European forests are undergoing large-scale changes in structure and species composition due to anthropogenic disturbances, climate change, and other canopy disturbances. Forest canopies across Europe are now opening up due to tree mortality associated with drought, pests, storms, and fire. Insights on how this will affect forest functioning are essential to understanding, predicting and managing our forests. Therefore, we want to better quantify the spatial and temporal relationships between forest structure, light and microclimate. For this purpose, we have set up a state-of-the-art edge-to-core transect in a temperate deciduous forest near Ghent (Aelmoeseneiebos, Gontrode, Belgium). The transect goes from the forest edge to 135 meters into the forest, covering both an oak-beach-dominated zone and an ash-dominated zone characterised by ash dieback. Here, we installed a spatially dense network of light and microclimate sensors every 15 meters, as well as a fiber optic sensing cable for distributed temperature sensing along the transect. The transect includes a 35 m high measuring tower, which also enables measuring light and microclimate along a vertical transect. Additionally, terrestrial laser scanning (TLS) data is collected from the transect monthly during leaf-off and leaf-on conditions. This setup will enable us to quantify the temporal and spatial variation of the microclimate and the forest structure in great detail. The continuous measurements will be complemented with campaign-based observations of (1) spectral components (reflectance, transmission & absorption) of leaves, bark and understorey using an ASD field Spectrometer to facilitate radiative transfer modelling (RTM) and (2) physiological measurements (photosynthesis, chlorophyll fluorescence, etc.) on the dominant tree species and understorey plants. In a further step of the project, we will reconstruct a 3D virtual forest (using the TLS data) that will be used as an input for radiative transfer modelling (RTM, which simulates the interaction between light and forest structure).

Capturing how tree growth, survival and recruitment vary through space and time is critical to understanding the processes that shape the structure and dynamics of tree-dominated ecosystems. However, characterising tree demography at scale has traditionally been challenging, as trees are relatively slow growing, long-lived and often cover vast expanses of land. Here we overcome this challenge by using repeat airborne laser scanning data acquired over a 25 km2 area of temperate woodland in Western Australia to track the height growth, crown expansion, survival and recruitment of >50,000 individual trees over the course of a decade. We show that these various demographic processes are all constrained by a combination of initial tree size, local competitive environment and topography. After initially investing in height growth when small, trees progressively shifted their growth allocation to crown expansion as they reached larger sizes, particularly when growing in isolation from neighbouring trees. Moreover, rates of tree growth, survival and recruitment were all consistently lower on ridges and outcrops, resulting in vegetation cover becoming increasingly spatially aggregated with respect to topography over time. Our results shed new light on the processes that shape the spatial structure and dynamics of Western Australia’s unique temperate woodlands, and provide a road map for using emerging remote sensing technologies to track tree demography at scale.

Boreal forests, covering a total area of 270 Mha in Canada, account for a third of the lumber produced worldwide. Current changes in temperature and water dynamics however bring uncertainty regarding the future productivity of this forest biome. For instance, warmer summer temperatures have led to increased water deficits in some regions, causing a decrease in the growth rate of black spruce, the most prevalent species in the boreal forest. The impact of drought events on black spruce growth is highly variable across the landscape, and is influenced by stand composition as well as forest structural attributes and site characteristics. Anticipating the impact of such climate-induced changes in productivity is challenging, but crucial to better forecast the consequences on timber supply, carbon storage and ecosystem services. Generating spatially explicit information on the vulnerability of boreal forests to drought would also allow identifying areas where targeted adaptive silvicultural actions could be implemented to promote more resistant and resilient stands.

In this study, we used dendrochronology in combination with aerial laser scanning data (ALS) to identify site and forest structure attributes affecting resistance to drought in black spruce stands. We collected over 1200 increment cores in 52 sample plots located within two forest management units in Quebec and Ontario, Canada, on sites presenting a gradient of soil water availability. Drought events were detected using the Standardized Precipitation Evaporation Index, and a growth resistance index was calculated from the difference in annual basal annual growth before and during the drought, as measured from the increment cores. We then analysed the influence of ALS-derived site and forest structural attributes on the changes in growth observed during drought events and produced spatially explicit information on the predicted resistance of black spruce stands in the study areas.

Forest height growth is a widely used indicator of site quality. Airborne laser scanning (ALS) data provide accurate measures of forest canopy height. These accurate measures, repeated over time, can provide useful data to improve existing forest growth models, and for the development of novel approaches for characterizing forest growth. Existing growth models rely on sample data that often represent historic growth conditions, acquired over limited spatial and temporal extents and site conditions. In contrast, 3D measures from airborne lidar data can be acquired over a much broader range of site conditions, augmenting investments in ground plot networks, and providing a foundation to bring together new and existing approaches for characterizing growth. However, over time, ALS data acquisitions use different sensors and parameters that can influence the derived growth increments and therefore careful pre-processing of the ALS data are required to minimize the impacts of these exogenous factors on height growth assessments. To demonstrate this, we assessed and quantified the magnitude of horizontal and vertical differences across four ALS datasets acquired over a 13-year period in a temperate mixedwood forest environment. We pre-processed (harmonized) the ALS datasets to account for differences in acquisition parameters to minimize error in assessment of change in canopy height. We then calculated periodic annual increments (PAI) of canopy height and quantified the impact of the pre-processing on the derived PAI. We compared and examined inconsistencies between field-measured height increments and those derived from the ALS data. Over the entire growth period (2005–2018) differences in growth increments between the harmonized and non-harmonized datasets were statistically significant (p<0.05), with harmonized ALS datasets resulting in height growth increments that were on average 37% greater than those derived from non-harmonized ALS data. Harmonization results in a more consistent PAI series that reduced uncertainties associated with the different ALS acquisitions.

It has taken over 20 years to get three-dimensional ecosystem structure measurement capabilities established via systematic lidar measurements from space. The Global Ecosystem Dynamics Investigation (GEDI) Lidar is the culmination of that objective. We present results exploring multiple methods for generating continuous maps of canopy structure metrics from GEDI Lidar data products, including canopy height, foliage height diversity and plant area volume density, derived in combination with remotely sensed data from imaging sensors. We focus on results assessing the relative importance of these new GEDI canopy structure data and gridded products in species distribution models (SDMs) of mammalian species occurrences and richness in Southeast Asia, derived from extensive camera trap networks. We also summarize the utility of GEDI Lidar relative to airborne laser scanning (ALS) data for estimating plant and tree diversity metrics across ecoregions of the United States and Colombia. Our efforts address the influence of sampling density, spatial scale (grain, extent), variable selection and different machine learning models. Finally, we explore the importance of canopy structure metrics relative to canopy cover alone for faunal species richness and threatened status, both within and outside of protected areas.