal/land/instructions, particularly the Front Matter section under Abstract.

I would like to encourage the authors to provide more comprehensive details about the study's methodologies to enhance the abstract. Additionally, it would be advantageous for the authors to clearly express the main objectives of the study for better reader understanding.

 

Introduction

Please provide a more explicit rationale for applying kNDVI employed as a monitoring indicator for vegetation dynamics methods over time in other regions, counties, or locations sharing similar characteristics is imperative. Although the importance of the study is evident, it is necessary to include more elements that justify this application in various counties and locations in terms of justification. The authors should illustrate more about the state of the art of kNDVI as a monitoring indicator for applying vegetation dynamics.

Another critical point that will bring more information to the reader is to describe directly and with greater clarity the objectives of the work. Of course, when reading the complete introduction, we know what the objective is. However, making it more explicit will help readers understand the work's importance and the hypotheses tested.

 

Methods 

1.     I kindly request a more comprehensive explanation that thoroughly covers all of the methods. The selection criteria and method assumptions should have been discussed in all sections.

 

2.     I kindly request that you provide a comprehensive justification for the selection of the criteria for the Mann-Kendall indices employed in the study. The current explanations are quite brief, and there is a need for more detailed elaboration. Please provide a more explicit rationale for applying Mann-Kendall indice.

 

3.     How can the differences in spatial resolution between all spatial data affects (MOD data, Climate data, Terrain factors) the results? I believe it's important to include this in a section of the methods or even in the discussion.

 

4.     Did you include a model validation step in your work? If you did, it's crucial to share the details and results.

 

5.     It's essential to reference the seminal paper when mentioning Google Earth Engine. I recommend including Remote Sens in the citation and references: 2023, 15(14), 3675; https://doi.org/10.3390/rs15143675.

 

6.     Another critical point to consider is that, given the substantial funding for the project ("National Program on Key Research Projects of China 555 [grant numbers 2017YFC1502706]" and the starting grant for introduced talents from Sun Yat-sen University (to X.L., 37000-12240012)), it would be beneficial to include the project's GitHub repository with the scripts used. This is a good practice for the community using GEE and a way to showcase the project to the academic community.

 

 

 

Results

1.     The authors should consider providing additional detail in the sections on Spatial-Temporal variations of vegetation dynamics, Time lag and accumulation effects of climate factors on kNDVI, and Contributions of human activity and climate change to vegetation dynamics. While the main results were presented accurately, more specific details could be included. Therefore, I recommend providing more detailed results for each region in all sections.

 

2.     Given that validating the application holds significant importance in the paper, it is recommended to establish a dedicated section to present and discuss the validation results.

 

Discussion

 

The authors should include a dedicated section that contrasts the outcomes obtained from the methods and analyses proposed in the article with existing findings in the literature from other countries and regions. This will help to alleviate the impression that the results found follow strictly local patterns, providing broader context to the discussion based on the results.

 

Conclusions

Authors need to add a specific section that compares the outcomes achieved from the suggested approaches and examinations in the article with earlier discoveries in the literature from various countries and areas. This addition will help the authors in dealing with possible limitations and lead them to provide a more thorough perspective of their research.

General

In the Google Earth Engine community, it's beneficial to share the code or the project's GitHub repository. This enables other researchers to replicate your findings and allows journal referees to evaluate the script implementation. Consider making your project's GitHub link available to the scientific community; it's a valuable practice.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf