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Tree-centric forest inventory across regions and biomes using airborne lidar

Airborne Laser Scanning (ALS) is the most effective technique to expand the sampling of fine-scale 3D forest structure across space and time beyond the limited range of inventory plots. However, the most commonly used ALS-based techniques to estimate biophysical metrics (e.g. biomass, basal area, tree density) apply the so-called area-based approach that relies on a large amount of field samples to calibrate the ALS signal and, therefore, they are not easily transferable across forest types and regions. Individual Tree Crown (ITC) approaches have been successfully applied at multiple sites; however, studies often focus on a single forest type with relatively simple forest structures, and the ability to expand ITC approaches across biomes and regions remains uncertain. We developed an ITC approach, called Adaptive Mean Shift (AMS3D, Ferraz et al., 2012), whose parameterization is driven by local tree height-crown area allometric equations that are extracted from the ALS measurements in a pre-processing stage. The AMS3D method has been successfully applied globally across different biomes (Tropical, Mediterranean, Alpine, and Temperate). Importantly, our method outperforms most ITC approaches over complex tropical forest structures for the characterization of both vertical and horizontal components (Aubry-Kientz et al., 2019; Ferraz et al., 2016). The AMS3D results have been validated in terms of several biophysical variables including stem density, tree height and crown area distributions, stem size spectrum, basal area, and biomass. We show the meaningfulness of our ITC maps to characterize large-scale tree structural variability across forest types and gradients of climate and edaphic conditions using nearly 600 ALS transects randomly sampled over the entire Brazilian Amazon (http://www.ccst.inpe.br/projetos/eba-estimativa-de-biomassa-na-amazonia/). Finally, we briefly discuss ongoing synergies between ALS-derived ITC maps with airborne spectral imagery to map forest functional traits (e.g. leaf chlorophyll and leaf carotenoids) in an object-based approach as opposed to the commonly used pixel-based methods.

Bibliography

Aubry-Kientz, M., Dutrieux, R., Ferraz, A., Saatchi, S., Hamraz, H., Williams, J., Coomes, D., Piboule, A., Vincent, G., 2019. A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sens. 11. doi:10.3390/rs11091086 Ferraz, A., Bretar, F., Jacquemoud, S., Gonçalves, G., Pereira, L., Tomé, M., Soares, P., 2012. 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sens. Environ. 121, 210–223. doi:10.1016/j.rse.2012.01.020 Ferraz, A., Saatchi, S., Mallet, C., Meyer, V., 2016. Lidar detection of individual tree size in tropical forests. Remote Sens. Environ. 183, 318–333. doi:10.1016/j.rse.2016.05.028

Antonio Ferraz
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
United States

Sassan Saatchi
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
United States

David Clark
Department of Biology, University of Missouri-St. Louis, MO, USA
United States

Marcos Longo
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
United States

Michael Keller
USDA Forest Service, International Institute of Tropical Forestry, San Juan, Puerto Rico
Puerto Rico

Victoria Meyer
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
United States

Fabian Schneider
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
United States

Clement Mallet
University Paris Est, IGN-ENSG, LaSTIG, Saint-Mandé, France
France

Jean Ometto
CCST/INPE, São José dos Campos, SP, Brazil
Brazil

 


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