1 Introduction
Plant species diversity is a fundamental goal for ecological restoration
particularly in mining areas where functional and often biodiverse
ecosystems need to be reinstated (Brancalion and Holl, 2020; McKenna et
al., 2020; Han et al., 2021; Chen et al., 2022). Ensuring complete
species mixes are reinstated in restoration programs can improve
ecosystem function and stability, particularly for alleviating impacts
of extreme or unexpected environmental events (Naeem et al., 1994;
Gaston, 2000; Isbell et al., 2015; Hauser et al., 2021; Fremout et al.,
2022). The UN Decade of Ecosystem Restoration highlights the importance
of re-vegetating with complex species mixes (Zhang et al., 2021), while
various ecological restoration standards (Gann et al. 2019, Young et al.
2022) emphasise the importance of matching native reference sites with
species mixes to ensure restored areas are resilient and functional
(Yang et al., 2022; Young et al., 2022). Despite major investments by
governments, mining companies, and land managers to restore ecosystems
after mining, restoration for many mines particularly those in native
ecosystems remains difficult and complex to achieve (Menz et al., 2013).
Importantly, once resource extraction has been completed many companies
are not willing to invest additional time, resources, and funding to
monitor and evaluate the effectiveness of restoration practices,
severely hindering the capacity to keep projects on track (Galatowitsch
and Bohnen, 2020). An observed phenomenon is that restoration projects
tend to prioritize short-term vegetation cover over long-term
biodiversity protection (Hoffmann, 2022), ultimately leading to their
failure (Chen et al., 2022). Thus, accurate and efficient vegetation
diversity monitoring is crucial for tracking the success of restoration
projects and guiding adaptive management strategies (Hoffmann, 2022).
Traditional vegetation survey approaches rely primarily on manual ground
surveys at plot level to obtain plant species diversity and abundance
and/or structural data but these are often costly, time-consuming, and
limited in their ability to monitor large areas (Anderson, 2018; Reddy,
2021). Satellite imagery, on the other hand, has proven to be a valuable
tool for automated or semi-automated and repeatable vegetation mapping
across a range of spatial scales, spanning from regional to
community-level, and from low to high spatial resolutions (Li et al.,
2021; Villoslada et al., 2020; Randin et al., 2020). However, mining
areas are often subject to intense disturbance (Hou et al., 2021; Jiang
et al., 2022), resulting in relatively small spatial extent confounded
by the often dense regeneration of uniformly aged plants in mine
restoration that make it difficult to accurately differentiate taxa when
deploying high altitude satellite approaches. In particular, the optical
detection of α diversity, which is the foundational biological
information in any restoration program (Rocchini, 2007).
To meet the requirements for mine site restoration where high temporal
and spatial resolution and site-based data integration are necessary
(Lawley et al., 2016), the more nuanced and higher resolution capacity
from drone-based analyses may provide useful capacity (Miller et al.,
2017; de Almeida et al., 2020). Unmanned Aerial Vehicles (UAVs) are an
increasingly common platform for remote sensing data acquisition
(Johansen et al., 2019). UAVs enable low-cost, rapid acquisition of
high-resolution data at a centimeter scale (Lu and He, 2017; Ren et al.,
2019; Alvarez-Vanhard et al., 2020; Belmonte et al., 2020), and can
coincide with the timing of site-based assessments (Lawley et al.,
2016), making them a convenient tool to aid in vegetation surveys in
mining areas.
The applications of UAVs in vegetation assessments in mining areas are
wide-ranging, including monitoring vegetation growth conditions such as
soil temperature (Ruan et al., 2022); mapping vegetation communities,
vegetation structure and height (Banerjee and Raval, 2022; Tang et al.,
2022); and assessing biomass (Ren et al., 2022). Multispectral sensors
can be used to monitor ecological indicators such as vegetation
coverage, aboveground biomass, and tree crown coverage (Villoslada et
al., 2020; Fernandez-Guisuraga et al., 2022), and can provide an
alternative to high-cost airborne hyperspectral and LiDAR approaches.
However, few studies have used spectral information provided by UAV
remote sensing integrated with site-based data to identify plant species
and provide estimates of diversity.
Studies estimating plant diversity using remote sensing can typically be
divided into two categories: direct identification of plant species and
their distribution through visual interpretation or image classification
algorithms, and indirect methods that establish a relationship between
diversity and spectral data, or derive species distribution through
habitat mapping (Rocchini, 2007; Madonsela et al., 2017; Wang and Gamon,
2019; Villoslada et al., 2020; Zhu et al., 2022). Many studies use
vegetation indices such as NDVI estimate species diversity indirectly,
although they do not discriminate well between vegetation communities
(Gillespie, 2005; Madonsela et al., 2018; Kacic and Kuenzer, 2022; Tian
and Fu, 2022). However, less attention has been given to the sensitivity
of vegetation indices to species distributional patterns through visual
interpretation or image classification algorithms (Lu and He, 2017;
Reddy, 2021).
In this study, we combine on-ground vegetation surveys with UAV
multispectral data to map vegetation diversity in mining areas in
Northwestern China on a sub-meter scale over tens of hectares and
develop classification algorithms to identify plant communities The
restoration biome is characterized by the co-existence of trees, shrubs
and herbaceous plants with high floral diversity compared to the
surrounding area, making fine-scale monitoring necessary to analyze the
spatial configuration and diversity distribution of vegetation
communities. Our objectives are to: (i) compare the performance of three
industry-standard supervised classification methods in identifying plant
species and mapping their distributions based on reflectance values,
(ii) estimate α-diversity at scales determined by the species-area curve
to assess the ability of UAV data, and vegetation indices calculated
from this data, to qualitatively and quantitatively map alpha floral
diversity. This gives restoration practitioners a robust method to
significantly improve the spatial detail and coverage of floral
diversity mapping for effective monitoring of restoration projects using
UAV-based inventory.