Vegetation types such as shrub, grassland, and meadow have the characteristics of low-height and distributed widely (Li et al., 2019), and play an important role in the global carbon cycle (Scurlock and Hall. 1998), prevention of land degradation (Liao et al., 2019), and biodiversity conservation (Dong et al., 2020). It was reported that the grassland has been seriously degraded since the 1980s, and accompanied with shrub encroachment (Eldridge et al., 2011), causing losses in grassland biodiversity, water retention capability, and soil nutrients (Andrade et al., 2015; Chen et al., 2017). Therefore, vegetation restoration of degraded grassland is essential to maintain the functions and services of grassland ecosystems.
From 2008 to 2017, we established a set of experimental plots for vegetation restoration of degraded grasslands, and selected suitable vegetation restoration species by comparing vegetation parameters (Li et al., 2019). Traditionally, the vegetation parameters for the low-height vegetation were estimated in combination with field surveys and optical remote sensing data (Psomas et al., 2011), the Light Detecting and Ranging (LiDAR) technology, which can provide three-dimensional (3D) information, has gradually replaced traditional investigation method for vegetation survey. Terrestrial laser scanning (TLS) can provide the vertical structure information of vegetation with millimeter-level accuracy (Liang et al., 2018), which has unique advantages for collecting vegetation information on sample scale (Liang et al., 2012; Liang and Hyyppä. 2013), such as diameter at breast height (Kankare et al., 2015), height (Tian et al., 2019), and biomass estimation (Calders et al., 2015). However, LiDAR as a tool for vegetation investigation were, so far, primarily used for forest inventories (Liang et al., 2016), the application in low-height eco-system was rarely investigated (Wachendorf et al., 2017; Li et al., 2019;).
TLS scanning are divided into the mode of single-scan (SS) and multiple-scan (MS) (Liang et al., 2016), most of researches on scanning method are to compare the difference between these two modes in the forest inventories (Pueschel et al., 2013; Saarinen et al., 2017). For the low-height vegetation, Li et al (2019) utilized the TLS date obtained by registering 18 scanning spots to assess the effects of topography on the revegetation. Xu et al (2020) estimated the grassland aboveground biomass from the TLS data obtained by 5 scanning spots. Both studies adopted TLS data registered by the mode of MS to obtain more complete information of the sample plot. Damian et al (2019) indicated that in the process of estimating grass biomass by TLS, two scans from opposite directions slightly increased the spatial information compared to one scan only. Therefore, it is important to arrange the location of the scanning spots reasonably for the date acquisition.
As a matter of fact, we should reduce the risk and cost of the vegetation investigation in some areas with harsh environmental conditions (e.g. the plateau region like Qinghai-Tibet Plateau), and improve the data acquisition in terms of efficiency, robustness, and precision. Trochta et al. (2013) mentioned that occlusion is one of the key factors that limited the potential of TLS data acquisition. Occlusion is caused by objects shadowing each other, so that some part of the objects of interest are not visible to the scanning spots with only one side. The effect of occlusion is usually mitigated by combining TLS scans from different locations (Hilker et al., 2012). But few studies provided a standard for the location determination of scanning spots, and explored the factors affecting the completeness of data collection. Therefore, we mainly discussed the difference of the scan mode in the data acquisition of low-height vegetation, and explored the influence of terrain features (i.e. the slope and the fluctuation of terrain) for setting scanning spots. We selected some vegetation parameters for comparison, including the number (N), height (H), and crown width (CW), because these parameters can directly reflect the growth status of the vegetation (Li et al., 2016). We provided two hypotheses in this study: (1) for the low-height shrub, the mode of SS could be competent for information collection, but the occlusions from the vegetation and the distance from the central scanning spot were the main limiting factors (2) the fluctuation of terrain has a great influence on setting scanning spots. In addition, we will provide a reasonable suggestion for setting the scanning spots and optimizing the scanning scheme according to the different data requirements.