# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
###### Abstract  
Moso bamboo (_Phyllostachys edulis_) is a crucial species among the 500 varieties of bamboo found in China and plays an important role in providing ecosystem services. However, remote sensing studies on the invasion of Moso bamboo, especially its impact on forest biodiversity, are limited. Therefore, we explored the feasibility of using Sentinel-2 multispectral data and digital elevation data from the Shuttle Radar Topography Mission and random forest (RF) algorithms to monitor changes in forest diversity due to the spread of Moso bamboo. From October to November 2019, researchers conducted field surveys on 100 subtropical forest plots in Zhejiang Province, China. Four biodiversity indices (Margalef, Shannon, Simpson, and Pielou) were calculated from the survey data. Subsequently, after completing 100 epochs of training and testing, we developed the RF prediction model and assessed its performance using three key metrics: coefficient of determination, root mean squared error, and mean absolute error. Our results showed that the RF model has a strong predictive ability for all indices except for the Pilou index, which has an average predictive ability. These results demonstrate the feasibility of using remote sensing to monitor forest diversity changes caused by the spreading of Moso bamboo.  
Moso bamboo; remote sensing; invasion; biodiversity; random forest +
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Recent developments in satellite remote sensing technology offer the potential to link satellite observations with ground-based measurements of biodiversity. In 2015, the European Space Agency launched Sentinel-2, a new satellite constellation for the Copernicus program. Sentinel-2 has a greater swath width (290 km) and spatial resolution (4 bands at 10 m, 6 bands at 20 m, and 3 bands at 60 m spatial resolution) than previous satellites, spectral resolution (13 bands including visible, red-edge, near-infrared, and short-wave infrared), and temporal resolution (3-5 day revisit) [13]. In addition, using the free atmospheric correction tool within the Sentinel Application Platform software (version 9.0, development by European Space Agency) can improve the Top-Of-Atmosphere (TOA) reflectance images to Bottom-of-Atmosphere (BOA) reflectance images [14; 15]. The longer wavelength red-edge bands present in Sentinel-2 data contribute significantly to the effectiveness of vegetation monitoring [16]. However, Sentinel-2 cannot be compared to hyperspectral instruments that typically carry hundreds of spectral bands, which is known as the spectral scale problem [17]. Additionally, as the spatial resolution remains limited (relative to individual canopy size), using Sentinel-2 measurements to detect species diversity is challenging because each pixel may include multiple individuals from different species, which is known as the spatial scale problem [10], as well as the soil background [18; 19].  
Most research on bamboo invasion has focused on the invasion mechanisms [20; 21], effects on plants [22; 23], and effects on soil physicochemical properties and microorganisms [4; 5; 6; 24]; few studies have used remote sensing techniques to investigate bamboo invasion, especially its effect on forest biodiversity. To address this research gap, we employed Sentinel-2 multispectral data and Shuttle Radar Topography Mission (SRTM) digital elevation data to investigate the feasibility of using the random forest (RF) algorithm in monitoring the changes in forest diversity caused by Moso bamboo invasion. The specific objectives of this study were to (1) assess the performance of the RF algorithm for predicting forest diversity changes caused by Moso bamboo invasion and (2) identify the relative importance of key variables (spectral indices, vegetation indices, and topographic feature variables) in the remote sensing model.