Desertification is a serious global environmental problem. Under the influence of natural environmental change and the anthropogenic causes of grassland degradation, ecological and environmental deterioration, as well as desertification have become more severe in Mongolia [1
]. Plant species from 1961 to 2006 in the forest steppe, real steppe, mountain steppe, desert steppe, and desert regions in Mongolia were reduced by 50.0%, 44.7%, 30.3%, 23.8%, and 26.7%, respectively [2
]. In 2007, more than 72% of Mongolia’s land was affected by desertification, with the range of desertification still expanding [3
]. In 2017, the most current data from the Ministry of Natural Environment and Tourism of Mongolia indicated that 76.8% of the country’s land suffered varying degrees of desertification with desertification continuing to spread at a rapid rate, affecting the country’s renowned grasslands, including those in Dornod and Khentii provinces [4
]. The increasing desertification problem on the Mongolian Plateau will have a strong effect on local sustainable development and may become the biggest obstacle to the transboundary cooperation in this area, for example, the China-Mongolia-Russia economic corridor issued by the governments of these three countries [5
International research on desertification monitoring by remote sensing began in the 1970’s [6
]. Initially, researchers used land degradation as reflected by the vegetation index to represent desertification [7
]. In the 1980’s, studies found that land surface albedo is one of the most important parameters of the ground radiant energy balance, which determine the radiant energy absorbed by the underlying surface [10
]. Some studies showed that increasing land surface albedo implies a degradation of land quality [11
]. At the beginning of the 21st century, a number of studies found that texture features, moisture content, and surface albedo changed with a change in ground object types [12
]. At the same time, some researchers found that monitoring changes in vegetation and land use was not the only way to measure desertification [13
]. Zeng et al. used the albedo and NDVI (normalized difference vegetation index) to build the Albedo-NDVI feature space to conduct a study on desertification [15
]. They found that multi-dimensional remote sensing data had clear biophysical significance and could reflect the surface coverage, hydrothermal combination, and the changes in land desertification.
However, due to the considerable influence of the soil background on the NDVI, the vegetation condition cannot be well expressed in areas with sparse vegetation. With a decrease in vegetation coverage, surface albedo and surface radiation temperatures increase accordingly. Therefore, the MSAVI (modified soil adjusted vegetation index) was introduced, which fully considers the bare soil line problem and can better eliminate or reduce the influence of the soil and vegetation canopy background [16
]. After comparing the correlation coefficients of NDVI, MSAVI, and the grassland vegetation cover, Wu et al. found that the MSAVI was significantly correlated with grassland vegetation cover [17
]. Feng et al. proposed building an Albedo-MSAVI feature space model by replacing NDVI with MSAVI and applying it to the study of soil salinization [18
]. However, Vova et al. carried out land degradation monitoring in the Govi-Altai province of Mongolia and found that the changes in the MSAVI were not sufficient to evaluate the land degradation process [19
]. This suggests that changes in pure MSAVI are not a major indicator of land degradation assessment. In fact, in the current research of desertification information extraction, studies on feature space are still dominated by the Albedo-NDVI feature space, and diverse feature spaces, such as the Albedo-MSAVI feature space, are rarely used.
In addition, due to different degrees of desertification, different topsoil textures are produced. More serious desertification corresponds to rougher the surface soil particle composition. Therefore, TGSI (topsoil grain size index) is recommended as an evaluation index of land degradation [20
]. Lamchin et al. used NDVI, TGSI, and albedo as representative indicators of vegetation biomass, landscape pattern, and micro-meteorology, respectively. Then, desertification information extraction was conducted on the Hogno Khaan Nature Reserve in Mongolia to complete a dynamic analysis of desertification [22
]. In 2017, Lamchin et al. found that the highest correlations were between TGSI and albedo at all levels of desertification [23
]. This provided a basis for constructing the Albedo-TGSI feature space model. Combined with the above information, it can be seen that the previous studies of desertification information extraction are mainly based on the combination of multiple indexes or on a single feature space model, which lacks the comprehensive evaluation ratio of various special space models. Determining what kind of feature space is suitable for the extraction of desertification information regarding the Mongolian Plateau remains a scientific problem that needs to be solved.
In response to this challenge, our study analyzed the effects and applicability of different feature space models in the extraction of desertification information, with the goal of finding appropriate extraction approaches for desertification relating to the Mongolian plateau. We used 30-m resolution remote sensing images to invert the NDVI, MSAVI, TGSI, and albedo data of the study area in Mongolia and investigated the desertification information based on the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI. By comparing and analyzing the results, we attempted to find the reasonable model(s) under different vegetation cover conditions. This study was expected to provide a reference for the methods used for the dynamic monitoring of desertification on the Mongolia Plateau.
In desertification extraction studies, researchers have tried to extract information by deciphering ground cover types. In terms of the landscape pattern, the land covers in desertification areas are usually classified as desert steppe and barren. However, when the desertification information is obtained through the interpretation of land cover, it is often interpreted from a macroscopic perspective. In this study, the results of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI were compared to the 2015 Mongolia land cover classification map. The contrasting results showed that the desertification area obtained by image interpretation accounts for 81.43% of the total area, which is lower than the desertification extraction results in this study. The land cover interpretation is not being sensitive enough regarding low quality vegetation information, resulting in the misclassification of some land cover information, such as desert steppe and barren areas. When the vegetation coverage of desert steppe remained at about 10% (using the land cover classification map of Mongolia), it was very similar to the real steppe with low coverage. In addition, it was easily classifiable as real steppe, leading to an underestimation in the area of desertification. When the vegetation coverage of desert steppe was reduced to about 5%, the similarity with land classified as barren became high. In the 30-m resolution image, it was difficult to distinguish between desert steppe and barren, resulting in the overestimation of severe desertification and high desertification, and the underestimation of medium desertification. Therefore, the actual results of the desertification area in this region should be greater than 81.43%, as indicated on the land cover interpretation map. In addition, it is more similar to the results of the above three models.
represents a comparison of the land cover interpretation product and the Albedo-MSAVI model inversion results. Figure 5
a,b do not represent the same type of results, with (a) being a land cover map reflecting desertification or non-desertification land cover types, while (b) is a model inversion result showing the degree of desertification. As desertification will inevitably lead to changes in land object types and special land cover types, land cover maps can, indirectly reflect the present situation of desertification. From our study, we found that the similarity between water- and sand information obtained by the two different methods was high, but the results of other land objects were quite different. Through visual comparison, five zones with significant differences were selected in Figure 5
. Regarding Zone 1, the non-desertification areas in the Eastern part of Uvs province and the Western part of Zavkhan province differed greatly. The results of the land cover map based on eCognition software showed much greater non-desertification than those of the distribution map, showing different degrees of desertification. For Zone 2, the results of the Albedo-MSAVI retrieval showed that the Southern part of Zavkhan province mainly exhibited areas of severe and high desertification, while the interpretation of images suggested that half of the Southern part of Zavkhan province consisted of non-desertification areas (interpretation classified as real steppe). With regards to Zone 3, the interpretation of images showed that the Southern part of Uvs province was barren, portraying areas of high and severe desertification. However, the Albedo-MSAVI model exhibited far fewer areas of high desertification than the interpretation of the image in the high desertification region. For Zone 4, the retrieved results of the Albedo-MSAVI model showed that the area East of Uvs Lake was mainly an area that contained high and severe desertification. However, the interpretation results showed that the degree of desertification in this region was relatively light (interpretation classified this area as desert steppe). For Zone 5, the land cover map showed that most of the areas of Khovd province and the Southern part of Govi-Altai province were barren, namely, the areas of high and severe desertification. On the other hand, the distribution map of the degree of desertification showed far fewer barren areas than the remote sensing image interpretation results in the high desertification region.
The results obtained from this study were very similar to the desertification data released by Mongolia. The 2007 desertification data of Mongolia also shows that the high and severe areas of desertification are strip-shaped and extend from the Northwest to the Southeast. Land degradation was indicated to have begun near the country’s rivers and lakes, most of which had become areas of low desertification [3
]. The area selected for this study lies in the Northwestern part of Mongolia, associated with serious desertification. In this region, there is less area that is covered by forests and steppe grasslands. Therefore, the actual desertification situation in the region should be higher than the average desertification level of 76.8% announced by Mongolia in 2017 [4
]. It may be closer to the results of this study.
Compared to previous studies on the extraction of desertification information, the data sources chosen in this study have a higher resolution, and a variety of surface reference variables were introduced to build corresponding feature spaces. Previous studies on desertification mostly used MODIS data as the basic data source to extract large-scale desertification information [40
]. However, the spatial resolution of MODIS data is 500 m, making the retrieval of fine detail difficult. Therefore, Landsat 8 images with a spatial resolution of 30 m, as used in this study, can greatly increase the level of detailed information and thereby, improve accuracy. Furthermore, most other studies on the extraction of desertification information, involved a variety of indexes that were chosen to form a decision tree classification or to construct an Albedo-NDVI feature space [43
]. In this study, MSAVI and TGSI were introduced to replace NDVI to build different models, and the results were more accurate than the single Albedo-NDVI model.
A comparison of the results from the three models reveals that the effects of desertification extracted by each method differs, but each method has its own advantages in extracting desertification information at different levels. The Albedo-TGSI model was used to extract the largest area of desertification, accounting for 86.99% of the total area, followed by the Albedo-NDVI model at about 86.20%, and the Albedo-MSAVI model at about 85.72%. At the same time, the area and accuracy of severe and high desertification areas extracted by the Albedo-TGSI model were the highest, far exceeding the results obtained by the Albedo-NDVI and Albedo-MSAVI models. Its spatial distribution extends from the Southwestern part of Zavkhan province to a wide area in the Southern part of Zavkhan province and from the Southern part of Khovd province and parts of Govi-Altai province to cover most of Khovd province and the Southern part of Govi-Altai province. However, the areas of medium desertification, low desertification, and non-desertification extracted by the Albedo-TGSI model were smaller than those obtained by the other two methods. The spatial changes were mainly reflected in the disappearance of the scattered non-desertification areas in the Northern part of Khovd province and the decrease in both the low and medium desertification levels. A comparison between Albedo-NDVI and Albedo-MSAVI showed that the results of the two classifications were highly similar. However, the areas of non-desertification, low desertification, and water extracted by the Albedo-MSAVI model were all larger than those obtained by the Albedo-NDVI. The accuracy of non-desertification, low desertification, and water extracted by the Albedo-MSAVI was far higher than that of the other two models. Thus, we can conclude that the Albedo-MSAVI model is the most sensitive to vegetation under the condition of low vegetation coverage. In addition, this model can fully extractinformation relating to areas of non-desertification and low desertification. The Albedo-TGSI model has the highest sensitivity to surface soil changes and can fully extract information relating to areas of high and severe desertification. Presently, the Albedo-NDVI model is a widely accepted model and can be used to accurately acquire desertification information in regions with higher vegetation coverage.
By constructing the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, this study, with a high resolution (30 m), has obtained the results regarding the extraction of desertification information of Northwestern Mongolia, analyzed the mechanism of three feature space models, and compared these with previous studies on desertification information extraction. This study has proven that it is feasible to extract desertification information using the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI. Moreover, these models are preferable to the traditional method of extracting desertification information by land cover classification. In addition, the results of the different desertification extraction models vary. Therefore, in the vast area that comprises the Mongolian plateau, different methods for the extraction of desertification information should be chosen for different regions and different surface features. The traditional Albedo-NDVI model can be used in areas with high vegetation coverage and high forest ratios (such as the Northern and Eastern parts of the Mongolian plateau). The Albedo-MSAVI model can be used to extract desertification information in areas with relatively low vegetation coverage on the Mongolian Plateau (such as the Western Mongolian Plateau and Eastern Mongolia). The Albedo-TGSI model can be used for regions with extremely low vegetation coverage and for the widely distributed Gobi Desert and bare land (such as the Southwestern regions of the Mongolian plateau). Based on the three models and the land cover characteristics of the Mongolian plateau, dynamic monitoring of desertification on the Mongolian plateau can be realized in the future.