Techniques of Geoprocessing via Cloud in Google Earth Engine Applied to Vegetation Cover and Land Use and Occupation in the Brazilian Semiarid Region

Round 1

Reviewer 1 Report

This manuscript demonstrates a useful application of data available through the MapBiomas Brazil project and NDVI available through Google Earth Engine, by highlighting some of the landcover and NDVI trends over a 36-year period. The results are interesting and many of the trends are expected. I think the overall content in this manuscript is worthy of publication, however some further details need to be provided first (these are mentioned below). Also, there are some long sentences throughout the manuscript. I suggest many of these could be cut into two sentences to help clarify the key messages and improve the readability of the text (I have mentioned some of these sentences in the Grammatical section below).

Line 159 – can you provide information on the accuracy and limitations of the MAPBiomas Brazil products in your manuscript? This would be useful to help understand the results shown.

Table 1 – I don’t think this information needs to be in a table, given five of the columns have the same information. Alternatively, you could just show the wavelengths from the different sensors in the table (and include the other information in the text).

Paragraph starting line 229 – you mention vegetation vigour and condition, however Figure 2 only shows extent of these classes rather than their condition. Maybe you need to re-phrase this paragraph or explain how you derive vigour/condition further.

Paragraph starting line 258 –Can you comment on some of the peaks and troughs seen in Figure 3a? Why is there a peak in Forest area for 1995? Is this likely to be real, or a result of limitations in the data?

Paragraph starting line 263 – Figure 3b looks like a slight negative trend, but your statistics say it is a positive trend. Also, Table 1 shows the area of ‘Non forest natural formation’ has reduced from 1985 to 2020.  Can you please comment further on this?

Line 268 – why don’t you comment on Figure 3c (farming) as well? You mention all the other sub-figures in Figure 3.

Line 314 – what do you mean by ‘(for example, Figure 3)’? I think you need to refer to which sub-figure(s) you mean, since the NDVI classes are not linked to landcover in the text.

 

Grammatical suggestions

Line 30 – ‘mainly in the dry and dry periods’? Should one of them be ‘wet’?

Line 76  ‘over time over time’ – typing error.

Paragraph from 74-80 is one long sentence. I suggest breaking it into two so it reads better.

Line 217 – ‘using data geospatial data’ – remove first ‘data’

Line 303 – Another very long sentence. I suggest breaking it up into two sentences.

Line 329 – Another very long sentence. I suggest breaking it up into two sentences.

Line 364 –‘,he highlighted the changing…’. What should ‘he’ be?

Author Response

Comments & Reviews

Dear Editor of the Geographies,

 

Reviewer 1

General Comment:

 This manuscript demonstrates a useful application of data available through the MapBiomas Brazil project and NDVI available through Google Earth Engine, by highlighting some of the landcover and NDVI trends over a 36-year period. The results are interesting and many of the trends are expected. I think the overall content in this manuscript is worthy of publication, however some further details need to be provided first (these are mentioned below). Also, there are some long sentences throughout the manuscript. I suggest many of these could be cut into two sentences to help clarify the key messages and improve the readability of the text (I have mentioned some of these sentences in the Grammatical section below).

 

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Comments for author:

 Note: Dear Reviewer 1, for better understanding, the authors point out that all changes and additions of new elements are highlighted throughout the structure of the manuscript, in yellow tone. All changes were based on the main questions and observations answered and commented on here.

 

General response: It is worth noting that from a new systematic review of the literature in front of relevant articles, the present study underwent a restructuring in all its topics. It also highlights unpublished points investigated, which were added and rewritten throughout the manuscript.

 

In the introduction, for example, the authors added clearer conditions about the problem studied, mainly highlighting the planning and management of land cover and use based on the search for new tools, methods and practical and efficient technologies to meet demands such as the absence of large-scale environmental and climate data in semiarid regions. In the topic of material and methods, new methodological information was highlighted, which clarifies and gives ample transparency to the present study. Also from the inclusion of more literature reviews, the authors brought to the topic of results and discussion new approaches and confrontation of results, giving greater breadth to the discussion and arguments necessary for the greatest impact of the manuscript. The statistical trend was widely discussed and restructured, bringing results and a pattern of behavior that are close to the semiarid reality.

 All grammatical suggestions have been reviewed and are highlighted in the text.

 

Specific Comments - Reviewer 1:

 

1. Line 159 – can you provide information on the accuracy and limitations of the MAPBiomas Brazil products in your manuscript? This would be useful to help understand the results shown.

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Authors' specific comments:

The conditions highlighted here have been incorporated into the text of this manuscript.

Response:

             The MapBiomas Brazil project is based on geospatial data derived from the Landsat series of satellites, for the development of thematic maps of land use and land cover. The spatio-temporal monitoring of the natural environment of Brazilian biomes was driven by the implementation of a low-cost, open-access methodology.

             It is worth noting that annual thematic maps contain up to 105 layers of information. Thus, to generate a single land cover and land use map for each year, so-called prevalence rules apply. In this case, it is when the same pixel is classified in two maps of different classes, in order to define which class it belongs to in the final map.

             And at the end of all digital processing, these are carefully evaluated/validated as to their statistical quality through accuracy analysis, with estimates of the general accuracy rates and also of accuracy and error for each of the cross-sectional themes mapped [38,39].

Table 1 presents the total annual amount of land cover and use specifically for the years 1985 and 2020, highlighting the loss and gain conditions of the study area. And also, the statistical data of accuracy by classes and general accuracy.

Table 1. Annual quantification of the different uses and land cover in the years of 1985 and 2020 for the semiarid region of Campina Grande-PB, Brazil.

Territory	Thematic class	Total annual quantification of land uses
		1985 (km2)	1985 (%)	Overall accuracy (%)	2020 (km2)	2020 (%)	Overall accuracy (%)
Municipality of Campina Grande, PB, Brazil	Forest (forest formation and savanna formation)	334.16	56.48	90.00	218.80	36.98	88.05
	Non forest natural formation (grassland)	6.62	1.12	17.89	6.31	1.07	19.48
	Farming (agriculture and pasture)	238.57	40.32	67.16	287.92	48.66	82.27
	Non vegetated area (urban area and other non-vegetated areas)	9.30	1.57	80.63	77.31	13.07	86.08
	Water	3.01	0.51	93.59	1.33	0.23	93.68
	Total	591.659	100	-	591.659	100	-
	Overall accuracy (1985 - 2020)	81.80%

Source: Adapted from [39].

 

Between 1985 and 2020, the municipality of Campina Grande-PB, in the Brazilian Semiarid Region, highlighted the loss of native vegetation, around 20% (115 km²), among the forest areas (specifically in thematic classes of forest formation and savanna formation), with statistical accuracy of 90% (1985) e 88.05% (2020), and non-forest natural formation (specifically in thematic class of grassland), with an accuracy of 17.89% (1985) and 19.48% (2020), being this class of low statistical precision, however, it is worth mentioning that its representativeness in the sample is only 1% (Table 1).

On the other hand, the farming (specifically in thematic classes of agriculture and pasture), with an accuracy of 67.16% (1985) and 82.27% (2020), and non-vegetated area (specifically in thematic classes of urban area and other non-vegetated areas), with an accuracy of 80.63% (1985) and 86.08% (2020), showed a percentage gain over time, around 12% and 8%, respectively (Table 1).

The water area of the semiarid region of study also highlighted the loss of water availability over time, with a significant accuracy of 93.59% (1985) and 93.68% (2020). Thus, it was observed through the thematic classes, in the period from 1985 to 2020, a general accuracy of 81.80% (Table 1).

 

Specific Comments - Reviewer 1:

 

2. Table 1 – I don’t think this information needs to be in a table, given five of the columns have the same information. Alternatively, you could just show the wavelengths from the different sensors in the table (and include the other information in the text).

Response: As recommended, table 1 was removed by the authors and the main characteristics of the satellites/sensors were incorporated into the text, for example:

For the proper application and effective use of Landsat images on the GEE platform, atmospheric correction/calibration factors, multiplier (0.0000275), and additive factors (-0.2) were applied for each multispectral band used of the surface reflectance product, collection 2 [33]. The surface reflectance product for the Landsat-5/TM, 7/ETM+, and 8/OLI satellites/sensors, presents temporal resolution between 14 and 16 days; spatial resolution of 30 m and radiometric resolution of 16 bits [34].

 

3. Paragraph starting line 229 – you mention vegetation vigour and condition, however Figure 2 only shows extent of these classes rather than their condition. Maybe you need to rephrase this paragraph or explain how you derive vigour/condition further.

Response: As noted, the paragraph has been reformulated and deals specifically with land use classes. Thus, the vigor of the vegetation was shown only in Figure 4, based on the NDVI.

                Over time, a reduction in the class of forest areas is observed, highlighted by the spectral condition of the vegetation cover of the Caatinga. The significant reduction in nat-ural vegetation in the semiarid environment is evidenced by the increase in agricultural areas (agriculture and pasture) and urban infrastructure over the last three decades. Such ones were strengthened mainly by livestock, with expansion of pasture areas in all regions of the municipality, as well as by the boosting of civil construction in the central region (Figure 2).

 

4. Paragraph starting line 258 –Can you comment on some of the peaks and troughs seen in Figure 3a? Why is there a peak in Forest area for 1995? Is this likely to be real, or a result of limitations in the data?
5. Paragraph starting line 263 – Figure 3b looks like a slight negative trend, but your statistics say it is a positive trend. Also, Table 1 shows the area of ‘Non forest natural formation’ has reduced from 1985 to 2020. Can you please comment further on this?
6. Line 268 – why don’t you comment on Figure 3c (farming) as well? You mention all the other sub-figures in Figure 3.
7. Line 314 – what do you mean by ‘(for example, Figure 3)’? I think you need to refer to which sub-figure(s) you mean, since the NDVI classes are not linked to landcover in the text.

Responses: In general, it is worth noting that the statistical discussion of trends has been restructured.

                In the forest area (Figure 3(a)), it is noteworthy that 92.79% of the vegetation cover is savannah while only 7.21% has a dense forest formation [39]. In view of this, it is worth noting that the vegetation of the Caatinga biome is characterized by its high resilience power, which favors the rapid formation of plant biomass soon after the rainy events, rain being a dominant and controlling factor in this semiarid environment. This helps to ex-plain some vegetation resilience peaks such as, for example, the significant increase in vegetation cover in 1995. This year presented an annual rainfall of around 984.1 mm, and the previous year, 1994, was also very rainy, 1028.5 mm [54]. That is, both presenting rainfall above the annual historical average (773.5 mm) of the study area.

                On the other hand, as this ecosystem is highly dynamic, it is also highlighted that from the events of severe drought (accentuated water deficit), a drastic reduction of the vegetation of the Caatinga is observed from the loss of the leaf canopy and the condition of plant biomass, physiological defense characteristic of this biome [55,56].

                Statistically, the time series of the forest area does not present a statistically signifi-cant trend, according to the negative Zs value = -1.51, however, it highlights a decrease, as a function of the true magnitude of the trend, by the Q test, which estimated an annual loss of these areas around 1.08 km², mainly alerting to the increase in deforestation (Figure 3(a)).

                The time series of the area of non-forest natural formation (grassland) also did not show a statistically significant trend, according to the characterization of the statistical values of positive Zs = 0.45. The true magnitude of the trend, by the Q test, only estimated an annual gain of 0.00193 km² (Figure 3(b)).

                The farming area (agriculture and pasture) also did not show a statistically signifi-cant trend, with negative Zs = -1.08. However, it is observed that the true magnitude of the trend, by the Q test, detected an annual loss of 0.45 km² (Figure 3(c)). Here, it is worth not-ing that within this area, agriculture lost strength and/or a reduction was observed over time. Especially between 2011 and 2020, which did not show recovery power due to the severe effects of drought in this period (Figure 3(c)). The pasture, predominant in the study area, remained stable, however in this period it also highlighted the loss. In this sense, it is highlighted that the drought and the pressures of anthropic activities are promoting in-tensified vulnerabilities to the natural environment in the study area.

                The time series for the urban area highlighted a significant increasing trend, with positive Zs = 8.57. The true magnitude of the trend, by the Q test, estimated an annual gain of 0.91 km², with trend significance at the level of 1% (p<0.01) (Figure 3(d)). The rise of the urban area over time directly affected the condition of reservoirs in the central region of the study area.

                Other non-vegeted areas highlighted a significant upward trend in the time series, positive Zs = 6.03. The Q test estimated an annual gain of 0.12 km², at a level of 1% (p<0.01) (Figure 3(e)). This behavior is linked to the behavior pattern of farming (agricul-ture and pasture). [49] observed that undisciplined anthropic activities also contribute to the increase in exposed soil areas in the semiarid region, because deforestation and burn-ing of Caatinga vegetation for agricultural purposes is common.

                The water coverage area highlights a decreasing trend, with negative Zs = -3.04. The magnitude of the trend by the Q test estimated a quantitative annual loss of -0.04 km² at the 1% level (p<0.01) (Figure 3(f)). So that, in addition to the effects of climate change such as severe drought events, rainfall variability in space and time affects water availability in rivers, lakes and reservoirs in the study area.

                In Panchkula District, Haryana, India, between 1980 and 2020, agricultural land classes increased by 73.71%, built-up areas by 84.66%, and forest by 4.07%, while river beds reduced by 50.86%, in spatial extent. And all these changes are strongly associated with industrial activities and buildings [25]. In Batticaloa Municipality, Eastern Province of Sri Lanka, between 1990-2020, there was a decrease in agricultural land use from 26.9% to 21.9% [24].

 

 

Yours sincerely,

 

Principal Author: Jhon Lennon Bezerra da Silva

Adress: National Institute of the Semiarid (INSA), Center for Information Management and Science Popularization, Campina Grande 58434-700, Paraiba, Brazil.

E-mail: [email protected]

Author Response File: Author Response.docx

Reviewer 2 Report

Summary:

This manuscript address on Techniques of geoprocessing via cloud in Google Earth Engine applied to vegetation cover and land use and occupation in the Brazilian Semiarid Region. Some interesting results could provide good information that are essential indicators to mitigate environmental impacts in the Brazilian semiarid region. The science and methodology of the manuscript appear sound, and adequately cited. The trend analysis of land cover and use detected, the loss of forest areas and water bodies, followed by the advance of exposed soil areas and urban infrastructure, give reader’s a picture. This study is a beneficial exploration of the modification of native vegetation directly influences water availability, and agricultural activities increase the pressure on water resources, mainly in the dry and dry periods. One point that could perhaps be strengthened is more of an indication to the readers of the level of uniqueness of the study comparing with previous studies if there is any. I believe a moderate level of revisions should be made to the paper before it is ready to be considered for publication with Geographies. See the detail comments below.

General Comment:

As I mentioned in summary, it will help readers to better understand the level of uniqueness of the study comparing with previous studies if there is any, add more sentences in the introduction section about what have been done, and include comparison (with previously study) results in your study in conclusion. 

Specific Comments:

How do authors deal with the cloud issues with Landsat time-series data?

Do authors have special models to deal with disturbances such as fire, forest cut, et al? If not, explain the reasons.

Author Response

Comments & Reviews

Dear Editor of the Geographies,

 

Reviewer 2

 General Comment:

 As I mentioned in summary, it will help readers to better understand the level of uniqueness of the study comparing with previous studies if there is any, add more sentences in the introduction section about what have been done, and include comparison (with previously study) results in your study in conclusion.

 ---------

Comments for author:

 Note: Dear Reviewer 2, for better understanding, the authors point out that all changes and additions of new elements are highlighted throughout the structure of the manuscript, in yellow tone. All changes were based on the main questions and observations answered and commented on here.

 

General response: It is worth noting that from a new systematic review of the literature in front of relevant articles, the present study underwent a restructuring in all its topics. It also highlights unpublished points investigated, which were added and rewritten throughout the manuscript.

 

In the introduction, for example, the authors added clearer conditions about the problem studied, mainly highlighting the planning and management of land cover and use based on the search for new tools, methods and practical and efficient technologies to meet demands such as the absence of large-scale environmental and climate data in semiarid regions. In the topic of material and methods, new methodological information was highlighted, which clarifies and gives ample transparency to the present study. Also from the inclusion of more literature reviews, the authors brought to the topic of results and discussion new approaches and confrontation of results, giving greater breadth to the discussion and arguments necessary for the greatest impact of the manuscript. The statistical trend was widely discussed and restructured, bringing results and a pattern of behavior that are close to the semiarid reality.

 

Specific Comments - Reviewer 2:

How do authors deal with the cloud issues with Landsat time-series data?

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Authors' specific comments:

The conditions highlighted here have been incorporated into the text of this manuscript.

Response:

          It is noteworthy that, specifically, Google Earth Engine provides a range of processing methods/algorithms for the Landsat series of satellites. From methods to calculate the radiance in sensors, as well as the reflectance at the top of the atmosphere (TOA), and also, as used in the present study, the surface reflectance (SR), Furthermore, the platform especially highlights models for scoring clouds and cloud-free composites.

          Regarding cloud problems, one of the criteria adopted for Landsat image processing was to develop a digital processing script for cloud mask and their shadows from a spe-cific band for both satellites/sensors. The script was adapted according to the indications of the rudimentary GEE ID algorithm: (for example: https://code.earthengine.google.com/54aabb24979858b32a59aaebe8ba125c?noload=true). Indications of these processing methods/algorithms as a function of Landsat sensors can be found in the following GEE repository (https://developers.google.com/earth-engine/guides/landsat).

 

Specific Comments - Reviewer 2:

Do authors have special models to deal with disturbances such as fire, forest cut, et al? If not, explain the reasons.

Response: The authors did not work with special models regarding specific fires and deforestation in the study area. Although there are already specific models and algorithms in this sense, where the characteristics of the vegetation cover are also conditioning factors of the quality and reliability of the detection of forest fires by satellites, for example.

In the present manuscript, the models used highlight the behavior pattern and the characteristics of land cover and use in the semiarid region. From these spatial indications, it is intended to observe the conditions of local changes in the sub-regions of the study area. However, the authors intend to adopt these specific models in future studies and arrive at more concrete results on deforestation, whether by fire or forest cutting.

 

Yours sincerely,

 

Principal Author: Jhon Lennon Bezerra da Silva

Adress: National Institute of the Semiarid (INSA), Center for Information Management and Science Popularization, Campina Grande 58434-700, Paraiba, Brazil.

E-mail: [email protected]

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Thank you for addressing my comments. I believe the manuscript is suitable for publication. I just have one minor question/comment for you to address:

Line 436 – Do you mean ‘(Figure 4)’?

Reviewer 2 Report

The authors did a decent job in revising the manuscript. They have addressed my main concerns and also taken care of minor comments/editorial changes. I recommend that the manuscript be accepted for publication.