Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Abstract

This systematic literature review (SLR) provides a comprehensive overview of remote sensing (RS) applications in northern peatlands from 2017 to 2022, utilising various platforms, including in situ, UAV, airborne, and satellite technologies. It addresses the challenges and limitations presented by the sophisticated nature of northern peatland ecosystems. This SLR reveals an in-creased focus on mapping, monitoring, and hydrology but identifies noticeable gaps in peatland degradation research. Despite the benefits of remote sensing, such as extensive spatial coverage and consistent monitoring, challenges persist, including high costs, underexplored areas, and limitations in hyperspectral data application. Fusing remote sensing data with on-site research offers new insights for regional peatland studies. However, challenges arise from issues like the cost of high-resolution data, coverage limitations, and inadequate field validation data in remote areas. This review suggests refining methodologies, validating with high-resolution data, and addressing these limitations for future research.

Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis

“…Until now, remote sensing (satellite, airborne, or UAV platforms) and proximal sensing (multispectral or hyperspectral imaging, LiDAR, thermal imaging, or electromagnetic radiation) have been the most common used techniques concerning the acquisition of information about plant growth and health status [17,18]. However, being close to the plant but not directly embedded in it, both approaches give partial although reliable information on plant growth and development.…”

A One-Dimensional Light Detection and Ranging Array Scanner for Mapping Turfgrass Quality

“…Furthermore, a relatively strong correlation (R 2 = 0.61) was found in terrestrial LiDAR applications in measuring the biomass of tall fescue, a grass, utilizing a single 2-D LiDAR operating at 50 Hz compared to using NDVI (normalized difference vegetation index) measurements on their own (R 2 = 0.56) [4]. Previous research utilizing drone-based LiDAR has primarily focused on crops with a much larger economic impact than sod, including area-wide mapping and forest canopy measurements, for which a ground-based vehicle would not be suitable [13,14].…”