Publications

The rapid vegetation line shift in response to glacial dynamics and climate variability in Himalaya between 2000 and 2014
Climate change is causing glaciers to retreat across much of the Himalaya, leading to a rapid shift of the vegetation cover to higher altitudes. However, the rate of vegetation shift with respect to glacier retreat, climate change, and topographic parameters is not empirically quantified. Using remote sensing measurements, we estimate (a) the rate of glacier-ice mass loss, (b) the upward vegeta- tion line shift rate, (c) regional greening trends, and (d) a relationship between the factors influencing the greenness of the landscape and vegetation change in the Himalaya. We find that the glacier mass loss rate is 10.9 ± 1.2 Gt/yr and the mean vegetation line shifts upward in altitude by 7–28 ± 1.5 m/yr. Considering the land use/land cover change pattern, the grassland area is found to be expanding the most, particularly in the de-glaciated regions. The vegetation change is found to be controlled by soil moisture and slope of the area. Bandyopadhyay (2022), Environmental Monitoring and Assessment

Tree species classification from complex laser scanning data in Mediterranean forests using deep learning
Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three-dimensional vegetation structure at high resolution, but the scalability of using these data for large-scale forest monitoring is limited by reliance on intensive manual data processing. Here, we apply a deep learning-based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre-processing such as leaf-wood separation or quantitative reconstructions.Using modern network architectures and data augmentation techniques, and without extensive pre-processing, we are able to achieve high overall and per-species accuracy that is comparable or higher than in existing work while using data from a water-limited ecosystem complicated by structural convergence and multi-stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring. Allen (2022) Methods in Ecology and Evolution

Tallo: A global tree allometry and crown architecture database
Data capturing multiple axes of tree size and shape underpin a wide range of ecological research. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallodatabase can be used to address a range of theoretical and applied questions in ecology. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle. Jucker (2022) Global Change Biology

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing
How ecologists think about above-ground forest structure and dynamics is fundamentally shaped by the data we can collect. Recent advances in remote sensing and data processing are revolutionising our ability to accurately measure tree and forest structure from leaves to landscapes. Here, we review the new opportunities these technologies bring us to measure the physical structure of trees and highlight the technological developments needed to maximise their value to the field of forest ecology. Lines (2022) Journal of Ecology

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.  Duncanson (2022) Remote Sensing of Environment

Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains unclear whether universal statistical models can be developed to predict traits from spectral information. It is also an open question whether the temporal changes of multiple leaf traits can be predicted successfully from hyperspectral data. To explore this question, monthly changes in 21 physiochemical leaf traits and plant spectra were measured for eight deciduous tree species from the UK. Partial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed. Our findings demonstrate that leaf spectra can successfully predict multiple functional foliar traits through the growing season, establishing one of the fundamentals for monitoring and mapping plant functional diversity in temperate forests from air- and spaceborne imaging spectroscopy. Chen (2021) Remote Sensing of Environment

Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites
Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, but reliable observations of Antarctic snow algae are difficult. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. Our results suggest snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized. Gray (2021) Frontiers in Plant Science

The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo
We used airborne LiDAR to quantify canopy architecture adjustments associated with logging and at least 11–14 years of recovery in Borneo’s ultra-complex tropical forest. We found a decline in PAI of ~28% in sites logged twice, and ~52% at sites logged four times, relative to old-growth forest. This sharp decline is associated with the near-complete loss of PAD above ~30-m, with further reductions in PAD above 10–15 m at high logging intensities. One impact of these structural changes is a drop in the diversity of canopy environments, in particular, the loss of a deep, shaded understorey. These results suggest that full recovery of foliage density, and its vertical distribution, is likely to take decades, leaving a long-lived legacy of logging in recovering forests in Borneo. Milodowski (2021) Journal of Applied Ecology

Competitive drivers of interspecific deviations of crown morphology from theoretical predictions measured with Terrestrial Laser Scanning
In this study, we calculate high‐resolution two‐ and three‐dimensional crown metrics from Terrestrial Laser Scanning data and test height‐crown metric scaling relationships. We demonstrate new TLS methods to define symmetric and asymmetric neighbourhood metrics based on tree height, crown size and neighbour projected crown area, and test the importance of neighbourhood genus diversity on crown morphology by separating competition from congeneric and heterogeneric neighbours. Owen (2021) Journal of Ecology

Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño
It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Niño event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300 m away. Nunes (2021) Nature Communications

Carbon flux and forest dynamics: Increased deadwood decomposition in tropical rainforest tree‐fall canopy gaps
Tree mortality rates are increasing within tropical rainforests as a result of global environmental change. Little is known however about the effect of tree‐fall canopy gaps on the activity of decomposer communities and the rate of deadwood decay in forests. Therefore, to determine the effect of canopy openings on wood decay rates and regional carbon flux, we carried out the first assessment of deadwood mass loss within canopy gaps in old‐growth rainforest. Our results provide the first insights into how small‐scale disturbances in rainforests can generate hotspots for decomposer activity and carbon fluxes. In doing so, we show that including canopy gap dynamics and their impacts on wood decomposition in forest ecosystems can help improve the predictive accuracy of the carbon cycle in land surface models. Griffiths (2021) Global Change Biology

Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing
Fungal ash dieback (Hymenoscyphus fraxineus) is posing an imminent threat to forest health in Europe. Using airborne hyperspectral imagery trained against 422 tree crowns of known species and ash dieback severity, we built PLS-DA and RF models that classified individual tree crowns (ITCs) into five species (>90% OA) and ash crowns into three disease severity classes (77% OA) respectively. Dark pixel filtering was found to improve the accuracy of species (+6%) but not disease classification. By incorporating automatic ITC segmentation and the classification models, we further demonstrated how species and fungal ash dieback can be mapped at a region scale for forest management and epidemiological research. Chan (2020) Remote Sensing in Ecology and Conservation

Riparian buffers act as microclimatic refugia in oil palm landscapes
We combined information from airborne LiDAR with field‐based microclimatic measurements to investigate the efficacy of forested riparian buffers of different widths and habitat composition for providing microrefugia within oil palm plantations. We deployed dataloggers across three riparian habitats: oil palm, riparian buffers and continuous logged‐forest, in Sabah, Malaysian Borneo. Our results are important to riparian buffer policies in human‐modified tropical landscapes, supporting suggestions that mandatory riparian buffer widths in the tropics should be wider than they currently are, that more attention should be given to buffer habitat quality, and that topography should also be considered when planning networks of buffers across landscapes. Williamson (2020) Journal of Applied Ecology

Resilience of Spanish forests to recent droughts and climate change
Time-series of canopy greenness derived from satellite imagery can be analysed alongside environmental factors, species composition and management regimes, to better understand forest resilience to drought. In Spain, forests are on average greening despite drying trends. This resilience manifests in the short-term with native species activating drought tolerance and avoidance mechanisms observable from space (i.e. losing and gaining little greenness like chestnuts to losing and gaining a lot of greenness like maritime pines). The non-native eucalypt dominated forests reveal a low short-term resilience (i.e. do not recover enough after droughts) and hence have a higher percentage of declining pixels. Factors such as water balance, elevation, and protection status greatly influence these drought response patterns. Khoury (2020) Global Change Biology

Dynamics of a human‐modified tropical peat swamp forest revealed by repeat lidar surveys
In this study, two lidar surveys are compared to map forest biomass dynamics of PSF in Kalimantan, Indonesia. We found that historically logged forests were recovering biomass near old canals and railways used by the concessions. Lidar detected substantial illegal logging activity of logging canals were located beneath the canopy. Unexpectedly, rapid growth was also observed in intact forest that had not been logged. Carbon sequestration in above‐ground biomass may have offset roughly half the carbon efflux from peat oxidation. This study demonstrates the power of repeat lidar survey to map fine‐scale forest dynamics in remote areas, revealing previously unrecognized impacts of anthropogenic global change. Wedeux (2020) Global Change Biology

Remote sensing reveals Antarctic green snow algae as important terrestrial carbon sink
We present the first estimate of green snow algae community biomass and distribution along the Antarctic Peninsula. Sentinel 2 imagery supported by two field campaigns revealed 1679 snow algae blooms, seasonally covering 1.95 × 106 m² and equating to 1.3 × 10³ tonnes total dry biomass. Ecosystem range is limited to areas with average positive summer temperatures, and distribution strongly influenced by marine nutrient inputs, with 60% of blooms less than 5 km from a penguin colony. A warming Antarctica may lose a majority of the 62% of blooms occupying small, low-lying islands with no high ground for range expansion. However, bloom area and elevation were observed to increase at lower latitudes, suggesting that parallel expansion of bloom area on larger landmasses, close to bird or seal colonies, is likely. This increase is predicted to outweigh biomass lost from small islands, resulting in a net increase in snow algae extent and biomass as the Peninsula warms. Davey (2020) Nature Communications

Combined InSAR and Terrestrial Structural Monitoring of Bridges
This paper examines advances in interferometric synthetic aperture radar (InSAR) satellite measurement technologies to understand their relevance, utilization, and limitations for bridge monitoring. Waterloo Bridge is presented as a case study to explore how InSAR data sets can be combined with traditional measurement techniques including sensors installed on the bridge and automated total stations. A novel approach to InSAR bridge monitoring was adopted by the installation of physical reflectors at key points of structural interest on the bridge, in order to supplement the bridge’s own reflection characteristics and ensure that the InSAR measurements could be directly compared and combined with in situ measurements. Selvakumaran (2020) IEEE Transactions on Geoscience and Remote Sensing