Greening and Browning Trends on the Pacific Slope of Peru and Northern Chile

Round 1

Reviewer 1 Report

Please see the attachment

Comments for author File: Comments.pdf

Author Response

We thank the reviewer for their comments. Below, we clarify the various questions and detail our amendments made to the paper.

Text in italic highlights our replies to the comments and text in standard font are the amendments made to the manuscript.

Title correctly describes the article.  

Abstract The abstract presents a concise and complete synthesis of the work. Keywords: MODIS; EVI; time series; greening; browning; Andes; Peru; climate zones; life zones; trend Some of these keywords were already mentioned in the title.

OK – we have removed these.

Introduction The introduction suitably depicts the problem about the climate influence, as the Köppen-Geiger climate system represents, on the terrestrial ecosystems.

104- 114 Above the cold desert lies the cold semi-arid steppe, (coded BSk), which extends along the Pacific slope in a narrow elevational strip that gradually ascends southwards into Chile and Bolivia. This zone differs from the cold deserts in that more than 50% of its precipitation falls outside a short wet season and is typified by dense thorn scrub and cacti. Ascending to the high Andes, the cold semi-arid steppe climate zone (coded BSk) gives way to the Polar Tundra (coded ET). This is a more simply defined climate with average monthly temperatures lying above freezing but not exceeding 10C and being a region typically covered in open grasslands with relictual patches of Polylepis woodlands. When mapped, these climatic zones produce a north to south arrangement of parallel zones following the Pacific slope of Peru and northern Chile as shown in Figure 1 which indicates our study area and major Köppen-Geiger climate zones [3,4].

The BSh region was not described here.

This brief description was added to the manuscript.

The hot arid steppe zone (BSh) only occupies a very small and fragmented area above hot deserts in the far north of Peru. As our area of interest lies to the south, this climate zone does not appear in our analysis.

The objectives are explicit and clearly exposed.

Material and Methods

Study Area. The region under study is appropriately described

All the methodology was thoroughly detailed.

Figure 2 is a complete synthesis of the procedure

Results The results obtained are presented in an orderly manner and they are analyzed in depth.

All figures are necessary and facilitate the interpretation of the work.

Discussion I believe that the specifications incorporated are appropriate.

Conclusions The conclusions are fully consistent with the results obtained

Reviewer 2 Report

This article presents the results of statistical analysis applied to MODIS EVI imagery in order to identify patterns of greening and browning along the coast of Peru and Chile. The research is relevant in the context of global warming and provides insights into understanding the effects of warming on ecosystems located in sensitive environments. The article is well structured and provides relevant examples to sustain the results. I have just few comments:

- I suggest shortening the part from the introduction with respect to Köppen-Geiger classification (rows 83-115), and alternatively relate it with the characteristics of the study area (section 2.1)

- you might check and cite in the methodology part the work of Cortés et al, 2021 (https://doi.org/10.1029/2020GL091496)  related to the global greening and browning trends (multiple testing procedure to control false positive outcomes)

-   the scale of maps in figures 3-8 is not really visible

-   in figures 4-8, the scale seems too coarse to compare the greening/browning spots with high-resolution imagery on the right; the outlined areas might be more visible if you zoom in to see more detail, at least in one of the examples (one of the figures).

Author Response

We thank the reviewer for their comments. Below, we clarify the various questions and detail our amendments made to the paper.

Text in italic highlights our replies to the comments and text in standard font are the amendments made to the manuscript.

This article presents the results of statistical analysis applied to MODIS EVI imagery in order to identify patterns of greening and browning along the coast of Peru and Chile. The research is relevant in the context of global warming and provides insights into understanding the effects of warming on ecosystems located in sensitive environments. The article is well structured and provides relevant examples to sustain the results. I have just few comments:

I suggest shortening the part from the introduction with respect to Köppen-Geiger classification (rows 83-115), and alternatively relate it with the characteristics of the study area (section 2.1).

OK – amended to focus more closely on study area.

you might check and cite in the methodology part the work of Cortés et al, 2021 (https://doi.org/10.1029/2020GL091496) related to the global greening and browning trends (multiple testing procedure to control false positive outcomes)

OK – we have connected our work to the findings of this paper.  

the scale of maps in figures 3-8 is not really visible .

OK – this has been amended.

in figures 4-8, the scale seems too coarse to compare the greening/browning spots with high-resolution imagery on the right; the outlined areas might be more visible if you zoom in to see more detail, at least in one of the examples (one of the figures).

Figures 4-8 aim to highlight a few large zones of greening and browning with direct anthropogenic influence, which we remove from the definition of the greening strip. More detailed observations of cities and fields for example are beyond the scope of this paper, however, we intend to make public the full GeoTIFF used to produce these images such that people can zoom into whichever area is of interest.  

Reviewer 3 Report

Lepage et al evaluate long term vegetation trends in western South America using EVI data. The paper is very well written, though the quality of the graphics could be improved somewhat.

Should we expect vegetation trends to correspond with Koppen zones? As the authors note there is regional variation in warming with a climate zone. What about comparisons to ecoregions? (e.g. WWF ecoregions? Are there distinctive vegetation conditions within the GS? Or perhaps they are showing relatively high greening because the starting condition was quite brown?

The manual nature of defining the greening strip is a bit concerning. From figure 1, it appears the strip could be broadened in some locations.

If there is 0% plant cover, which seems to be the case in at least some of this region, why would there be any EVI trend at all? Is any of the greening strip ~0% vegetation cover?

Minor comments

Line 45: I disagree, there are lots of studies on climate drivers of vegetation dynamics (per lines 118-119), but the statement in line 52 (lack of studies in arid upland regions) is true.

Lines 46-48: Do you have a citation to back up this statement?

Lines 59-60: “low levels of biodiversity”, but you state in the abstract “biologically rich”, these seem at odds.

Line 76: “only up to 300m above any given point”. This statement isn’t clear to me, so at location below 800 m asl, C02 can vary within 300 m of the ground surface?

Lines 163-169: No hypothesis?

Line 206: There is no EVI band on MODIS, rather there are red, NIR, and blue bands, that are used to calculate EVI.

Line 207: You are not necessarily looking at land cover change (e.g forest to grassland, grassland to urban, etc.), rather land condition change.

Line 208: This was already described in the introduction, no need to do it again.

Lines 230-231: “the construction of houses or factories and corresponding largely to the expansion of urban areas” all of this can be replaced with simply “urbanization.”

Line 244: So in other words, “points” are simply a single data point from a 16 day composite?

Figure 2: All the abbreviations used in the figure will need to be define in the caption. Also why are lomas removed?

Lines 288-289: In this case the short temporal observation window is the 16-day compositing period of the raw EVI data or the 21 year time-series? If the latter, there certainly could be non-linear trends.

Line 302: What years of LCCS data were used? Is this an annual product?

Line 304: why only look at greening pixels? Browning pixels were excluded from all further analysis? It appears not based on 336-337?

Figure 3. Recommend describing what the readers are roughly looking at first. Something like: change in EVI from 2000 to 2020 using MODIS….

“where change below -10% and above 60% were clipped” I think were “capped in this graphic” is more accurate. Clipped implies removed.

Would be helpful to label the boxes inset map with the corresponding higher resolution figure.

Lines 344-345: Why define the acronym GS for greening strip, then not use it?

Figure 4: Any ideas why the glaciers are showing as greening? These seem suspect.

Figure 5: It seems odd that a mine (upper middle) is showing as greening. Is this a recently abandoned mine?

Figure 7: It seems that several of the agricultural areas in this image show a browning trend? Any thoughts on this?

Figure 9: Is this plot only for the greening strip? What about the gap from figure 4? Is that the most narrow part in the north of this plot?

Line 381: “which sharply drops off after a positive trend greater than 30%.” This means there isn’t much area outside the GS with a EVI trend >30%?

Lines 394-396: Or perhaps the zones were not properly defined to begin with.

Line 408: And agricultural abandonment?

Lines 422-423: You could confirm with a different remote sensing dataset (eg Landsat) which goes back to 1985.

Lines 426-429: Given the context the temperature generally decreases from north to south (in addition to elevation) in the study area? I think this is important to note.

Figure 11: It seems that most of the greening didn’t start until ~2016.

Table 3: To see the relative difference in the GS, we need to see the correlations in the climate zones outside the GS.

Line 461: All else is rarely equal in this context, precipitation almost always increases with elevation as it does in the study area.

Lines 462-463 “where the most intense relative greening rises in altitude as the strip progresses southward”, again, it seems important, that temperature generally decreases (all else equal) from north to south. So temperature gradients would be expected to run counter that of the GS. Specifically, in the south at say 1000 m elevation, the temperature would generally be cooler than in the north at 1000m. Meaning in the north you have to go further upslope to encounter similar temperatures.

Author Response

We thank the reviewer for their comments. Below, we clarify the various questions and detail our amendments made to the paper.

Text in italic highlights our replies to the comments and text in standard font are the amendments made to the manuscript.

Lepage et al evaluate long term vegetation trends in western South America using EVI data. The paper is very well written, though the quality of the graphics could be improved somewhat. 

OK - We have upgraded graphics where we can by including images as PDF rather than raster format. Much of the compression of the images is due to size requirements of the full manuscript. We intend to make public the full GeoTIFF used to produce these images such that people can zoom into whichever area is of interest and see the highest resolution version of our results.

Should we expect vegetation trends to correspond with Koppen zones? As the authors note there is regional variation in warming with a climate zone. What about comparisons to ecoregions? e.g. WWF ecoregions?

Perhaps not exactly, but the Koppen-Gieger model was designed to explain vegetation patterns using climate data. The Koppen-Geiger model roughly defines broad climate zones intended to correspond to vegetation zones. We have used this as an existing model to usefully assess the greening strip and we have found two things 1) that the greening strip does in fact largely mirror the pattern of the Koppen-geiger climate zones on the Pacific slope, although without fully corresponding to them and 2) that it follows the same elevational/latitudinal trends as in the Koppen-Geiger model. Interestingly, these trends were not previously reported for Koppen-geiger on the Pacific slope. As far as we know.

Added in the text:

We discuss causation of greening patterns, correlating calculated vegetation response with known climatic drivers and the geospatial patterning identified by the Köppen-Geiger climate classification system in the region. Thus, we are proposing that greening patterns will be largely determined by climatic drivers and mirror the climate classification zonation of the Köppen-Geiger climate model.

Are there distinctive vegetation conditions within the GS? Or perhaps they are showing relatively high greening because the starting condition was quite brown?

There are distinctive vegetation conditions on the Pacific slope. The Greening strip largely corresponds to an elevational range that is covered in vegetation. It does change seasonally and in accordance with the El Nino climatic cycle. We are describing relative change and so even if we were looking at a desert, there would still be a trend, however hard to see on the ground. The observation is that greening has taken place across the Pacific slope in a perhaps counterintuitive pattern, although one that mirrors the Koppen-geiger climate zones.

Added in the text:

This zone differs from the cold deserts in that more than 50\% of its precipitation falls outside a short wet season and is characterised by dense thorn scrub and cacti.

The manual nature of defining the greening strip is a bit concerning.

It was observed from MODIS EVI satellite data. We have described it, analysed the data and undertaken substantial ground truthing of notable points of greening and browning. A functional or automated way of highlighting the strip would start to include agricultural areas and other unrelated features in the strip.

From figure 1, it appears the strip could be broadened in some locations.

We agree. Originally, the greening strip appeared significantly wider than shown in our figures.  Data analysis narrowed the elevational band in which ‘statistically significant’ greening appears.  It is our belief that the GS in fact distributed across the entire Pacific slope from 1100-2800/4000m in an ascending zone from north to south.

If there is 0% plant cover, which seems to be the case in at least some of this region, why would there be any EVI trend at all? Is any of the greening strip ~0% vegetation cover?

In the GS there is really no area with 0% vegetation. It is not a forested region but it is covered in vegetation, although some areas are only covered with ephemeral plants in accordance with the El Nino cycle.

Added in the text:

This zone differs from the cold deserts in that more than 50\% of its precipitation falls outside a short wet season and is characterised by dense thorn scrub and cacti.

This important region is characterised by highly variable geospatial contrasts in human impact and vegetation. The coastal valleys to 1200m asl being intensively developed, while valleys from 1200-3500m asl are farmed less intensively and the steeper slopes of the Andes, covering much of the region, above 1200m being used only for extensive grazing.

Minor comments Line 45:

I disagree, there are lots of studies on climate drivers of vegetation dynamics (per lines 118-119), but the statement in line 52 (lack of studies in arid upland regions) is true.

This is true. There are a great many studies on the climatic drivers of vegetation dynamics. However, it is widely commented that the ‘ Spatio-temporal vegetation dynamics are not well understood, especially in mountainous regions.’  We have reworded as follows.

The climatic drivers of spatio-temporal vegetation dynamics are not well understood in mountainous regions

Lines 46-48: Do you have a citation to back up this statement?

OK, added

• Piao, S.; Wang, X.; Park, T.; Chen, C.; Lian, X.; He, Y.; Bjerke, J.W.; Chen, A.; Ciais, P.; Tømmervik, H.; et al. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth & Environment 2020, 1, 14–27.
• Prieto-Torres, D.A.; Lira-Noriega, A.; Navarro-Sigüenza, A.G. Climate change promotes species loss and uneven modification of richness patterns in the avifauna associated to Neotropical seasonally dry forests. Perspectives in Ecology and Conservation 579 2020, 18, 19–30. 580
• Ruhm, J.; Böhnert, T.; Weigend, M.; Merklinger, F.F.; Stoll, A.; Quandt, D.; Luebert, F. Plant life at the dry limit—Spatial patterns of floristic diversity and composition around the hyperarid core of the Atacama Desert. PLoS One 2020, 15, e0233729.

Lines 59-60: “low levels of biodiversity”, but you state in the abstract “biologically rich”, these seem at odds.

We have reworded to read ‘unique’

However, we would argue that you can have tremendous biological richness in an area of low diversity. A better-known example would be Madagascar. Here the levels of biodiversity are not high, the biological richness is extraordinary.

Line 76: “only up to 300m above any given point”. This statement isn’t clear to me, so at location below 800 m asl, C02 can vary within 300 m of the ground surface?

We have rewritten this sentence as follows

However, below 800m asl but only to 300m above any given point CO2 concentration is more variable. This is related primarily to plant productivity and is enhanced in regions characterised by semi-permanent atmospheric stability as in this region.

Lines 163-169: No hypothesis?

We have rewritten this sentence as follows

We discuss causation of greening patterns, correlating calculated vegetation response with known climatic drivers and the geospatial patterning identified by the Köppen-Geiger climate classification system in the region.

Line 206: There is no EVI band on MODIS, rather there are red, NIR, and blue bands, that are used to calculate EVI.

We changed the word ‘band’ to ‘dataset’.

Line 207: You are not necessarily looking at land cover change (e.g forest to grassland, grassland to urban, etc.), rather land condition change.

We agree and have reworded as follows.

In this analysis, we used the EVI dataset of the Terra module of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor as a proxy for land condition changes

Line 208: This was already described in the introduction, no need to do it again.

Ok, We have removed the repetition.

Lines 230-231: “the construction of houses or factories and corresponding largely to the expansion of urban areas” all of this can be replaced with simply “urbanization.”

Ok, We have changed this.

Line 244: So in other words, “points” are simply a single data point from a 16 day composite?

Yes. We have clarified the text to read

`Points' refer to single, 16-day composite, temporal data points in the time series of each pixel.

Figure 2: All the abbreviations used in the figure will need to be define in the caption.

OK, these have all been added to the caption as well as the definitions section at the end of the paper.

The abbreviations used in this workflow are for the Large Scale International Boundary (LSIB), the Mann-Kendall test (M-K), the European Centre for Medium-Range Weather Forecasts reanalysis (ERA-5), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the National Oceanic and Atmospheric Administration (NOAA), and the Consultative Group for International Agricultural Research (CGIAR).

Also why are lomas removed?

We have removed all the coastal greening and browning from the analysis of the pacific slope. This is because the greening strip as seen on the EVI imagery is separate from the Lomas. It is also the case that the coastal desert and Lomas have an entirely different microclimatic driver. We are currently looking at the greening Lomas in a separate study. We clarified this in the text:

We have not included the Lomas in our definition of the greening strip because they are both geographically separate and driven by different micro-climatic factors.

Lines 288-289: In this case the short temporal observation window is the 16-day compositing period of the raw EVI data or the 21 year time-series? If the latter, there certainly could be non-linear trends.

This is the 21-year time series. There most likely are non-linear trends, but we found that trying to model this change with non-linear models would risk overfitting the data and thus producing convincing trends that are in fact not true.

Line 302: What years of LCCS data were used? Is this an annual product?

We used the data from 31.03.2021. We have made this clear in the text and linked the exact version used in the reference.

Line 304: why only look at greening pixels? Browning pixels were excluded from all further analysis? It appears not based on 336-337?

This is a mistake, we originally phrased it that way because almost all statistically significant pixels are greening within the greening strip. However, we did also consider browning pixels. We have reworded the sentence to read more accurately:

Only statistically significant trends, as described in Section \ref{sec:statisticalsignificance} were included in our time series analysis.

Figure 3. Recommend describing what the readers are roughly looking at first. Something like: change in EVI from 2000 to 2020 using MODIS…. “where change below -10% and above 60% were clipped” I think were “capped in this graphic” is more accurate. Clipped implies removed.

Ok, We have changed this to match the reviewer’s suggestion.

Would be helpful to label the boxes inset map with the corresponding higher resolution figure.

OK, we have amended the figure accordingly.

Lines 344-345: Why define the acronym GS for greening strip, then not use it?

We use the acronym GS in Table 2 and Table 3.

Figure 4: Any ideas why the glaciers are showing as greening? These seem suspect.

So, this is interesting. We are currently undertaking field study to determine what is happening in the icefields. Glacial retreat is taking place across practically all ice fields in the Peruvian Andes. We have found vegetation present in the areas that show up as greening, but we are currently investigating to see if the change observed with the satellite data is caused by vegetation or if it is connected to the character of the rocky surfaces revealed. This will be the subject of another publication, and, given our lack of concrete results on glaciers, we prefer not to make an official statement in this paper.

Figure 5: It seems odd that a mine (upper middle) is showing as greening. Is this a recently abandoned mine?

In mining areas, we do find a mix of greening and browning. Newly exposed rock in areas of sparse vegetation shows as browning. However, as ponds and waterways are often installed for mining process, where there was no surface water beforehand, greening will take place. Also, if a mined area is abandoned it will green up. We added the following in the text:

Areas of mining can appear as greening if they are abandoned or involve processes that deliver surface water where there was none previously.

Figure 7: It seems that several of the agricultural areas in this image show a browning trend? Any thoughts on this?

This is caused by a number of things. 1) Changes in crop type (eg from maize to avocado would produce browning, 2) the cessation of irrigation (We have found numerous examples of this).

We have added the following to the caption.

Larger red patches correspond to areas urbanisation and changes in agricultural areas. The largest red patch in the above image is the Majes irrigation project, where a combination of changes in crops and reduced irrigation have produced marked browning more recently.

Figure 9: Is this plot only for the greening strip? What about the gap from figure 4? Is that the most narrow part in the north of this plot?

This is only for the greening strip. The ‘greening gap’ in figure 4 shows in figure 9 between Casma and Barranca towards the left of the figure. Even here the greening is apparent and it is offset as a result of the parallel cordilleras, appearing to the east on the west facing slope of the Cordillera blanca. We have clarified this in the figure caption:

Visualisation of relative EVI change in the greening strip as a function of both latitude and altitude. Latitude cross-sections were taken from Northern Peru to Northern Chile. The greening gap described in Figure 4 can be seen between Casma and Barranca.

Line 381: “which sharply drops off after a positive trend greater than 30%.” This means there isn’t much area outside the GS with a EVI trend >30%?

Yes.

Lines 394-396: Or perhaps the zones were not properly defined to begin with.

We agree that this is a possibility.

Line 408: And agricultural abandonment?

Ok, We have altered the sentence as follows.

We see highly differentiated patterns of change below 1100m in the coastal deserts, with greening hot spots associated with agricultural expansion and browning hot spots associated with urbanisation, mining, land slides and agricultural abandonment.

Lines 422-423: You could confirm with a different remote sensing dataset (eg Landsat) which goes back to 1985.

Ok, We have reworded this section as follows.

This could be the case if the period coincided with a particularly dry phase in the El Niño cycle. It is also possible that the observed 20-year trend is an anomalous phenomenon within much longer climatic cycles. As satellite data only covers the period from the 1980s, alternative data sets indicating vegetation changes would be needed to indicate whether these recent trends are part of longer-term cycles or the consequence of climate change.

Landsat data is complicated to use as the satellites and their associated sensors have changed several times since the 1980s.  

Lines 426-429: Given the context the temperature generally decreases from north to south (in addition to elevation) in the study area? I think this is important to note.

Ok, We have added this as follows.

That is, since plant productivity would be expected to decline as temperature declines with increasing altitude and latitude.

Figure 11: It seems that most of the greening didn’t start until ~2016.

Yes, but the variance since 2016 will blur the statistical significance of the trends. We opted to analyse the longer-term data set in order to minimize the effect of a particularly good or bad year.

Table 3: To see the relative difference in the GS, we need to see the correlations in the climate zones outside the GS.

Table 2 shows the correlations of climate zones inside and outside the strip with each other. Table 3 shows correlations of climate zones inside and outside the strip with climate factors. We didn’t analyse other climate zones because they fall outside of our study area and would be driven by other factors.

Line 461: All else is rarely equal in this context, precipitation almost always increases with elevation as it does in the study area.

Ok, This has been reworded as follows.

When comparing the greening strip with land surface temperature (LST), we see complex patterns of warming and cooling across the Pacific slope and coastal deserts with clearly differentiated and largely altitudinal micro-climatic zonation. It is to be expected that LST cooling is a consequence of increased vegetative productivity. However, it is not always the case that increased plant growth results in LST cooling and we do see a highly variegated patterning of cooling and warming across the Pacific slope in greening areas. Understanding the geo-spatial relationship between LST and vegetation dynamics in the region requires a more in-depth analysis of regional climatological conditions. However, we cannot conclusively determine that LST is not a driver to vegetation growth in the greening strip since we also see Pacific slope warming highlighted by the Climate Change Institute's reanalyzer .

Although the complex geospatial interplay between LST and greening or browning is beyond the scope of this research paper, it is the subject of ongoing research with this preliminary observation indicating that vegetation-climate feedback plays a complex role in vegetation dynamics, if one largely circumscribed by atmospheric circulation and oceanic currents.

Lines 462-463 “where the most intense relative greening rises in altitude as the strip progresses southward”, again, it seems important, that temperature generally decreases (all else equal) from north to south. So temperature gradients would be expected to run counter that of the GS. Specifically, in the south at say 1000 m elevation, the temperature would generally be cooler than in the north at 1000m. Meaning in the north you have to go further upslope to encounter similar temperatures.

Indeed, we agree with this statement. We find it strange that the greening strip would run counter to what we would have expected. We have reworded this sentence to read:

Exploring causality for this phenomenon is problematic, since as one moves south, the greening strip ascends, which would seem counter intuitive. That is, since plant productivity would be expected to decline as temperature declines with increasing altitude and latitude.

Reviewer 4 Report

It is disappointing that the authors spend a great deal of time on data pre-processing and very little time on the uncertainty of the data sources themselves and the methodology of the trend study. The study area is also not of broad interest.

With the intensification of human activities, its impact on vegetation changes cannot be ignored. E.g. Global land change from 1982 to 2016.

The authors mention high resolution imagery, I don't see any information about it, and what do you tell us by showing high resolution imagery side by side with vegetation greenness?

Are the rectangular and circular areas meant to illustrate images of human activity on vegetation greenness? If so, this is too crude and unacceptable.

L246, add a full stop before Pixels.

The authors only used EVI to explore the greenness of the vegetation, but there is a large variation between vegetation indices and MODIS products have been reported to have over-correction problems, so how are you sure your results are necessarily correct? (Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?)

The Quality of English is fine.

Author Response

We thank the reviewer for their comments. Below, we clarify the various questions and detail our amendments made to the paper.

Text in italic highlights our replies to the comments and text in standard font are the amendments made to the manuscript.

It is disappointing that the authors spend a great deal of time on data pre-processing and very little time on the uncertainty of the data sources themselves and the methodology of the trend study.

See our response to the last comment.

The study area is also not of broad interest.

I believe we have addressed the question of why we have studied this region and why it is so important. We have clarified several points to meet this comment as follows.

It is of interest because it provides an excellent latitudinal and altitudinal transect as follows:

1. the region is both arid and mountainous with the Andes rising rapidly from the arid coastal plain to 6000m at the continental divide, and is only dissected by rivers that cut deep east-west valleys running to the Pacific Ocean;

It is particularly important with regard to biodiversity and those involved in understanding the impact of climate change on the natural world find this region of great interest, as follows:

2. while the region supports relatively low levels of biodiversity, it is characterised by environmental fragility and high levels of biological endemism with four discrete centres of endemicity.
3. Given the character of the region it also offers distinct climatic zonation with clear vegetation zones. The region is characterised by sharp east-west humidity and temperate gradients, producing distinct vegetation zones that, in large part, remain natural or semi-natural; cities and regional agriculture are totally dependant on water from headwater rivers rising on the pacific slope making it a region of particular interest for much of the Peruvian population.
4. the Pacific slope is a highly vulnerable, water-scarce region with significant pressures on natural resources from several large cities in the coastal deserts including Lima, Trujillo and Arequipa in Peru to Antofagasta and Calama in northern Chile, which are totally dependent on scarce water resources available in this region.

We have clarified this in the manuscript as follows:

Although studies have been undertaken into climate change impacts on vegetation dynamics in the highly biodiverse, but sparsely populated, Amazon basin, there are few such studies of the Pacific slope of the Andes, despite high levels of biological endemism in the region and extreme urban vulnerability to water scarcity.

This important region is characterised by highly variable geospatial contrasts in human impact and vegetation. The coastal valleys to 1200m asl being intensively developed, while valleys from 1200-3500m asl are farmed less intensively and the steeper slopes of the Andes, covering much of the region, above 1200m being used only for extensive grazing.

With the intensification of human activities, its impact on vegetation changes cannot be ignored. E.g. Global land change from 1982 to 2016.

Ok. The greening strip we defined does not include land cover directly managed by humans. The change we see here is for natural vegetation. While it may be impacted by global human activity, we cannot make a conclusive claim that it is or isn’t caused by an anthropogenic source.

We have added citations to the suggested reference in our work.

The authors mention high resolution imagery, I don't see any information about it, and what do you tell us by showing high resolution imagery side by side with vegetation greenness?

The captions describe the EVI greening and browning and the satellite image shows the area it refers to. In it, one can see that certain areas of greening can be readily identified as fields and browning either urbanisation or abandoned fields. This goes some way to validate the observation, since if we can see that landslides or newly built areas of housing do indeed correspond to browning and that newly irrigated lands correspond to greening, then this will apply to the entire area of interest. Nevertheless, we have undertaken an exhaustive statistical processing of pixel data to confirm validity. We also have spent many years working in the region and have checked dozens of these areas to confirm that what we see as greening or browning does correspond to these processes.  

We have amended our figure captions to better describe what the images show:

Areas of mining can appear as greening if they are abandoned or involve processes that deliver surface water where there was none previously.

Larger red patches correspond to areas urbanisation and changes in agricultural areas. The largest red patch in the above image is the Majes irrigation project, where a combination of changes in crops and reduced irrigation have produced marked browning more recently.

Are the rectangular and circular areas meant to illustrate images of human activity on vegetation greenness? If so, this is too crude and unacceptable.

The rectangular areas of greening and browning do indicate areas of human activity, many of which we have visited and verified in ground-truthing exercises. Areas of browning present a mix of urban development in previously agricultural land together with areas of agricultural land that is no longer irrigated or which has been abandoned. In some cases, browning may not be strong and this can obtain from a change in crop type or crop cycle.  Indeed, further investigation using these images as presented in the paper is too crude, so we intend to make available the full resolution GeoTIFF image of our post-processed data. This way, readers can zoom into any area of interest. It would currently be too heavy to include this full resolution image in the manuscript.

L246, add a full stop before Pixels.

OK, added.

The authors only used EVI to explore the greenness of the vegetation, but there is a large variation between vegetation indices and MODIS products have been reported to have over-correction problems, so how are you sure your results are necessarily correct? (Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?)

The pre-processing has ensured that we have correctly described the greening processes on the Pacific slope and verified the trend. As it happens data processing has greatly reduced the area of greening that we are describing. We have also cross-checked our results with ground observations and high-resolution satellite imagery. As such we are very certain of the findings presented here.

We spent a great deal of time on data pre-processing to remove as much uncertainty as possible. Cloudy days and low-quality pixels will negatively affect any analysis of the data so we have carefully taken care of these issues. The trend itself was also carefully calculated to ensure that we are only talking about ‘statistically significant’ changes in EVI when describing the greening strip.

In accordance to the reference suggested by the reviewer, we have used the updated Collection 6 from MODIS to ensure the best possible data was used. We have also added more citations to this particular reference.

The following text had been added to the manuscript to clarify these points:

We chose MODIS as it offers a reliable data set over the past two decades, with any systematic errors being consistent for each data point as part of the updated Collection 6.

Validation at stage 3 has been achieved for the MODIS Vegetation Index (VI) product suite. https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13.

We have also performed this analysis using NDVI rather than EVI and have obtained the same results. For the sake of clarity, we only present EVI data in this paper.

Round 2

Reviewer 3 Report

The authors have addressed my concerns/comments in the previous draft. Only remaining suggestion is that Figure 3 caption lacks a description of the thin grey line that outlines the GS. This needs to be added to caption or to the graphic itself. Additionally, I would suggest something a bit more apparent than this thin grey line.

Author Response

Thank you for your comments. Figure 3 has been amended by adding a bolder black outline and defining it in the caption.

Reviewer 4 Report

No more question.

Author Response

Thank you for your comments!