Optical remote sensing of vegetation canopy properties from local to global scales

We are in the midst of a new era of Earth Observation for vegetation monitoring. Recent enhancements in the spectral, spatial and temporal resolution, as well as availability, of optical sensors provides opportunities to significantly advance the retrieval of vegetation properties at multiple scales. However, directed research linking optical imagery to field-collected vegetation measurements is required to optimally harness current and future advances in remote sensor technologies.
This thesis aims to investigate the optical remote sensing of plant functional traits at four scales: the leaf, unmanned aerial vehicle (UAV), aircraft and satellite level. This thesis has four objectives, each of which are addressed through a research article.
The main objective of the first article, entitled ‘Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2 data’, is to evaluate the use of multispectral UAV imagery for mapping biochemical leaf traits in high Arctic tundra. The High Arctic is an ideal environment for establishing a baseline potential for mapping leaf traits using UAV imagery, as species richness is low and vegetation cover lacks the structure and confounding effects of a forest canopy. The article starts by using field spectroscopy to classify plant species and quantify biochemical plant traits (water content, nitrogen (N), phosphorus (P) and C:N ratios). At the UAV level, it classifies three plant functional types (mosses, graminoids and dwarf shrubs) at 72% accuracy and generates maps of plant chemistry. (r2 = 0.44–0.54, RMSEmean = 26%–48%). Lastly, the UAV results were used to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2 imagery.
The objective of the second article, entitled ‘Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery’ is to evaluate the use of hyperspectral UAV imagery for mapping biochemical leaf traits across a species-rich, structurally complex forest canopy in Ghana, West Africa. Similarly, the article starts by using field spectroscopy to estimate six biochemical plant traits (leaf mass per area (LMA), N, P, calcium (Ca) and magnesium (Mg)). At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected hyperspectral imagery: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). Mapping the leaf economic spectrum (LMA, N and P) across a precipitation gradient reflected carbon cycling data, and revealed precipitation and substrate as co-dominant drivers of trait distributions and relationships.
The objective of the third article, entitled ‘Mapping photosynthetic capacity in a Bornean tropical forest from airborne and satellite imagery: challenges and recommendations’, expands focus from mapping biochemical leaf traits to mapping photosynthetic capacity (Vcmax25 and Jmax25) across a forest disturbance gradient in Malaysian Borneo using aircraft-collected hyperspectral imagery and Sentinel-2 imagery. In addition, it explores increased image quality issues associated with expanded spatial coverage. Aircraft-collected hyperspectral imagery was found to exhibit poor radiometric calibration and Sentinel-2 imagery was found to suffer from processing errors related to cloud masking and inconsistent surface reflectance values, especially over humid tropical forests. The strongest model performance (r2=0.36 and 0.43 for Vcmax25 and Jmax25 respectively) was produced by tuning the high spectral and spatial resolution of the aircraft-collected hyperspectral imagery with the improved signal-to-noise ratio of a Sentinel time-series composite, demonstrating the importance of diagnosing and minimising data quality issues.
The last article entitled ‘Global mapping of the photosynthetic capacity of tropical forests’, combines Sentinel-2A imagery, environmental datasets and photosynthesis data from 35 tropical forest GEM plots to create a pan-tropical map of Vcmax25 and Jmax25. To the best of my knowledge, this is the first research that uses Sentinel-2 imagery to map tropical forest Vcmax25 and Jmax25 globally. Models captured 38% and 57% of variation in Vcmax25 and Jmax25 across lowland forest, and 62% and 61% of variation across lowland-and-montane forest. In lowland-only forest, mean climatic water deficit (MCWD) and solar radiation were most important predictors of Vcmax25 and Jmax25 respectively. In lowland-and-montane forest, the strongest predictor of both Vcmax25 and Jmax25 was mean annual temperature (MAT). At intercontinental scales, Vcmax25 and Jmax25 values were mainly controlled by climate. At the regional level, trait distributions were shaped by soil type. At local levels, trait patterns reflected spectral variation arising from topography and canopy structure.
Overall, this thesis offers new methodologies to measure and monitor tropical forest functional diversity and productivity. It builds from a simple ecosystem to a floristically and structurally complex tropical forest, and explores the trade-offs of expanding spatial coverage and decreasing spectral resolution for the retrieval of vegetation canopy traits. Alongside, it highlights current limitations of optical remote sensing, and presents best-practice recommendations for future imaging studies. The results are pertinent for high resolution, low cost mapping of tundra and tropical forest ecosystems (and potentially for other terrestrial ecosystems), and for improving the representation of vegetation properties, especially photosynthetic capacity, in the next generation of Earth System Models.

Keywords: remote sensing
satellite data
environmental change
drones
tropical forests
plant traits
machine learning
biogeography
Advisor: Malhi, Y
Doughty , C