Tree growth and biomass production are key ecosystem services that forests provide and are essential for carbon sequestration and wood production. However, the within-season dynamics of tree height and stem growth has been difficult to measure hampering the understanding of the interplay between these processes. Here, we investigated the feasibility of a laser scanning system in estimating tree height dynamics, aiming to study how tree height and stem diameter growth dynamics vary and interact, and how environmental variables explain the tree height and stem diameter growth dynamics within season. We used a laser scanner fixed to a 35-meter tower to measure tree height near-daily and dendrometers to measure stem diameter growth from April to October in a Scots pine dominated boreal forest. We found that the change in the 99.95th height percentile of segmented point clouds gave a reliable estimate of tree height growth with less errors than maximum height change within the season. We then investigated the temporal dynamics of tree height and stem diameter growth and found that early growth of both occurred approximately at the same time, but deviated towards late season. On average, stem diameter growth showed a longer growing period (80 days) than tree height growth (56 days). Based on the dendrometer measurements, we computed a growth response function that aimed at characterizing the current state of stem diameter development, whether it is increasing or decreasing from its past state. When these stem diameter growth responses were compared against environmental variables, we found that the growth response was mostly controlled by water balance, vapor pressure deficit, temperature and standardized precipitation evapotranspiration index. Our findings support the utilization of laser scanning time series for measuring intra seasonal changes in tree height and increase our understanding of the interactions between tree height and stem diameter growth.

Forests have important ecosystem functions and contribute substantially to biodiversity. Function, structure, and composition of a forest have been recognized as primary ecosystem attributes to constitute its biodiversity. Recent improvements in remote sensing methods such as airborne laser scanning (ALS) allow the assessment of forest structural parameters like canopy height or vegetation density. Such parameter estimations have been linked to plant diversity and ecosystem functioning and can be provided over continuous areas.

Repeated large-scale ALS acquisitions are getting more common, opening up possibilities for multitemporal analyses and long-term monitoring programs of forest structure using ALS data.

ALS data from different years are often recorded with different sensor and flight settings. While simple traits like canopy height are reported to be more robust, the derivation of more complex traits is often more susceptible to varying acquisition parameters. Especially methods for forest density retrieval like plant area index (PAI) are often strongly influenced by different sensor and flight parameters such as flying altitude, scan angle, beam divergence, and pulse repetition frequency.

To ensure comparability between the different ALS acquisitions, robust methods for the derivation of forest parameters are required.

In this study we analyzed the robustness of multiple derivation algorithms for PAI retrieval from ALS acquisitions. Multitemporal ALS acquisitions (2014, 2019, 2020) of the Canton of Aargau, Switzerland were available, showing a large variety of different scanning geometries and sensor specifications.

Comparisons of different methods reveal how sensitive some of them react to sensor and flight parameter changes. The most robust method uses a ray-tracing approach that incorporates many of these acquisition parameters, resulting in a less susceptible PAI. We show that by including many sensor and flight parameters in the computation of the forest structural traits, high-quality multitemporal analyses using ALS data can be conducted.

Tropical forests are critical components of the Earth's climate system but the degree to which they are threatened by climate change is poorly constrained. Current dynamic global vegetation models (DGVMs) that are used to predict interactions of tropical forests with climate rely on simplified representations of forest composition and dynamics and fail to accurately simulate observed fluxes. To improve our understanding of how these complex ecosystems respond to environmental changes there is a need for more granular observation and characterisation of tropical forest structure and function. In particular, we need a clearer understanding of how the myriad of deciduous behaviours combine to moderate the flow of carbon and water between the biosphere and atmosphere.

The development of drone mounted sensor systems has radically improved the spatiotemporal resolution with which we can monitor vast forest landscapes. With fieldwork and AI/machine learning techniques applied to remote sensing data, we mapped 3000 tree crowns (with species identification) in the flux tower footprint at Paracou, French Guiana. By scanning the area every three weeks with UAV lidar and modelling light extinction in a 3D voxelised space (with AMAPvox) we were able to track the variation in PAD through time down to the individual crown level. Our analysis revealed a high degree of spatiotemporal variability in PAD, with species showing distinct phenological patterns. By comparing PAD to spectral metrics (from concurrent RGB and MS scans) we were able to contrast the variation through time of “leaf amount” and “leaf quality” (photosynthetic capacity). Linking these signals to the observed fluxes will shed light on how seasonal changes of individual trees combine to produce overall patterns of forest productivity.

Our results demonstrate the potential for UAV-mounted lidar to combine with complementary RS data sources and ground-based measurements to study the subtle phenological patterns of tropical forests.

Habitat structural complexity is widely recognised as a key indicator of biodiversity and ecosystem functioning. Yet despite its importance in shaping ecological processes, we continue to lack a robust, standardised approach for quantifying structural complexity across our increasingly threatened biomes. In response to this challenge, a general theoretical framework based on three key features – height range, fractal dimension and surface rugosity – was recently put forward. This theoretical framework was tested using data from coral reefs, but was developed specifically to be applicable to any system whose 3D structure can be quantified via remote sensing. Here, we provide the first comprehensive assessment of this framework for quantifying the 3D structural complexity of a terrestrial ecosystem and its response to both human and natural disturbance. Using high-resolution airborne laser scanning (ALS) datasets acquired across a forest disturbance gradient in Malaysian Borneo, we show that the framework captures the variation in 3D structural complexity across a tropical forest landscape and can be used to track the loss of complexity, and its recovery, through time. With further evaluation of the framework in different environments, we anticipate that this approach could enhance our understanding of 3D habitat complexity and how it relates to biodiversity and ecosystem functioning.

Thanks to advances in Light Detection and Ranging (LiDAR) technologies such as Terrestrial Laser Scanning (TLS), we are now able to capture the structure of forest stands in great detail. However, measurement of microclimatic properties is often still tied to sensors such as thermocouples that can only provide point measurements. The microclimate in forest stands can change rapidly in space and time due to the complex three-dimensional structure of forests themselves. Therefore, we propose a novel technical approach to map the surface temperatures of a mature forest stand based on sensor fusion of TLS and thermal imaging. Repeated measurements along a survey path allow us to create a time series of temperature changes in the forest stand. At least the lower parts of the stand (up to 10 m) have been captured in high coverage and detail. We gain insight into small-scale temperature changes such as sunspots or different bark structures, which are difficult to capture with other sensing systems, but contribute to the microclimatic variability of the stand. We also show that tree trunks cool by absorbing cold groundwater, which is statistically directly related to sap flow in the tree. Although only an initial proof of concept of this technology, our study provides a successful characterization of the spatiotemporal dynamics of stem temperatures and suggests a promising correlation with ecohydrological processes. Our results suggest that this novel technology opens up several opportunities for future research and ecosystem monitoring.