Temporal and Spatial Variation of Land Use and Vegetation in the Three–North Shelter Forest Program Area from 2000 to 2020

Round 1

Reviewer 1 Report

This manuscript addressed an important and interesting problem-land use changes in the Three-North Shelter Forest Program Area. The authors analyzed spatial and temporal dynamics characteristics and explored the driving factors of changes. And the manuscript is well organized. However, some major issues still need to be improved.

(1)   The novelty of submitted manuscript is lack of novelty. Scientific problems are not clear, and the manuscript fails to draw novel conclusions.

(2)   The name of figure 1 is inappropriate. In fact, it is land cover map of the Three-North Shelter Forest Program Area. Figure 1 needs to indicate the year.

(3)The accuracy of land cover data needs to be examined carefully and articulated clearly. Data accuracy is not high so that some main conclusions are not credible. For example, forest area fell from 26×10⁴ km² in 2000 to 25.82 ×10⁴ km² in 2020. The number of 26×10⁴ km² is also smaller than that consensual number.

Author Response

Response to Editor and Reviewers’ Comments

Dear editor and reviewer:

Thanks for your valuable suggestions and comments on our manuscript, which are valuable for revising and improving our manuscript with important guiding significance. We have made correction according to the comments, and the revised portions are marked in red in the revised manuscript. The responds to the reviewer's comments are as follows:

 

This manuscript addressed an important and interesting problem-land use changes in the Three-North Shelter Forest Program Area. The authors analyzed spatial and temporal dynamics characteristics and explored the driving factors of changes. And the manuscript is well organized. However, some major issues still need to be improved.

 

(1) The novelty of submitted manuscript is lack of novelty. Scientific problems are not clear, and the manuscript fails to draw novel conclusions.

Reviewing:

Thank you for your suggestion. In our replies, we made corresponding replies to the research background, significance and innovation points of this paper.

Research background and significance

The unreasonable economic development mode and weak environmental protection awareness have made the ecological environment worse in northern China. Therefore, in the 1970s, combined with the change of national economic development strategy and the strengthening of environmental protection awareness, the Chinese government proposed the construction of the Three-North Shelter Forest Program Area. The main purpose of the program construction is to basically control the damage of sand and soil erosion and improve the ecological environment in the middle of the 21st century. Construction began in 1978 and will end in 2050 with three phases and eight stages. Among them, the research time scale of this paper coincides with the second phase (the fourth and fifth stage project). Therefore, the research results of this paper can also be used as the acceptance of the results of the second phase of the Three-North Shelter Forest Program Area, and provide data basis for the decision-making of national government agencies.

Innovation

1. Previous studies on land use and vegetation change mostly focused on one or two aspects of Land Use, Vegetation Coverage and Gross Primary Productivity. In this study, the three were analyzed comprehensively. Among them, Land Use Cover Change usually focuses on the study of the impact of human activities on the Earth surface and global change. Human activities directly or indirectly affect surface biophysical parameters such as surface albedo, specific emissivity, surface roughness, photosynthetic active radiation and evapotranspiration through the interaction between the biosphere and the atmosphere. It has a profound influence on the surface radiation energy balance, biogeochemical cycle and ecosystem service function. The quality assessment of vegetation natural ecosystem refers to the HJ1172-2021 Technical Specification for National Ecological Status Investigation and Assessment - Ecosystem Quality Assessment issued by the Institute of Standards, Ministry of Ecology and Environment, PRC in 2021. We selected Vegetation Coverage and Gross Primary Productivity as evaluation indexes. Among them, Vegetation Coverage is the percentage of the vertical projection area of vegetation (including leaves, stems and branches) on the ground in the total area of the statistical area, which mainly represents the horizontal structure of vegetation, and is of great significance for revealing the change of ecosystem environment, vegetation restoration and reconstruction layout. Gross Primary Productivity refers to the total amount of organic carbon fixed by green vegetation through photosynthesis in unit time and unit area, which is the starting point and important component of atmospheric CO₂ entering the terrestrial ecosystem and mainly represents the strength of vegetation photosynthesis capacity.
2. At first, Hurst index method was used to analysis the change of vegetation coverage and productivity in past research, without considering the influence of the coefficient of variation. Generally speaking, the coefficient of the variation larger area by the small value but generally change larger area, is often mentioned the ecological fragile district, worthy of our attention. Secondly, previous studies on changes of vegetation coverage and productivity were limited to interannual scale, and few studies considered the seasonal changes of ecological indicators. This study explored the seasonal changes based on the four seasons division of agricultural season distribution in China, providing a good model for the study of the growth rules of seasonal vegetation.
3. In addition to discussing the effect of climate factors on vegetation growth on section 4.1, this study also discussed the influence of changes in land use types on vegetation growth on section 4.2. Taking the mutual conversion of cultivated land, forest and grassland as an example, the non-transformed area was taken as the reference group to compare the differences in the variation of vegetation coverage and productivity, and the differences were quantified. Finally, it is necessary to further explore the quantitative analysis and driving mechanism of human activities that lead to land use and vegetation change on section 4.3.

(2) The name of figure 1 is inappropriate. In fact, it is land cover map of the Three-North Shelter Forest Program Area. Figure 1 needs to indicate the year.

Reviewing:

Thank you for your suggestion. We made some modifications about the title of Figure 1 and labelled the time of the data source in the new version (Section 2.1).

Figure 1. Map of Land use and land cover type in the Three-North Shelter Forest Program Area in 2020.

(3) The accuracy of land cover data needs to be examined carefully and articulated clearly. Data accuracy is not high so that some main conclusions are not credible. For example, forest area fell from 26×10⁴ km² in 2000 to 25.82 ×10⁴ km² in 2020. The number of 26×10⁴ km² is also smaller than that consensual number.

Reviewing:

Thank you for your suggestion. We examined the accuracy of the LUCC data and modified it. The unit of the data is square kilometers in the new version (Section 3.1).

Table 4. LUCC in the TNSFPA from 2000 to 2020

Type	Area/ km2			Change rate/%
	2000	2010	2020	2000~2010	2010~2020	2000~2020
cultivated land	551888	558765	568458	1.25	1.73	3.00
forest	260019	262100	258214	0.80	-1.48	-0.69
grassland	1385544	1378018	1385111	-0.54	0.51	-0.03
water body	101978	101304	84762	-0.66	-16.33	-16.88
constructive land	47804	52282	73716	9.37	41.00	54.21
unused land	1721767	1716532	1698740	-0.30	-1.04	-1.34

 

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper touches upon an important issue Temporal and spatial variation of land use and vegetation in China. Author use the case study of “the Three-North Shelter Forest Program to assess the vegetation changes from 2000 to 2020”.

In this time of global mutation and climate change, this paper raised the question of   LULC and tit relationship with climate change.

 

 The experimental data used by author’s show a method that author mastered.  Based on  LUCC and MODIS image data, they created a dataset including land use/cover, annual and seasonal vegetation coverage and vegetation productivity.

 

 The analytical framework and the theoretical scheme are absent, this have to be done. I suggest to create to rephrase the discussion section.

Before providing detailed comments to the specific sections, I have some general suggestions to strengthen the analytical consistency.

Overall comment

The authors need to reframe, the discussion The manuscript need an English edition.

Detailed comment

line 34: what is "unused land", use the technical term

line 36: we can used the TNSFPA abbreviation

line 39-41: the first sentence is not complete, please check

line 48: Bond?? change the word

line 85-88: yes, but author can provide the layout of the paper here

line 109-110: it is still not clear what is the TNSFP. what is the objective

figure1 and all Maps: the North arrow and the source is lacking

line39: what are the characteristics of image used

figure 2: review this "unused land" concept

line 238: the real reason of these changes need to be stated

in figure 4: d and b are the  same title, please check

4.3 is discussion

discussion section: the discussion section can be more developed,

line 447: this Modis data is not well mentioned in the method section

 

All remarks and comments are in the manuscript.

 

 Hope these comments are helpful to improve the manuscript for submission Sustainability.

Author Response

Response to Editor and Reviewers’ Comments

Dear editor and reviewer:

Thanks for your valuable suggestions and comments on our manuscript, which are valuable for revising and improving our manuscript with important guiding significance. We have made correction according to the comments, and the revised portions are marked in red in the revised manuscript. The responds to the reviewer's comments are as follows:

 

This paper touches upon an important issue Temporal and spatial variation of land use and vegetation in China. Author use the case study of “the Three-North Shelter Forest Program to assess the vegetation changes from 2000 to 2020”.

 

In this time of global mutation and climate change, this paper raised the question of LULC and tit relationship with climate change.

 

The experimental data used by author’s show a method that author mastered. Based on LUCC and MODIS image data, they created a dataset including land use/cover, annual and seasonal vegetation coverage and vegetation productivity.

 

The analytical framework and the theoretical scheme are absent, this have to be done. I suggest to create to rephrase the discussion section.

 

Before providing detailed comments to the specific sections, I have some general suggestions to strengthen the analytical consistency.

 

Overall comment

 

The authors need to reframe, the discussion The manuscript need an English edition.

 

Detailed comment

 

line 34: what is "unused land", use the technical term

Reviewing:

Thank you for your suggestion. We added a table for illustrating the classification of land types. We revised "unused land" to “unutilized land” on the section 2.2.1.

Table 1. the classification system of LUCC

Code	The primary classification	The secondary classification
1	cultivated land	dry land, paddy fields
2	forest	woodland, shrubland, open woodland, others
3	grassland	high, medium, ground cover grassland
4	water body	rivers, lakes, reservoirs, glaciers, seashores, mudflats
5	constructive land	urban, rural settlement, industrial, mining construction
6	unutilized land	sandy, gobi, saline, swampy, bare soil, bare rock, others

 

line 36: we can use the TNSFPA abbreviation

Reviewing:

Thank you for your suggestion. We abbreviated the LUCC in the new version.

 

line 39-41: the first sentence is not complete, please check

Reviewing:

Thank you for your suggestion. We made some modifications about the first sentence of the abstract in the new version (Section 1).

Land use and vegetation information are the linkage between human socio-economic activities and natural ecosystem processes. Both of them have been a staple and hot topic in the research of socio-economic and environmentally sustainable development [1].

 

line 48: Bond?? change the word

Reviewing:

Thank you for your suggestion. We revised "bond" to “node” and then modified the structure of this sentence.

Vegetation, an important node among the atmosphere, soil, biosphere and hydrosphere [7], can indicate the change in terms of global energy transfer, biogeochemistry and hydrological cycle [8-9].

 

line 85-88: yes, but author can provide the layout of the paper here

Reviewing:

Thank you for your suggestion. We added the layout of the paper (Figure 2) on section 2.3.

In order to reflect the research idea of this paper more clearly, we chose to add a mind map (Figure 2).

Figure 2. Mind map of this study.

 

line 109-110: it is still not clear what is the TNSFP. what is the objective

Reviewing:

Thank you for your suggestion. We modified TNSFPA. It’s the research area of this paper in the new version.

 

figure1 and all Maps: the North arrow and the source is lacking

Reviewing:

Thank you for your suggestion. We added the North arrow and source of the Figure.1 in the new version.

 

Figure 1. Map of Land use and land cover type in the Three-North Shelter Forest Program Area in 2020.

 

line39: what are the characteristics of image used

Reviewing:

Thank you for your suggestion. We added the characteristics of image used on the section 2.2.

2.2. Data

2.2.1. Land Use and Land Cover

In order to obtain land use change information, we adopted the national land use status and dynamic change database developed by Liu et al [49-54]. It is a dataset obtained through the integration of higher resolution remote sensing, unmanned aerial vehicle, ground survey and observation technology system, combined with the human and computer interactive interpretation method based on geoscience knowledge. There are six land use types in this dataset, namely cultivated land, forest, grassland, body of water, constructive land, and unutilized land (Table.1). We selected the data in 2000, 2010 and 2020, with a spatial resolution 1 km, derived from Resource and Environment Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/).

Table 1. the classification system of LUCC.

Code	The primary classification	The secondary classification
1	cultivated land	dry land, paddy fields
2	forest	woodland, shrubland, open woodland, others
3	grassland	high, medium, ground cover grassland
4	water body	rivers, lakes, reservoirs, glaciers, seashores, mudflats
5	constructive land	urban, rural settlement, industrial, mining construction
6	unutilized land	sandy, gobi, saline, swampy, bare soil, bare rock, others

2.2.2. MODIS Products

In this study, we used MOD/MYD13A1 Vegetation Indices and MOD/MYD17A2H Gross Primary Productivity products to obtain vegetation change information. MOD and MYD with a spatial resolution of 500 m are acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, respectively. In order to reduce the impact of atmospheric and geometric deformation on data accuracy, we adopted the average value of MOD and MYD data in the same time period. They were obtained from the National Space Administration of the United States of America (https://modis.gsfc.nasa.gov/).

2.2.3. Meteorological Data

The meteorological data from 2000 to 2020 was adopted to analyze the climate trends and its effect on vegetation changes, which have a spatial resolution of 0.5°×0.5°. Based on the air temperature and precipitation data from 2472 national meteorological stations in China newly compiled by the Basic Program of National Meteorological Information Center (http://data.cma.cn/), this dataset was generated by spatial using Thin Plate Spline (TPS) interpolation method.

 

figure 2: review this "unused land" concept

Reviewing:

Thank you for your suggestion. We added a table for illustrating the classification of land types. We revised "unused land" to “unutilized land” on the section 2.2.1.

Table 1. the classification system of LUCC

Code	The primary classification	The secondary classification
1	cultivated land	dry land, paddy fields
2	forest	woodland, shrubland, open woodland, others
3	grassland	high, medium, ground cover grassland
4	water body	rivers, lakes, reservoirs, glaciers, seashores, mudflats
5	constructive land	urban, rural settlement, industrial, mining construction
6	unutilized land	sandy, gobi, saline, swampy, bare soil, bare rock, others

 

line 238: the real reason of these changes need to be stated

Reviewing:

Thank you for your suggestion. We explained the reason of these changes on section 3.1.

In order to further analyze the change patterns of different land use types in the TNSFPA, the change pattern mapping was established by integrating the land use data of 2000, 2010 and 2020 (as shown in Figure 3). Among them, 72.61% of the land use area remained unchanged from 2000 to 2020. The land use change pattern was dominated by later change (95.75%), followed by previous change (2.47%) and repeated change (1.03%). It implies that the transfer of land use type from 2010 to 2020 was stronger than that from 2000 to 2010, which is closely related to the national strategy for high-quality development and ecological civilization construction, as well as the impact of climate change. In addition, the previous change type mainly showed the mutual transformation between cultivated land and grassland. The later change type mainly showed the mutual transformation between grassland and unutilized land. The repeated change type mainly presented the “grassland-cultivated land-grassland” model. The difference of continuous change type is less than others type.

LUCC patterns of cultivated land, forest and grassland show that cultivated land and forest were mainly transformed into grassland, with an area of 95681 km² (51.16%) and 65163 km² (59.07%) respectively. Grassland was mainly transformed into cultivated land (112699 km², 27.54%) and unutilized land (207211 km², 50.64%). It reflected the implementation of the policy of “returning cultivated land to forest land and grassland” and the current situation of grassland reclamation and degradation. It is worth noting that the area of forest has decreased a little. It is related to the older age of trees, the harsh growth environment, the single tree species and the great threat of pests and diseases in some degree.

 

in figure 4: d and b are the same title, please check

Reviewing:

Thank you for your suggestion. We checked and corrected it on the new version.

Figure 5. Trend of vegetation coverage in the TNSFPA and its sub-regions from 2000 to 2020.

 

4.3 is discussion

discussion section: the discussion section can be more developed.

Reviewing:

Thank you for your suggestion. The discussion part is divided into three parts. In addition to discussing the effect of climate factors on vegetation growth on section 4.1, this study also discussed the influence of changes in land use types on vegetation growth on section 4.2. Taking the mutual conversion of cultivated land, forest and grassland as an example, the non-transformed area was taken as the reference group to compare the differences in the variation of vegetation coverage and productivity, and the differences were quantified. Finally, it is necessary to further explore the quantitative analysis and driving mechanism of human activities that lead to land use and vegetation change on section 4.3. We have made some corrections to the language in the discussion section.

 

 

line 447: this Modis data is not well mentioned in the method section

Reviewing:

Thank you for your suggestion. We added the characteristics of MODIS products used on the section 2.2.2.

2.2.2. MODIS Products

In this study, we used MOD/MYD13A1 Vegetation Indices and MOD/MYD17A2H Gross Primary Productivity products to obtain vegetation change information. MOD and MYD with a spatial resolution of 500 m are acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, respectively. In order to reduce the impact of atmospheric and geometric deformation on data accuracy, we adopted the average value of MOD and MYD data in the same time period. They were obtained from the National Space Administration of the United States of America (https://modis.gsfc.nasa.gov/).

 

All remarks and comments are in the manuscript.

 

Hope these comments are helpful to improve the manuscript for submission Sustainability.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

The authors have attempted to showcase LUCC over the period 2000 - 2020 using MODIS data. Land use and land cover change information are important in the monitoring and management of landscapes hence the relevance of this study. However, my main concern with this manuscript was that the authors failed to clearly describe their methodology in clear detail that is repeatable and justifiable in the literature. The methodology is too abstract and often articulated with jargon that is difficult to understand. For instance the Trend analysis and Trend test lines 154. 

I suggest that the authors justify their use and relate with literature showing the advantages and disadvantages of each method that they are presenting as well as the innovation in their choice of methodology. As the manuscript stands, I am not convinced that there is an innovation compared to what is already in the literature.

The introduction must show the gaps in knowledge that the methodologies will answer.

LUCC must be expanded at first mention line 15

Cannot start a sentence with a number line 26

Consistent use of capital letters must be observed e.g., line 42

Lines 57-58 need to be rephrased

There is an over-assumption that all readers would understand what MOD/MYD13A1 is on line 84. These must be defined or expanded for ease of understanding.

The aim of the study is ambiguous and not clearly defined enough see lines 85-87

TNSFPA must be expanded at first mention in the text line 69

Authors must provide a description of the change mappings in Table 1. I observed these were described in the results but I suggest that a column be added for the description of what each of the categories mean.

Overall, the methodology must be redone with a better description of each step and the authors must provide at the end of the methodology a summarised flowchart.

 

 

Author Response

Response to Editor and Reviewers’ Comments

Dear editor and reviewer:

Thanks for your valuable suggestions and comments on our manuscript, which are valuable for revising and improving our manuscript with important guiding significance. We have made correction according to the comments, and the revised portions are marked in red in the revised manuscript. The responds to the reviewer's comments are as follows:

 

The authors have attempted to showcase LUCC over the period 2000 - 2020 using MODIS data. Land use and land cover change information are important in the monitoring and management of landscapes hence the relevance of this study. However, my main concern with this manuscript was that the authors failed to clearly describe their methodology in clear detail that is repeatable and justifiable in the literature. The methodology is too abstract and often articulated with jargon that is difficult to understand. For instance, the Trend analysis and Trend test lines 154.

Reviewing:

Thank you for your suggestion. In our replies, we have made some changes in the revised draft.

 

I suggest that the authors justify their use and relate with literature showing the advantages and disadvantages of each method that they are presenting as well as the innovation in their choice of methodology. As the manuscript stands, I am not convinced that there is an innovation compared to what is already in the literature.

Reviewing:

Thank you for your suggestion. In our replies, we have made corresponding replies to the innovation points of this paper.

Innovation

1. Previous studies on land use and vegetation change mostly focused on one or two aspects of Land Use, Vegetation Coverage and Gross Primary Productivity. In this study, the three were analyzed comprehensively. Among them, Land Use Cover Change usually focuses on the study of the impact of human activities on the Earth surface and global change. Human activities directly or indirectly affect surface biophysical parameters such as surface albedo, specific emissivity, surface roughness, photosynthetic active radiation and evapotranspiration through the interaction between the biosphere and the atmosphere. It has a profound influence on the surface radiation energy balance, biogeochemical cycle and ecosystem service function. The quality assessment of vegetation natural ecosystem refers to the HJ1172-2021 Technical Specification for National Ecological Status Investigation and Assessment - Ecosystem Quality Assessment issued by the Institute of Standards, Ministry of Ecology and Environment, PRC in 2021. We selected Vegetation Coverage and Gross Primary Productivity as evaluation indexes. Among them, Vegetation Coverage is the percentage of the vertical projection area of vegetation (including leaves, stems and branches) on the ground in the total area of the statistical area, which mainly represents the horizontal structure of vegetation, and is of great significance for revealing the change of ecosystem environment, vegetation restoration and reconstruction layout. Gross Primary Productivity refers to the total amount of organic carbon fixed by green vegetation through photosynthesis in unit time and unit area, which is the starting point and important component of atmospheric CO₂ entering the terrestrial ecosystem and mainly represents the strength of vegetation photosynthesis capacity.
2. At first, Hurst index method was used to analysis the change of vegetation coverage and productivity in past research, without considering the influence of the coefficient of variation. Generally speaking, the coefficient of the variation larger area by the small value but generally change larger area, is often mentioned the ecological fragile district, worthy of our attention. Secondly, previous studies on changes of vegetation coverage and productivity were limited to interannual scale, and few studies considered the seasonal changes of ecological indicators. This study explored the seasonal changes based on the four seasons division of agricultural season distribution in China, providing a good model for the study of the growth rules of seasonal vegetation.
3. In addition to discussing the effect of climate factors on vegetation growth on section 4.1, this study also discussed the influence of changes in land use types on vegetation growth on section 4.2. Taking the mutual conversion of cultivated land, forest and grassland as an example, the non-transformed area was taken as the reference group to compare the differences in the variation of vegetation coverage and productivity, and the differences were quantified. Finally, it is necessary to further explore the quantitative analysis and driving mechanism of human activities that lead to land use and vegetation change on section 4.3.

We have to apologize the innovation in methodology is lacking. However, it’s worthy that the innovation of this paper mainly focuses on the analysis part. Combined with the methods of statistics and econometric geography, the paper has deepened the time scale of the previous relevant studies, and analyzed the seasonal changes. In the discussion part, the effects of land use change on vegetation coverage and productivity change, which were neglected by predecessors, are also added.

 

LUCC must be expanded at first mention line 15

Reviewing:

Thank you for your suggestion. In our replies, we made some modifications in the new version.

Based on Land Use and Cover Change (LUCC) and MODIS image data, we created a dataset including land use/cover, annual and seasonal vegetation coverage and vegetation productivity in the TNSFPA from 2000 to 2020, then analyzed their spatial and temporal dynamics characteristics and explored the driving factors of changes.

 

Cannot start a sentence with a number line 26

Reviewing:

Thank you for your suggestion. In our replies, we checked and corrected this sentence on the new version.

There are 51.66% of the TNSFPA showed an increasing vegetation productivity (3.41 gC·m^-2·a^-1), mainly in the basic stable state and significantly increased state.

 

Consistent use of capital letters must be observed e.g., line 42

Reviewing:

Thank you for your suggestion. In our replies, we checked and corrected the consistent use of capital letters on the new version.

In the IPCC special report on Climate Change and Land, Desertification, Land Degradation, Sustainable Land Management and Food Security are listed as key issues of concern [2].

 

Lines 57-58 need to be rephrased

Reviewing:

Thank you for your suggestion. In our replies, we rephrased the sentence on the new version.

The evolution of LUCC and vegetation induced by climate change is mainly reflected in changes of land use types, vegetation coverage and productivity [2]. Therefore, it is of strategic importance to grasp their spatiotemporal evolution characteristics and driving mechanisms for improving land use efficiency, promoting rational utilization of land resources and comprehensively managing land degradation.

 

There is an over-assumption that all readers would understand what MOD/MYD13A1 is on line 84. These must be defined or expanded for ease of understanding.

Reviewing:

Thank you for your suggestion. We added the characteristics of MODIS products used on the section 2.2.2.

2.2.2. MODIS Products

In this study, we used MOD/MYD13A1 Vegetation Indices and MOD/MYD17A2H Gross Primary Productivity products to obtain vegetation change information. MOD and MYD with a spatial resolution of 500 m are acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, respectively. In order to reduce the impact of atmospheric and geometric deformation on data accuracy, we adopted the average value of MOD and MYD data in the same time period. They were obtained from the National Space Administration of the United States of America (https://modis.gsfc.nasa.gov/).

 

The aim of the study is ambiguous and not clearly defined enough see lines 85-87

Reviewing:

Thank you for your suggestion. In our replies, we supplemented the research background and significance of this study on the revised draft. Of course, to ensure the readability and completeness of the manuscript, this part was not mentioned on the new version.

Research background and significance

The unreasonable economic development mode and weak environmental protection awareness have made the ecological environment worse in northern China. Therefore, in the 1970s, combined with the change of national economic development strategy and the strengthening of environmental protection awareness, the Chinese government proposed the construction of the Three-North Shelter Forest Program Area. The main purpose of the program construction is to basically control the damage of sand and soil erosion and improve the ecological environment in the middle of the 21st century. Construction began in 1978 and will end in 2050 with three phases and eight stages. Among them, the research time scale of this paper coincides with the second phase (the fourth and fifth stage project). Therefore, the research results of this paper can also be used as the acceptance of the results of the second phase of the Three-North Shelter Forest Program Area, and provide data basis for the decision-making of national government agencies.

 

TNSFPA must be expanded at first mention in the text line 69

Reviewing:

Thank you for your suggestion. In our replies, we made some modifications in the new version.

However, the overall understanding of LUCC and vegetation changes in the Three-North Shelter Forest Program Area (TNSFPA) and their response to climate change in the past two decades is lack due to differences in research periods, data sources, methods and indicators.

 

Authors must provide a description of the change mappings in Table 1. I observed these were described in the results but I suggest that a column be added for the description of what each of the categories mean.

Reviewing:

Thank you for your suggestion. In our replies, we added the relevant content about the description of the change mappings after the table.2.

Table 2. Types of LUCC.

Code	AAA	ABB	AAB	ABA	ABC
Type	No change	Previous change	Later change	Repeated change	Continuous change

(1) No change. It denotes that the type of land remains unchanged from 2000 to 2020.

(2) Previous change. It refers to the type of land transfer that occurred from 2000 to 2010.

(3) Later change. It refers to the type of land transfer that occurred from 2010 to 2020.

(4) Repeated change. It refers to the type of land that transferred only in 2010 and remained consistent in 2000 and 2020.

(5) Continuous change. It denotes that the type of land was different in 2000, 2010 and 2020.

 

Overall, the methodology must be redone with a better description of each step and the authors must provide at the end of the methodology a summarized flowchart.

Reviewing:

Thank you for your suggestion. we made some modifications in the new version (section 2.3).

2.3. Methods

The global schema of data process is shown in Figure 2 and some key methods are provided in the following sections.

Figure 2. Mind map of this study.

2.3.1. Land Use Change Mapping

In this paper, we adopted the map fusion proposed by Bao [55] and Wang [56] to obtain the pattern of land use change, which can be implemented by map algebraic operation on each pixel of land use images in different periods. The operation is as follows:

                                    (1)

where C is the evolution mapping of land use pattern during the study period, the values of i are 1, 2 and 3, corresponding to different years (2020, 2010 and 2000), respectively. A_i is the pixel values of land use types in different periods. The types of LUCC are listed in Table 2.

Table 2. Types of LUCC.

Code	AAA	ABB	AAB	ABA	ABC
Type	No change	Previous change	Later change	Repeated change	Continuous change

(1) No change. It denotes that the type of land remains unchanged from 2000 to 2020.

(2) Previous change. It refers to the type of land transfer that occurred from 2000 to 2010.

(3) Later change. It refers to the type of land transfer that occurred from 2010 to 2020.

(4) Repeated change. It refers to the type of land that transferred only in 2010 and remained consistent in 2000 and 2020.

(5) Continuous change. It denotes that the type of land was different in 2000, 2010 and 2020.

2.3.2. Vegetation Coverage Extraction

The Vegetation Coverage (VC) is the percentage of vegetation’s vertical projection on the ground over the area of the statistical zone [57]. Common methods for calculating the VC are mainly divided into ground survey and remote sensing monitoring. The former includes sampling method and calculation by instrument, while the latter includes regression model, vegetation index method and pixel decomposition model [58]. Limited by the influence of the scope of the study area and the accuracy of the model, this study chose remote sensing monitoring. Firstly, we used the Maximum Value Composites method to obtain the maximum of inter-annual and seasonal NDVI [59-61]. Then, the pixel dichotomous model was applied to calculate the VC, considering all pixels as mixed ones including soil and vegetation [62].

                            (2)

where VC is the vegetation coverage of individual pixel, NDVI_S and NDVI_V represent the minimum (pure soil pixel) and maximum (pure vegetation pixel) of NDVI among all pixels in the study area, respectively. Neither of them is constant, due to the influence of some factors such as atmospheric environment, surface roughness, soil properties and vegetation type [63]. Therefore, we selected the NDVI corresponding to the cumulative frequency at 5% and 95% confidence intervals as NDVI_S and NDVI_V, respectively [64].

2.3.3. Linear Regression Analysis

The linear regression analysis method can calculate the slope of cells and reflect the spatial distribution characteristics of variable changes [11, 65]. In this study, we used the least square method to calculate the parameters of the linear regression. The principle of the method is to find the best function by minimizing the sum of error squares.

                               (3)

where S is the trend rate of change; n is the number of time points; X_i is the corresponding ecological indicator.

2.3.4. Sen’s Trend Degree, M-K Significance Test

The Sen’s trend degree has been widely applied on qualitative description of time series data. It mainly calculates by the median of the time series data, which can effectively reduce the influence of the abnormal value and extreme value.

                             (4)

where β is a Sen’s trend degree. X_i and X_j are the sequences of the ecological indicators; i and y are the sequences of the time. When β is greater than 0, it means that the time series data has an overall increasing trend. When β is less than 0, it means that the time series data has a reducing trend. When β is equal to 0, it means that the time series data presents a basic stability state.

The Mann-Kendall test method is a non-parametric statistical test method and has been widely applied on the time series data, as well as raster data on a pixel scale [66-67]:

                                 (5)

                                (6)

                              (7)

                              (8)

where X_i and X_j are the sequences of the ecological indicators. i and y are the sequences of the time. We assumed that the confidence level α was set at 0.05, then Z_1-a/2 is the corresponding value (1.96) of the distribution table of the standard normal function at this confidence level. If |Z|˃Z_1-a/2, it indicates that there is a significant trend of change. Whereas |Z|˂Z_1-a/2, it indicates that there is a slight trend of change.

According to the combination of β and Z values, five categories of vegetation coverage and productivity trend theoretically exist. Table 3 provides the classification criteria and their corresponding trends.

Table 3. Classification criteria of the trend.

Coefficient reference range	Trend
β<0, |Z|>1.96	Significant reduction
β<0, |Z|≤1.96	Slight reduction
β=0	Basic stability
β>0, |Z|≤1.96	Slight increase
β>0, |Z|>1.96	Significant increase

 

The introduction must show the gaps in knowledge that the methodologies will answer.

Reviewing:

Thank you for your suggestion. In our replies, we made some modifications in the new version.

At first, we introduced some knowledge and research significance of LUCC, vegetation coverage and productivity in the first paragraph of the abstract. The conclusion of this part is: in the context of global climate change, quantifying LUCC and vegetation change at different spatial scales and their relationship with climate change has become a common scientific issue of climate change and terrestrial surface ecosystem, as well as one of the main elements of global change research [17-18].

Then, we introduced relevant studies on LUCC, vegetation coverage and productivity, and summarized some existing researches on the TNSFPA. At present, studies on LUCC mainly focus on its effects on land productivity [19-22], land use efficiency [23-26] and carbon emissions [27-29]. In the fields of vegetation coverage and productivity, scholars paid attention on regional monitoring and trend analysis [30] and their response to climate change. Most of these researches were concluded in this study area (e.g., administrative divisions [31-32], ecological zones [33-35], watersheds [18, 36-38] and project areas [39-41]), and found that vegetation coverage and productivity in most areas showed an increasing trend. By summarizing the existing researches, we pointed out the shortcomings of existing research. There are: the overall understanding of LUCC and vegetation changes in the Three-North Shelter Forest Program Area (TNSFPA) and their response to climate change in the past two decades is lack due to differences in research periods, data sources, methods and indicators.

Finally, we illustrated the feasibility of this study from the data accessibility and method operability. Satellite remote sensing technology enables us to classify land use and monitor vegetation on a large extent and long time series, therefore remote sensing data have become the main data source for long-term vegetation studies [30, 42-43]. Data products such as common vegetation index and primary productivity can fully reflect the growth status of plants. Meanwhile, we again raised the significance of this study. Therefore, it is of great significance to explore the current situation and changing trends of land use and vegetation in the TNSFPA, especially for optimizing the structure of regional land use types, as well as coordinating eco-environment protection and economic development. At the end of the abstract, we put forward a series of specific contents such as research content methods, and significance. In this study, we constructed a dataset including land use, annual and seasonal vegetation coverage and productivity in the TNSFPA from 2000 to 2020 based on LUCC, MOD/MYD13A1 and MOD/MYD17A2H data. Moreover, their spatiotemporal evolution characteristics and impact factors were analyzed. This study aims to provide theoretical support and scientific basis for the quantitative and dynamic monitoring of vegetation coverage, ecological benefits and sustainable development in the TNSFPA.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The innovation of this manuscript is weak.  The first sector of the authors' responses is not an  innovative point. The original interpreted data used in the manuscript is not highly accurate so that some conclusions do not fit with common senses. It is also hard to cerrect it so that the authors ignore this problem.

 

Author Response

(1) The innovation of this manuscript is weak. The first sector of the authors' responses is not an innovative point.

Reviewing:

Thank you for your suggestion. In our replies, we have rearranged the innovation points of this paper.

Innovation

1. We updated of the time scale of the related research. We considered the seasonal change trend of Vegetation Coverage and Gross Primary Productivity.
2. From the perspective of statistics, we analyzed the mean value, coefficient of variation, slope and trend of related indexes in the analysis section.
3. It is worth noting that many studies on vegetation coverage and productivity focused on single land use type or did not consider the impact of LUCC on it. By comparing different land use change models, we explored the impact of LUCC on vegetation coverage and productivity. The basic conclusion is that: ① When the cultivated land was changed into forest, of which the vegetation coverage and productivity were greater than the unchanged cultivated land.

② When the cultivated land was changed into grassland, of which the vegetation coverage and productivity were lower than the unchanged cultivated land.

③ The vegetation coverage and productivity of the unchanged forest were greater than the changed forest.

In future studies, we hope to obtain more precise LUCC or experimental field data, so as to explore the mechanism of land use change on vegetation growth.

(2) The original interpreted data used in the manuscript is not highly accurate so that some conclusions do not fit with common senses. It is also hard to correct it so that the authors ignore this problem.

Reviewing:

Thank you for your suggestion. In the revised manuscript, we replaced the original interpreted data – MCD12Q1 (section 2.1.1), used the Land Use and Land Cover data with higher spatial and temporal resolution and re-analyzed it (section 3.1).

 

2.2.1 Land Use and Land Cover

In this study, we used MCD12Q1 Land Cover products to obtain LUCC. It is a suite of science data sets (SDSs) that map global land cover at 500 meters spatial resolution at annual time step for six different land cover legends, including 5 legacy classification schemes (IGBP, UMD, LAI, BGC, and PFT), a new three layers legend based on the Land Cover Classification System (LCCS) from the Food and Agriculture Organization [49-51] and a Quality Assurance layer. It is created using supervised classification of MODIS reflectance data [52-53]. We selected the International Geosphere – Biosphere Program (IGBP) legend to reflect LUCC in the study area during 2001-2020.

The IGBP scheme was classified using the C4.5 decision tree algorithm that ingested a full year of 8-day MODIS Nadir BRDF-Adjusted Reflectance data (MCD43A2 and MCD43A4) [54]. There are seventeen land use types in this dataset. In order to better analyze LUCC in the study area, the original 17 categories were integrated into 6 categories (Table.1). It was obtained from the National Space Administration of the United States of America (https://modis.gsfc.nasa.gov/).

Table 1. the classification system of LUCC.

Code	The primary classification	The secondary classification and code
1	cultivated land	croplands (12), cropland/natural vegetation mosaics (14)
2	forest	evergreen needleleaf forests (1), evergreen broadleaf forests (2), deciduous needleleaf forests (3), deciduous broadleaf forests (4), mixed forest (5), closed shrublands (6), open shrublands (7)
3	grassland	woody savannas (8), savannas (9), grasslands (10)
4	water body	permanent wetlands (11), permanent snow and ice (15), water bodies (17)
5	constructive land	urban and built-up lands (13)
6	barren	barren (16)

3.1. Change of land use and land cover in the TNSFPA from 2000 to 2020

There is an obvious change of land use and land cover area in the TNSFPA from 2001 to 2020 (as shown in Table 4). In 2020, the land use type having the largest area was barren, followed by grassland, accounting for 44.33% and 40.52%, respectively. The area of water body and constructive land were fewer, accounting for 1.18% and 0.72% respectively. From 2001 to 2020, the area of all land use types increases, except for the barren. Among them, the water body witnessed the largest area change rate, which reflected the increasing trend of warming and humidification in the Northwest China.

Table 4. LUCC in the TNSFPA from 2001 to 2020.

Type	Area/ km2			Change rate/%
	2000	2010	2020	2000~2010	2010~2020	2000~2020
cultivated land	408632	428480	465158	4.86	8.56	13.83
forest	60992	68576	73803	12.43	7.62	21.00
grassland	1637223	1648584	1648891	0.69	0.02	0.71
water body	34259	40942	47936	19.50	17.08	39.92
constructive land	26102	27158	29247	4.04	7.69	12.05
barren	1901790	1855261	1803965	-2.45	-2.76	-5.14

Compared with MCD12Q1 IGBP legacy classification schemes in the TNSFPA during 2001-2020, we found that evergreen broadleaf forest, closed shrublands, woody savannas, grasslands and barren showed an obvious decreasing trend (as shown in Figure 3). In addition, the other types showed an increasing trend. Grassland and barren decreased at the rate of 1663.49 km²/a and 4542.32 km²/a, respectively. Savannas and croplands increased at the rate of 1699.17 km²/a and 3098.16 km²/a, respectively.

Figure 3. Trend of LUCC in the TNSFPA from 2001 to 2020. (a-q) trend of 17 land use types in secondary classification system; (r) rate of 17 land use types.

In order to further analyze the change patterns of different land use types in the TNSFPA, the change pattern mapping was established by integrating the land use data of 2001, 2010 and 2020 (as shown in Figure 4). Among them, 90.05% of the land use area remained unchanged from 2001 to 2020. The land use change pattern was dominated by later change (58.06%) and previous change (28.94%). It implies that the transfer of land use type from 2010 to 2020 was stronger than that from 2001 to 2010, which is closely related to the national strategy for high-quality development and ecological civilization construction, as well as the impact of climate change. In addition, the previous change type mainly showed the mutual transformation between cultivated land and grassland. The later change type mainly showed the mutual transformation between grassland and barren. The repeated change type mainly presented the “grassland-cultivated land-grassland” model. The difference of continuous change type (2.38%) is less than others type.

Figure 4. Geo-spectrum of LUCC change model.

LUCC patterns of cultivated land, forest and grassland show that cultivated land and forest were mainly transformed into grassland, with an area of 9699.75 km² and 43705 km² (60.92%) respectively. Grassland was mainly transformed into cultivated land (102090.25 km²) and barren (25710.25 km²). It reflected the implementation of the policy of “returning cultivated land to forest land and grassland” and the current situation of grassland reclamation and degradation.

Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

The authors have revised the relevant section to make this manuscript clearer.

I think this current manuscipt can be accepted for publication.