Vegetation structure and function measurement and modelling using drone based sensing techniques

Abstract

This thesis explores the use of drone-based data acquisitions for deriving structural plant traits and spectral reflectance of vegetation which are variables of interest for carbon stock estimations and for understanding vegetation functioning. Previous work demonstrated that fine-grained spatio-temporal insights gained from drone-acquired data are critical for understanding local processes but also for interpreting dynamics in coarse scale representations of landscapes from Earth observing satellite data. This work builds on this by assessing uncertainties in data acquisition and processing workflows and demonstrating novel applications of drone acquired data. The findings from the individual chapters presented herein allow conclusions on which metrics can be used with confidence to assess the status and track changes in vegetation structure over time. An example of how the finely resolved spatial information on structural plant traits can be used for simulations of radiation propagation, towards functional landscape representations is demonstrated. The presented work includes experiments conducted in the United Kingdom, Italy and Malaysia arising from international collaborations and addresses three critical questions in the proximal sensing of vegetation: 1. To what extent can drone Structure-from-Motion (SfM) photogrammetry-derived products deliver accurate information about vegetation height parameters? Using SfM photogrammetry for deriving robust vegetation height parameters is of particular interest when seeking to derive growing stock volume and biomass for plantation and forest management purposes and for the assessment of carbon stocks. The associated study focused on an oil palm plantation in Sarawak, Malaysia and examined the quality of SfM-based estimates of palm height and inferred stem height, both metrics which are commonly used for allometric estimates of biomass. Further, the impact of acquisition methodology on point cloud precision was investigated. Results showed that SfM could provide palm height metrics at the individual tree level with mean relative errors between 11.7% and 18.9% dependant on palm age and that for mature palms (>10 years) flight plans favouring coverage over spatial resolution and overlap did not decrease the accuracy. 2. How accurate and consistent are surface reflectance and vegetation index products acquired from drone-based sensors over vegetation canopies? Quantifying the spatial and temporal consistency of surface reflectance and vegetation index data acquired by lightweight sensors mounted on drone platforms is essential for applications in precision agriculture, for species classification and for studying vegetation functioning. This topic was addressed through two studies. The first study compared drone acquired spectral data over a maize field in Grosseto, Italy against reference datasets from near simultaneous airborne and satellite based image acquisitions. While uncertainties in drone acquired surface reflectance were found to be greater than anticipated (5-28% relative errors over the maize field), VIs were highly correlated and comparable across scales. The second study investigated the use of vegetation index data to track phenology related changes over time for mostly deciduous tree species in Cornwall, UK. VIs proved to be sufficiently consistent for both, acquisitions under overcast and cloud free skies to resolve phenological changes with illumination based uncertainties an order of magnitude smaller than the total increase in index values across Spring green-up. 3. Can drone-based data be used to constrain and drive models of radiative transfer for understanding photon-plant interactions in complex heterogeneous canopies? Combining drone acquired canopy height models and vegetation index information to represent vegetation in a 3D radiative transfer model represents a new opportunity for simulating the interaction of light with vegetation at fine spatial scales. Previously, the information required for modelling heterogeneous vegetation canopies could only be acquired through laborious measurements in-situ or financially costly laser-scanning methods. This topic was explored by creating a representation of a local wildlife conservation site in Cornwall, UK within the Discrete Anisotropic Radiative Transfer (DART) model. The model was used for a case study focused on simulating the photosynthetically active radiation (PAR) reaching the understory as hourly fractions and spatially explicit (1 m spatial resolution) daily light integrals across Spring green-up. Results showed that while the drone-data parameterised model could represent the variability across discontinuous vegetation cover, PAR reaching the understory was considerably overestimated at start-of-peak greenness due to uncertainties in modelled plant area density and leaf angular distributions. The primary data acquisitions of all the presented studies were performed exclusively with lightweight multi-rotor drones, trialling relatively low-cost consumer grade and multi-spectral cameras which have since been widely adopted by research groups globally. The presented results therefore represent a timely contribution with relevant insights from appropriate acquisition methodologies to novel applications of drone acquired data for representing vegetation in a radiative transfer modelling context.