1. Introduction
Grassland is an important part of terrestrial ecosystems, accounting for 40.5% of the total global land area (except ice caps and ice sheets) [
1,
2], and has important ecological and food production functions [
3,
4]. Alpine meadows are the main grassland ecosystems on the Tibetan Plateau, and their unique biodiversity and ecological functions are important for maintaining the ecological stability of the Tibetan Plateau region and the livelihoods of local herders [
5]. However, in recent years, alpine meadows have experienced the emergence of diverse types of bald patches, characterized by limited or absent vegetation cover. These patches have arisen primarily due to overgrazing and rodent activities, which may have been compounded or intensified by the effects of climate change, such as altered precipitation patterns and increased temperature extremes [
6]. As shown in
Figure 1B, the restoration stages of alpine meadow patches are mainly divided into active patches (Stage 0), inactive patches (Stage 1), recovering patches (Stage 2), and healthy alpine meadow (Stage 3). The alpine meadow patches in the active-patch stage after the increase in patches; connectivity; and the formation of a large area of bare land, after continuous degradation, will form a ‘Heitutan’ degraded grassland (
Figure 1A), seriously affecting the production and life of local herders [
7]. Xilai [
8] pointed out that Heitutan degraded grassland can be recovered, but the recovery requirements for different degrees of Heitutan degradation vary greatly. Based on model simulations, Heitutan degraded grassland can form after 21 years of high-intensity grazing. Recovery requires at least 50 years with virtually no external interference, while under typical grazing conditions, the recovery period for Heitutan ranges from 115 to 500 years [
9,
10].
The timely intervention and development of management measures at the stages of alpine meadow patchiness can effectively reduce the difficulty of the subsequent management of Heitutan degraded grasslands. Previous studies mainly focused on the distribution of patches in degraded alpine meadows, succession patterns, soil microorganisms, etc. Huo et al. [
11] carried out patch-scale investigations in alpine meadows at different stages of degradation in the Tibetan Plateau. The study revealed that the changes in patch properties and vegetation shifts in alpine meadows were mainly affected by climate change, human activities, and soil erosion. This research underscores the importance of patch dynamics as indicators of alpine meadow degradation, providing valuable insights for sustainable grassland management. Song et al. [
12] used model simulations and field experiments to investigate the impact of grazing activities on the construction of plant communities in the degradation of grasslands. The study revealed the remodeling effects of environmental variables on plant community structure under patchy degradation scenarios, and the effects of different herbivores on plant community construction during grassland degradation. Du et al. [
13] focused on the phenomenon of patchiness in degraded alpine meadows of the Sanjiangyuan, and analyzed the characteristics of plant communities at the center and edge of the patches and the role of their associated soil physical properties in combating soil erosion through field investigations and laboratory tests. The results showed that the root–soil complex in the patches had a positive effect on the erosion resistance of degraded soils. Duan et al. [
14] carried out a field-based biodiversity study on patches with different degradation levels in alpine meadows and explored the positive effects of β-diversity of fungi on soil multifunctionality in the process of the natural restoration of degraded patches in alpine meadows. Additionally, they elucidated the key roles of soil pH and moisture in regulating the relationship between microbial diversity and soil function. These findings provide an important scientific basis for improving the recovery ability of degraded grassland ecosystems. Li et al. [
15] conducted a containment experiment on patchy degraded alpine meadows in the Yellow River source area of the Tibetan Plateau. The results showed that a 5-year enclosure could effectively improve soil nutrients and carbon sequestration, and could maintain grassland productivity without long-term enclosures. These studies mostly rely on field surveys, which are subjective and destructive to alpine meadows [
16], while fewer studies have been conducted on alpine meadow patches objectively and non-destructively from the perspective of images.
In recent years, hyperspectral imaging (HSI) technology has been widely used in smart agriculture because of its ability to capture the rich spectral information of features [
17]. HSI technology combines the advantages of machine vision and spectral analysis and is able to acquire three-dimensional data cubes containing numerous consecutive spectral bands. Through the in-depth analysis of these data, the detailed spectral features of each pixel point can be used as an effective prediction basis for stage identification [
18,
19]. In addition, machine learning techniques have demonstrated excellent capabilities in processing large amounts of complex data. By combining different features of images, efficient regression and classification models can be constructed. Mansour et al. [
20] studied grassland degradation in Okhombe shared grazing land in South Africa by analyzing canopy hyperspectral reflectance to differentiate between four classes of grasses representing different levels of degradation. They used the random forest (RF) algorithm combined with the forward variable selection technique to select a set of effective feature sets containing eight key wavelengths among a large number of wavelengths, achieving good classification accuracy (88.64%). Guan et al. [
21] applied hyperspectral technology to the estimation of soil organic matter content in the degraded grassland of Sanjiangyuan, and confirmed the excellent performance of the RF model in predicting soil organic matter content. Gu et al. [
22] conducted a study on the non-destructive detection of early tomato spotted wilt virus (TSWV) infection in tobacco by using HSI and machine learning technology. They used multiple wavelength selection methods with different classification models for comparative analysis, and finally determined that the model combining successive projection algorithms and boosted regression tree performed the best, with an accuracy of 85.2%. This consequently provides an effective method to achieve a fast and accurate non-invasive diagnosis of TSWV in early stages. Fu et al. [
23] innovatively fused multispectral images from the Jilin-1 satellite (JL101K) and UAV platforms, and used the Gram–Schmidt algorithm to improve image quality, then made a breakthrough in the karst wetland vegetation classification problem. The results show that the light gradient boosting optimization model is the most advantageous classification model.
In addition, a series of studies have emphasized the importance of multi-feature fusion for improving model performance [
24]. Guo et al. [
25] combined spectral and textural features to identify the tasseling date of summer maize. The integration of spectral and texture features to generate a new index using the improved adaptive feature weighting method resulted in a reduction in the root-mean-square error for the tasseling date prediction to 5.77 days. Johari et al. [
26] successfully identified different instar stages of Metisa plana larvae using HSI and machine learning techniques. A weighted k-nearest neighbor (KNN) constructed based on 506 nm and 538 nm reflectances combined with significant morphological parameters achieved the best identification results. Guo et al. [
27] focused on the utilization of crop height to identify the critical stages of maize growth and development. They obtained RGB vegetation indexes (VIs), texture features (TFs), and multispectral VIs, and constructed a maize plant height prediction model by linear regression analysis. The results showed that constructing a maize height model based on multi-source images is an important complementary tool for extracting different maize phenology. Ali et al. [
28] conducted a classification study of six types of maize seeds by machine learning methods. The researchers integrated color features (CFs), TFs, and spectral features to construct a hybrid feature set, and used the correlation-based feature selection for feature preference. And, the multi-layer perceptron model constructed based on the preferred features achieved an overall classification accuracy of 98.93%. Yan et al. [
29] used RF and neural network models combined with multi-source data from geography, meteorology, plants, and microorganisms to predict the degradation degree of grassland in northern China. Among them, the RF model showed the best prediction performance, with a relative error of only 16.9%, which provided theoretical support for the design of a grassland degradation early warning system.
Although the applications of HSI technology and machine learning methods have achieved remarkable results, few studies on the use of these methods integrated with the combination of spectral reflectance, VIs, CFs, and TFs exist to identify the restoration stages of degraded alpine meadow patches in the Tibetan Plateau. Therefore, this study aims to develop an effective method for identifying the restoration stages of alpine meadow patches based on hyperspectral images. This could further be used to provide a scientific reference for the analysis of plant community composition and dominant species on patches in degraded alpine meadows. More specifically, this study has the following purposes: (1) to validate the applicability of HSI in identifying the restoration stages of alpine meadow patches in the Tibetan Plateau; (2) obtain the optimal wavelengths (OWs), prominent VIs, significant CFs, and effective TFs using CARS, reliefF, RFE, and F-test feature selection algorithms, respectively; (3) develop identification models with different machine learning techniques, including the support vector machine (SVM), KNN, RF, and extreme gradient boosting (XGBoost); and (4) determine the optimal combination of feature sets and predictive models for the identification of alpine meadow patches at the restoration stage.
4. Discussion
The applications of HSI in the field of grassland ecology mainly include the disease monitoring, biomass estimation, and discrimination of grass species, as well as the detection of different grass cover types [
61]. Hyperspectral imaging possesses the unique advantage of providing information in hundreds of consecutive spectral wavelengths to reveal spectral features that cannot be directly perceived by the human eye.
The purpose of this study is to identify different restoration stages based on spectral, color, and texture features extracted from hyperspectral images of degraded alpine meadow patches. The experimental results show that the importance of spectral features is greater than that of CFs, and the importance of CFs is greater than that of TFs. In this study, six OWs (421.1, 485.6, 545.8, 566.1, 663.3, and 689.2 nm) were selected near the two absorption bands (450 nm and 670 nm) and the reflectance peak (550 nm) in the visible region, which are important for distinguishing the different restoration stages. And these OWs are strongly correlated with the chlorophyll content [
62]. In addition, the OWs and prominent VI fusion feature dataset combined with the optimized SVM model demonstrated the best performance, which is consistent with the results of the study by Wu et al. [
63].
The results based on the fused feature dataset show that the SVM model is the most effective in identifying the restoration stages of alpine meadow patches, and all of its evaluation indexes are greater than 0.8. The performance of the XGBoost model is second only to the SVM, and improves with the increase in features. RF and KNN models are not as effective as the SVM and XGBoost, but they are also able to identify restoration stages of degraded alpine meadows patches. In addition, the average accuracy of the model based on a single preferred feature was 0.7828, while that of the model constructed by fusing preferred features was improved to 0.8626. This indicates that fusing multiple features can more effectively improve the overall performance of the model compared with a single preferred feature in the model construction process. This finding is consistent with that found in related studies [
64,
65]. It is worth noting that the overall performance of the model can be significantly improved by considering the prominent VIs or OWs. This assertion is vividly substantiated by a comparison of the experimental outcomes. Compared with the experimental results based on dataset F (effective TFs + significant CFs), the experimental results of dataset K (OWs + prominent VIs + effective TFs + significant CFs) show a significant improvement. Specifically, the accuracies of KNN, RF, and XGBoost improved by more than 10%. Furthermore, the fact that the best model is based on dataset A (OWs + prominent VIs) underscores the clear advantages of these two features in this study. Thus, in interdisciplinary grassland ecology and computer science research, prioritizing OWs and VIs is a judicious and effective strategy. While the SVM model excelled in identifying alpine meadow restoration stages, our approach mainly focuses on vegetation production and recovery, leaving room for a deeper exploration of plant community structure and species diversity. Future work will integrate ground-based ecological assessments with remote sensing data, collaborating with grassland ecologists to broaden our ecological indicators. This multi-dimensional approach aims to enhance our understanding of restoration impacts on biodiversity and community dynamics, offering more comprehensive insights into grassland ecosystem health.
For each stage of identification, S0 and S3 were the most effective. S1 and S2 were also effectively identified, but some models encountered challenges in identifying S2. This difficulty stems from the unique ecological community structure of S2, which contains not only 1–2-year-old weeds from S1, but also plants such as the sedge family and perennial weeds that dominate in S3 [
66]. Furthermore, the plant community of S2 exhibits a higher degree of species diversity and compositional complexity, with some species emerging as dominant players, affecting the recovery trajectory. Understanding the specific roles and interactions of these species is crucial for optimizing restoration strategies. These findings not only reveal the advantages and limitations of the model in dealing with different categories, but more importantly, they provide valuable insights for subsequent studies, especially for the exploration of strategies on how to improve the recognition accuracy of S2. In the future, we plan to delve deeper into the plant community dynamics of S2, incorporating multi-scale analysis and additional environmental factors. By leveraging ensemble methods and focusing on the interactions between species, we aim to enhance the robustness and accuracy of our model, especially for S2.
The method proposed in this study opens up new perspectives for the practical application of hyperspectral technology in degraded meadows, and the adoption of this advanced digital agricultural technology in the Tibetan Plateau region also has broad social and policy implications. Among them, S0 is an important node in the degradation process of alpine meadows, and a timely and effective intervention is the key to stop the spread of localized active patches and prevent them from being transformed into irreversible Heitutan deteriorated grassland. The method can be used as a key tool for identifying the restoration stages of alpine meadow patches, thus determining the optimal timing of degraded grassland management. The adoption of this digital agriculture approach has great potential for rural communities in the Tibetan Plateau. By facilitating early detection and targeted interventions, local farmers and herders can benefit from improved land management practices that increase rangeland productivity and maintain livestock health. This may help alleviate poverty and promote sustainable development in remote areas, in line with national policies aimed at rural revitalization and ecological conservation. In addition, the integration of solutions, such as hyperspectral imaging and machine learning algorithms, into agricultural practices may require specialized personnel capable of operating and maintaining complex equipment. This shift could drive the demand for education and training programs focused on digital literacy and advanced agricultural technologies, creating new jobs in rural areas and promoting a knowledge-intensive economy. Thus, the proposed method not only provides a scientific reference for grassland ecology, but also has more far-reaching implications for social well-being and policy development in the Tibetan Plateau region.
5. Conclusions
This study investigated the potential of utilizing HSI and machine learning techniques to identify the restoration stages of degraded alpine meadows patches. An integrated approach for the identification of the restoration stages of alpine meadow patches was developed using an HIS system combined with feature selection algorithms (CARS, ReliefF, RFE, and F-test) and machine learning algorithms (SVM, KNN, RF, and XGBoost). Furthermore, 20 OWs were selected based on CARS, 10 prominent VIs were selected by ReliefF, 11 significant CFs were selected by RFE, and 10 effective TFs were selected by F-test. The models constructed based on the preferred features dataset, as well as the fused features dataset, achieved satisfactory identification results. It is worth mentioning that the SVM model constructed based on OWs and prominent VIs obtained the best values for accuracy (0.9320), precision (0.9369), recall (0.9308), and F1 score (0.9299). Therefore, this study proposes an objective, non-invasive method to identify the restoration stages of degraded alpine meadows patches, so as to provide references for the sustainable use and intelligent monitoring or management of alpine meadows. However, this study only identified four types of degraded alpine meadow patches in one watershed in Qilian County, in the Qinghai–Tibet Plateau. In the future, we plan to improve the hyperspectral unmanned aerial vehicle image acquisition system to obtain patch data in different watersheds, and further improve the accuracy and applicability of the classification model to realize the digital and accurate identification of the restoration stages of degraded patches on a larger scale. Furthermore, we plan to strengthen our close collaboration with grassland ecologists to delve deeper into ecosystem health from a more nuanced perspective, such as species diversity and composition, aiming for a more precise identification of the restoration stages of patches, particularly for S2. Lastly, extending the application of this research methodology to a wider range of cases is also one of the crucial directions for our future work.