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- 01HHM4QG0C59DH5RQ7P7GVQEHS classification C3.
- 01HHM4QG0C59DH5RQ7P7GVQEHS date "2023".
- 01HHM4QG0C59DH5RQ7P7GVQEHS language "eng".
- 01HHM4QG0C59DH5RQ7P7GVQEHS type conference.
- 01HHM4QG0C59DH5RQ7P7GVQEHS hasPart 01HHM50Z63Q3MT4A801DFAK77N.pdf.
- 01HHM4QG0C59DH5RQ7P7GVQEHS subject "Earth and Environmental Sciences".
- 01HHM4QG0C59DH5RQ7P7GVQEHS presentedAt urn:uuid:66bac634-0c4e-4305-ae4c-b724965d00df.
- 01HHM4QG0C59DH5RQ7P7GVQEHS abstract "Terrestrial laser scanning (TLS) is being recognised as a key technology in forest monitoring by providing highly detailed in-situ measurements of 3D vegetation structure. While applications of TLS forest point clouds are manifold, including plant functional trait analysis and plant area index (PAI) estimation, it is a particularly valuable tool within the context of aboveground biomass (AGB) estimation (Calders et al., 2020) and how this is derived through spaceborne sensors. Here, we highlight (1) how TLS can be used as reference data for in situ AGB estimates; and (2) how TLS can be used to build digital twins to support cal/val activities of remote sensing. We demonstrate both applications using TLS data from a typical deciduous forest, Wytham Woods in the UK. Through 3D reconstruction of individually identified trees using quantitative structure models (QSMs), TLS data allows for nondestructive estimation of tree volumes, which, combined with species specific wood-density, can serve as a highly accurate ground-truth reference for AGB quantification (Figure 1). Research has shown that TLS derived AGB is favourable both in terms of sampling strategy and AGB accuracy when compared to traditionally used allometric models (Calders et al., 2015). Furthermore, these detailed QSMs can be parameterised as so-called digital twins which can serve as input for radiative transfer models (RTMs). RTMs are a powerful tool to establish the physically-based link between in situ structure and how it is monitored with satellite remote sensing, hence enabling the upscaling of AGB estimation. RTMs enhance our ability to monitor and understand the coupling between emitted, reflected or scattered electromagnetic (EM) waves, and a scene characterised by the structure and spectral signature of its biophysical components (e.g. leaves, bark, understorey). Figure 2 shows a case study of the first explicit digital forest twin derived from TLS data, which was further parameterized for optical RTM. This 1-hectare proof of concept RTM scene was generated for Wytham Woods, a deciduous forest near Oxford (United Kingdom) consisting of all 559 individual trees modelled explicitly from TLS, and showed the ability to simulate satellite data such as Sentinel-2 (Calders et al., 2018). Whereas optical RTMs have commonly been used in vegetation remote sensing to simulate optical (see Figure 2 as an example) or lidar data using 3D input scenes (Widlowski et al., 2015), microwave RTMs using 3D canopies as input is more recent (Tanase et al., 2019), partly driven by recent radar satellite missions, such as ESA BIOMASS, targeting vegetation specifically. However, currently established methods all are based on assumptions to generate their 3D input models and do not use an exact replica of real trees and forest, as is possible from TLS data. Ongoing and future work within the context of the SPACETWIN project (https://spacetwin.ugent.be/), focuses on enhancing our understanding on forest disturbances and recovery from space. SPACETWIN aims to integrate realistic TLS-derived digital twins of forest structure as 3D RTM input to build an emulator for optical, lidar and radar satellite data that will allow for near real-time interpretation of disturbances (Figure 3). This includes, among others, using the MIPERS4D RTM to simulate microwave (C-, L-, and P-band) backscatter, which can be parameterised using field-collected dielectric permittivity to represent the water content (Tanase et al., 2019). The combination of structurally accurate 3D digital twin forests with a parameterized microwave RTM would allow for a powerful instrument to facilitate the calibration and validation of remote sensing data and derived biophysical products such as biomass.".
- 01HHM4QG0C59DH5RQ7P7GVQEHS author 7CC47BCA-94E1-11E7-8073-9A50AE28A064.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author 9286A620-A6F4-11E7-8878-09B7AD28A064.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author CBA470CC-2D6C-11E4-A9C7-B2F1B4D1D7B1.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author f5c023f5-b884-11ed-8b0c-962de55f9d08.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author urn:uuid:0a2fecb7-1e48-4dea-a0ab-19f2bae5dcef.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author urn:uuid:4160bbd8-c7a1-4c06-9a5d-45d2d99f0b97.
- 01HHM4QG0C59DH5RQ7P7GVQEHS author urn:uuid:db0e51f6-c328-4964-84c8-a95dc5be15c2.
- 01HHM4QG0C59DH5RQ7P7GVQEHS dateCreated "2023-12-14T12:27:24Z".
- 01HHM4QG0C59DH5RQ7P7GVQEHS dateModified "2024-10-29T18:40:06Z".
- 01HHM4QG0C59DH5RQ7P7GVQEHS name "Terrestrial laser scanning has potential to support Cal/Val activities of radar biomass estimates".
- 01HHM4QG0C59DH5RQ7P7GVQEHS sameAs LU-01HHM4QG0C59DH5RQ7P7GVQEHS.
- 01HHM4QG0C59DH5RQ7P7GVQEHS sourceOrganization urn:uuid:7c310c0a-8c41-4b93-9cca-921609441d68.
- 01HHM4QG0C59DH5RQ7P7GVQEHS type C3.