Amazeco Pipelines:

AMAZECO: Covering the Amazon with an Ecosystem Structure EBV product combining satellite and airborne lidar

AMAZECO aims to provide maps of Essential Biodiversity Variables (EBVs), based on lidar remote sensing. The project wants to support research and decision-making on the conservation, restoration, and management of tropical forest ecosystems. Our products included Ecosystem Morphological Traits such as canopy mean and maximum height, canopy cover, and heterogeneity. Here, on Amazeco's Github, we are going to provide an R-based tutorial to produce Ecosystem Morphological Traits from airborne and satellite lidar. Anyone with ALS or GEDI data can now produce EBV maps based on different interest areas. Amazeco's statistical validation and methods will be published in Almeida et al. (in prep).

Authors: Danilo Roberti Alves de Almeida, Carlos Alberto Silva, Eric Bastos Gorgens, Mauro de Assis, Michael Keller, Jean Ometto and Ruben Valbuena.

Team

Amazeco members are specialists in the use of lidar data in Brazilian Amazon. Ruben Valbuena is the principal investigator of the Amazeco. He has highlighted the need for standardized morphological traits to support public policy and decision-making (Valbuena et al. 2020). Eric Gorgens recently published a paper about the giant trees of the Amazon using lidar (Gorgens et al. 2021). Carlos Silva is expert on GEDI data (Silva et al. 2021) and he is the developer of important lidar R packages (e.g., “ForestGapR” and “Treetop”), including the rGEDI (Silva et al. 2020). Danilo Almeida worked with Brazilian Amazon lidar data during his Masters and Ph.D. studies, and has experience in leaf area density modeling (developer of the R package “leafR”). Mauro Assis has extensive experience with lidar data processing and cloud computing. He has been working with Jean Ometo, coordinator of the EBA project, the largest lidar database in the Brazilian Amazon. Michael Keller is the coordinator of the Sustainable Landscape project, the first and largest multitemporal data collection in the Brazilian Amazon.

Acknowledgements

Amazeco funded by Microsoft Artificial Intelligence for Earth programme, EBVs (Essential Biodiversity Variables) on the cloud joint call with GEO BON (Group of Earth Observations—Biodiversity Observation Network).

References

Almeida et al. (in prep.) Ecosystem Morphological Traits from airborne and satellite lidar.

Almeida, D. R. A. D., Stark, S. C., Shao, G., Schietti, J., Nelson, B. W., Silva, C. A., ... & Brancalion, P. H. S. (2019). Optimizing the remote detection of tropical rainforest structure with airborne lidar: Leaf area profile sensitivity to pulse density and spatial sampling. Remote Sensing, 11(1), 92.

Gorgens, E. B., Nunes, M. H., Jackson, T., Coomes, D., Keller, M., Reis, C. R., ... & Ometto, J. P. (2021). Resource availability and disturbance shape maximum tree height across the Amazon. Global Change Biology, 27(1), 177-189.

Roussel, J. R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R., Meador, A. S., ... & Achim, A. (2020). lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment, 251, 112061.

Silva, C. A., Duncanson, L., Hancock, S., Neuenschwander, A., Thomas, N., Hofton, M., ... & Dubayah, R. (2021). Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253, 112234.

Silva,C.A; Hamamura,C.; Valbuena, R.; Hancock,S.; Cardil,A.; Broadbent, E. N.; Almeida,D.R.A.; Silva Junior, C.H.L; Klauberg, C. rGEDI: NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing. version 0.1.9, accessed in October. 22 2020, available at: https://CRAN.R-project.org/package=rGEDI

Valbuena, R., O’Connor, B., Zellweger, F., Simonson, W., Vihervaara, P., Maltamo, M., ... & Coops, N. C. (2020). Standardizing ecosystem morphological traits from 3D information sources. Trends in Ecology & Evolution, 35(8), 656-667.