4D Remote Sensing of Forests
Overview
Our research group is focused on developing and applying remote sensing technologies to monitor and analyse forest ecosystems. This includes the use of LiDAR (Light Detection and Ranging) and other remote sensing tools to gather data on forest structure, biomass, and health. We develop innovative, physically-based methods for extracting detailed information from multi-scale remote sensing data, contributing to our understanding of forest dynamics, biodiversity, and carbon cycling. Additionally, we use radiative transfer models to deepen our knowledge of the fundamentals of remote sensing within the challenging 4D environment of forests.
Team
- Bornand, Aline
- Helfenstein, Isabelle
- Koch, Tiziana
- Kükenbrink, Daniel
- Morsdorf, Felix (Group leader)
Publications
ZORA Publication List
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Publications
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Planting contexts affect urban tree species classification using airborne hyperspectral and LiDAR imagery. Landscape and Urban Planning, 257:105316.
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Satellite observations reveal a positive relationship between trait‐based diversity and drought response in temperate forests. Global Change Biology, 31(2):e70059.
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Completing 3D point clouds of individual trees using deep learning. Methods in Ecology and Evolution, 15(11):2010-2023.
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Evaluation of the terrain elevation estimates over forested areas from spaceborne full-waveform Lidar Missions: GLAS and GEDI. In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 7 July 2024 - 12 July 2024. Institute of Electrical and Electronics Engineers, 6238-6241.
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Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products. Remote Sensing of Environment, 306:114116.
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Limitations of estimating branch volume from terrestrial laser scanning. European Journal of Forest Research, 143(2):687-702.
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Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches. Agricultural and Forest Meteorology, 341:109654.
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Remote sensing‐based forest modeling reveals positive effects of functional diversity on productivity at local spatial scale. Journal of Geophysical Research: Biogeosciences, 128(6):e2023JG007421.
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Remotely sensed functional diversity and its association with productivity in a subtropical forest. Remote Sensing of Environment, 290:113530.
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A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA. Science of the Total Environment, 867:161365.
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Tree volume estimation with terrestrial laser scanning — Testing for bias in a 3D virtual environment. Agricultural and Forest Meteorology, 331:109348.
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Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing. International Journal of Applied Earth Observation and Geoinformation, 114:103074.
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Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity. Remote Sensing of Environment, 275:113024.
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Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270:112845.
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Clumping effects in leaf area index retrieval from large-footprint full-waveform LiDAR. IEEE Transactions on Geoscience and Remote Sensing, 60:4406220.
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Above-ground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 128(6):709-724.
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Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests. IEEE Transactions on Geoscience and Remote Sensing, 59(10):8153-8167.
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Remotely sensed between‐individual functional trait variation in a temperate forest. Ecology and Evolution, 11(16):10834-10867.
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Correcting crown-level clumping effect for improving leaf area index retrieval from large-footprint LiDAR: A study based on the simulated waveform and GLAS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:12386-12402.
