The World's Tallest Tropical Tree in Three Dimensions

We would like to thank NERC for funding the airborne remote sensing campaign (HMTF grant NE/K016377/1 to the BALI consortium, YM, DC and DB) + direct access grant to MC, DSB, GM and DB), analyses (grants NE/P004806/1 to MC, DSB, GF, DB, GH, and NE/I528477/1 to GH, DSB, GF), and ground-based work (grant NE/P012337/1 to YM, MD and LPB); an ERC Advanced Investigator Award (321131) to YM for funding the UAV work; LAStools’ LASmoons program for a free academic license; and an Anne McLaren Research fellowship by the University of Nottingham to GH for funding the tree climbing. YM is supported by the Jackson Foundation. Data availability statement Generated Statement: The datasets generated for this study are available on request to the corresponding author.Peer reviewedPublisher PD

Exploring temporality in socio-ecological resilience through experiences of the 2015–16 El Niño across the Tropics

In a context of both long-term climatic changes and short-term climatic shocks, temporal dynamics profoundly influence ecosystems and societies. In low income contexts in the Tropics, where both exposure and vulnerability to climatic fluctuations is high, the frequency, duration, and trends in these fluctuations are important determinants of socio-ecological resilience. In this paper, the dynamics of six diverse socio-ecological systems (SES) across the Tropics – ranging from agricultural and horticultural systems in Africa and Oceania to managed forests in South East Asia and coastal systems in South America – are examined in relation to the 2015–16 El Niño, and the longer context of climatic variability in which this short-term ‘event’ occurred. In each case, details of the socio-ecological characteristics of the systems and the climate phenomena experienced during the El Niño event are described and reflections on the observed impacts of, and responses to it are presented. Drawing on these cases, we argue that SES resilience (or lack of) is, in part, a product of both long-term historical trends, as well as short-term shocks within this history. Political and economic lock-ins and dependencies, and the memory and social learning that originates from past experience, all contribute to contemporary system resilience. We propose that the experiences of climate shocks can provide a window of insight into future ecosystem responses and, when combined with historical perspectives and learning from multiple contexts and cases, can be an important foundation for efforts to build appropriate long-term resilience strategies to mediate impacts of changing and uncertain climates

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Estimating canopy chlorophyll concentration from both field and airborne spectra

This paper presents the interim results of an on-going study into the effects of both soil contamination and nitrogen application on the red edge-chlorophyll concentration relationship. Canopy reflectance data were collected using a field spectrometer in conjunction with substantial ground-based measurements of chlorophyll concentration across a grass al-id wheat study site in the U.K. There was a strong red edge-chlorophyll concentration relationship across both study sites and the correlation between red edge position (REP) and canopy chlorophyll concentration was r = 0.86 and 0.81 for the grassland and winter wheat study sites respectively. The strength of these relationships indicated the potential of the REP as a means oi estimating canopy chlorophyll concentration and thereby both vegetation stress and productivit