1. Introduction
Intense levels of human activity, such as mining, have caused extensive vegetation removal or degradation in many areas [
1,
2]. In recent years, important ecological restoration efforts have gained popularity; for instance, the United Nations recently declared 2021–2030 as the decade for ecosystem restoration [
3]. Vegetation, as a vital component in ecological restoration, plays a crucial role by contributing significantly to the structure and function of ecosystems [
4,
5]. To ensure the stability and resilience of a restored ecosystem, land managers usually employ a variety of species during revegetation with the goal of creating a mixed-species plant community. However, the restoration of vegetation on degraded land is often limited by nutrient deficiency, which hinders the process of ecological restoration [
6]. Therefore, monitoring the nutrient levels and functions of an ecosystem has become important to ensure that these characteristics develop according to the expected recovery trajectory [
7,
8].
For a restored plant community, carbon (C), nitrogen (N), and phosphorus (P) constitute fundamental building blocks required for plant growth and development. Carbon is the most important element with the highest concentration in leaf dry matter, while N and P are essential nutrients for building plant structures [
9,
10,
11]; these three elements serve as important indicators of ecological health. Their availability and stoichiometric ratios not only indicate the nature of conditions related to plant growth and community composition [
12,
13] but also reveal the nutrient dynamics and limitations of plants under changing climatic conditions [
14,
15,
16]. Nutrient limitations, specifically in the form of C, N, and P limitations, can impede the absorption and utilization of nutrients by plants, affecting their growth rates, metabolic processes, and the decomposition of litter [
17,
18]. This, in turn, influences the overall health of a restored ecosystem. Therefore, for the assessment of ecosystem health, and to ensure the success of restoration efforts, it is imperative to conduct rapid monitoring of foliar C, N, and P concentrations in a mixed plant community within an ecological restoration area.
Recent monitoring and assessment of restoration efforts have focused more on vegetation coverage or greenness rather than on the chemical or biological indicators of vegetative conditions [
19,
20]. In particular, limited research is available on the rapid monitoring of C, N, and P in mixed plant communities within ecological restoration areas. Traditional extensive surveys of foliar concentrations of C, N, and P in the canopy are time-consuming and labor-intensive, making them impractical for the large-scale mapping of C, N, and P and applications employed in post-restoration management. In the recent two decades, remote sensing has been adopted as an efficient alternative monitoring method, providing a crucial means for the large-scale monitoring of plant growth and quantification of the biochemical properties of vegetation [
21,
22,
23]. Unmanned Aerial Vehicle (UAV)-based laser imaging, detection, and ranging (LiDAR) and hyperspectral remote sensing are two advanced remote sensing technologies, each possessing unique advantages in acquiring surface information [
24]. Specifically, LiDAR is characterized by a high level of accuracy and an ability to penetrate the canopy, providing high-resolution three-dimensional structural information. This technique enables the accurate and effective monitoring of plant communities in spatial dimensions [
25]. Hyperspectral remote sensing offers rich spectral information, exhibiting high sensitivity to vegetation health and types, which is valuable for monitoring vegetation health and conducting biochemical composition studies [
26]. It has been found from recent research progress that the integration of LiDAR and hyperspectral data has a strong capacity to enable the classification of vegetation and assessment of structural and morphology parameters, such as biomass, diversity, and canopy coverage [
27,
28]. In addition, the two technological methods have been successfully applied to assess biochemical parameters such as N and P. However, those application cases mainly focus on individual species such as
phoenix trees [
29],
rice [
30],
apple trees [
31],
Phragmites communis [
32], and
maize [
33]. Studies that discuss the feasibility of mapping the foliar C, N, and P of restored ecosystems with mixed plant communities are lacking. Considering that the combination of LiDAR and hyperspectral data may provide information about the chemical signal of different plants with various morphologies, this synergistic approach may contribute to a better understanding of spatial heterogeneity in C, N, and P among different plant communities and their responses to ecological restoration.
Therefore, the present study is an attempt to examine the possibility of using LiDAR and hyperspectral data in the spatial mapping of the C, N, and P concentrations in a mixed plant community of an ecological restoration area. The main research goals include the following: (1) to establish the optimal model for estimating foliar C, N, and P concentrations; (2) to investigate how the LiDAR and hyperspectral data could contribute to the estimation accuracy; and (3) to discuss the ability of LiDAR and hyperspectral data to reveal the heterogeneity of foliar C, N, and P across plant species and spatial regions. The research aims to serve as a reference for identifying C, N, and P nutrient limitations and assessing the health of an ecosystem.
3. Results
3.1. Selected Feature
The feature selection results are shown in
Figure 4. The blue box plot corresponds to the minimum, mean, and maximum Z scores (importance) of the shadow features. The red, yellow, and green box plots represent the Z scores for the rejected, tentative, and confirmed features, respectively. In this study, important variables (green box) were picked out as the input variables for modeling.
3.2. Accuracy of the Estimation Model
The causal bands, multiple linear regression, and random forest models were built using these selected feature combinations. Specifically, the causal band model takes as input the spectral bands causally related to foliar C, N, and P. The multiple linear regression model takes as input the combination of the hyperspectral features and hyperspectral + LiDAR features, while the random forest model takes the same input combination as the multiple linear regression model.
Table 5 presents the R
2 and RMSE for different models and feature combinations. For C, the accuracy of the three models ranged from 0.07 to 0.56, with the RMSE ranging from 1.19% to 9.49%. Among them, the multiple linear regression and random forest models outperformed the causal bands model. Additionally, combinations with LiDAR features outperformed those with hyperspectral features. The best performance was observed in the random forest model with hyperspectral + LiDAR, where R
2 reached 0.56. For N, the accuracy of the models ranged from 0.20 to 0.53, with the RMSE ranging from 0.57% to 0.92%. Similar to C, the multiple linear regression and random forest models outperformed the causal band model. The difference in R
2 between the models with hyperspectral + LiDAR and hyperspectral features was 0.00. The best performance was observed in the random forest model with hyperspectral features, reaching an R
2 of 0.53. For P, the accuracy of the models ranged from 0.32 to 0.44, with the RMSE ranging from 0.40 g/kg to 0.52 g/kg. The multiple linear regression and random forest models outperformed the causal band model; the difference in R
2 between the models with hyperspectral + LiDAR and hyperspectral features was only 0.01. The best performance was observed in the random forest model with hyperspectral + LiDAR, achieving an R
2 of 0.44.
In comparison, the causal band model exhibited the best accuracy in estimating the foliar P concentration (
Figure 5), the multiple linear regression model performed best in estimating the foliar N concentration (
Figure 6), and the random forest model excelled in estimating the foliar C concentration (
Figure 7).
3.3. Map of Foliar C, N, and P Concentrations
Foliar C, N, and P concentration maps were made to visualize the spatial distribution of these three nutrients. These maps were generated by applying random forest models to the images of the feature variables corresponding to the C, N, and P concentrations.
Figure 8a–c illustrates the foliar C, N, and P concentrations in the study area, respectively. The spatial distribution of C, N, and P concentrations in each plant community was significantly different, which could effectively reflect the differences in C, N, and P concentrations among different plant communities.
The maximum C value, 48.70%, was observed in the Pinus sylvestris community, while the minimum, 38.20%, was found in the Herbaceous community. The average C concentration was 45.17%, with a median of 45.20%. The majority of C values were concentrated within the range of 42.20–48.00%. The high-value areas of C concentration were mainly found in the Sea buckthorn and Pinus sylvestris communities in the middle of the study area, and the low-value areas were mainly found in the Populus spp. and Herbaceous communities in the study area.
The maximum N value, 3.69%, was observed in the Caragana spp. community, while the minimum value, 1.35%, was found in the Herbaceous community. The average N concentration was 2.56%, with a median of 2.54%. The majority of values were concentrated within the range of 1.50–3.50%. The high-value areas of N concentration were mainly found in the Sea buckthorn community in the middle of the study area and scattered Populus spp. and Caragana spp. communities, while the low-value areas were mainly found in the Pinus sylvestris community near the main road and the Populus spp. community in the east.
The maximum P value, 2.42 g/kg, was observed in the Artemisia oleifera community, while the minimum value, 1.12 g/kg, was found in the Herbaceous community. The average P concentration was 1.65 g/kg, with a median of 1.63 g/kg. The majority of values were concentrated within the range of 1.30–2.20 g/kg. The high-value area of P concentration was mainly found in the Artemisia oleifera community in the middle of the study area, and the low-value areas were mainly found in the Pinus sylvestris, Populus spp., and Herbaceous communities in the study area.
4. Discussion
4.1. The Role of Features in the Model
Table 6 presents the hyperspectral and LiDAR features involved in constructing the models to measure the foliar C, N, and P concentrations. A total of 32 features were used in estimating both the C and N concentrations, including nine hyperspectral features and 23 LiDAR features for C along with 14 hyperspectral features and 18 LiDAR features for N. In the case of the P concentration estimation, 16 features were considered, consisting of 12 hyperspectral features and four LiDAR features.
For C, red-edge bands, height variables, and vegetation structure parameters were identified as comparatively important. For N, textural features, height percentiles of 40–95%, and vegetation structure parameters were deemed significant. As for P, spectral features, a height percentile of 80%, and a 1 m foliage height diversity were considered crucial. Among these, the 80% height percentile, height standard deviation, height variance, and 1 m leaf height diversity were identified as common features contributing to the construction of C, N, and P concentration estimation models, indicating their importance in estimating C, N, and P concentrations. Additionally, during the construction of the C concentration estimation model, the incorporation of LiDAR features significantly enhanced the predictive accuracy, suggesting that the C concentration is sensitive to plant height and can differentiate plant communities based on height. However, when constructing the N and P concentration estimation models, the inclusion of LiDAR features did not notably improve predictive accuracy, indicating that the sensitivity of N and P concentrations to plant height is limited; in addition, enhancing the accuracy of the N and P estimates did not rely heavily on the LiDAR features.
4.2. Implications of the Foliar C, N, and P Map
Considering the fact that the restoration of vegetation on degraded land is often limited by nutrient deficiency, C, N, and P maps can be used to examine nutrient limitation and storage in an area, so as to provide information supporting the post-restoration management of vegetation.
The C:N ratio reflects the vegetation’s ability to assimilate carbon (C) in relation to nitrogen (N) and its efficiency in utilizing nitrogen; this ratio also indicates the carbon sequestration capacity of the plant [
72]. A high C:N ratio implies a relatively higher content of C and a lower content of N in the leaves. This may occur in environments with limited N supply, or when plants grow under conditions rich in C but deficient in N. Plants with a high C:N ratio exhibit high N use efficiency but grow slowly because they require more time to acquire sufficient N to support growth [
73]. However, a low C:N ratio signifies a relatively low C content and a higher N content in the leaves. This typically indicates that the studied plants have an ample N supply, possibly due to being in an N-rich environment or receiving N fertilizer. Plants with a low C:N ratio have rapid decomposition traits and faster growth, with a relatively lower demand for C [
73]. Plants with a moderate C:N ratio have a balanced content of C and N in their leaves. This usually suggests that plants are in a relatively balanced state in terms of C and N supply, are capable of adapting to various environmental conditions, and thrive under suitable conditions.
In the present study, the C:N ratios were calculated (
Figure 9a). This figure shows that the
Pinus sylvestris and
Populus spp. communities had higher C:N ratios. This suggests that
Pinus sylvestris and
Populus spp. have relatively high N use efficiencies and stronger C sequestration capabilities, whereas
Sea buckthorn,
Artemisia oleifera,
Amorpha fruticosa, and
Caragana spp. have lower N use efficiencies and weaker carbon sequestration capabilities. The C:N ratios for the
Salix spp. and
Herbaceous plant communities were relatively balanced.
The N:P ratio reflects the availability of N and P in plants; the availability of N and P is a critical factor that can limit plant growth [
74]. Therefore, analyzing the N:P ratio in plants can provide insights into the growth and nutrient limitations of plants. Typically, when the N:P ratio is between 14 and 16, plants are not limited by N or P or are co-limited by both. In contrast, when the N:P ratio is less than 14, plants are primarily limited by N, while when the N:P ratio is greater than 16, plants are mainly limited by P [
75]. The N:P ratios for different plant canopies in the study area were generated from the N and P concentration maps (
Figure 9b).
The blue region (46.62%) in
Figure 9b primarily represents communities of
Amorpha fruticosa,
Sea buckthorn, and
Caragana spp., with an N:P ratio greater than 16, indicating that these three plant communities were limited by P. The green region (20.06%) in the same figure mainly represents
Pinus sylvestris,
Artemisia oleifera, and
Salix spp. communities, with an N:P ratio of less than 14, indicating that these three plant communities were limited by N. The orange region (30.32%) in that figure mainly represents
Populus spp. and
herbaceous communities, with an N:P ratio between 14 and 16, suggesting that these two plant communities may either not have been limited by N or P or they may have been co-limited by both.
4.3. Limitations and Future Work
This study has some limitations that need to be noted. First, the plant communities in the study area are mixed. Although LiDAR provides high accuracy, the richness of plant community types results in lower estimation accuracy when compared with conditions in communities with vegetation types composed of a single species such as
wheat or
corn [
75,
76]. Second, the limited spectral bands (398–1002 nm) in the hyperspectral data allow for only a partial inversion of C, N, and P concentrations, because sensitivity to these elements is lacking in certain bands (1000–2000 nm). Third, seasonal or monthly variations may affect the effectiveness of the developed approach. We suggest assessing the C, N, and P concentrations during the maturity period of vegetation growth. Moreover, the C, N, and P concentrations of different vegetation types should be conducted in the same observing time.
The developed approach is more suitable for semi-arid regions with less diverse vegetation. It can also be used in farmland, plantations, and natural forests to quickly assess foliar C, N, and P concentrations for fertilization management and ecosystem health assessment. For implementing the developed approach, the recommended procedures include, first, acquiring LiDAR and hyperspectral data by UAV, extracting the sensitive features using LiDAR 360 V6.0 software and ENVI 5.0 software, as shown in
Table 6, training random forest models to generate maps of C, N, and P concentrations, and then validating the results. If the accuracy cannot be accepted, change the sensitive features and re-train the models, and, if accepted, the maps could be applied to show the status of foliar C, N, and P.
The technology employed in this study demonstrates significant potential, offering a synergistic approach for estimating foliar C, N, and P concentrations in plant communities and revealing their responses to ecological changes. Moreover, the monitoring results can be applied to ecological restoration management, ecosystem health assessment, and plant stress identification. The research shows substantial spatial heterogeneity, significantly reducing estimation accuracy, particularly in forests restored by humans that are often under C, N, and P limitations. The maps of C, N, and P could be used to explain the driving factors, such as soil, terrain, and vegetation, of restoration success. Considering that plant community types have a significant impact on LiDAR and hyperspectral features, in the future, it can be considered to classify mixed plant communities first, and then establish estimation models for C, N, and P concentration for each plant community type separately, reducing inter-class differences in data and achieving the goal of improving evaluation accuracy. In addition, this study only used shallow features from remote sensing images, such as plant height and vegetation index information. In the future, deep network models can be used to extract deep features, such as abstract semantic information from remotely sensed images and LiDAR point cloud data, to improve evaluation accuracy and efficiency. Expanding monitoring capabilities using satellite data to acquire C, N, and P information for ecological assessments or vegetation health monitoring is crucial for overcoming the limitations of traditional monitoring based on vegetation indices such as coverage and greenness.
5. Conclusions
This study used LiDAR and hyperspectral data to extract spectral features, textural features, vegetation indices, height features, and vegetation structural parameters for eight dominant plant communities in the Shendong Mining Area in China. Three models, namely, causal bands, multiple linear regression, and random forest models, were developed and tested in an ecological restoration area in northern China.
The key findings and conclusions of this research suggest that LiDAR and hyperspectral data can be used to effectively monitor the C, N, and P concentrations in a mixed plant community zone within an ecological restoration area. The random forest model demonstrated the best performance, with the optimal feature combination being hyperspectral + LiDAR data. This study revealed that C, N, and P concentrations effectively reflect differences among various regions and communities, indicating the stress levels on plants often caused by a lack of these elements. This approach offers a rapid and cost-effective means of acquiring related data, providing valuable information for the management and successful assessment of an ecological restoration area.
Based on the experience of this study, it is necessary to utilize hyperspectral data with a broader range of bands and perform classification for plant communities in this type of study. An extended spectral range can provide richer information for analyzing the spatial heterogeneity of foliar C, N, and P concentrations and their stress on vegetation growth after restoration. Plant classification may reduce the spatial and spectral uncertainties introduced by plant community heterogeneity, offering the potential to enhance the accuracy of predicting plant C, N, and P concentrations. Using this information for the analysis of C, N, and P limitations and ecosystem health assessment will contribute to a more precise understanding of ecosystem restoration.