**1. Introduction**

Desertification is defined as land degradation mainly characterized by aeolian activity in arid and semi-arid areas, due to the inharmonious man-land relationship [1]. Desertification has become one of the most severe ecological environmental issues, which causes economic losses of up to 540 billion RMB annually in the world [2–4]. The Gonghe Basin is one of the centralized distribution regions of desertification in the northeast of the Qinghai-Tibet Plateau [5], in which the ecological environment is fragile. Desertification has become increasingly prominent in the Gonghe Basin due to global climate change and unreasonable human activities, which not only affect the lives of local people but also pose a huge threat to the safety of the Longyangxia Reservoir, has hindered socioeconomic development [6,7]. Thus, it is urgent to strengthen research on desertification in this area.

Gonghe Basin is affected by the Asian monsoon circulation and the mid-latitude westerly circulation, and it is a part of the boundary between the deserts and loess in China [8]. Its unique geographical location provides an ideal research site for exploring

**Citation:** Jia, H.; Wang, R.; Li, H.; Diao, B.; Zheng, H.; Guo, L.; Liu, L.; Liu, J. The Changes of Desertification and Its Driving Factors in the Gonghe Basin of North China over the Past 10 Years. *Land* **2023**, *12*, 998. https:// doi.org/10.3390/land12050998

Academic Editors: Li Ma, Yingnan Zhang, Muye Gan and Zhengying Shan

Received: 11 April 2023 Revised: 25 April 2023 Accepted: 29 April 2023 Published: 1 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the formation and changes of the aeolian activity environment. So far, multiple time scales research had been conducted on the formation, evolution, and driving mechanism of the aeolian activity in the Gonghe Basin [6,9,10]. The studies of desertification of the Gonghe Basin during its geological history mainly focused on geomorphological evolution, sedimentary strata, the history of aeolian activity, and the driving mechanisms [11,12]. Previous studies have shown that the oldest formation age of aeolian sand is 33.5 ± 2.1 ka BP in the Gonghe Basin, dune fields developed in the early and middle Holocene, and fixed in the late Holocene [10]. The formation of the aeolian sand environment in the Gonghe Basin is influenced by multiple factors, such as the evolution process of the geomorphology, regional climate, and wind strength, the main control factors were different in different periods [13,14].

The research about the modern process of desertification in the Gonghe Basin mainly includes different aspects such as aeolian landforms [15], wind conditions [16], spatial distribution and dynamic monitoring of desertification, driving mechanism of desertification and countermeasures for land desertification prevention and control [6,17–19]. Remote sensing images provide an effective data source for desertification monitoring and information extraction [20,21]. Collado et al. assessed the desertification process in the crop-rangeland boundary of Argentina by using remote sensing data [22]. Qi et al. analyzed the spatiotemporal changes of desertification from1986 to 2003 through supervised classification method in the agropastoral transitional zone of northern Shaanxi Province in China [23]. In the Gonghe basin, Yan et al. used Landsat data from 1975 to 2005 through visual interpretation to explore aeolian desertification trends and driving factors in the Longyangxia Reservoir next to the Yellow River [24]. Ma et al. used TM data from the three periods of 1990, 2000, and 2010 in the Gonghe Basin, it was concluded that the desertification situation was worsening from 1990 to 2010 [25]. However, previous monitoring methods were mainly based on visual interpretation and supervised classification, resulting in low utilization rate and classification accuracy of remote sensing information [24,26,27]. In recent years, many studies have used single indicator (e.g., NDVI, EVI, MSAVI) to assess desertification [28,29]. Nonetheless, due to the complex causes of desertification evolution, using a single index cannot comprehensively reflect desertification information [30]. The Albedo-NDVI feature space basing on the negative correlation between Albedo and NDVI established by Zeng et al. provides an efficient approach for quantitative analysis of desertification [31], which has been used in many desertified land, such as Moulouya basin in Morocco [32], central Mexico [33], Mongolian plateau [34], source region of Yellow River in China [35]. For the Gonghe Basin, there is lack of study on the desertification evolution process over the past 10 years, and short time scale desertification research is of practical significance in assessing the effectiveness of desertification prevention. There is relatively little research on desertification monitoring in the Gonghe Basin compared to other typical desertification regions, and only part of Gonghe Basin was studied, such as around the Longyangxia Reservoir, Gonghe county, and Guinan county [24,36–38]. Additionally, the research on the driving mechanism of desertification evolution in the Gonghe Basin mainly focuses on qualitative research [24,25,38], while quantitative research can better reveal the potential impact factors of desertification process.

In this study, we obtained two desertification monitoring indicators, Albedo and NDVI, to constructed Albedo-NDVI feature space basing on Landsat images. Additionally, we quantitatively explored the spatiotemporal evolution patterns of desertification and the underlying causes of desertification from 2010 to 2020 using Geodetector model, which provided a theoretical basis for desertification combat.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Gonghe Basin is surrounded by mountains on three sides, including the Qilian Mountains, Kunlun Mountains and Qinling Mountains (Figure 1), geographic coordinates are 35◦270–36◦560 N, 98◦460–101◦220 E, with an elevation of 2400–3200 m. It is administratively subordinate to Qinghai Province, including Gonghe county, Guinan county, Xinghai county and Wulan county. The dune fields in the Gonghe Basin are mainly spread in the central and eastern parts of the basin, such as Talatan Plain and Mugetan Plain, with moving dunes, sand ridges, and sand belts [9]. 80% of the precipitation in the Gonghe Basin is mainly concentrated from May to September, accompanying high evaporation. Strong winds prevail in the Gonghe Basin, and the maximum wind velocity reaches 40 m s−1 in spring [39]. Aeolian activities are common in this area, resulting in wide dune fields and severe land desertification [10].

The Gonghe Basin is surrounded by mountains on three sides, including the Qilian Mountains, Kunlun Mountains and Qinling Mountains (Figure 1), geographic coordinates are 35°27′–36°56′ N, 98°46′–101°22′ E, with an elevation of 2400–3200 m. It is administratively subordinate to Qinghai Province, including Gonghe county, Guinan county, Xinghai county and Wulan county. The dune fields in the Gonghe Basin are mainly spread in the central and eastern parts of the basin, such as Talatan Plain and Mugetan Plain, with

The climate type in the Gonghe Basin is a typical alpine and semi-arid climate, the annual average temperature is about 3.7 °C and annual precipitation is about 300 mm.

*Land* **2023**, *12*, x FOR PEER REVIEW 3 of 17

moving dunes, sand ridges, and sand belts [9].

**2. Materials and Methods**

*2.1. Study Area*

**Figure 1.** Geographical location of the Gonghe Basin. **Figure 1.** Geographical location of the Gonghe Basin.

*2.2. Data Sources* We used the Landsat TM/OLI images to monitor land desertification in the Gonghe Basin in our study, and the images were obtained from the geospatial data cloud platform (http: //www.gscloud.cn/ (accessed on 10 February 2023)). A total of 8 images for 2010 (2009–2011) and 2020 were collected during the vegetation growing season (especially in June and August) with cloud coverage of less than 10%. These images were preprocessed The climate type in the Gonghe Basin is a typical alpine and semi-arid climate, the annual average temperature is about 3.7 ◦C and annual precipitation is about 300 mm. 80% of the precipitation in the Gonghe Basin is mainly concentrated from May to September, accompanying high evaporation. Strong winds prevail in the Gonghe Basin, and the maximum wind velocity reaches 40 m s−<sup>1</sup> in spring [39]. Aeolian activities are common in this area, resulting in wide dune fields and severe land desertification [10].

#### mainly using ENVI5.3 software, including radiometric calibration and atmospheric cor-*2.2. Data Sources*

rection. The vector boundary data of the Gonghe Basin was used to clip and mosaic the Landsat images to obtain the entire Landsat image of the Gonghe Basin. The annual average temperature, annual precipitation and annual average wind velocity data from 2010 to 2019 were calculated basing the ERA5 data set on the Google Earth Engine (GEE) platform. Annual interpolation data for meteorological data and relevant regional socio-economic data included datasets of land use (1:100,000), population density (1 km) and GDP density (1 km) were downloaded from the Resource and Environment Science and Data Center (RESDC, https://www.resdc.cn/ (accessed on 5 March 2023)). We used the Landsat TM/OLI images to monitor land desertification in the Gonghe Basin in our study, and the images were obtained from the geospatial data cloud platform (http://www.gscloud.cn/ (accessed on 10 February 2023)). A total of 8 images for 2010 (2009–2011) and 2020 were collected during the vegetation growing season (especially in June and August) with cloud coverage of less than 10%. These images were preprocessed mainly using ENVI5.3 software, including radiometric calibration and atmospheric correction. The vector boundary data of the Gonghe Basin was used to clip and mosaic the Landsat images to obtain the entire Landsat image of the Gonghe Basin.

The annual average temperature, annual precipitation and annual average wind velocity data from 2010 to 2019 were calculated basing the ERA5 data set on the Google Earth Engine (GEE) platform. Annual interpolation data for meteorological data and relevant regional socio-economic data included datasets of land use (1:100,000), population density (1 km) and GDP density (1 km) were downloaded from the Resource and Environment Science and Data Center (RESDC, https://www.resdc.cn/ (accessed on 5 March 2023)).

#### *2.3. Methods*

### 2.3.1. Normalized Difference Vegetation Index (NDVI)

NDVI is an important biophysical parameter that reflects the state of surface vegetation, with a range of −1 to 1, and the higher the vegetation coverage, the closer NDVI value is to 1. NDVI can be used to indicate vegetation growth status and reflect vegetation coverage, and we can calculate it using the reflectance of the following two bands in remote sensing images [40].

$$\text{NDVI} = (\wp\_{\text{nir}} - \wp\_{\text{red}}) / (\wp\_{\text{nir}} + \wp\_{\text{red}}) \tag{1}$$

where ρnir, ρred represent near infrared band and the red band, respectively.

#### 2.3.2. Land Surface Albedo

Land Surface albedo is a physical parameter that reflects the reflection characteristics of the surface to solar shortwave radiation. With the aggravation of desertification, surface vegetation is severely damaged, and surface roughness increases, manifested as an increase in Albedo values in remote sensing images. The value of Albedo is between 0 and 1. In this study, we calculated Albedo using the calculation method proposed by Liang [41].

$$\text{Albedo} = 0.\text{556} \times \rho\_{\text{blue}} + 0.1\text{50} \times \rho\_{\text{red}} + 0.\text{573} \times \rho\_{\text{nir}} + 0.085 \times \rho\_{\text{swiri}1} + 0.072 \times \rho\_{\text{swiri}2} - 0.0018 \tag{2}$$

where ρblue, ρred, ρnir, ρswir1 and ρswir2 represent blue band, red band, near infrared band and the shortwave infrared bands, respectively.

#### 2.3.3. Data Normalization

The dimensions of NDVI and Albedo are different, so that Albedo-NDVI feature space cannot be directly established, we normalized the values of NDVI and Albedo to between 0 and 1. The NDVI and the Albedo values were normalized using following equations.

$$\text{N} = (\text{NDVI} - \text{NDVI}\_{\text{min}}) / (\text{NDVI}\_{\text{max}} - \text{NDVI}\_{\text{min}}) \tag{3}$$

$$\text{A} = (\text{Albedo} - \text{Albedo}\_{\text{min}}) / (\text{Albedo}\_{\text{max}} - \text{Albedo}\_{\text{min}}) \tag{4}$$

For NDVI, NDVImax, NDVImin refer to maximum and minimum values, respectively, N was the normalized value; For Albedo, Albedomax and Albedomin refer to maximum and minimum values, respectively, A was the normalized value.

#### 2.3.4. Albedo–NDVI Feature Space

Zeng et al. [31] conducted research on the feature space composed of NDVI and Albedo, and summarized the desertification situation under different vegetation coverage conditions in an ideal feature space (Figure 2). A, B, C, and D points represent the extreme states in the Albedo-NDVI feature space, respectively. A represents areas with severe drought and no vegetation cover, B represents areas with high water content and no vegetation cover, C represents areas with low water content and high vegetation cover, and D represents areas with high water content and high vegetation cover. The upper boundary AD represents a high albedo line, reflecting drought conditions, the bottom BC is the low albedo line, representing the condition of sufficient surface water. And the distribution of different land cover types presented by NDVI and Albedo has a significant distribution rule in the feature space, which can well distinguish water, high vegetation coverage land, low vegetation coverage land and completely bare land [42]. Overall, there is a significant negative correlation between Albedo and NDVI in the feature space.

Based on the ROI function of ENVI5.3 version, 900 sample points were randomly selected from different degrees of desertification land in the study area [30], extracting the NDVI and Albedo values after normalization in 2010 and 2020, respectively. And then selecting the NDVI values as independent variables, Albedo values as dependent variables, we can construct a linear regression equation between them.

Then the linear regression equation represents negative correlation between Albedo and NDVI was calculated using the following formula:

$$\text{Albedo} = \mathbf{k} \times \text{NDVI} + \mathbf{b} \tag{5}$$

where k refers to the slope of the linear expression, and b refers to the parameter.

**Figure 2.** Albedo-NDVI feature space [31]. **Figure 2.** Albedo-NDVI feature space [31].

#### 2.3.5. Desertification Difference Index (DDI) 2.3.5. Desertification Difference Index (DDI)

Based on previous research findings [43], dividing the Albedo-NDVI feature space in the vertical direction representing the trend of desertification change can effectively distinguish different types of desertification land, represented by the Desertification Difference Index (DDI). we can use the following two formulas to calculate the DDI index for Based on previous research findings [43], dividing the Albedo-NDVI feature space in the vertical direction representing the trend of desertification change can effectively distinguish different types of desertification land, represented by the Desertification Difference Index (DDI). we can use the following two formulas to calculate the DDI index for 2010 and 2020.

selecting the NDVI values as independent variables, Albedo values as dependent varia-

where k refers to the slope of the linear expression, and b refers to the parameter.

Then the linear regression equation represents negative correlation between Albedo

Albedo = k NDVI + b (5)

bles, we can construct a linear regression equation between them.

and NDVI was calculated using the following formula:

$$\mathbf{k} \times \mathbf{a} = -1 \tag{6}$$

$$\text{ADD} = \text{a} \times \text{NDVI} - \text{Albedo} \tag{7}$$

DDI = a NDVI − Albedo (7) where a represent the slope of DDI linear expression.

#### where a represent the slope of DDI linear expression. 2.3.6. Accuracy Verification

2010 and 2020.

2.3.6. Accuracy Verification Confusion matrix is also called error matrix, is an effective method for evaluating the accuracy of classification results. In the confusion matrix, each row represents the real category of desertification degree, and each column represents the prediction category [44]. We obtained evaluation indicators, including the overall accuracy (OA), producer's Confusion matrix is also called error matrix, is an effective method for evaluating the accuracy of classification results. In the confusion matrix, each row represents the real category of desertification degree, and each column represents the prediction category [44]. We obtained evaluation indicators, including the overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA), and Kappa coefficient, which can be used to verify the accuracy of the desertification classification results using Albedo-NDVI feature space method. The specific calculation formulas are as follows:

$$OA = \sum\_{i=1}^{i=5} X\_{ii} / N \tag{8}$$

$$PA = \mathbf{X}\_{\text{ii}} / \mathbf{X}\_{\text{i}+} \tag{9}$$

(8)

(9)

$$
\mathcal{U}A = X\_{\text{ii}} / X\_{+\text{i}} \tag{10}
$$

$$Kappa = \frac{\left[N \times \sum\_{i=1}^{i=5} X\_{ii} - \left(\sum\_{i=1}^{i=5} X\_{+i} + X\_{+i}\right)\right]}{N^2 - \sum\_{i=1}^{i=5} X\_{+i} + X\_{+i}} \tag{11}$$

*ii*

1

=

*i*

where *N* refers to the total number of samples; *Xii* refers to the sample quantity in row *i* and column *i*, that is, the number of sample points correctly identified for a certain type of desertification degree; *Xi+* refers to the sample quantity in row *i*, is the total real sample size of a certain type of desertification; and *X+i* refers to the sample quantity in column *i*, is the predicted total sample size of a certain type of desertification.

#### 2.3.7. Desertification Land Transfer Matrix

The land transfer matrix has been widely applied in land use change. The land transfer matrix can reflect the transformation area from one degree of desertification land to another degree of desertification land within a certain period of time, and can reflect the transformation relationship between different degrees of desertification land [45]. In this study, we used the land transfer matrix to calculate the conversion areas between different grades of desertification land basing ArcGIS 10.8. The formula used is as follows:

$$\mathbf{S}\_{ij} = \begin{bmatrix} \mathbf{S}\_{11} & \mathbf{S}\_{12} & \dots & \mathbf{S}\_{1n} \\ \mathbf{S}\_{12} & \mathbf{S}\_{22} & \dots & \mathbf{S}\_{2n} \\ \dots & \dots & \dots & \dots \\ \mathbf{S}\_{n1} & \mathbf{S}\_{n2} & \dots & \mathbf{S}\_{nn} \end{bmatrix} \tag{12}$$

where *i* and *j* represent different grades of land desertification, *Sij* represents the transition area from the grade *i* to *j* (km<sup>2</sup> ), and *n* represents the number of desertification grades.

#### 2.3.8. Dynamic Degree of Desertification Land

The dynamic degree indicates the area change of one grade of desertification land within a certain specific time range in a certain research area [46]. The formula is as follows:

$$K = \frac{u\_2 - u\_1}{u\_1} \times \frac{1}{t\_2 - t\_1} \times 100\% \tag{13}$$

where *K* represents the dynamic degree from 2010 to 2020, *u*<sup>1</sup> refers to the initial area (km<sup>2</sup> ), *u*<sup>2</sup> represents the final area (km<sup>2</sup> ), *t*<sup>1</sup> and *t*<sup>2</sup> represent the starting and end time, respectively.

#### 2.3.9. Changes in Desertification Degree

The degree of desertification development was divided into five categories [30]: severe deterioration (desertification degree increased by more than one level), deterioration (desertification degree increased by one level), no change (desertification degree remained stable), restoration (desertification degree decreased by one level), obvious restoration (desertification degree decreased by more than one level).

#### 2.3.10. Geodetector Model

The Geodetector is a widely used statistical model that reveal spatial variability and potential driving forces. Geodetector can detect the spatial heterogeneity of a single factor, and can also reveal possible causal relationships between two factors through calculating their consistency of spatial distribution [47]. This study takes the degree of desertification as the dependent variable Y, and selects independent variable indicators including temperature, precipitation, wind velocity, population density, GDP, and land use. The factor interpretation power in Geodetector is represented by the q value. The expressions used are as follows:

$$q = 1 - \frac{\sum\_{h=1}^{L} N\_h \sigma\_h^2}{N \sigma^2} = 1 - \frac{SSW}{SST} \tag{14}$$

$$SSW = \sum\_{h=1}^{L} \text{N}\_{h} \sigma\_{h}^{2} \text{ } SST = N\sigma^{2} \tag{15}$$

where *h* refers to the stratification of the independent variable; *N<sup>h</sup>* and *N* represent the number of units within layer *h* and the entire area, respectively; *σ<sup>h</sup>* <sup>2</sup> and *σ* 2 represent the discrete variances of layer *h* and the entire area, respectively; SSW refers to the sum of intralayer variances; SST refers to the regional total discrete variance; q refers to the explanatory power of the independent variable to the degree of desertification, the range of q is between 0 and 1, the larger the q value represents the stronger the explanatory power of the selected factor. intralayer variances; SST refers to the regional total discrete variance; q refers to the explanatory power of the independent variable to the degree of desertification, the range of q is between 0 and 1, the larger the q value represents the stronger the explanatory power of the selected factor. The purpose of interaction detection is to assess whether interaction between two factors can increase the explanatory power of the degree of desertification or whether the impact of these factors on the degree of desertification is independent [48].

their consistency of spatial distribution [47]. This study takes the degree of desertification as the dependent variable Y, and selects independent variable indicators including temperature, precipitation, wind velocity, population density, GDP, and land use. The factor interpretation power in Geodetector is represented by the q value. The expressions used

2

*N SST*

*SSW*

*SST N*=

2

(14)

(15)

<sup>2</sup> and *σ*<sup>2</sup> represent the

2 1 1

2

= − = −

*h h*

where *h* refers to the stratification of the independent variable; *N<sup>h</sup>* and *N* represent the

discrete variances of layer *h* and the entire area, respectively; SSW refers to the sum of

*h h*

1

=

1

<sup>=</sup> ,

=

*h SSW N*

*L*

*N*

*h*

*q*

number of units within layer *h* and the entire area, respectively; *σ<sup>h</sup>*

*L*

The purpose of interaction detection is to assess whether interaction between two factors can increase the explanatory power of the degree of desertification or whether the impact of these factors on the degree of desertification is independent [48]. **3. Results**

#### **3. Results** *3.1. Desertification Classification*

are as follows:

#### *3.1. Desertification Classification* The scatterplots of Albedo and NDVI in 2010 and 2020 are shown in Figure 3, there

The scatterplots of Albedo and NDVI in 2010 and 2020 are shown in Figure 3, there was presented a trapezoidal shape in the Albedo-NDVI feature space [46]. The R<sup>2</sup> values of the linear regression equations were 0.7106 and 0.7044, respectively, the results indicated there was a significant negative correlation between Albedo and NDVI. Using Equation (6) to calculate the k value to obtain the final expression of the desertification difference index (DDI) in 2010 and 2020, as shown in Table 1. was presented a trapezoidal shape in the Albedo-NDVI feature space [46]. The R<sup>2</sup> values of the linear regression equations were 0.7106 and 0.7044, respectively, the results indicated there was a significant negative correlation between Albedo and NDVI. Using Equation (6) to calculate the k value to obtain the final expression of the desertification difference index (DDI) in 2010 and 2020, as shown in Table 1.

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**Figure 3.** Albedo-NDVI linear regression analysis. **Figure 3.** Albedo-NDVI linear regression analysis.

**Table 1.** Linear relationship of desertification difference index (DDI) in 2000 and 2020. **Table 1.** Linear relationship of desertification difference index (DDI) in 2000 and 2020.


DDI can be used to obtain the desertification classification, we used the natural breaks (Jenks) method combined with field survey data and Google Earth map to classify the desertification intensity into 5 categories, including extremely severe desertification, severe desertification, moderate desertification, slight desertification, and non-desertification. Finally, the spatial distribution maps of desertification degree in 2010 and 2020 were made by using ArcGIS 10.8 (Figure 4).

**Year**

**Extremely Severe (%)**

made by using ArcGIS 10.8 (Figure 4).

(**b**) 2020

**Figure 4.** Spatial distribution of desertification in the Gonghe Basin. **Figure 4.** Spatial distribution of desertification in the Gonghe Basin.

**Table 2.** Classification accuracy of desertification. **Severe (%) Moderate (%) Slight (%) Non-Desertification (%) Kappa Coefficient PA UA PA UA PA UA PA UA PA UA** 2010 95 95 94.74 90 94.74 90 86.36 95 100 100 0.93 94 To test the accuracy of desertification land classification results, we used Landsat true color image and Google Earth map as reference data, randomly selected 20 sample points in different desertification types (100 points in total) to construct the confusion matrix through visual interpretation. The accuracy evaluation results are shown in Table 2, the overall evaluation accuracy was 94%, and Kappa coefficient was 0.93 in 2010. In 2020, the overall accuracy was 95%, Kappa coefficient was 0.94. The phenomenon of misclassification mainly occurred in slight desertification areas. Overall, the Albedo-NDVI feature space method has certain feasibility and applicability to evaluate desertification level.

**OA (%)**

DDI can be used to obtain the desertification classification, we used the natural breaks (Jenks) method combined with field survey data and Google Earth map to classify the desertification intensity into 5 categories, including extremely severe desertification, severe desertification, moderate desertification, slight desertification, and non-desertification. Finally, the spatial distribution maps of desertification degree in 2010 and 2020 were

To test the accuracy of desertification land classification results, we used Landsat true color image and Google Earth map as reference data, randomly selected 20 sample points in different desertification types (100 points in total) to construct the confusion matrix through visual interpretation. The accuracy evaluation results are shown in Table 2, the overall evaluation accuracy was 94%, and Kappa coefficient was 0.93 in 2010. In 2020, the overall accuracy was 95%, Kappa coefficient was 0.94. The phenomenon of misclassification mainly occurred in slight desertification areas. Overall, the Albedo-NDVI feature space method has certain feasibility and applicability to evaluate desertification level.



#### *3.2. Temporal Distribution Characteristics of Desertification*

Table 3 shows the area, proportion, and dynamic changes of different desertification levels in the study area. As shown in Table 3, extremely severe desertification land areas had decreased by 2335.32 km<sup>2</sup> from 2010 to 2020, with the proportion decreasing from 17.4% to 5.6% and the dynamic degree of 6.8%. Severe desertification, moderate desertification, and slight desertification have a slight increasing trend, with the area increasing 592.13 km<sup>2</sup> , 687.33 km<sup>2</sup> and 227.40 km<sup>2</sup> , respectively, and their proportions had increased 3.0%, 3.5% and 1.1%, respectively. Meanwhile, the non-desertification land areas accounted for 19.9% in 2020, and the area increased by 827.46 km<sup>2</sup> . The dynamic degrees of the severe, moderate, slight and non-desertification land area were 1.3%, 1.5%, 0.5% and 2.7%, respectively.


**Table 3.** Dynamic changes of desertification land area in the Gonghe Basin from 2010 to 2020.

In the past 10 years, there existed a certain upward trend in the non-desertification area. The moderate desertification had always accounted for a large proportion of the total study area, which was the main type of desertification in the Gonghe Basin. It can be concluded that the desertification status in the Gonghe Basin had generally improved from 2010 to 2020, and the degree of desertification had been mainly reversed from extremely severe to other degrees of desertification.

#### *3.3. Spatial Distribution Characteristics of Desertification*

According to the spatial distribution maps of desertification in the Gonghe Basin (Figure 4), we analyzed the dynamic changes in the spatial distribution pattern of desertification in the Gonghe Basin from 2010 to 2020. As shown in Figure 4, desertification was widespread in the Gonghe Basin, the lowlands in the central part of the basin were a concentrated distribution area of desertification, non-desertification areas were mainly spread in the south and southeast areas or on the mountains around the basin.

As shown in Figure 4a, there were large areas of extremely severe desertification around the Shazhuyu River, Mugetan, Talatan and other areas around the Longyangxia Reservoir in 2010. And in the periphery of extremely severe desertification, there were large areas of severe desertification, such as in the center of the basin or around the Longyangxia Reservoir. Moderate desertification was mainly spread in the east of Longyangxia Reservoir, such as Shagou Town, Longyangxia Town, etc. Slight desertification land was spread mainly in the southern and southeastern parts of the study area, such as the northern part of Heka Town and the northwest part of the mobile dunes in Mugetan. Non-desertification was distributed mainly in the Heka Town, Taxiu town, and Senduo town. Which were in the southern edge and southeast of the basin.

Compared with 2010, the overall desertification area had obviously reduced in 2020 (Figure 4b). The extremely severe desertification land spread in the western of the basin had been reduced significantly, mainly reversed to severe or moderate desertification. Meanwhile, the slight desertification and non-desertification land in the Gonghe Basin expanded to the south and southeast, and the non-desertification areas distributed around the northern marginal region increased during the study period.

#### *3.4. Changes in Desertification Intensity*

We obtained the transition matrix of desertification in this study from 2010 to 2020, as shown in Table 4. During the research period, the transformation of desertification intensity occurred between different levels of desertification. The conversion area accounted for 83.69% of the total land area, which was 8959.19 km<sup>2</sup> . The main desertification transformations were mainly from extremely severe to severe, from severe to moderate, from moderate to slight, and from slight to non-desertification, covering land areas of 2213.94 km<sup>2</sup> , 1736.12 km<sup>2</sup> , 1418.13 km<sup>2</sup> , and 1256.56 km<sup>2</sup> , respectively, accounting for 24.71%, 19.38%, 15.83% and 14.03% of the land area. Extremely severe desertification significantly decreased by 2335.32 km<sup>2</sup> , and severe, moderate, slight and non-desertification increased by 592.13 km<sup>2</sup> , 687.33 km<sup>2</sup> , 228.4 km<sup>2</sup> , and 827.46 km<sup>2</sup> , respectively. This showed that the overall desertification condition in the Gonghe Basin had a great improvement from 2010 to 2020, and sand prevention and control achieved effective results.



As shown in the changes in desertification intensity from 2010 to 2020 (Figure 5), the degree of desertification development was divided into five categories. The desertification grade unchanged land was sporadically scattered, mainly in the mobile sand dunes of Mugetan and Talatan, accounting for 42.6% of the land in basin (Table 5). The deterioration areas were primarily spread in the southwest, southeast, and northeast of the Gonghe Basin. The restoration and obvious restoration areas were mainly distributed around Chaka Salt Lake and the east of Longyangxia Reservoir. Additionally, the proportion of desertification deterioration and restoration areas were 6.8% and 50.6%, respectively. Desertification restoration areas were 11,067 km<sup>2</sup> larger than desertification deterioration areas. *Land* **2023**, *12*, x FOR PEER REVIEW 11 of 17

**Figure 5.** Changes in desertification intensity from 2010 to 2020. **Figure 6.** Comparisons of q value for different factors (2010 and 2020). **Figure 5.** Changes in desertification intensity from 2010 to 2020.

*3.5. The Influencing Factors of Desertification*

3.5.1. Singer Factor

power of different factors on desertification in the Gonghe Basin.

Geodetector for single factor and interactive factors analysis to explore the explanatory

As shown in Figure 6, for single factor analysis, the order of explanatory power of different factors on desertification in 2010 was precipitation > land use > GDP > population density > wind velocity > temperature. The precipitation was the main interfering factor of desertification, followed by human activities such as land use, GDP, and the impacts of the population density, wind velocity and temperature were relatively weak. The explanatory power of the q value on precipitation reached 0.29, but the explanatory power of temperature was only 0.03. In 2020, the explanatory power of q values on different factors was precipitation > land use > temperature > wind velocity > population density > GDP. The precipitation factor still had the greatest explanatory power on desertification, with a value of 0.22. The explanatory power of temperature and land use increased relatively, with values of 0.18 and 0.21, respectively. The q value of GDP had decreased to 0.02.


In this research, we selected temperature, precipitation and wind velocity as natural

**Table 5.** Area and proportion of changes in desertification intensity.

*Land* **2023**, *12*, x FOR PEER REVIEW 11 of 17

#### *3.5. The Influencing Factors of Desertification* factor indicators, population density, GDP, and land use as human factor indicators, used

In this research, we selected temperature, precipitation and wind velocity as natural factor indicators, population density, GDP, and land use as human factor indicators, used Geodetector for single factor and interactive factors analysis to explore the explanatory power of different factors on desertification in the Gonghe Basin. Geodetector for single factor and interactive factors analysis to explore the explanatory power of different factors on desertification in the Gonghe Basin. 3.5.1. Singer Factor

#### 3.5.1. Singer Factor As shown in Figure 6, for single factor analysis, the order of explanatory power of

As shown in Figure 6, for single factor analysis, the order of explanatory power of different factors on desertification in 2010 was precipitation > land use > GDP > population density > wind velocity > temperature. The precipitation was the main interfering factor of desertification, followed by human activities such as land use, GDP, and the impacts of the population density, wind velocity and temperature were relatively weak. The explanatory power of the q value on precipitation reached 0.29, but the explanatory power of temperature was only 0.03. In 2020, the explanatory power of q values on different factors was precipitation > land use > temperature > wind velocity > population density > GDP. The precipitation factor still had the greatest explanatory power on desertification, with a value of 0.22. The explanatory power of temperature and land use increased relatively, with values of 0.18 and 0.21, respectively. The q value of GDP had decreased to 0.02. different factors on desertification in 2010 was precipitation > land use > GDP > population density > wind velocity > temperature. The precipitation was the main interfering factor of desertification, followed by human activities such as land use, GDP, and the impacts of the population density, wind velocity and temperature were relatively weak. The explanatory power of the q value on precipitation reached 0.29, but the explanatory power of temperature was only 0.03. In 2020, the explanatory power of q values on different factors was precipitation > land use > temperature > wind velocity > population density > GDP. The precipitation factor still had the greatest explanatory power on desertification, with a value of 0.22. The explanatory power of temperature and land use increased relatively, with values of 0.18 and 0.21, respectively. The q value of GDP had decreased to 0.02.

**Figure 6. Figure 6.** Comparisons of q value for different factors (2010 and 2020). Comparisons of q value for different factors (2010 and 2020).

#### 3.5.2. Interactive Factors

In this study, the influence between two factors was non-linear enhanced after interaction (Figures 7 and 8). In 2010, the order of explanatory power of interaction factors on desertification was precipitation ∩ land use > precipitation ∩ wind velocity > precipitation ∩ population intensity > temperature ∩ precipitation > precipitation ∩ GDP intensity > GDP intensity ∩ land use > population density ∩ GDP intensity. The dominant interactive factor was precipitation ∩ land use (0.392), followed by precipitation ∩ wind velocity (0.365) and precipitation ∩ population intensity (0.345). The q value of temperature ∩ population density was the smallest, which is 0.079. In 2020, the order of explanatory power of interaction factors on desertification was precipitation ∩ land use > temperature

∩ land use > temperature ∩ precipitation > precipitation ∩ wind velocity > temperature ∩ population intensity > precipitation ∩ population intensity > precipitation ∩ GDP intensity. Among them, the precipitation ∩ land use also had the greatest explanatory power on desertification evolution, the q value increased to 0.447, followed by temperature ∩ land use and temperature ∩ precipitation, their q values were 0.351 and 0.340, respectively. All in all, the precipitation ∩ land use was the essential factor influencing the spatiotemporal distribution of desertification in the Gonghe Basin during the study period. factors on desertification was precipitation ∩ land use > temperature ∩ land use > temperature ∩ precipitation > precipitation ∩ wind velocity > temperature ∩ population intensity > precipitation ∩ population intensity > precipitation ∩ GDP intensity. Among them, the precipitation ∩ land use also had the greatest explanatory power on desertification evolution, the q value increased to 0.447, followed by temperature ∩ land use and temperature ∩ precipitation, their q values were 0.351 and 0.340, respectively. All in all, the precipitation ∩ land use was the essential factor influencing the spatiotemporal distribution of desertification in the Gonghe Basin during the study period.

In this study, the influence between two factors was non-linear enhanced after interaction (Figures 7 and 8). In 2010, the order of explanatory power of interaction factors on desertification was precipitation ∩ land use > precipitation ∩ wind velocity > precipitation ∩ population intensity > temperature ∩ precipitation > precipitation ∩ GDP intensity > GDP intensity ∩ land use > population density ∩ GDP intensity. The dominant interactive factor was precipitation ∩ land use (0.392), followed by precipitation ∩ wind velocity (0.365) and precipitation ∩ population intensity (0.345). The q value of temperature ∩ population density was the smallest, which is 0.079. In 2020, the order of explanatory power of interaction

*Land* **2023**, *12*, x FOR PEER REVIEW 12 of 17

3.5.2. Interactive Factors

**Figure 7. Figure 7.** The q values of The q values of interactive factors in 2010 (P < 0.01). interactive factors in 2010 (P < 0.01).

**Figure 8.** The q values of interactive factors in 2020 (P < 0.01). **Figure 8.** The q values of interactive factors in 2020 (P < 0.01).

**4. Discussion**

fication evolution of the Gonghe Basin from 2010 to 2020.

Previous studies have shown that desertification evolution is influenced by natural factors and human activities [26,27,49]. For natural factors, the terrain of the Gonghe Basin is flat and open, providing a good deposition site for the aeolian activity. The basin con-

and ancient aeolian sand [9], which are easily eroded by wind [26], which provided material sources for aeolian activity, causing widespread distribution of desertification land [12]. Among all natural factors, climate change has an essential impact on the development of desertification, temperature, precipitation and wind velocity are the main influencing factors [50,51]. The explanatory power of precipitation factors on desertification was highest among all factors in 2010 and 2020, the explanatory power of temperature and wind velocity on desertification evolution increased between 2010 and 2020, with q values increasing by 0.15 and 0.02, respectively. In northwestern China, the climate is arid all year with scarce precipitation [15], so the precipitation factor played a vital role in the desertification evolution. As shown in Figure 9 and Figure 10, in the Gonghe Basin, we can see a fluctuating downward trend in the annual average temperature from 2010 to 2019, but there was an upward trend in the annual precipitation, which revealing that the climate had become colder and more humid over the past 10 years. The decrease in temperature could effectively reduce evaporation, while accompanied by the increase of precipitation, the improvement of hydrothermal conditions was conducive to the vegetation recovery, affecting the efficiency of sand material acquisition, which reduced aeolian activity [12,52– 55]. The annual mean wind velocity also presented a relative downward trend (Figure 11), and reduced wind strength led to the weakening of aeolian activity [56]. In general, these favorable natural factors changes were beneficial to the desertification reversal. Among the changes in human factors, the q values of land use and population density increased by 0.08 and 0.03 respectively, while the value of GDP density decreased by 0.06. From this, it can be seen that the impact of human activities gradually increased during the deserti-

However, the interaction between two different impact factors will increase the explanatory power on desertification compared to single factors [57]. The dominant

#### **4. Discussion**

Previous studies have shown that desertification evolution is influenced by natural factors and human activities [26,27,49]. For natural factors, the terrain of the Gonghe Basin is flat and open, providing a good deposition site for the aeolian activity. The basin contained a large amount of Quaternary loose sediments, such as fluvial-lacustrine sediments and ancient aeolian sand [9], which are easily eroded by wind [26], which provided material sources for aeolian activity, causing widespread distribution of desertification land [12]. Among all natural factors, climate change has an essential impact on the development of desertification, temperature, precipitation and wind velocity are the main influencing factors [50,51]. The explanatory power of precipitation factors on desertification was highest among all factors in 2010 and 2020, the explanatory power of temperature and wind velocity on desertification evolution increased between 2010 and 2020, with q values increasing by 0.15 and 0.02, respectively. In northwestern China, the climate is arid all year with scarce precipitation [15], so the precipitation factor played a vital role in the desertification evolution. As shown in Figures 9 and 10, in the Gonghe Basin, we can see a fluctuating downward trend in the annual average temperature from 2010 to 2019, but there was an upward trend in the annual precipitation, which revealing that the climate had become colder and more humid over the past 10 years. The decrease in temperature could effectively reduce evaporation, while accompanied by the increase of precipitation, the improvement of hydrothermal conditions was conducive to the vegetation recovery, affecting the efficiency of sand material acquisition, which reduced aeolian activity [12,52–55]. The annual mean wind velocity also presented a relative downward trend (Figure 11), and reduced wind strength led to the weakening of aeolian activity [56]. In general, these favorable natural factors changes were beneficial to the desertification reversal. Among the changes in human factors, the q values of land use and population density increased by 0.08 and 0.03 respectively, while the value of GDP density decreased by 0.06. From this, it can be seen that the impact of human activities gradually increased during the desertification evolution of the Gonghe Basin from 2010 to 2020. *Land* **2023**, *12*, x FOR PEER REVIEW 14 of 17 interactive factor in 2010 and 2020 was precipitation ∩ land use, with an increase of 0.055. In 2020, The explanatory power of temperature ∩ land use on desertification had significantly increased, with an increase of 0.17 compared to 2010. In 2010 and 2020, the q values of temperature ∩ precipitation were relatively high, both greater than 0.3. The results showed that the natural factors such as precipitation ∩ temperature played a fundamental role in the desertification change. Furthermore, the improvement of desertification conditions was the combination consequences of natural and human factors, the impact of human activity intensity had been increasing over the past 10 years. This is consistent with previous research on the Qinghai Tibet Plateau [26,27], the source of the Yellow River [35], and the surrounding areas of Qinghai Lake [30]. However, the highest explanatory power of precipitation and land use on desertification in this study is only 0.447, which may be related to the specific geological environment in the Gonghe Basin and the limited factors selection [35,58]. Since 1991, numerous measures have been applied to combat desertification. Tree planting and return the grain plots to forestry reforestation projects can help increase vegetation coverage and improve ecological diversity [59,60]. The photovoltaic power generation base and closed protection zone were established in Talatan [61,62], it is conducive to prevent wind and fix sand, thereby reducing local aeolian activities. *Land* **2023**, *12*, x FOR PEER REVIEW 14 of 17 interactive factor in 2010 and 2020 was precipitation ∩ land use, with an increase of 0.055. In 2020, The explanatory power of temperature ∩ land use on desertification had significantly increased, with an increase of 0.17 compared to 2010. In 2010 and 2020, the q values of temperature ∩ precipitation were relatively high, both greater than 0.3. The results showed that the natural factors such as precipitation ∩ temperature played a fundamental role in the desertification change. Furthermore, the improvement of desertification conditions was the combination consequences of natural and human factors, the impact of human activity intensity had been increasing over the past 10 years. This is consistent with previous research on the Qinghai Tibet Plateau [26,27], the source of the Yellow River [35], and the surrounding areas of Qinghai Lake [30]. However, the highest explanatory power of precipitation and land use on desertification in this study is only 0.447, which may be related to the specific geological environment in the Gonghe Basin and the limited factors selection [35,58]. Since 1991, numerous measures have been applied to combat desertification. Tree planting and return the grain plots to forestry reforestation projects can help increase vegetation coverage and improve ecological diversity [59,60]. The photovoltaic power generation base and closed protection zone were established in Talatan [61,62], it is conducive to prevent wind and fix sand, thereby reducing local aeolian activities.

**Figure 9.** Average annual temperature in from 2010 to 2019. **Figure 9.** Average annual temperature in from 2010 to 2019. **Figure 9.** Average annual temperature in from 2010 to 2019.

**Figure 10.** Annual precipitation from 2010 to 2019. **Figure 10.** Annual precipitation from 2010 to 2019. **Figure 10.** Annual precipitation from 2010 to 2019.

**Figure 11.** Average annual wind velocity from 2010 to 2019.

**Figure 11.** Average annual wind velocity from 2010 to 2019.

**Figure 10.** Annual precipitation from 2010 to 2019.

**Figure 9.** Average annual temperature in from 2010 to 2019.

**Figure 11. Figure 11.** Average annual wind velocity from 2010 to 2 Average annual wind velocity from 2010 to 2019. 019.

However, the interaction between two different impact factors will increase the explanatory power on desertification compared to single factors [57]. The dominant interactive factor in 2010 and 2020 was precipitation ∩ land use, with an increase of 0.055. In 2020, The explanatory power of temperature ∩ land use on desertification had significantly increased, with an increase of 0.17 compared to 2010. In 2010 and 2020, the q values of temperature ∩ precipitation were relatively high, both greater than 0.3. The results showed that the natural factors such as precipitation ∩ temperature played a fundamental role in the desertification change. Furthermore, the improvement of desertification conditions was the combination consequences of natural and human factors, the impact of human activity intensity had been increasing over the past 10 years. This is consistent with previous research on the Qinghai Tibet Plateau [26,27], the source of the Yellow River [35], and the surrounding areas of Qinghai Lake [30]. However, the highest explanatory power of precipitation and land use on desertification in this study is only 0.447, which may be related to the specific geological environment in the Gonghe Basin and the limited factors selection [35,58]. Since 1991, numerous measures have been applied to combat desertification. Tree planting and return the grain plots to forestry reforestation projects can help increase vegetation coverage and improve ecological diversity [59,60]. The photovoltaic power generation base and closed protection zone were established in Talatan [61,62], it is conducive to prevent wind and fix sand, thereby reducing local aeolian activities.

interactive factor in 2010 and 2020 was precipitation ∩ land use, with an increase of 0.055. In 2020, The explanatory power of temperature ∩ land use on desertification had significantly increased, with an increase of 0.17 compared to 2010. In 2010 and 2020, the q values of temperature ∩ precipitation were relatively high, both greater than 0.3. The results showed that the natural factors such as precipitation ∩ temperature played a fundamental role in the desertification change. Furthermore, the improvement of desertification conditions was the combination consequences of natural and human factors, the impact of human activity intensity had been increasing over the past 10 years. This is consistent with previous research on the Qinghai Tibet Plateau [26,27], the source of the Yellow River [35], and the surrounding areas of Qinghai Lake [30]. However, the highest explanatory power of precipitation and land use on desertification in this study is only 0.447, which may be related to the specific geological environment in the Gonghe Basin and the limited factors selection [35,58]. Since 1991, numerous measures have been applied to combat desertification. Tree planting and return the grain plots to forestry reforestation projects can help increase vegetation coverage and improve ecological diversity [59,60]. The photovoltaic power generation base and closed protection zone were established in Talatan [61,62], it is conducive to prevent wind and fix sand, thereby reducing local aeolian activities.

#### **5. Conclusions**

In this paper, we used the Albedo-NDVI feature space method based on Landsat images to explore the spatiotemporal evolution of desertification and its driving mechanism in the Gonghe Basin over the past 10 years, and then provide some scientific references for desertification prevention. The main conclusions are as follows:

(1) Desertification in the Gonghe Basin was divided into 5 categories by constructing the Albedo-NDVI feature space. There was high accuracy in the desertification classification by using the feature space method, reaching 94% in 2010 and 95% in 2020.

(2) From 2010 to 2020, the desertification situation in the Gonghe Basin generally improved, especially in the western part of the basin. The proportion of desertification area decreased from 84.3% in 2010 to 80.1% in 2020. The transformation from extremely severe desertification to severe desertification is the main form of desertification reversal.

(3) The improvement of desertification in the Gonghe Basin from 2010 to 2020 is a result of the combined effects of natural and human factors. In natural factors, precipitation played an important role in desertification evolution, and the impact of human factors was gradually increasing.

However, our study still has some limitations. Due to limited data in this study, there are some errors in the classification results of desertification. It is crucial to explore the dominant driving mechanism of desertification on different time scales, and provide targeted suggestions for desertification control in the Gonghe Basin.

**Author Contributions:** Conceptualization, H.J. and J.L.; data curation and investigation, H.J., R.W., H.L., B.D. and H.Z.; methodology, H.J., L.G., L.L. and J.L.; writing—original draft, H.J.; writing—review and editing, H.J. and J.L.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the the National Natural Science Foundation of China (No. 41730639), and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK0906).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The source of relevant data acquisition has been described in the text.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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