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Partnerships for enhanced engagement in research (PEER)
Cycle 4 (2015 Deadline)

Lidar remote sensing of Brazilian Amazon forests: Analysis of forest biomass, forest degradation, and secondary regrowth

PI: Jean Ometto (, Instituto Nacional de Pesquisas Espaciais (INPE)
U.S. Partner: Michael Keller, USDA - Forest Service
Project Dates: November 2015 - December 2020

Project Overview

4-209 Georeferencing of plots
Georeferencing of field plots.

Recently, the Earth System Science Center (CCST) at INPE received substantial funding from the Amazon Fund-BNDES to improve knowledge of land use change and carbon budgets in the Brazilian Amazon. As part of this project, INPE plans to contract LIDAR (light detection and ranging or laser scanning) remote sensing flights to acquire forest structure information over the Amazon that can be used to improve our knowledge of forest biomass. These data will substantially overcome the current limitations of insufficient and biased sampling of the Brazilian Amazon forest and provide the first large-scale, statistically balanced characterization of forest carbon stocks across the Amazon region. The experimental design will not only estimate carbon stocks across the region but also identify the proportions of forest that are currently secondary or degraded and thus are potentially large carbon sinks in the future. The LIDAR data calibrated by a network of ground-based forest inventories will be used to achieve three objectives:

1. Reduce the uncertainty in the quantification of the above-ground carbon stocks of the forests of the Brazilian Amazon
2. Provide improved estimates of carbon emissions from deforestation and avoided carbon loss from reducing deforestation policies in the Brazilian Amazon region
3. Improve our ability to predict future carbon fluxes in the Brazilian Amazon by quantifying the current biomass status of secondary and degraded forests.

This project should improve the estimation of carbon emissions from deforestation in the Brazilian Amazon and construct a sound basis for future emissions scenarios, considering different options of land use and land cover change. Several strategies, policies, and compensation mechanisms have been proposed to minimize the impact of human actions on the natural forest. Among those, effective implementation of REDD+, soy and beef moratoria, and establishment of conservation units can benefit from better calculation of the balance between carbon emission by deforestation and forest degradation, and uptake, by secondary vegetation and mature forest growth. Through capacity building activities, this project will train young researchers in LIDAR estimates of biomass, statistical biomass modeling and mapping, and emissions modeling. The data gathered will be a resource for the Brazilian Ministry of Science, Technology, and Innovation to produce the National Inventories on GHG emissions. The Brazilian Earth System Model, currently under development at INPE and associated universities and research institutes, is also an obvious client of the planned database. Finally, through its leadership in forest monitoring activities, Brazil has the potential to share its knowledge and technologies with neighboring countries, especially those of the Amazonian Cooperation Treaty Organization. 

Summary of Recent Activities

During the final quarter of 2019, Dr. Domingues’s PhD student Graciela Tejada published a peer-reviewed paper related to the project activities. The paper can be accessed using the link: PhD student Catherine Almeida has also had a paper accepted for publication recently, with PEER providing support towards the collection of field data used to validate the scientific methodology. As previously reported, the team received new LiDAR data from the Fototerra company. They are currently working to process, validate, and calculate forest biomass values for each dataset so they can add the information into their biomass map.

4-209 Data Collection 24-209 Data Collection
The team conducts extensive data collection activities in the Amazon (Photos courtesy of Dr. Ometto).

In December 2019, members of this PEER team presented four papers at the 2019 AGU Fall Meeting in San Francisco. A list of the five papers they have published recently is included below. During the first half of 2020, as the project moves towards its extended close at the end of the year, the PI and his colleagues will continue to install and access new field plots to increase the sample size of their forest measurements. The continuous implementation of these field plots is very important in providing reference points to compare with the LiDAR data and determine the appropriate scientific methodology to be applied. Likewise, the acquisition of new LiDAR transects is equally valuable in validating the field data.

Recent Publications
  • Nascimento, N., T.A.P. West, L. Biber-Freudenberger, E.R. Sousa-Neto, J. Ometto, and J. Börner. 2019. A Bayesian network approach to modelling land-use decisions under environmental policy incentives in the Brazilian Amazon. Journal of Land Use Science
  • Almeida, C.T., L.S. Galvão, L.E.O.C. Aragão, J.P.H.B Ometto, A.D. Jacon, F.R.S. Pereira, L.Y. Sato, A.P. Lopes, P.M.L.A. Graça, C.V.J. Silva, J. Ferreira-Ferreira, and M. Longo. 2019. Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms. Remote Sensing of Environment 232, 111323.
  • Pereira, I., H.M. Nascimento, B.M. Cicari, M. Disney, E. Delucia, T.F. Domingues, B. Kruijt, D. Lapola, P. Meir, R. Norby, J. Ometto, C. Quesada, A. Rammig, and F. Hofhansl. 2019. Performance of Laser-Based Electronic Devices for Structural Analysis of Amazonian Terra-Firme Forests. Remote Sensing 11(5), 510. 
  • Görgens, E.B., A.Z. Motta, M. Assis, M.N. Nunes, T. Jackson, D. Coomes, J. Rosette, L.E.O.C. Aragão, and J.P. Ometto. 2019. The giant trees of the Amazon basin. Frontiers in Ecology and the Environment 17(7), 373-374. 
  • Tejada, G., E.B. Görgens, F.D. Espírito-Santo, R.Z. Cantinho, and J.P. Ometto. 2019. Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon. Carbon Balance and Management 14, 11.  
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