4 Discussion
4.1 Feasibility of Vegetation Species
Identification
The
comparative accuracy of species identification algorithms by remote
sensing at different scaled has not been extensively studied (Camarretta
et al., 2020). This study utilized pixel-based supervised classification
to identify plant species by training and validating machine learning
algorithms. SVM outperformed maximum likelihood and neural network
classifiers in fragmented land plots with mixed vegetation types,
exhibiting higher accuracy with limited training samples and minimal
Hughes phenomenon. However, pixel-based supervised classification may
not fully utilize the textural and structural information capability of
remote sensing imagery, leading to ”salt and pepper” and ”spectral
confusion” phenomena, particularly for species with similar spectral
characteristics. Future studies may introduce texture features and
spatial information (Camarretta et al., 2020), such as LiDAR and digital
elevation models, to address these limitations (Shokirov et al., 2023).
Monitoring and evaluation are crucial steps in restoration projects,
emphasizing the implementation of practical indicators instead of
broadly defined and often ambiguous monitoring standards (Australian
Government, 2016; Nilsson et al., 2016; Young et al., 2022). The
transition from conceptual ideas to on-ground implementation aims at
cost-effective and successful restoration (Evju et al., 2020). This
study highlights the benefits of low-altitude UAV multispectral data for
cost-effective, easy-to-use, and high-efficiency ecological monitoring
at a fine scale, thereby improving the ease and accuracy of monitoring
efforts.
Firstly, the accuracy of low-altitude UAV imagery surpasses that
of satellite remote-sensing data (Johansen et al., 2019). Satellite
remote sensing covers a wide spatial extent and diverse landscape types,
making it difficult to capture detailed vegetation information (Peng et
al., 2021), leading to less precise classification. This is especially
true in arid and semi-arid areas where sparse vegetation, a mixture of
herbaceous shrubs, and different soil reflectance values complicate
accurate vegetation monitoring (Rossi et al., 2022).
Secondly, UAV multispectral data is more cost-effective than
hyperspectral sensors and LiDAR for many applications (de Almeida et
al., 2021; Haneda et al., 2023). Hyperspectral data offers a multitude
of spectral bands that enhance diversity detection potential; however,
this might result in a decrease in classification accuracy in high
spectral dimensions (Gholizadeh et al., 2018). Despite its advantages in
characterizing vegetation vertical structure (Fernandez-Guisuraga et
al., 2022), the widespread application of LiDAR is significantly limited
due to its high expenses. Therefore, practitioners need to weigh both
the economic costs of LiDAR and the time costs associated with
hyperspectral data analysis. In light of these considerations,
integrating UAV multispectral data emerges as the optimal approach for
monitoring ecological restoration in mining areas. The
cost-effectiveness of UAV multispectral data, compared to hyperspectral
sensors and LiDAR, makes it a favorable choice for various applications
The present study has some limitations regarding species classification,
which may be improved by combining deep learning and spectral features
to increase accuracy and efficiency (Saadeldin et al., 2022) such as the
integration of vegetation height and canopy extraction and species
classification. Further in-depth research could also explore the effects
of lower flight altitudes (e.g. 10-50m) and adjusted flight attitudes
(e.g. oblique photogrammetry) for more accurate monitoring of
low-growing herbaceous or recently emerged seedlings.
4.2 Feasibility of UAV-based Vegetation Diversity
Monitoring
Field measurements are essential for validating the accuracy of results.
Figure 11 shows the relative importance values of vegetation species
obtained through field surveys. Poplar, Artemisia, and Salix were ranked
as the top three species by importance value, consistent with results
from UAV imagery. Poplar dominated the tree layer, while Artemisia
dominated the shrub layer. Notably, Artemisia had the largest relative
abundance and relative frequency, values of 0.48 and 0.94, respectively,
indicating it is the most abundant and widely distributed species in the
sampled area and therefore a key dominant species.