Leaf phenology amplitude derived from MODIS NDVI and EVI: maps of leaf phenology synchrony for Meso‐ and South America

The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution

Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology

Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests

• Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. • Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. • The model performed well for independent Brazilian sunlit and shade canopy leaves (R2 = 0.75–0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R2 = 0.27–0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment–trait linkages – either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments – we achieved a more general model that well-predicted leaf age across forests and environments (R2 = 0.79). • Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments

TLS2trees: A scalable tree segmentation pipeline for TLS data

1. Above-ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. / 2. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. / 3. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. / 4. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software

Impacts of leaf age on the spectral and physiochemical traits of trees in Amazonian forest canopies

This doctoral research presents the first comprehensive analysis of the morphological, biochemical and spectral leaf traits of canopy and emergent tropical trees during natural (in situ) leaf ageing. It adopts an interdisciplinary approach and combines multiple scales of analysis to generate insights into the effects of natural leaf ageing on our current understanding of tropical leaf trait variation, chemometric models used to spectrally predict leaf traits, and together with other leaf phenological processes, on remotely-sensed vegetation indices (VIs) commonly used to monitor canopy dynamics in tropical evergreen forests. The first research paper of this thesis (Chapter 4) examines the effects of leaf age on morphological and biochemical leaf traits and demonstrates that leaf age is a significant driver of leaf trait variation in Amazonian canopy trees and that leaf age differences could potentially account for a significant fraction of what we currently understand as intra- and interspecific leaf trait variation. It also highlights that age-related trait variation within and between individual trees could play a significant role in shaping community composition and structure of tropical canopies. The leaf traits examined in Chapter 4, among others, have been shown to directly influence the spectral reflectance behaviour of leaves. Therefore, Chapter 5 investigates the effects of leaf age on leaf spectral properties within and across a tropical canopy tree community. This study reveals that trees with diverse leaf properties age in a similar manner in terms of spectral properties. This is one of the most important findings of this thesis and lead to the development of a novel chemometric partial least square regression (PLSR) model to predict leaf age from hyperspectral data. This model extends the utility of current spectroscopic methods and introduces a simple and efficient approach for predicting and monitoring leaf age in lowland tropical forests with important implications for remote sensing. Additionally, this study is the first to provide evidence of age-related reflectance changes in leaves that have significant impacts on vegetation indices commonly used to monitor productivity and canopy dynamics in tropical evergreen forests. Considering the findings of the previous two research chapters, Chapter 6 investigates if chemometric PLSR models used to spectrally predict some of the important leaf traits for plant physiology and economy (leaf mass per area, LMA; water content, LWC; phosphorous, P; nitrogen, N; and carbon, C content) investigated in the previous two research chapters could be significantly biased by variation in leaf age. This is particularly relevant as the current standard protocol is to use only "fully expanded mature leaves" to calibrate these models. This study demonstrates that PLSR models developed using the current standard protocol display age/temporal sensitivity, which has important implications for forest canopy communities with both synchronised and unsynchronised leaf phenology. The final research chapter of this thesis, Chapter 7, demonstrates that the phenological age-related changes in leaf spectral properties reported in Chapter 5 are also expressed at the canopy scale but influenced by both canopy leaf area (CLA) and the leaf phenological behaviour of individual trees. This study also reveals that the seasonality of greenness VIs such as NDVI and EVI2 are more strongly correlated to phenological changes in CLA then changes in leaf reflectance and proposes that NDWI (water content VI) which was found to be strongly correlated to age-related changes in leaf reflectance should complement greenness VIs in phenological studies. Furthermore, by combing leaf, canopy and community scale phenological observations, this study shows that complex and diverse leaf phenological behaviours exhibited by tropical canopy trees, at both the individual and community scale, challenge our current ability to remotely sense tropical canopy dynamics. Finally, this chapter highlights the need for more widespread phenological studies that examine the interaction, covariation, asynchrony and unique behaviours of tropical phenological processes at different scales. Such studies would enable the development of a significant mechanistic understanding of what creates and drives different phenological mosaics identified by remote sensing studies across tropical forests and in modelling their effects on water and carbon fluxes in tropical forest ecosystems.</p

Impacts of leaf age on the spectral and physiochemical traits of trees in Amazonian forest canopies

This doctoral research presents the first comprehensive analysis of the morphological, biochemical and spectral leaf traits of canopy and emergent tropical trees during natural (in situ) leaf ageing. It adopts an interdisciplinary approach and combines multiple scales of analysis to generate insights into the effects of natural leaf ageing on our current understanding of tropical leaf trait variation, chemometric models used to spectrally predict leaf traits, and together with other leaf phenological processes, on remotely-sensed vegetation indices (VIs) commonly used to monitor canopy dynamics in tropical evergreen forests. The first research paper of this thesis (Chapter 4) examines the effects of leaf age on morphological and biochemical leaf traits and demonstrates that leaf age is a significant driver of leaf trait variation in Amazonian canopy trees and that leaf age differences could potentially account for a significant fraction of what we currently understand as intra- and interspecific leaf trait variation. It also highlights that age-related trait variation within and between individual trees could play a significant role in shaping community composition and structure of tropical canopies. The leaf traits examined in Chapter 4, among others, have been shown to directly influence the spectral reflectance behaviour of leaves. Therefore, Chapter 5 investigates the effects of leaf age on leaf spectral properties within and across a tropical canopy tree community. This study reveals that trees with diverse leaf properties age in a similar manner in terms of spectral properties. This is one of the most important findings of this thesis and lead to the development of a novel chemometric partial least square regression (PLSR) model to predict leaf age from hyperspectral data. This model extends the utility of current spectroscopic methods and introduces a simple and efficient approach for predicting and monitoring leaf age in lowland tropical forests with important implications for remote sensing. Additionally, this study is the first to provide evidence of age-related reflectance changes in leaves that have significant impacts on vegetation indices commonly used to monitor productivity and canopy dynamics in tropical evergreen forests. Considering the findings of the previous two research chapters, Chapter 6 investigates if chemometric PLSR models used to spectrally predict some of the important leaf traits for plant physiology and economy (leaf mass per area, LMA; water content, LWC; phosphorous, P; nitrogen, N; and carbon, C content) investigated in the previous two research chapters could be significantly biased by variation in leaf age. This is particularly relevant as the current standard protocol is to use only "fully expanded mature leaves" to calibrate these models. This study demonstrates that PLSR models developed using the current standard protocol display age/temporal sensitivity, which has important implications for forest canopy communities with both synchronised and unsynchronised leaf phenology. The final research chapter of this thesis, Chapter 7, demonstrates that the phenological age-related changes in leaf spectral properties reported in Chapter 5 are also expressed at the canopy scale but influenced by both canopy leaf area (CLA) and the leaf phenological behaviour of individual trees. This study also reveals that the seasonality of greenness VIs such as NDVI and EVI2 are more strongly correlated to phenological changes in CLA then changes in leaf reflectance and proposes that NDWI (water content VI) which was found to be strongly correlated to age-related changes in leaf reflectance should complement greenness VIs in phenological studies. Furthermore, by combing leaf, canopy and community scale phenological observations, this study shows that complex and diverse leaf phenological behaviours exhibited by tropical canopy trees, at both the individual and community scale, challenge our current ability to remotely sense tropical canopy dynamics. Finally, this chapter highlights the need for more widespread phenological studies that examine the interaction, covariation, asynchrony and unique behaviours of tropical phenological processes at different scales. Such studies would enable the development of a significant mechanistic understanding of what creates and drives different phenological mosaics identified by remote sensing studies across tropical forests and in modelling their effects on water and carbon fluxes in tropical forest ecosystems.</p

LONG-TERM REGENERATION PATTERNS AND CONSERVATION STATUS OF A REMNANT OLD-GROWTH OAK FOREST: WISTMAN'S WOOD, DARTMOOR

Wistman's Wood is one of three small oakwoods growing in isolated positions amongst granite blocks (clitter) on steep slopes at > 250m altitude on Dartmoor, an upland area of moorland in south-west England (Barkham, 1978). The aim of this study was to (1) reveal the long-temn pattern of regeneration and woodland expansion at Wistman's Wood, (2) investigate the effects a 40 year old enclosure inside the woodland has had on its structure, and (3) uncover the ecological history and conservation status of the stand. Successful natural regeneration throughout Wistman's Wood appears to have been continuous for 100 years (1886-1985) but particularly successful during 1926-1965. After 1985, an almost complete lack of regeneration was observed across all sites outside the enclosure. Grazing has probably been the main factor determining success or failure in regeneration but growth rate has also played a part. Significant differences in the long-term recruitment and woodland structure inside the enclosure were observed: a much higher proportion of the sampled trees inside the enclosure had been released after enclosure and this site displayed the highest tree density with mean tree crowding 17% to 76% higher than that of the other 3 sites open to grazing. Leaf are index (LAI) was also found to be highest inside the enclosure. All of the above indicates that regeneration has been much more successful inside the enclosure than at sites open to grazing and has remained so until the present time. Wistman's Wood stand structure has been dramatically changed after this wave of regeneration. It has increased its woodland area by 106%, the canopy has risen in height and infilled, and the growth form of the trees has altered. Today, the wood consists of a mosaic in which the South and Middle groves display an uneven-aged structure with most trees within the younger class ages but with a distinct lack of seedlings and saplings. The enclosure also displays an unevenaged structure but with mean age here significantly lower than that of the two groves. In contrast, an even-aged stand structure can be observed at the regeneration site. This site is the result of recent regeneration and expansion and could be considered as a single demographic unit. Tree mortality at these last two sites is high and could be attributed to self-thinning. Monitoring of the effects of grazing and canopy structure on present and future regeneration within the groves is required in order to ensure a sustained woodland canopy. Key Words: Quercus robur, Tree-rings; Long-term regeneration; Historical expansion; Age distributions; GrazingFaculty of Science, English Nature, Devon Area Team, Renslade House, Exeter EX4 SAW, Devon, Englan

Deriving hyper spectral reflectance spectra from UAV data collected in changeable illumination conditions to assess vegetation condition

Hyperspectral imaging is a recent development in the evolving field of UAV remote sensing and a new avenue for habitat condition monitoring. We present preliminary results of a pilot study evaluating the use of UAV hyperspectral imaging to detect early stages of Acute Oak Decline (AOD) in a broadleaved forest in the UK. Field observations revealed that, compared to asymptomatic trees, leaves of symptomatic trees show lower levels of water and higher reflectance in the near-infrared part of the spectrum. The observed changes in leaf level reflectance spectra were subtle but statistically significant. Normalised hyperspectral UAV canopy radiance spectra suggest the opposite is occurring: symptomatic trees have lower near-infrared radiances. UAV campaigns suffer from changing illumination conditions and in our case normalizing between image frames is not sufficient. We plan to derive reflectance spectra to enable us to adequately evaluate the observed differences between leaves and canopies