SP3: Novel Earth Observation techniques for forest structure analyses and multi-scale characterization of forests

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Earth observation is a powerful means to derive spatially explicit information about the Earth’s surface at large scales and monitor its change over time. Various remote sensing applications related to forests exist. This is based on the sensitivity of active sensor systems (such as LIDAR and SAR) mainly to forest structure (horizontal & vertical), and of passive – predominantly optical – systems mainly to biochemical properties of the canopy (e.g. multi- or hyperspectral sensors). However, recent scientific work demonstrated the capability of very high spatial resolution passive optical sensors onboard UAVs to quantify forest features on the ground (below-canopy). Despite these new findings, there is no one-fits-all sensor in place. While there is a trend of increasingly combining different sensor systems sensitive to specific properties (e.g. small-scale LIDAR measurements as proxy for forest structure at local scale and wide-coverage spaceborne multispectral images to transfer local information to larger scales), there is hardly any exhaustive study that accounts for all sensor types and their spatial, spectral and temporal resolution characteristics.Within SP3 we want to close this gap, and hence our objective is to explore a huge coverage of sensor systems with heterogeneous sensor characteristics, including UAV (very high spatial resolution at cm-range, multispectral, thermal & LIDAR), airborne (high spatial resolution <5 m, hyperspectral) and spaceborne (medium spatial resolution 10 m to 1 km, multispectral, hyperspectral, SAR). We aim at categorizing the temporality of forest disturbances (Enhancement of Structural Beta Complexity (ESBC)) within the controlled experiments of the RU, i.e. if, under which conditions and how long disturbances are detectable for the various sensors to which extent. Our main goal is to identify parameters derived from the remote sensing data that are sensitive to the disturbances, hence forest structure, and to the taxa records in the field, and use those parameters to map β- and γ-diversity at larger scales. Consequently, our findings will bridge the scales of in-situ measurements of forest structure, biodiversity and functionality, fine-scale near-distance remote sensing (UAV type, airborne) and spaceborne satellite remote sensing. As a result, we expect improved understanding if and how the joint analyses of multi-sensor data at different spectral, spatial and temporal resolutions can contribute to in-situ assessments of biodiversity and ecosystem functions.