Publications

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.

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.

Selective logging has affected large areas of tropical forests and there is increasing interest in how to manage selectively logged forests to enhance recovery. However, the impacts of logging and active restoration, by liberation cutting and enrichment planting, on tree community composition are poorly understood compared to trajectories of biomass recovery. Here, we assess the long-term impacts of selective logging and active restoration for biomass recovery on tree species diversity, community composition, and forest structure. We censused all stems ≥2 cm diameter at breast height (DBH) on 46 permanent plots in unlogged, primary forest in the Danum Valley Conservation Area (DVCA; 12 plots, totalling 0.6 ha) and in sites logged 23–35 years prior to the census in the Ulu Segama Forest Reserve adjacent to DVCA (34 plots, totalling 1.7 ha) in Sabah, Malaysian Borneo. Active restoration treatments, including enrichment planting and climber cutting, were implemented on 17 of the logged forest plots 12–24 years prior to the census. Total plot-level basal area and pole (5–10 cm DBH) stem density were lower in logged than unlogged forests, however no difference was found in stem density amongst saplings (2–5 cm DBH) or established trees (≥10 cm DBH). Neither basal area, nor plot-level stem density varied with time since logging at any size class, although sapling and pole stem densities were lower in actively restored than naturally regenerating logged forest. Sapling species diversity was lower in logged than unlogged forest, however there were no other significant effects of logging on tree species richness or diversity indices. Tree species composition, however, differed between logged and unlogged forests across all stem size classes (PERMANOVA), reflected by 23 significant indicator species that were only present in unlogged forest. PERMANOVA tests revealed no evidence that overall species composition changed with time since logging or with active restoration treatments at any size class. However, when naturally regenerating and actively restored communities were compared, two indicator species were identified in naturally regenerating forest and three in actively restored forests. Together our results suggest that selective logging has a lasting effect on tree community composition regardless of active restoration treatments and, even when species richness and diversity are stable, species composition remains distinct from unlogged forest for more than two decades post-harvest. Active restoration efforts should be targeted, monitored, and refined to try to ensure positive outcomes for multiple metrics of forest recovery.

Work using HyMap has shown a relationship between hyperspectral reflectance and the degree of humification of in situ upland peats in the south Pennines, especially in the shortwave infrared. The contribution of moisture content, however, could not be excluded. In order to decouple moisture and humification effects, a controlled drying experiment was carried out in the laboratory on three peat blocks of low, intermediate and poor humification. The NERC ASD FieldSpec Pro was used in contact probe mode to measure peat reflectance spectra at different gravimetric moisture contents. Analysis has concentrated on water absorptions centred on 1400 and 1940 nm and ligno-cellulose absorption at 2100 nm. As the peat dried out, albedo increased and changes occurred in the position of the shoulders, the slope between them and the position, depth, width and asymmetry of features below the continuum line. Spectral indices were used to express these changes. Humification signals became more pronounced in dry peat, and were stronger for poorly humified peat. The 1200 nm feature associated with water, cellulose and lignin disappeared on drying, even for the poorly humified peat, suggesting that it was primarily due to water. Absorptions at 1400 and 1900 nm remain even in dry peat. Early results suggest that the albedo of the shoulders of the 1900 nm feature, and to a lesser extent the 1400 nm feature, are the most sensitive to moisture, while the shape of the features below the continuum line is also sensitive to humification. On-going work is testing the value of combinations of candidate indices to separate peat into moisture-humification classes.

Tropical forests are undergoing drastic transformations, putting at risk the species that rely on them. On the island of Borneo, between 1973 and 2015, 50% of the forest has been lost, much of this to oil palm and other industries. We explore the impacts of these four decades of forest loss on the functionally connected habitat of 245 forest birds and mammals. First, we map potential suitable habitat in 1973 and 2015 by refining reported ranges by elevation, forest cover and patch size. We find that, on average, these species have lost 28% of habitat within their ranges. Then we use graph-theory connectivity models to calculate the amount of area that is functionally connected for each species, according to their natal dispersal abilities. This analysis shows a mean loss of 35% of their connected habitat and reveals the larger, and often hidden, impacts from deforestation. Losses in functionally connected habitat are largely driven by area of habitat loss, yet maximum elevational range limit also explain some of the differences modelled across species with lowland species being the most affected. We present a vulnerability index of threat arising from functionally connected habitat loss. The spatial aggregation of this vulnerability index serves as a tool for setting conservation priorities for forest remnants on Borneo, given that most of the ranges of these species are not protected. We make recommendations for the use of connectivity models to prioritize resources for research and conservation on Borneo and other biodiversity hotspots.

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.