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Article
Peer-Review Record

Cold Wave-Induced Reductions in NDII and ChlRE for North-Western Pacific Mangroves Varies with Latitude and Climate History

Remote Sens. 2021, 13(14), 2732; https://doi.org/10.3390/rs13142732
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Remote Sens. 2021, 13(14), 2732; https://doi.org/10.3390/rs13142732
Received: 29 May 2021 / Revised: 17 June 2021 / Accepted: 9 July 2021 / Published: 12 July 2021

Round 1

Reviewer 1 Report

Abstract

L21: We isolated 21 of the cold wave effects on the forest canopy from phenology. How ? did you use  any phonological approaches such as using BBCH scale?

L31-32: This is not the result section. I recommend changing these sentences to summarize the importance of your study or the implication of the study findings.

Introduction :

In general, more literature need to be added to this section

L44-53: what about precipitation?

L75-85: The authors need to add relevant information about the importance of remote sensing data in the study related to mangroves, including the pros and cons.

Materials and Methods

L166-188: I did not find anything related to the plot size and how they are matched with sentinel-2 data? Please revise and add relevant information

L213-240: The authors investigated how the cold waves would affect canopy reflectance signature using NDII and ChIRE. I recommend adding more spectral indices and canopy properties; for example, measuring LAI, Chlorophyll, and canopy water content will be an option.   Also, if the size of the selected plots is big enough, you may use MODIS LST data as well.

Results and discussion: well written

Author Response

 L21: We isolated 21 of the cold wave effects on the forest canopy from phenology. How ? did you use  any phonological approaches such as using BBCH scale?

Reply: The effects of cold wave were separated from the phenology by comparing the 2021 VI values to the values in the 5-years baseline (without cold wave) as explained in lines 16-17 “Using a cold wave event from 2021, we evaluated the effects of low temperatures on vegetation index (VI) change (relative to a recent five-year baseline) […]”. For clarity, we added the information in lines 22-23 “We isolated the cold wave effects on the forest canopy from phenology (i.e., cold wave-induced deviation from a five-year baseline) and […]”. The baseline method is explained in section 2.4.2 starting line 260.

 

L31-32: This is not the result section. I recommend changing these sentences to summarize the importance of your study or the implication of the study findings.

Reply: The two last sentences were modified to highlight the implications of our study, it now reads “Our results confirm that local baseline (i.e., recent past) climate predicts canopy resistance to cold wave damage across K. obovata stands in the northern Pacific, and in congruence with findings from New World mangroves, they imply geographic variation in mangrove leaf physiological resistance to cold for Northern Pacific mangroves” (lines 31-34).

 

In general, more literature need to be added to this section

Reply: We added more references to this section:

  1. Asbridge, E., et al., The extent of mangrove change and potential for recovery following severe Tropical Cyclone Yasi, Hinchinbrook Island, Queensland, Australia. Ecol. Evol., 2018. 8(21): p. 10416-10434.
  2. de Beurs, K.M., et al., Hurricane damage detection on four major Caribbean islands. Remote Sensing of Environment, 2019. 229: p. 1-13.
  3. Devaney, J.L., et al., Low humidity and hypersalinity reduce cold tolerance in mangroves. Estuarine, Coastal and Shelf Science, 2021. 248: p. 107015.
  4. Friess, D.A., et al., The state of the world's mangrove forests: past, present, and future. Annual Review of Environment and Resources, 2019. 44(1): p. 89-115.
  5. Giri, C., Observation and monitoring of mangrove forests using remote sensing: opportunities and challenges. Remote Sensing, 2016. 8(9): p. 783.
  6. Kuenzer, C., et al., Remote sensing of mangrove ecosystems: a review. Remote Sensing, 2011. 3(878-928).
  7. Otero, V., et al., An analysis of the early regeneration of mangrove forests using Landsat time series in the Matang Mangrove Forest Reserve, Peninsular Malaysia. Remote Sensing, 2019. 11: p. 774.
  8. Pham, T.D., et al., Remote sensing approaches for monitoring mangrove species, structure, and biomass: opportunities and challenges. Remote Sensing, 2019. 11: p. 230.
  9. Wang, L., et al., A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 2019. 231: p. 111223.
  10. Zhang, K., et al., Remote sensing of seasonal changes and disturbances in mangrove forest: a case study from South Florida. Ecosphere, 2016. 7(6): p. e01366.
  11. Zhang, C., S.D. Durgan, and D. Lagomasino, Modeling risk of mangroves to tropical cyclones: a case study of Hurricane Irma. Estuarine, Coastal and Shelf Science, 2019. 224: p. 108-116.

 

L44-53: what about precipitation?

Reply: We added a reference to the work of Devaney et al. (2021, already cited in Section 4.2 see https://www.sciencedirect.com/science/article/pii/S0272771420307460) about the effect of aridity in conjunction with low temperatures in mangroves in lines 48-52, the sentence now reads “Low, but non-freezing temperatures (i.e., chilling) lead to physiological stress, which often manifests as reductions in leaf chlorophyll concentration, photosynthetic rate, stomatal conductance, and stem sap flow, which sometimes results in sapling mortality for cold-sensitive species or in conjunction with low humidity.” The effect of precipitation is discussed in Section 4.2 and the measured rainfall values of winter 2021 and the 5-year baseline are shown in Table S2 (Supplementary data).

 

L75-85: The authors need to add relevant information about the importance of remote sensing data in the study related to mangroves, including the pros and cons.

Reply: We added relevant information in lines 77-89 to highlight the importance of remote sensing in mangrove studies as well as its limitations. The sentence read: “Numerous studies of the effects of cold events on mangroves are based on field observations, however, satellite imagery and remote sensing techniques have also been used to examine mangrove phenology, forest dynamics as well as to observe and model their responses to disturbances from local to regional scales. Remote sensing approaches allow for standardized temporal analyses over large areas, which makes them ideal to monitor mangrove environments that are otherwise difficult to study through field-based surveys. On the other hand, mangrove remote sensing is limited by the frequent cloud cover in coastal tropical and subtropical regions, and by the relationships between image-derived indices (e.g., vegetation indices) and forest structural or canopy characteristics. Nevertheless, ground-based studies have validated that remote sensed-derived indices, such as the normalized difference infrared index, reflect real changes in mangrove canopy properties following cold and wind disturbances, including both canopy damage and recovery dynamics.”

 

L166-188: I did not find anything related to the plot size and how they are matched with sentinel-2 data? Please revise and add relevant information

Reply: We thank the reviewer for pointing out the missing information regarding plots size. Section 2.1 now indicates the area of each site at line 129 “The northernmost site was located in Tanegashima Island (15 ha) […]” for Tanegashima, line 135 “[…] from the estuaries of the Danshui (40 ha) and Xinfeng (14 ha) rivers […]” for Danshui and Xinfeng, line 137 “The fourth site, a mangrove stand boarding the Kuira and Hidori rivers of Iriomote Island (51 ha) […]” for Iriomote, line 140 “[…] two mangrove stands were selected from southern Taiwan in the Hailiao (3 ha) and Yanshui (7 ha) estuaries […]” for Hailiao and Yanshui, and line 143 “[…] we selected two stands outside the influence of the Kuroshio Current in the Jiulongjiang (22 ha) and Zhangjiang (31 ha) estuaries […]” for Jiulongjiang and Zhangjiang. We clarified plot-Sentinel-2 matching at the end of Section 2.3.2 lines 252-254: “We reprojected plot shapefiles to match Sentinel-2 scenes projection before extracting data from all scenes with the ‘raster’ package in R. Values of common pixels across all dates (i.e., never covered by clouds or cloud shadows) were used for subsequent analyses.

 

L213-240: The authors investigated how the cold waves would affect canopy reflectance signature using NDII and ChIRE. I recommend adding more spectral indices and canopy properties; for example, measuring LAI, Chlorophyll, and canopy water content will be an option.   Also, if the size of the selected plots is big enough, you may use MODIS LST data as well.

Reply: The nature of our study (solely based on remote sensing) did not allow us to use ground-based measurements of LAI or chlorophyll or canopy water content, however the NDII and the ChlRE are both related to the canopy characteristics as explained in lines 231-233 “NDII is a one index that is representative of canopy leaf water and chlorophyll content. NDII values range between −1 and 1, with higher values indicating healthy vegetation cover.” for NDII and canopy water content, and lines 238-248 “We used the ChlRE to monitor the variation in canopy leaf chlorophyll content associated with cold stress. The ChlRE is based on the first RE band of Sentinel-2 (RE1, band 5) and NIR (RE3, band 7, equation 2). Band 5 of Sentinel-2 imagery is centered on the 705nm wavelength, which corresponds to the RE position of K. candel leaves with low chlorophyll concentrations in southern China. Therefore, ChlRE decreases when the quantity of chlorophyll decreases and the RE position shifts from longer wavelengths toward 705nm. In addition, Sun et al. [67] compared the three RE bands of Sentinel-2 for crops, and they found that the band 5 was the most sensitive and band 7 the least sensitive to crop chlorophyll content; moreover, the authors showed that ChlRE had a strong relationship with crop canopy LAI.” for ChlRE and canopy chlorophyll content and LAI.

We also agree with the reviewer suggestion to use MODIS LST. However, the mangrove stands of Northeast Asia are relatively small (i.e., the areas that calculated for the reply above), and hence do not fit easily in MODIS LST imagery spatial resolution (1 km).

Author Response File: Author Response.docx

Reviewer 2 Report

This is an interesting, rigorous, and well-written paper. I have included a few tiny minor revisions below:

Introduction:

Page 2, Line 87: Please change "there is a need to standardized comparisons" to either "there is a need to standardize comparisons" or "there is a need for standardized comparisons".

Page 3, Line 98: Please add "the" between "ask" and "following".

 

Materials and Methods:

Page 3, Line 113-116: Please consider revising this sentence for clarity. What do the authors mean by "natural origin"? Why is it important that the mangroves selected for the study be in riverine-estuarine settings?

Page 4, Line 140: I like Figure 1 a lot!

Author Response

Page 2, Line 87: Please change "there is a need to standardized comparisons" to either "there is a need to standardize comparisons" or "there is a need for standardized comparisons".

Reply: We thank the reviewer for its help regarding grammatical errors, the sentence (lines 97-98) now reads: “Finally, there is a need for standardized comparisons of cold temperature effects across forest stands dominated by the same species […]”.

 

Page 3, Line 98: Please add "the" between "ask" and "following".

Reply: We added the missing word, the sentence is now: “Comparing winter 2021 to previous five-year baseline, we ask the following questions: […]” (line 109).

 

Page 3, Line 113-116: Please consider revising this sentence for clarity. What do the authors mean by "natural origin"? Why is it important that the mangroves selected for the study be in riverine-estuarine settings?

Reply: Following the reviewer suggestion, we improved the sentence (lines 124-127) into “Our selection was based on the following criteria: natural and protected forests (e.g., within reserves and not planted), in a riverine-estuarine settings (i.e., moderate salinity), and with >1 ha area so that they could be identified in the USGS Global Distribution of Mangroves dataset 1.3 (GDM)”.

We wanted to control for the hydromorphological setting because it can have implications on the salinity in the mangrove. Mangrove stands without fresh water can be exposed to higher salinity, which is also a source of stress for the mangrove trees, which can affect canopy remote sensing data. This was discussed in Section 4.2. (lines 501-503): “Indeed, no sites had experienced other stressors, such as strong winds or hypersalinity in conjunction with the cold wave – as the eight mangroves are in comparable estuarine settings.

 

Page 4, Line 140: I like Figure 1 a lot!

Reply: We thank the reviewer for their comment regarding Figure 1.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

I am satisfied that the authors have addressed all my comments now.

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