## 4 Remote Sensing Platforms Used in Peatland Research
### UAV-Based Remote Sensing
#### 4.3.4 Limitations and Challenges  
UAV-based remote sensing on peatlands has limitations--for instance, limited spatial and temporal coverage of UAV surveys. The study by [PERSON] et al. (2019) acknowledges that the extent of UAV mapping is limited compared to satellite-based systems, which can cover larger areas [117]. Similarly, [PERSON] et al. (2021) point out that their study focused on specific locations within a montane peat bog, limiting the generalizability of their findings [122]. The small footprint of UAV surveys and their reliance on favourable weather conditions also restrict the temporal coverage of data collection, as mentioned by [PERSON] et al. (2020) in [119]. The cost associated with UAV remote sensing can be a significant challenge. [PERSON] et al. (2022) highlight the cost and technical challenges of using high-precision trace gas analysers in UAV-based methane emission mapping [120]. Moreover, accessing remote and challenging peatland sites for data collection can be difficult, as [PERSON] et al. (2018) mentioned in their study on bog-heathland mosaics [48]. These limitations may hinder the widespread application of UAV remote sensing in peatland research. The processing and analysis of large volumes of remote sensing data can be complex and time-consuming. The study by [PERSON] et al. (2021) involves advanced data processing techniques, such as orthophoto plan compilation and structural equation modelling, which require expertise and computational resources [116]. Additionally, [PERSON] et al. (2018) mention the need for accurate georeferencing, image overlap, and external control data to ensure data quality and precise analysis [48]. The selection and integration of appropriate sensors and instruments pose technical challenges in UAV remote sensing. [PERSON] et al. (2019) discuss the implementation of a UAV equipped with a hyperspectral system, highlighting the limitations of airborne hyperspectral remote sensing due to its relatively high cost and low temporal resolution compared to satellite platforms [13]. Furthermore, [PERSON] et al. (2022) highlight the need for precise georeferencing and control data to ensure accurate elevation measurements using UAV Structure-From-Motion (SfM) [118]. Interpreting and validating UAV remote sensing data can be challenging. [PERSON] and [PERSON] (2019) discuss the importance of ground truth data and the limitations of vertical accuracy in their study on vegetation patterning in recovering blanket peatlands [121]. The authors emphasise the need for continued monitoring and validation of topography and morphology in self-restoring peatlands to enhance the understanding of revegetation processes. [PERSON] et al. (2018) also mention the site-specific nature of relationships between vegetation proxies and below-ground C stocks, emphasising the need for further research to assess the generalizability of findings [32]. The relationships between vegetation attributes and C stocks are likely site-specific, depending on geographic location, species composition, and peatland status [123]. Generalising the findings across different landscapes requires further investigation and validation. Furthermore, due to the intricate nature of peatland ecosystems, it is imperative to employ advanced modelling techniques like support vector machines (SVMs) and random forests (RFs) [117; 121; 124] to effectively capture the complex interplay among canopy reflectance, vegetation attributes, and C gradient variations.