1. Introduction
Desertification refers to the phenomenon that land in arid and semi-arid areas, or even some semi-humid areas, gradually degenerates into a desert due to the combined effects of human activities and natural factors, such as drought, low rainfall, overgrazing, soil and water loss, and vegetation destruction [
1]. As one of the global ecological–social–economic problems experienced by the international community, it is attracting extensive scientific attention all over the world. Desertification leads to negative changes occurring in the properties of vegetation (such as biomass, density, vegetation cover), loss of biodiversity and soil fertility, and changes in landscape patterns in dry regions at different geographical scales [
2]. It is also increasingly damaging the local natural, ecological environment and is rapidly becoming one of the greatest obstacles to the sustainable development of both society and the economy. Therefore, it is critical to dynamically monitor desertification processes and quantitatively analyze the spatiotemporal distribution characteristics, which can provide a scientific basis for environmental protection and sustainable development practices [
3].
At present, there are numerous methods used for acquiring desertification information, which focus on field investigations and remote sensing. Among them, the field investigations method mainly relies on taking the measurements of sample grids in small areas, which is a time-consuming practice and requires a considerable effort when employed at the city level. Compared with field investigations, remote sensing methods have been widely used in the field to monitor the spatiotemporal information relating to desertification because of their large spatial coverage, easy accessibility, and reasonable costs [
4]. The primary monitoring method is dependent on manual visual interpretation. Although this method provides good mapping results for desertified land, it has high labor costs and its accuracy is dependent on its interpreters [
5]. Thus, this method is not optimal for desertification monitoring, especially for large regions. Furthermore, the monitoring results are affected by the interpretations and professional qualities of the individual personnel. With the ongoing development of remote sensing and computer technologies, numerous evaluation indexes have been proposed in the research to assess the degree of desertification in certain areas [
6]. Some relevant studies have tried to extract desertification information through thematic indices, such as the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), modified soil-adjustment vegetation index (MSAVI), and enhanced vegetation index (EVI) [
7,
8,
9,
10]. However, since the relevant vegetation index is greatly affected by precipitation fluctuations and soil background characteristics, especially in open vegetation environments, the use of a single vegetation index cannot entirely and accurately reflect the desertification information in semi-arid areas [
11]. Therefore, the method of combining multiple indexes and constructing a desertification degree index was proposed to better extract the necessary desertification information [
12,
13,
14,
15]. Based on the combination of NDVI–Albedo, MSAVI–Albedo, etc., feature space models were constructed to evaluate the desertification degree and study the spatial distribution in the region [
16,
17,
18]. The abovementioned methods can be used to extract the required desertification information; however, all these methods are based on the spectral characteristics of individual pixels, rarely considering the texture, geometric characteristics, or additional information of the land’s surface, which are difficult to use when attempting to distinguish areas of the same objects with different spectra or different objects with the same spectra. The results make it easy to generate the salt and pepper phenomenon, that is, due to the spectral differences of the same object, the original complete and uniform land-types are classified into different categories, usually presenting as discrete pixels [
19]. In addition, the environment of interaction among pixels is ignored in the studies, which can easily cause misclassifications in land desertification monitoring practices and can also affect the classification results [
20].
In recent years, the object-oriented multi-scale segmentation method has been extensively used in remote sensing image interpretation practices. Multi-scale segmentation can segment an image into geographical units with multiple similar attributes that are classified according to their spectral characteristics, texture characteristics, and additional information, effectively overcoming problems, such as spectral confusion or the salt and pepper phenomenon [
21]. Image segmentation is deemed to be a critical prerequisite for object-oriented classification because its quality considerably affects the final result of geo-object recognition activity. Understanding how to effectively determine the optimal segmentation scale is crucial to the improvement of segmentation quality in this field of research [
22]. Therefore, a variety of quantitative evaluation methods for selecting optimal segmentation scales was proposed, such as the maximum area method, objective function method, mean variance method, and homogeneity and heterogeneity index model [
23,
24,
25]. Quantitative evaluation approaches can achieve a wide range of segmentation results. If the segmentation scale is larger than the classified target object, under-segmentation occurs, whereas if the segmentation scale is smaller than the classified target object, over-segmentation occurs. As a result, it is challenging to understand how to successfully obtain the optimal segmentation scale for a certain class of objects. In terms of land cover classification methods [
26], vegetation information extraction [
27,
28], and farmland identification practices [
29], the optimal segmentation scale has been thoroughly studied in the literature and improved classification accuracy has been achieved by using the object-oriented classification method based on optimal segmentation. However, few studies focus on optimal segmentation scale selection in terms of the monitoring and mapping of land desertification behavior.
China is one of the countries with the largest desert areas and the fastest speed of desertification in the world. Sandstorm disasters frequently occur and land desertification causes considerable economic losses [
30]. The Mu Us Sandy Land is one of the four major sandy-land areas in China, which is located at the junction of arid and semi-arid areas. It is a typical agricultural–pastoral ecotone formed by both the natural environment and human activities. To a certain extent, the desertification process occurring in the Mu Us Sandy Land Ecological Function Reserve represents the desertification characteristics of arid and semi-arid areas [
31]. Following years of management, the Mu Us Sandy Land Ecological Function Reserve has experienced significant improvements, and the desertification area has also been considerably reduced. However, the ecological environment in this region remains fragile and sensitive, and the results of governance are still unstable. In order to prevent the phenomenon of desertification from recurring, the effective monitoring and assessment of land desertification processes needs to be continuously conducted.
In order to solve the problems of spectral confusion and the salt and pepper phenomenon by using the pixel classification method, to assess different types of land desertification, this paper addresses the Mu Us Sandy Land Ecological Function Reserve located in northwest China as the main study area to explore the effects of using the object-oriented method to successfully extract the appropriate desertification information. An improved method for desertified land classification is developed by incorporating spectral and spatial features as well as thematic index information with the object-oriented technique to improve the efficiency and accuracy of identifying the degree of desertification that occurs in the selected site. Based on these methods, we monitor and analyze the spatiotemporal changes in land desertification activity from the year 2001 to 2021. The results presented in this study provide a scientific basis for the prevention and control of desertification in the Mu Us region in the future.
5. Discussion
Desertification is a major obstacle to global sustainable development. The effective monitoring of desertification is particularly important for environmental protection and ecological restoration practices. In this study, object-oriented classification based on an optimal segmentation scale was used to obtain desertification information for the Mu Us Sandy Land Ecological Function Reserve from 2001 to 2021. Compared with the previous studies [
49,
50,
51], the unit of analysis used in our study was not a single pixel, but a similar object with the same spectral, geometric, and thematic characteristics, which overcame the problems of spectral confusion and the salt and pepper phenomenon in sparse-vegetation areas.
Image segmentation is a special operation of object-oriented classification. The selection of a segmentation scale determines the size of the image objects and quality of the classification results; therefore, it is very important to select the appropriate segmentation scale. In previous studies, some scholars have tried to use object-oriented classification methods to extract desertified land information, and the classification results were significantly improved. However, the selection of a segmentation scale mainly relies on repeated visual comparisons, which causes increased subjective interference; therefore, classification accuracy may be improved further [
52]. In this study, the mean variance and maximum area methods were used to determine the optimal segmentation scale of 140 in the study area, which can avoid over-segmentation and under-segmentation behaviors, to a certain extent, and reduce the influence of human interference. In a study conducted on Hunshandak Sandy Land [
53], Li et al. presented an optimal segmentation scale for extracting vegetation types, such as arbors and shrubs, which was determined to be 145 by the ESP2 tool of eCognition software, while the optimal segmentation scale for only extracting sandy land was 200. This effect is similar to the optimal scale determined in this study when the land class of the vegetation, such as grassland, was classified as non-desertification land to participate in the segmentation process, indicating that the optimal segmentation scale for land desertification determined by this method presents a certain level of reliability.
In fact, in the process of segmentation, the determination of the optimal scale was affected by several factors. The different selection methods, the setting of the segmentation parameters, the type of ground objects present in the study area, and the resolution of the remote sensing images led to differences in the results [
41]. Based on the Landsat images, Song et al. used the object-oriented method to extract ground feature information for the desertification area in the northwestern Liaoning Province and determined the optimal segmentation scale of vegetation types as 135 by using the mean variance method [
54]. Gao et al. used the GF-2 image to identify shelterbelts in the desert oasis area of Dengkou County. When the segmentation scale of shelterbelts in this area was determined as 82 by using the ESP2 tool, the segmentation effect of the image was the best [
55]. In addition, the optimal segmentation scale is closely related to the spatial resolution of the image. Generally speaking, the higher the spatial resolution, the smaller the corresponding optimal segmentation scale, and the better the segmentation effect; however, this does not mean that the classification accuracy will be higher. Lian and Chen used the object-oriented method in their study to classify the ground objects of images with different resolutions and observed that the classification accuracy of SPOT images with a resolution of 2.5 m was higher than that of Quick Bird images with a resolution of 0.6 m. Classification accuracy and spatial resolution do not simply have a linear relationship [
56]. Similar conclusions were determined in the study conducted by Jiang et al. [
57]. It was observed that the optimal segmentation scale was not completely consistent when providing results for different regions, research objects, and resolutions.
Although similar studies exist that are concerned with the dynamics of desertification in the study region, this study has provided long-term mapping trends of desertified land since 2001. It is helpful to comprehensively analyze the change in desertified areas and explore the spatial characteristics of the distribution of desertified areas, which has rarely been analyzed in previous studies. To effectively identify the different types of severe-, moderate-, and mild-desertification areas, the spectral, spatial, and thematic index features were selected to construct feature spaces, and the random forest algorithm was used to classify the desertification degree based on these features. Compared with the pixel-based classification method, the overall mapping accuracy of this method increased by 8.06%, and the Kappa coefficient increased by 0.1114. The mapping accuracy significantly improved for different degrees of desertification. In the relevant research of this region [
58], since 2000, land desertification has been mainly moderate and severe and has gradually changed to moderate and mild. The area of moderate desertification increased first and then decreased, the area of mild desertification increased annually, and the area of severe desertification decreased annually, which is the same pattern presented for the land desertification activity observed in the present study. In addition, the spatial distribution of desertified land was consistent with the research results obtained by Wang et al. [
49]; the change in desertification in the southeast region was more stable than that in the northwest, due to climate and topographic factors, as well as human activity, and the research results are reliable. In general, this research method meets the requirements of the classification of land desertification degree, at present, and can provide methodological support for the monitoring of desertification in the future.
However, there are certain issues that should be considered in future analyses. From the perspective of classification accuracy, whether pixel-based or object-oriented methods are employed, the classification accuracy of mild- and moderate-desertification areas is lower than that of non- and severe-desertification areas (
Table 4 and
Table 5). The main reason for this is that the characteristics between mild and moderate, and moderate and severe are relatively similar, which makes it difficult to perform the classification; therefore, some misclassifications may occur [
59]. Future studies should consider improving this consequence through spectral information enhancement [
60]. At the same time, the determination of the optimal segmentation scale is affected by several factors; whereas the method we utilized could effectively extract land desertification data, its robustness was not tested in other regions [
5]. Therefore, for different desertified areas, the selection of remote sensing data with appropriate resolutions, a reasonable determination of the desertification segmentation scale, and the extraction of desertification information should also be key factors in the research conducted in the future.
6. Conclusions
In view of the deficiencies of pixel-based land desertification information extraction, this study combined the mean variance and maximum area methods to determine the optimal segmentation scale and adopted the object-oriented random forest algorithm to obtain the land desertification information for the Mu Us Sandy Land Ecological Function Reserve from 2001 to 2021.
In the segmentation process, when there are multiple segmentation scales, a smaller segmentation scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient was increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km2 and the area of severely desertified land decreased by 4115.92 km2. The governance effect was remarkable. In the past ten years, the restoration rate of the entire area increased by 22% in the whole area, while the restored effect of the western Gobi Desert was not satisfied. The climate and topographic factors were the main reasons for land desertification, and human activities also aggravated the desertification process. Enhanced measurements are required for further successful governance in the future. This study explored the application of the object-oriented classification method and the optimal segmentation scale for obtaining land desertification information for the study area. The results show a significant improvement in the desertification classification accuracy. The long-term-mapping results provide effective decision-making ideas and support for land desertification restoration and management projects.