On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine
Round 1
Reviewer 1 Report
This paper is generally well written, in terms of producing the EMSI using multi-source satellite observations and evaluating the EMSI across various spatiotemporal scales. The only suggestion from the reviewer is about the improvement of the Introduction and Conclusions. From the Introduction, it's hard to clearly know what the current challenges and limitations of remote-sensing-based vegetation indices are, and why the EMSI is needed? The Conclusions read more like a part of Discussion, and should be reorganized to better state the main findings and implications of this research.
Author Response
Reviewer's comments:
This paper is generally well written, in terms of producing the EMSI using multi-source satellite observations and evaluating the EMSI across various spatiotemporal scales.
The only suggestion from the reviewer is about the improvement of the Introduction and Conclusions.
From the Introduction, it's hard to clearly know what the current challenges and limitations of remote-sensing-based vegetation indices are, and why the EMSI is needed?
Author's response:
We have added the following sentences and created a new section 1.1 to the Introduction to more clearly state the justification for standardized remote sensing based vegetation indices, and why the EMSI might be a useful application for monitoring ecosystem changes over time.
“Current challenges to measuring the ‘health’ of an ecosystem from EOS remote sensing relate to the establishment of what is the ‘normal’ or ‘average’ condition for a given location at any point in time in the present or the past. The justification for a multi-temporal standardized score is that it characterizes ecosystem health in terms of standardized greenness, rather than relative greenness. Areas that are always green, e.g. rainforests, or only briefly reach their peak greenness, e.g. desert grasslands, change dynamically, requiring a standardized value for their health within a season or period of time.
The availability of legacy remote sensing data for Earth systems study is both an asset and a liability. Decades of spaceborne remote sensing supply a rich source of data on systems dynamics; but spectral, spatial and temporal resolutions are often mixed. The application of multitemporal standard scores serves to address such challenges and limitations in remote sensing data; and in the present study, we apply multitemporal standard scores to ecosystem monitoring, adopting the term “Ecosystem Moisture Stress Index”. The EMSI represents a subset of potential applications of multitemporal standard scores, which we believe can apply more broadly to land use changes produced, for example, by urbanization and wildland fire disturbances. Our chief aim here is monitoring effects of climate forcing (i.e., temperature changes and precipitation deficits) through time.”
In our discussion (section 4.3) we do discuss the utility of a standardized index, versus a relative greenness index as part of the challenge to remote sensing and its limitations when looking at very green areas, e.g. tropical rain forests, versus very brown areas, e.g. desert grasslands.
Reviewer's comments:
The Conclusions read more like a part of Discussion, and should be reorganized to better state the main findings and implications of this research.
Author's response:
We have shortened and reorganized the Conclusions to more concisely state the main findings of the research.
Reviewer 2 Report
This manuscript presents a study to implement z score of NDVI in the google earth engine platform and then evaluate the indices in quantifying vegetation conditions in different locations. I think the idea of this z score is not new (which is essentially part of the Standard vegetation index), however, I think the efforts of developing the analyze framework based on google earth engine platform which can accommodate various EOS are encouraging. I have several concerns that need to be clarified and addressed:
- I think the term EMSI (Ecosystem Moisture Stress Index) is not appropriate since the z score itself can also reflect other conditions just as the authors mentioned in the manuscript, i.e. fire, land cover change, etc.
- Following (1) Is it possible to identify moisture stress events in combination with meteorology data (e.g. precipitation & evaporation)? According to the authors, various climate datasets are also included in their platform but it is not clear how these data are used.
- I think the current presentation is a little bit lengthy and boring. Some figures (e.g. Fig.6,7,etc.) can be moved to appendix as well.
Author Response
Reviewer's comments:
This manuscript presents a study to implement z score of NDVI in the google earth engine platform and then evaluate the indices in quantifying vegetation conditions in different locations.
I think the idea of this z score is not new (which is essentially part of the Standard vegetation index), however, I think the efforts of developing the analyze framework based on google earth engine platform which can accommodate various EOS are encouraging.
Author's response:
We agree the use of multi-temporal Z-scores is not new and its application as the SVI are complementary to the present work (re: section 1.2 of the manuscript). Yes, our primary intention is to show how the Google Earth Engine can be used to generate the SVI (as it was used specifically in drought monitoring in Peters et al., as well as for disturbance as the Fuel Moisture Stress Index (FMSI) (Yool 2001), and additionally for recognizing disturbance, long term ecosystem change.
Reviewer's comments:
I have several concerns that need to be clarified and addressed:
I think the term EMSI (Ecosystem Moisture Stress Index) is not appropriate since the z score itself can also reflect other conditions just as the authors mentioned in the manuscript, i.e. fire, land cover change, etc.
Author's response:
We agree, ‘EMSI’ as a description is not appropriate for fire or land cover change applications. We have modified the text to state that the EMSI is explicit for monitoring ecosystem conditions, and that rapid changes, such as those from herbivory or fire are recognized as rapid shifts in the EMSI score, as they are in the SVI.
Reviewer's comments:
● Following (1) Is it possible to identify moisture stress events in combination with
meteorology data (e.g. precipitation & evaporation)? According to the authors, various climate datasets are also included in their platform but it is not clear how these data are used.
Author's response:
In our Appendix A, we state the materials are hosted online in our GitHub repository: “In our GitHub repository, we provide scripts for analyzing fuel moisture, temperature, and precipitation from the DAYMET, GridMET, and PRISM. We also provide .js scripting for extracting data from GEE, and for calculating EMSI directly in the GEE.”
Reviewer's comments:
● I think the current presentation is a little bit lengthy and boring. Some figures (e.g. Fig.6,7,etc.) can be moved to the appendix as well.
Author's response:
We have removed all of the multipanel figures and two histograms from the main text and put them into the Appendix A. We believe that this tightens up the narrative and hopefully keeps readers interested.
Reviewer 3 Report
The authors have comprehensively provided all the required information in this study. The manuscript is also well-written and well-structured. They also provided a novel index in this study. I think the manuscript can be accepted without further revisions.
Author Response
We thank the reviewer for these kind comments
