Figure 3 Location of vegetation plots.
2.4 Data Processing
2.4.1 Image
Interpretation
Standard false-color images are
useful to highlight vegetation features as these are sometimes better
suited for identifying certain vegetation than true-color images. To
obtain more accurate visual interpretation results, we conducted this
study based on standard false-color and true-color images, and the
characteristics of vegetation morphology and distribution (Figure 4). We
visually interpreted orthophoto images from vegetation plots and
selected over 100 training samples for each plant species. Three
pixel-based supervised classifiers (Support Vector Machine, Maximum
Likelihood, and Artificial Neural Network) in ENVI were used to identify
the main vegetation species. Classification results were verified,
modified, and interpreted, and their accuracy was validated using
synchronous field data. Two accuracy indices were selected: overall
accuracy and Kappa coefficient.
Figure 4 Typical vegetation in the study area .
2.4.2 Diversity
Assessment
Alpha-diversity is commonly used to assess species richness and relative
dominance within a target community (Rocchini et al., 2016).
Alpha-diversity encompasses different aspects, including species
richness, evenness, and diversity (Peet, 1974). Species richness
measures the abundance of species in a community, often quantified using
the Margalef index (Clifford and Stephenson, 1975). Evenness
quantifies the distribution of
species within a community, often assessed by the Pielou index (Pielou,
1966); Diversity reflects the overall species richness and evenness and
can be evaluated by Simpson’s
index and Shannon-Wiener index (Simpson, 1948; Shannon, 1949).
Grids ranging from 1 x 1m to 100
x 100m were generated using ArcGIS as research units, and vegetation
diversity indices were calculated for each grid.
Moran’s I analysis was used to explore spatial correlation and
clustering patterns of plant diversity (Lozada & Bertin, 2022). Global
Moran’s I analysis, drawing upon geostatistical theory, is employed to
assess the overall spatial clustering of plant diversity (Moran, 1950).
Local Moran’s I analysis was applied to identify specific local
clusters, thereby supplementing the limitations of global spatial
autocorrelation in delineating precise clustering regions.
2.4.3 Spectral Vegetation Index
Considering the influence of soil reflectance on diversity assessment
within arid and semi-arid regions, the following vegetation indices were
selected (Kacic & Kuenzer, 2022): Normalized Difference
Vegetation Index (NDVI) (Tucker, 1979), Difference Vegetation
Index (DVI) (Tucker, 1979), Visible-Band Difference Vegetation
Index (VDVI) (Wang et al., 2015), Soil Adjusted Vegetation Index(SAVI) (Huete, 1988), Modified Soil Adjusted Vegetation Index(MSAVI) (Qi et al., 1994), and Excess Green - Excess Red (EXG -
EXR) (Meyer and Neto, 2008). Subsequently, correlation and regression
analyses were conducted using the R4.3.0 software to explore the
relationship between these vegetation indices and diversity indices.