Next Article in Journal
Declines in Livestock Numbers Accompany Cropping Deagrarianisation Processes in the Eastern Cape, South Africa
Previous Article in Journal
Spatio-Temporal Evolution of Key Areas of Territorial Ecological Restoration in Resource-Exhausted Cities: A Case Study of Jiawang District, China
Previous Article in Special Issue
Impact of Land Cover on Wind Erosion in Arid Regions: A Case Study in Southern Tunisia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 12
Issue 9
10.3390/land12091734
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Should Desert and Desertification Regions Be Confused? New Insights Based on Vegetation Quality and Its Inter-Decadal Variations

by
Luguang Jiang
1,2,* and
Ye Liu
1
1
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(9), 1734; https://doi.org/10.3390/land12091734
Submission received: 20 July 2023 / Revised: 29 August 2023 / Accepted: 31 August 2023 / Published: 6 September 2023
(This article belongs to the Special Issue Desert Ecosystems and Landscapes: Structure, Functioning and Threats)
Browse Figures
Versions Notes

Abstract

:
As the most unique ecosystem on the Earth’s surface, desert and desertification region cannot be confused. The current research on spatial distinction of desert and desertification region is still lacking. Based on NDVI (normalized difference vegetation index) data from 1998 to 2020, we aimed to distinguish the differences between desert and desertification region. Improvement and degradation of vegetation quality in China have coexisted in the past 20 years. Within the low value classification in 1998, the regions where vegetation quality remained High increase were mainly concentrated in Loess Plateau. Within the medium value classification in 1998, the High increase classifications were mainly distributed in the west of the Taihang Mountains, north of the Qinling–Daba Mountains, east of the Qinghai–Tibet Plateau, Yunnan–Guizhou Plateau, and the Northeast Plain. Within the high value classification in 1998, the High increase classification was distributed in the south of the Yangtze River. In 1998 and 2020, China had a total area of 2.50 million km2 of desert regions, accounting for 26% of China’s land area. After 20 years of large-scale ecological protection, desert regions have hardly undergone significant changes. Desertification regions decreased from 2.80 million km2 to 1.67 million km2, a decrease of 40.3%.

1. Introduction

Desertification is one of main forms of land degradation [1]. Currently, more than one-third of land has been degraded globally [2,3,4], which makes achieving the United Nations’ (UN) sustainable development goals a huge challenge [5,6]. In June 1994, the UN unanimously adopted the United Nations Convention to Combat Desertification [7]. In 2015, it made mitigating, containing, and reversing the degradation of terrestrial ecosystems its 15th sustainable development goal [8]. In 2019, the UN General Assembly unanimously approved the implementation of the United Nations’ 10-year ecosystem restoration plan for 2021–2030, which aims to address serious degradation of wetlands and aquatic ecosystems in the world [9,10]. Desertification presents very serious environmental risks [11]. In the context of global warming, desertification is likely to continually expand [12].
However, as a dynamic process, desertification and deserts cannot be confused. The controversial arguments are mainly divided into two types. Some scholars believe that desertification is a process of land degradation, while others believe that it is the ultimate result of land degradation [13,14,15,16,17]. The most comprehensive and widely accepted definition currently comes from the UNCCD (1994), which states that “desertification refers to land degradation in arid, semi-arid and sub-humid arid areas caused by various factors, including climate variability and human activities” [7]. Desert refers to a region with a dry climate, sparse precipitation, large evaporation and poor vegetation, or a land that is almost completely devoid of vegetation and well-developed soil. In addition, the focus of controversy also lies in two aspects: some scholars believe that at least one-third of global desertification is caused by humans; other scholars believe that there is insufficient evidence of human activities causing desertification [18,19,20]. To date, there have been three systematic assessments of land degradation worldwide: Global Assessment of Soil Degradation (GLASOD) in 1990 [21,22], the research work of Dregne and Chou (1992) [23], and the World Atlas of Desertification [24]. The GLASOD is based on expert opinions, and there are significant variations in the quality. The information relied on by Dregne and Chou (1992) is insufficient. Their evidence comes from anecdotes, various research reports, description of travelers, personal opinions, and personal experiences of local residents. The description of desertification in the first edition of the World Atlas of Desertification directly refers to GLASOD [25]. Deterministic maps of global land degradation are not included in the third edition of the World Atlas of Desertification [26]. In addition, there are many desertification-related monitoring and evaluation standards in China, such as the Fourth National Technical Regulations on Desertification Monitoring issued by the State Forestry and Grassland Administration, as well as national standards GB/T20483-2006 [27], GB/T24255-2009 [28], etc.
As previously indicated, practically all current research conflates desert with desertification without taking into account their distinctions. Many academics investigate desertification using trend analysis of the vegetation [29,30] and even examine the effects of precipitation stripping [31], but they still fail to detect the aforementioned distinctions. At the same time, it is difficult to control uncertainty with these methods because of the excessive factor consideration, and it is not possible to reveal macroscopic patterns when factors like climate change, soil change, and vegetation productivity changes are taken into account. They all primarily focus on the quantitative link between desertification and other variables, with little or no geographical pattern boundaries being shown. These studies primarily concentrate on the places where they altered, paying little attention to areas that have not altered. The region that does not change should be thought of as a desert, which is a very significant ecosystem on the Earth’s surface and also produces rich natural scenery.
The application of remote sensing is the most advantageous method for revealing macroscopic patterns and changes. Overall, remote sensing provides benefits including effectiveness, comprehensiveness, and affordability. Although various studies have explored the driving factors and control measures of desertification expansion, the difference between desert and desertification from a spatial perspective has not been explained by remote sensing either. Desertification monitoring research methods include field surveys, remote sensing image classification, and desertification indicator monitoring [32,33,34,35,36]. Desertification indicator monitoring includes vegetation index [37] and surface albedo [38]. Supervised classification based on machine learning is another commonly used remote sensing monitoring method [39]. The application of remote sensing in desertification evaluation has developed from visual interpretation and single-indicator evaluation to comprehensive evaluations of multiple remote sensing indicators [40,41,42].
Based on the cited results, current research on the spatial differences of deserts and desertification region is still relatively lacking. Therefore, the main content of our research has two aims: (1) based on the vegetation quality data for China from 1998 to 2020, we use the classification method to reveal spatiotemporal vegetation quality changes over the past 20 years; and (2) on this basis, we attempt to explain the difference between deserts and desertification region, providing a basis for more reasonable ecological protection. Rather than examining the mechanics of desertification, our study goal is to construct a geographical zoning of desert and desertification. Instead of focusing just on quantitative links, we wish to characterize the distinctions between deserts and desertification in terms of spatial patterns. This can offer a fresh perspective on achieving global sustainable development goals and stopping desertification.

2. Materials and Methods

2.1. Materials

The normalized difference vegetation index (NDVI) can accurately reflect the quality of surface vegetation. NDVI time series data obtained from the moderate-resolution imaging spectroradiometer (MODIS) satellite remote sensing images have been used for monitoring vegetation dynamic changes at various scales (Figure 1). We used MOD13Q1 data on the Google Earth Engine platform to extract annual maximums of NDVI for 1998–2020. This dataset is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC) with a spatial resolution of 250 m (https://ladsweb.modaps.eosdis.nasa.gov/search/ (accessed on 1 July 2023). The data of land use, annual average precipitation, and soil erosion intensity are from the Resource and Environment Science and Data Center, Chinese Academy of Sciences (https://www.resdc.cn/ (accessed on 1 July 2023).
The data format of the original MODIS is hierarchical data format (HDF), and the projection method of the data is the sine curve projection. MODIS data are preprocessed in advance, including image mosaic, clipping, and projection transformation. We take the average of three years as one year to reduce the impact of outlier on NDVI. For instance, the data for 2020 take the averages of 2018, 2019, and 2020. The data for 1998 adopt the mean values for 1998, 1999, and 2000. This dataset accurately depicts the distribution and alterations in the vegetation cover across the nation on both a geographical and temporal scale, without exhibiting significant abrupt shifts.

2.2. Classification Standard of NDVI

The NDVI classification threshold is based on 1998 data, and the 2020 data refer to the 1998 classification standards for unified comparison. The determination of the threshold mainly refers to three important geographical boundaries, namely, the boundaries of China’s wet and dry zones, China’s annual average precipitation, and China’s vegetation type zones. At the same time, the 20-year NDVI change threshold grading also refers to the above three boundaries. Firstly, the regions with the largest investment in ecological protection in China are located in arid, semi-arid, and semi-humid regions, and the boundaries between the three regions have become important grading references. Secondly, precipitation lines such as 200 mm, 400 mm, and 600 mm are important factors for ecological protection and vegetation growth. Thirdly, the boundaries of deserts, grasslands, and forests are also important references for grading standards, which directly determine the value and significance of NDVI. Since 1998, China has implemented a variety of key ecological protection projects, including natural forest protection, Grain for Green and grassland, returning grazing land to grassland, water and soil conservation projects, and comprehensive management of rocky desertification areas. Among them, most key ecological protection projects are closely related to arid, semi-arid, desert, and grassland areas. Therefore, the way we refer to geographical boundaries is in line with practical needs. The quantile grading based on statistics is also within the scope of consideration, so as to avoid too-large differences in the area of grading.
The specific threshold division was based on comprehensive considerations. The thresholds for NDVI data in 1998 were 0.27 and 0.71, which were divided based on the following three points: firstly, based on statistical results, the distribution curve of NDVI was bimodal, and the threshold needed to include complete peaks; secondly, the threshold needed to ensure that there was no order of magnitude difference in grading; thirdly, the threshold referred to the threshold of important geographical boundaries to ensure ecological significance. After multiple attempts, the thresholds were ultimately determined to be 0.27 and 0.71. The threshold division in 2020 was based on the threshold selection in 1998, as the data need be comparable. The threshold for NDVI changes were 0.05 and 0.12, which were divided based on the following three points: Firstly, based on statistical results, the NDVI change curve was unimodal, and the threshold needed to include complete peaks. Secondly, NDVI data had a certain degree of error, with some parts within 0.05 having a significant error. Therefore, we considered the parts within 0.05 to be stable. Finally, 0.12 was the location of sudden changes in the rate of change, with some changes greater than 0.12 tending to be moderate. In summary, we selected 0.05 and 0.12 as thresholds.

2.3. Methods

Based on NDVI data from 1998 to 2020, we conducted a spatial analysis of vegetation quality during large-scale ecological construction in China over the past 20 years. Firstly, we divided the NDVI range (0–0.92) in 1998 into three classifications: Low value (I; 0–0.27), Medium value (II; 0.27–0.71), and High value (III; 0.71–0.92) (Figure 1). Secondly, the NDVI change range is obtained from the NDVI range in 2020 minus the NDVI range in 1998 (−0.92–0.92). We divided the NDVI change range (−0.92–0.92) from 1998 to 2020 into four classifications: Decrease (a; <−0.05), Stable (b; −0.05–0.05), Low increase (c; 0.05–0.12), and High increase (d; >0.12). Based on these hierarchical arrangements and combinations, we divided the vegetation quality changes into 12 classifications: Ia, Ib, Ic, Id, IIa, IIb, IIc, IId, IIIa, IIIb, IIIc, and IIId (Table 1 and Table 2).
Taking the classifications of Ia, Ib, Ic, and Id as an example, the practical significance is that vegetation quality has undergone different changes over 20 years in regions that had low vegetation quality in 1998. Ia refers to areas with a further reduction in already-low vegetation quality since 1998; Ib refers to areas with low vegetation quality that remained stable since 1998; Ic refers to areas with a small increase in the low vegetation quality since 1998; and Id refers to a large increase in areas with low vegetation quality in 1998. The meanings of IIa, IIb, IIc, and IId are similar to those of IIIa, IIIb, IIIc, and IIId. The same hierarchical processing was performed on the 2020 NDVI data. Based on classification comparisons between 1998 and 2020, we obtained the boundary changes for the 12 classifications over these 20 years.
The areas with low, medium, and high NDVI values in 1998 were 3.27 × 106 km2, 2.68 × 106 km2, and 3.51 × 106 km2, accounting for 34.59%, 28.32%, and 37.09% of the land area, respectively (Figure 2). In 2020, the areas with low, medium, and high NDVI values were 2.89 × 106 km2, 2.14 × 106 km2, and 4.40 × 106 km2, accounting for 30.65%, 22.69%, and 46.66% of the land area, respectively. The areas of Decrease, Stable, Low increase, and High increase in NDVI changes were 6.82 × 105 km2, 3.77 × 106 km2, 2.73 × 106 km2, and 2.27 × 106 km2, accounting for 7.21%, 39.89%, 28.85%, and 24.01% of the land area, respectively.

3. Results

3.1. Spatiotemporal Variations of Vegetation Quality

According to NDVI data from 1998 and 2020, the NDVI value of China steadily declines from the southeast coast to the northwest interior, indicating that the southeast region has better vegetation than the northwest region (Figure 3). China’s vegetation has greatly improved over the previous 20 years. In 1998, China’s high and low NDVI values were primarily focused in the 0.6 to 0.8 and 0–0.2 ranges, respectively. At the same time, the low values remained unchanged, and the high value of NDVI in China in 2020 steadily concentrated over 0.8. This indicates that the vegetation has improved in the eastern section with a better backdrop, whereas the improvement in the northwest region with a bad background is not immediately apparent. The area of high value region (above 0.8) is growing, while the area of low value region (between 0.0 and 0.2) is decreasing.
In the past 20 years, vegetation quality in China has significantly improved. According to NDVI statistics, approximately one-third of China’s regions (3.0 × 106 km2) have experienced an increase (average NDVI increases are greater than 0.1). A decrease occurred in 3.73% of the regions (0.35 × 106 km2; average NDVI decreases are greater than 0.1). The area with significant declines is divided into two parts. First, urban built-up regions have experienced vegetation quality degradation caused by urbanization. Second, desert and steppe regions have also experienced vegetation quality degradation caused by special physical and geographical conditions. The areas where vegetation quality has improved significantly are concentrated in three regions: Loess Plateau, Yunnan–Guizhou Plateau, and the semi-arid region. The average NDVI in the Loess Plateau increased by 0.13 and that of the Yunnan–Guizhou Plateau increased by 0.11. NDVI increased significantly in regions around the Tarim Basin.

3.2. Classification Based on Vegetation Quality

Within the low value classification in 1998, the area that remained stable was the largest (Ib = 2.30 × 106 km2), exceeding the sum of the decrease, low increase, and high increase areas (Figure 4a,b). Within the medium value classification in 1998, the area of High increase was the largest (IId = 1.60×106 km2), exceeding the sum of the other three areas. Within the high value classification in 1998, the Low increase and High increase areas were the main classifications, with a total combined area of 2.22 × 106 km2 (Table 3).
The hierarchical features present significant regional differences in terms of spatial characteristics. Within the low value classification in 1998, the regions where vegetation quality remained stable were mainly concentrated in northwestern China and in the alpine regions of the Qinghai–Tibet Plateau. However, under the influence of alpine ice and snow meltwater, the vegetation quality around the Tarim Basin and at the foot of the Tianshan Mountains has increased significantly. The vegetation quality in alpine areas also has a sporadic increase and did not increase uniformly. The vegetation quality in the middle and upper reaches of the Yellow River has increased significantly. Within the medium value classification in 1998, the High increase and Low increase classifications account for the majority, which were distributed in the west of the Taihang Mountains, north of the Qinling–Daba Mountains, and east of the Qinghai–Tibet Plateau, Yunnan-Guizhou Plateau, and the Northeast Plain. The vegetation quality in the Hengduan Mountains and southern Qinghai–Tibet Plateau slightly increased. Within the high value classification in 1998, the High increase classification accounts for the majority, distributed in the Greater Khingan Mountains and vast areas south of the Yangtze River. The areas of Decrease are concentrated in the large urban agglomerations in China, mainly in the Beijing–Tianjin–Hebei urban agglomerations, Yangtze River Delta urban agglomerations, Guangdong–Hong Kong–Macao Greater Bay Area, Chengdu–Chongqing Group Cities, Triangle of Central China urban agglomerations, Guanzhong Plain City Group, and the Central Plains Urban Agglomeration.

3.3. Differences of Classification between 1998 and 2020

Regions with originally low vegetation quality values (Ia, Ib) have not significantly improved after 20 years of large-scale ecological construction (Figure 5). The vegetation quality in these regions has remained stable or decreased. This region is a true desert. After large-scale ecological construction, some areas with low values have significantly improved (Ic, Id), and these regions should be key areas for desertification control.
After large-scale ecological construction, the vast majority of areas with medium values have shown improvement (IIc, IId), and these regions also should be key areas for desertification control. Small portions of Stable or Decrease areas are distributed in the typical dry steppe of Ujumucin. The natural geographical characteristics of the dry steppe are that the average annual precipitation is less than 400 mm, but the evaporation is very high. Water conditions play a decisive role in vegetation quality changes. For these regions, attention should be paid to balancing preserving grassland and reasonable grazing.
Regions with inherently high value also have ecological construction necessity. Natural forest protection projects have produced positive results in improving vegetation quality [43]. Forest land in regions with inherently high value is the land use type with the most significant improvements in vegetation quality. Simultaneously, large-scale urbanization development occupies a higher proportion of areas with high vegetation quality. Coordination of the relationship between urbanization and ecological protection must always be considered.
Based on the research findings, it is clear that desertification mostly affects the northern area and also predominates there. Although separating desertification from desert differences is our major objective, we also need to highlight that there are many forms of desertification in China. Geographically, the northern region is mostly defined by desertification. Eleven provinces, including Inner Mongolia, Shanxi, Shaanxi, Ningxia, Gansu, Qinghai, and Xinjiang, have desertification regions. Rock desertification is the dominant feature of the southern area. Stone desertification is mostly found in the provinces of Guangxi, Guizhou, and Yunnan. Upstream of the Yangtze River, the Jinsha River, Wujiang River, and Pearl River are home to most of the rocky desertification terrain [43,44,45,46,47].

3.4. Distinction of Desert Regions or Desertification Regions

Deserts are a key component of natural ecosystems and a product of long-term evolution of the natural environment. Although a desert is arid with sparse vegetation, it also breeds unique animal and plant types. Many people believe that deserts are a threat to humanity and want to eliminate them, which violates the laws of compliance with nature [48,49]. What really needs to be addressed is the land desertification caused by irrational development activities, rather than naturally occurring deserts [50]. Desert ecosystems are fragile and difficult to recover once destroyed. Therefore, the differences in desert regions and desertification regions have very important practical significance for desertification control.
We attempted to divide the scope of desert and desertification based on the results of two levels and calculated their areas (Figure 6). The statistical results show that in 1998 and 2020, China had a total area of 2.50 million km2 and 2.52 million km2 of desert regions, accounting for 26% and 26.3% of China’s land area, respectively. After 20 years of large-scale ecological construction, desert regions have hardly undergone significant changes. The desertification regions decreased from 2.80 million km2 to 1.67 million km2, a decrease of 40.3% (Table 4).
It was worth noting that the classification of IIc and IId as desertification regions has been carefully considered. Firstly, these two classifications were the regions with the largest investment in ecological protection in China over the past 20 years. Secondly, from the perspective of vegetation types, they belong to deserts, desert grasslands, typical dry grasslands, and natural forest areas. Although the NDVI value in the past was moderate, it belongs to an ecologically fragile area, and the ecological condition was easily damaged. Through continuous protection, the IIc and IId areas have made significant improvements, and they are worth paying attention to. Therefore, we also divided the IIc and IId regions into desertification regions.

4. Discussion

4.1. Classification Boundary Movement

Based on 1998 NDVI data, we superimposed the changes after 20 years to classify them. A comparison of the two classifications can reveal spatially whether the boundaries of vegetation quality classifications have changed over 20 years (Figure 7).
The boundary with low vegetation quality in the northwestern part of China has not changed much (Ia, Ib), which indicates that ecological construction in this region did not have any effect during the 20-year period, and the original natural geographical landscape should be maintained. There are three regions with obvious boundary changes: the Yunnan–Guizhou Plateau, the semi-arid regions north of the Qinling–Daba Mountains and west of the Taihang Mountains, and the Northeast Plain. The Yunnan–Guizhou Plateau and Hengduan Mountains are key areas of large-scale natural forest protection projects. The semi-arid regions north of the Qinling–Daba Mountains and west of the Taihang Mountains are regions where large-scale reforestation and conversion of cropland to grassland and forest are conducted. In the Northeast Plain, better crop growth has led to vegetation quality improvements. Simultaneously, to the north of the Qinling–Daba Mountains and west of the Taihang Mountains, the region with low vegetation quality has significantly decreased after more than 20 years of ecological protection.

4.2. Comparison of Classification Results’ Accuracy

In order to illustrate the reliability of the results, we compared land use data, precipitation data, and soil erosion data. Figure 8a,b show land use in 2000 and 2020. Figure 8c shows the distribution of annual average precipitation in China, and Figure 8d shows the distribution of soil erosion intensity.
In terms of land use, the distribution of deserts is highly consistent with the distribution of unused land. According to statistics, the unused land area in the arid regions in 2000 and 2020 was 204.70 km2 and 198.56 km2, respectively, with a change of only 3%. The area of unused land has remained stable over the past 20 years, which is consistent with our research results. At the same time, our results overestimate the area of deserts because weak NDVI changes are also classified as desert regions, and this area of land is not classified as unused land in land use. Land with minimal NDVI changes over the past 20 years should also be considered a desert region, despite some sparse grass cover. In terms of annual average precipitation, desert regions are mainly located in areas with precipitation below 200 mm. According to statistics, the area with precipitation below 200 mm in the arid region is 277.44 km2, which is higher than our research results. From the perspective of soil erosion intensity, the area of wind erosion region is 228.76 km2, slightly lower than our research results. In summary, the spatial distribution of desert is highly consistent, and the accuracy in quantity may also exceed 85%. The presentation of the above data also fully proves that the reliability of our methodology is trustworthy.

4.3. Comparison with Climatic Regionalization Research

The subject of our research is not only desertification hotspots but also the distinctions between desert and desertification. To guarantee the validity of the research findings, we compared the distinctions and hotspots to earlier studies on desertification. The major focus in recent years has been on how climate change is affecting desertification. To investigate how precipitation affects desertification, these studies primarily look at the link between changes in vegetation trends and rainfall. For instance, the significant link between precipitation and desertification was explored by Higginsbottom et al. [29].
Furthermore, despite the fact that temperature variations are significant in many places, many people think that vegetation in arid regions is unrelated to variations in rainfall. Some people think that the main sign of land degradation is a decline in vegetation’s ability to use rainwater. As a result, it has been suggested that this signal’s temporal fluctuation serves as a deterioration indicator; however, there is still a lot of disagreement about this. For instance, based on an investigation of vegetation rainwater usage efficiency, Hein et al. presented a desertification evaluation of the Sahel area [32,33].
Compared to existing research on desertification, we proposed a method to distinguish the spatial differences between desert and desertification. The similarity lay in the fact that they were all based on research in desertification regions, and we have discussed the areas of vegetation change. The difference was that we focused not only on vegetation change but also on vegetation stability, which has rarely appeared in previous research. In terms of ecological protection, it had more practical significance. In addition, our method was based on the concept of spatial zoning, which was easier for management departments to implement and operate.
In conclusion, there is still a lot of debate and ambiguous research about desertification and climate change. Our study is not limited to this, though. The unchanging regions have been neglected in favor of an excessive amount of study of the regions that are changing. It appears that the only areas worth studying are those experiencing change. Unchanged areas still have a lot of practical use. Natural rules should be observed during the process of controlling desertification in places that have not altered in the last 20 years in order to preserve the desert ecosystem’s original appearance. Future studies on the altered locations will require deeper investigation.

5. Conclusions

Based on NDVI data from 1998 to 2020 in China, we used classification to reveal the spatiotemporal behavior of vegetation quality changes behind China’s ecological construction. On this basis, we defined the differences between desert and desertification. The results show that improvement and degradation of vegetation quality coexist in China during the past 20 years. Approximately one-third of China’s regions have experienced an increase (average NDVI increase of more than 0.1) and 3.73% of China’s regions have experienced a decrease (average NDVI decrease of more than 0.1). This reflects the significant achievements made in ecological protection in China. Comparing the boundaries of the classifications in 1998 and in 2020, most regions with low values (Ia and Ib, desert regions) had not changed, and the original physical and geographical appearance should be maintained. However, some regions with low and medium values (Yunnan–Guizhou Plateau and Hengduan Mountains, semi-arid regions north of the Qinling–Daba Mountains and west of the Taihang Mountains, and the Northeast Plain) have significantly improved in vegetation quality, and these regions are desertification regions. For regions with inherently high values of NDVI, more than 20 years of natural forest protection projects have produced positive vegetation quality improvements. Simultaneously, large-scale urbanization has occupied large areas with high vegetation quality. China had a total area of 2.50 million km2 of desert regions, accounting for 26% of China’s land area. After 20 years of large-scale ecological protection, desert regions have hardly undergone significant changes. The desertification regions decreased from 2.80 million km2 to 1.67 million km2, a decrease of 40.3%. The contribution of this study is to address the spatial confusion between desert and desertification over a long period of time, which can offer a fresh perspective on achieving global sustainable development goals and stopping desertification.

Author Contributions

Conceptualization, L.J. and Y.L.; methodology, Y.L.; formal analysis, L.J.; resources, L.J. and Y.L.; data curation, L.J. and Y.L.; writing of original draft, L.J. and Y.L.; writing, review, and editing, L.J. and Y.L.; supervision, L.J. and Y.L.; project administration, L.J.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant number 42071253) and the Environmental Defense Fund (grant number 20220088).

Data Availability Statement

We used MOD13Q1 data on the Google Earth Engine platform to extract annual maximums of NDVI for 1998–2020. This dataset is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC) with a spatial resolution of 250 m (https://ladsweb.modaps.eosdis.nasa.gov/search/ (accessed on 1 July 2023).

Acknowledgments

The authors thank the editors and anonymous referees for their valuable comments on an earlier version of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xu, D.Y.; Wang, Y.Q.; Wang, Z.Y. Linking priority areas and land restoration options to support desertification control in northern China. Ecol. Indic. 2022, 137, 108747. [Google Scholar] [CrossRef]
  2. Christian, B.A.; Dhinwa, P.S. Ajai Long term monitoring and assessment of desertification processes using medium & high resolution satellite data. Appl. Geogr. 2018, 97, 10–24. [Google Scholar] [CrossRef]
  3. Türkeş, M.; Öztaş, T.; Tercan, E.; Erpul, G.; Karagöz, A.; Dengiz, O.; Doğan, O.; Şahin, K.; Avcioğlu, B. Desertification vulnerability and risk assessment for Turkey via an analytical hierarchy process model. Land. Degrad. Dev. 2020, 31, 205–214. [Google Scholar] [CrossRef]
  4. Vogt, J.V.; Safriel, U.; Von Maltitz, G.; Sokona, Y.; Zougmore, R.; Bastin, G.; Hill, J. Monitoring and assessment of land degradation and desertification: Towards new conceptual and integrated approaches. Land. Degrad. Dev. 2011, 22, 150–165. [Google Scholar] [CrossRef]
  5. Barbier, E.B.; Hochard, J.P. Land degradation and poverty. Nat. Sustain. 2018, 1, 623–631. [Google Scholar] [CrossRef]
  6. Stavi, I.; Lal, R. Achieving Zero Net Land Degradation: Challenges and opportunities. J. Arid. Environ. 2015, 112, 44–51. [Google Scholar] [CrossRef]
  7. United Nations Convention to Combat Desertification (UNCCD). Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa. United Nations: New York, NY, USA, 1994. Available online: https://wedocs.unep.org/20.500.11822/27569 (accessed on 1 July 2023).
  8. Chasek, P.; Safriel, U.; Shikongo, S.; Fuhrman, V.F. Operationalizing Zero Net Land Degradation: The next stage in international efforts to combat desertification. J. Arid. Environ. 2015, 112, 5–13. [Google Scholar] [CrossRef]
  9. Aronson, J.; Goodwin, A.; Orlando, L.; Eisenberg, C.; Cross, A.T. A World of Possibilities: Six Restoration Strategies to support the United Nation’s Decade on Ecosystem Restoration. Restor. Ecol. 2020, 28, 730–736. [Google Scholar] [CrossRef]
  10. Chasek, P.; Akhtar-Schuster, M.; Orr, B.J.; Luise, A.; Ratsimba, H.R.; Safriel, U. Land degradation neutrality: The science-policy interface from the UNCCD to national implementation. Environ. Sci. Policy 2019, 92, 182–190. [Google Scholar] [CrossRef]
  11. Wang, X.; Song, J.L.; Xiao, Z.Q.; Wang, J.; Hu, F.Z. Desertification in the Mu Us Sandy Land in China: Response to Climate Change and Human Activity from 2000 to 2020. Geogr. Sustain. 2022, 3, 177–189. [Google Scholar] [CrossRef]
  12. Yan, X.; Cai, Y.L. Multi-Scale Anthropogenic Driving Forces of Karst Rocky Desertification in Southwest China. Land. Degrad. Dev. 2015, 26, 193–200. [Google Scholar] [CrossRef]
  13. Ajai; Arya, A.S.; Dhinwa, P.S.; Pathan, S.K.; Ganesh Raj, K. Desertification/land degradation status mapping of India. Curr. Sci. 2009, 97, 1478–1483. Available online: http://www.jstor.org/stable/24107342 (accessed on 1 July 2023).
  14. Bai, X.-Y.; Wang, S.-J.; Xiong, K.-N. Assessing spatial-temporal evolution processes of Karst rocky desertification land: Indications for restoration strategies. Land. Degrad. Dev. 2013, 24, 47–56. [Google Scholar] [CrossRef]
  15. Puigdefabregas, J.; del Barrio, G.; Hill, J. Ecosystemic Approaches to Land Degradation. In Advances in Studies on Desertification: Contributions to the International Conference in Memory of Johm B Thornes; Romero Díaz, A., Belmonte Serrato, F., Alonso Sarría, F., López Bermúdez, F., Eds.; Universidad de Murcia: Murcia, Spain, 2009; pp. 77–87. [Google Scholar]
  16. Reynolds, J.F.; Smith, D.M.S.; Lambin, E.F.; Turner, B.L., II; Mortimore, M.; Batterbury, S.P.J.; Downing, T.E.; Dowlatabadi, H.; Fernández, R.J.; Herrick, J.E.; et al. Global Desertification: Building a Science for Dryland Development. Science 2007, 316, 847–851. [Google Scholar] [CrossRef]
  17. Glantz, M.H.; Orlovsky, N.S. Desertification: A review of the concept. Desertif. Control Bull. 1983, 9, 12–22. [Google Scholar]
  18. Herrmann, S.M.; Hutchinson, C.F. The changing contexts of the desertification debate. Arid. Environ. 2005, 63, 538–555. [Google Scholar] [CrossRef]
  19. Hein, L.; Ridder, N.D. Desertification in the Sahel: A reinterpretation. Glob. Chang. Biol. 2006, 12, 751–758. [Google Scholar] [CrossRef]
  20. Prince, S.D.; Wessels, K.J.; Tucker, C.J.; Nicholson, S.E. Desertification in the Sahel: A reinterpretation of a reinterpretation. Glob. Chang. Biol. 2007, 13, 1308–1313. [Google Scholar] [CrossRef]
  21. Oldeman, L.R.; Hakkeling, R.T.A.; Sombroek, W.G. World Map of the Status of Human Induced Soil Degradation; International Soil Reference and Information Center (ISRIC): Wageningen, The Netherlands, 1991. [Google Scholar]
  22. Sonneveld, B.G.J.S.; Dent, D.L. How good is GLASOD? J. Environ. Manag. 2009, 90, 274–283. [Google Scholar] [CrossRef]
  23. Dregne, H.E.; Chou, N.T. Global desertification dimensions and costs. In Degradation and Restoration of Arid Lands; Texas Tech University: Lubbock, TX, USA, 1992. [Google Scholar]
  24. Cherlet, M.; Hutchinson, C.; Reynolds, J.; Hill, J.; Sommer, S.; von Maltitz, G. (Eds.) World Atlas of Desertification; Publication Office of the European Union: Luxembourg, 2018. [Google Scholar]
  25. Barrow, C.J. World atlas of desertification (United Nations Environment Program), edited by N. Middleton and D. S. G. Thomas. Edward Arnold, London, 1992. Land Degrad. Dev. 1992, 3, 249. [Google Scholar] [CrossRef]
  26. Spinoni, J.; Carrao, H.; Naumann, G.; Antofie, T.; Barbosa, P.; Vogt, J.A. A Global Drought Climatology for the 3rd Edition of the World Atlas of Desertification Geophysical Research Abstracts. In Proceedings of the EGU General Assembly Conference Abstracts 2013, Vienna, Austria, 7–12 April 2013; EGU2013-9192. Volume 15. [Google Scholar]
  27. GB/T20483-2006; Land desertification monitoring method. National Forestry and Grassland Administration: Beijing, China, 2006.
  28. GB/T24255-2009; Technical code of practice on the sandified land monitoring. National Forestry and Grassland Administration: Beijing, China, 2009.
  29. Brandt, M.; Mbow, C.; Diouf, A.A.; Verger, A.; Samimi, C.; Fensholt, R. Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Glob. Change Biol. 2015, 21, 1610–1620. [Google Scholar] [CrossRef]
  30. Brandt, M.; Romankiewicz, C.; Spiekermann, R.; Samimi, C. Environmental change in time series—An interdisciplinary study in the Sahel of Mali and Senegal. J. Arid. Environ. 2014, 105, 52–63. [Google Scholar] [CrossRef]
  31. Higginbottom, T.P.; Symeonakis, E. Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions. Remote Sens. 2014, 6, 9552–9575. [Google Scholar] [CrossRef]
  32. Su, S.L.; Yang, C.X.; Hu, Y.N.; Luo, F.H.; Wang, Y.P. Progressive landscape fragmentation in relation to cash crop cultivation. Appl. Geogr. 2014, 53, 20–31. [Google Scholar] [CrossRef]
  33. Wu, J.; Liu, Y.; Wang, J.; He, T. Application of Hyperion data to land degradation mapping in the Hengshan region of China. Int. J. Remote Sens. 2010, 31, 5145–5161. [Google Scholar] [CrossRef]
  34. Lamchin, M.; Lee, J.-Y.; Lee, W.-K.; Lee, E.J.; Kim, M.; Lim, C.-H.; Choi, H.-A.; Kim, S.-R. Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Adv. Space Res. 2016, 57, 64–77. [Google Scholar] [CrossRef]
  35. Eswaran, H.; Lal, R.; Reich, P.F. Land Degradation: An Overview; Bridges, E.M., Ed.; CRC Press: Boca Raton, FL, USA, 2001; pp. 20–35. [Google Scholar]
  36. Moumane, A.; Al Karkouri, J.; Benmansour, A.; El Ghazali, F.E.; Fico, J.; Karmaoui, A.; Batchi, M. Monitoring long-term land use, land cover change, and desertification in the Ternata oasis, Middle Draa Valley, Morocco. Remote Sens. Appl. Soc. Environ. 2022, 26, 100745. [Google Scholar] [CrossRef]
  37. Wang, X.M.; Xiao, F.Y.; Geng, X.; Hu, S.; Wang, Z.S. Spatiotemporal variations in surface water and its significance to desertification in China from 2000 to 2019. CATENA 2022, 213, 106182. [Google Scholar] [CrossRef]
  38. Li, Y.S.; Li, H.K.; Xu, F. Spatio-temporal changes in desertification land in rare earth mining areas under different disturbance conditions. Environ. Sci. Pollut. Res. 2021, 28, 30323–30334. [Google Scholar] [CrossRef]
  39. Xu, E.-Q.; Zhang, H.-Q.; Li, M.-X. Object-Based Mapping of Karst Rocky Desertification using a Support Vector Machine. Land. Degrad. Dev. 2015, 26, 158–167. [Google Scholar] [CrossRef]
  40. Hua, D.; Hao, X.M. Spatio-temporal change and drivers analysis of desertification in the arid region of northwest China based on geographic detector. Environ. Chall. 2021, 4, 100082. [Google Scholar] [CrossRef]
  41. Nijsen, M.; Smeets, E.; Stehfest, E.; van Vuuren, D.P. An evaluation of the global potential of bioenergy production on degraded lands. GCB-Bioenergy 2012, 4, 130–147. [Google Scholar] [CrossRef]
  42. Wang, X.F.; Yin, L.C.; Xiao, F.Y.; Zhang, M.M.; Liu, L.L.; Zhou, Z.X.; Ao, Y. The desertification process in the Silk Road Economic Belt in the past 15 years: A study using MODIS data and GIS analysis. Geol. J. 2018, 53 (Suppl. S1), 322–331. [Google Scholar] [CrossRef]
  43. Xiao, J.; Xiong, K. A review of agroforestry ecosystem services and its enlightenment on the ecosystem improvement of rocky desertification control. Sci. Total Environ. 2022, 852, 158538. [Google Scholar] [CrossRef] [PubMed]
  44. Guo, B.; Yang, F.; Fan, Y.; Zang, W. The dominant driving factors of rocky desertification and their variations in typical mountainous karst areas of southwest China in the context of global change. CATENA 2023, 220, 106674. [Google Scholar] [CrossRef]
  45. Jiang, Z.; Lian, Y.; Qin, X. Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Sci. Rev. 2014, 132, 1–12. [Google Scholar] [CrossRef]
  46. Wu, Z.; Zhu, D.; Xiong, K.; Wang, X. Dynamics of landscape ecological quality based on benefit evaluation coupled with the rocky desertification control in South China Karst. Ecol. Indic. 2022, 138, 108870. [Google Scholar] [CrossRef]
  47. Zhang, J.; Liu, M.; Liu, X.; Luo, W.; Wu, L.; Zhu, L. Spectral analysis of seasonal rock and vegetation changes for detecting karst rocky desertification in southwest China. Int. J. Appl. Earth Obs. Geoinf. 2021, 100, 102337. [Google Scholar] [CrossRef]
  48. Chen, Y.; Tang, H. Desertification in north China: Background, anthropogenic impacts and failures in combating it. Land. Degrad. Dev. 2005, 16, 367–376. [Google Scholar] [CrossRef]
  49. Wang, J. Respect and awe of nature is the fundamental law of human being existence. J. Arid. Land. 2018, 32, 1–4. [Google Scholar] [CrossRef]
  50. Liu, B.L.; Wu, B.; Lei, Z.Q.; Tang, J.N.; Zheng, Y.C. A misleading way to transform the natural desert into farmland. Innovation 2022, 3, 100237. [Google Scholar] [CrossRef] [PubMed]
Land 12 01734 g001
Figure 1. The raw image data used in the study. The years in the sequence are (a) 1998, (b) 1999, (c) 2000, (d) 2018, (e) 2019, (f) 2020.
Figure 1. The raw image data used in the study. The years in the sequence are (a) 1998, (b) 1999, (c) 2000, (d) 2018, (e) 2019, (f) 2020.
Land 12 01734 g001
Land 12 01734 g002
Figure 2. Distributions and classifications of normalized difference vegetation index (NDVI) in China: (a) NDVI classification for 1998; (b) NDVI classification for 2020; (c) NDVI change classification from 1998 to 2020.
Figure 2. Distributions and classifications of normalized difference vegetation index (NDVI) in China: (a) NDVI classification for 1998; (b) NDVI classification for 2020; (c) NDVI change classification from 1998 to 2020.
Land 12 01734 g002
Land 12 01734 g003
Figure 3. Distributions and spatiotemporal variations of normalized difference vegetation index (NDVI) in China: (a,b) NDVI spatial distribution and value statistics for 1998; (c,d) NDVI spatial distribution and value statistics for 2020; (e,f) NDVI change spatial distribution and value statistics.
Figure 3. Distributions and spatiotemporal variations of normalized difference vegetation index (NDVI) in China: (a,b) NDVI spatial distribution and value statistics for 1998; (c,d) NDVI spatial distribution and value statistics for 2020; (e,f) NDVI change spatial distribution and value statistics.
Land 12 01734 g003
Land 12 01734 g004
Figure 4. Vegetation quality change classifications in China. (a) Classification based on 1998 Normalized Difference Vegetation Index (NDVI) data; (b) different classification area statistics. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Figure 4. Vegetation quality change classifications in China. (a) Classification based on 1998 Normalized Difference Vegetation Index (NDVI) data; (b) different classification area statistics. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Land 12 01734 g004
Land 12 01734 g005
Figure 5. Classification based on 1998 and 2020 normalized difference vegetation index (NDVI) data. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Figure 5. Classification based on 1998 and 2020 normalized difference vegetation index (NDVI) data. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Land 12 01734 g005
Land 12 01734 g006
Figure 6. Desert and desertification region based on data in 1998 and 2020. (a) 1998; (b) 2020. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Figure 6. Desert and desertification region based on data in 1998 and 2020. (a) 1998; (b) 2020. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Land 12 01734 g006
Land 12 01734 g007
Figure 7. Classification boundary changes based on normalized difference vegetation index (NDVI) in 1998 and 2020. (a) 1998; (b) 2020. The pink arrow represented the movement and change of the boundary. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Figure 7. Classification boundary changes based on normalized difference vegetation index (NDVI) in 1998 and 2020. (a) 1998; (b) 2020. The pink arrow represented the movement and change of the boundary. Ia: Low value/Decrease, Ib: Low value/Stable, Ic: Low value/Low increase, Id: Low value/High increase, IIa: Medium value/Decrease, IIb: Medium value/Stable, IIc: Medium value/Low increase, IId: Medium value/High increase, IIIa: High value/Decrease, IIIb: High value/Stable, IIIc: High value/Low increase, and IIId: High value/High increase.
Land 12 01734 g007
Land 12 01734 g008
Figure 8. Spatial distribution of land use, annual average precipitation and soil erosion intensity in China. (a) Land use in 2000; (b) land use in 2020; (c) average annual precipitation; (d) soil erosion intensity.
Figure 8. Spatial distribution of land use, annual average precipitation and soil erosion intensity in China. (a) Land use in 2000; (b) land use in 2020; (c) average annual precipitation; (d) soil erosion intensity.
Land 12 01734 g008
Table 1. Comparison of thresholds for NDVI grading by important geographical boundaries.
Table 1. Comparison of thresholds for NDVI grading by important geographical boundaries.
ReferenceImportant Geographical Boundaries
China’s Average Annual Precipitation200 mm, 400 mm, 600 mm precipitation line
China’s Wet and Dry RegionArid, semi-arid, and semi-humid region boundaries
China’s Vegetation Types RegionTemperate desert area, temperate grassland area, warm temperate broad-leaved deciduous forest area
Table 2. Classification based on changes in normalized difference vegetation index (NDVI).
Table 2. Classification based on changes in normalized difference vegetation index (NDVI).
1998–2020 NDVI Change RangeNDVI Range in 1998 and 2020
Low ValueMedium ValueHigh Value
0–0.270.27–0.710.71–0.92
<−0.05DecreaseIaIIaIIIa
−0.05–0.05StableIbIIbIIIb
0.05–0.12Low increaseIcIIcIIIc
>0.12High increaseIdIIdIIId
Table 3. Classification area statistics based on normalized difference vegetation index (NDVI) data in 1998 (106 km2).
Table 3. Classification area statistics based on normalized difference vegetation index (NDVI) data in 1998 (106 km2).
1998–2020 NDVIChange RangeNDVI Range in 1998
0–0.270.27–0.710.71–0.92
<−0.050.220.330.13
−0.05–0.052.300.650.82
0.05–0.120.300.571.19
>0.12 0.311.601.03
Table 4. Area statistics of desert regions and desertification regions (103 km2).
Table 4. Area statistics of desert regions and desertification regions (103 km2).
YearDesert RegionDesertification RegionOther Region
IaIbIcIdIIa IIbIIcIIdIIIaIIIbIIIcIIId
1998213.332289.05296.76304.07195.77475.68395.261132.4019.36109.93154.26181.15
Total2502.382799.94464.70
2020258.142266.33197.7731.88111.18379.39327.66624.961.6367.0497.07240.12
Total2524.471672.84405.86
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jiang, L.; Liu, Y. Should Desert and Desertification Regions Be Confused? New Insights Based on Vegetation Quality and Its Inter-Decadal Variations. Land 2023, 12, 1734. https://doi.org/10.3390/land12091734

AMA Style

Jiang L, Liu Y. Should Desert and Desertification Regions Be Confused? New Insights Based on Vegetation Quality and Its Inter-Decadal Variations. Land. 2023; 12(9):1734. https://doi.org/10.3390/land12091734

Chicago/Turabian Style

Jiang, Luguang, and Ye Liu. 2023. "Should Desert and Desertification Regions Be Confused? New Insights Based on Vegetation Quality and Its Inter-Decadal Variations" Land 12, no. 9: 1734. https://doi.org/10.3390/land12091734

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop