Advanced search
Start date
Betweenand

Quantifying tree mortality with lasers: using a state-of-the-art model-data fusion approach to estimate biomass loss in tropical forests

Grant number: 19/21662-8
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): August 01, 2020
Effective date (End): January 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal researcher:Luiz Eduardo Oliveira e Cruz de Aragão
Grantee:Ricardo Dal'Agnol da Silva
Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil

Abstract

The Amazon Forest is an essential component of the global climate system. It consists in ca. 40% of tropical forests, store ca. 120 ± 30 Pg of carbon in biomass, and constantly exchange carbon with the atmosphere by productivity, mortality and decomposition, and also due to deforestation and fires. Our limited knowledge of tree mortality processes constrains our ability to accurately quantify the tropical forest carbon budget, and affects our predictions of climate and environmental change effects on the future states of forest ecosystems. In this context, remote sensing is the only way to obtain spatially-consistent observations of tree mortality over large regions. Moreover, airborne LIght Detection And Ranging (LiDAR) data can precisely retrieve forest structure and canopy gap information, which has the potential to be used for parametrizing and constraining tree mortality processes in ecosystem models, such as the LPJ-GUESS. The spatial variability of forest structure and gaps across contrasting environmental and climate conditions in Amazon Forest is largely unknown; and we do not fully understand the influence of local-scale drivers (topography) and/or regional-scale drivers such as climate (precipitation, seasonality), and edaphic properties on forest structure and gap dynamics, nor have these gap dynamics been converted into tree mortality rates. In this project, my goal is to provide a systematic and spatially-unbiased assessment of tree mortality rates and related carbon cycling in tropical forests using a novel model-data fusion approach, leveraging the recent availability of a massive single-pass LiDAR dataset. I aim to answer the following questions: (Q1) How does tropical forest structure and gap dynamics derived from airborne LiDAR data vary across the Brazilian Amazon Forest? (Q2) What local- and/or regional-scale factors drive forest dynamics in Brazilian Amazon? (Q3) Can forest structure and canopy gap information derived from single-pass airborne LiDAR data be used to characterize tree mortality rates and improve their representation in large-scale ecosystem models? (Q4) How does this constraint on forest dynamics improves our understanding of the Amazon Forest carbon cycle and what is the contribution of small-scale mortality to carbon turnover? The outcome of this research will consist in novel data of forest gap size-frequency distribution, height and crown sizes, and their spatial distribution across the Brazilian Amazon; a new model-data fusion method to derive tree mortality rates that has potential to be applied worldwide; and more accurate estimates of carbon cycle for the Amazon Forest. (AU)

Matéria(s) publicada(s) na Revista Pesquisa FAPESP sobre a bolsa::
La tecnología al servicio de la selva 
Forest technology 
News published in Agência FAPESP Newsletter about the scholarship:
Articles published in other media outlets (0 total):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications (6)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
KUCK, TAHISA NEITZEL; SILVA FILHO, PAULO FERNANDO FERREIRA; SANO, EDSON EYJI; BISPO, POLYANNA DA CONCEICAO; SHIGUEMORI, ELCIO HIDEITI; DALAGNOL, RICARDO. Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks. REMOTE SENSING, v. 13, n. 23 DEC 2021. Web of Science Citations: 0.
JACON, ALINE DANIELE; GALVAO, LENIO SOARES; DALAGNOL, RICARDO; DOS SANTOS, JOAO ROBERTO. boveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-. GIScience & Remote Sensing, v. 58, n. 7 AUG 2021. Web of Science Citations: 3.
ZHANG, HUIXIAN; HAGAN, DANIEL FIIFI TAWIA; DALAGNOL, RICARDO; LIU, YI. orest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observation. REMOTE SENSING, v. 13, n. 12 JUN 2021. Web of Science Citations: 0.
PONTES-LOPES, ALINE; SILVA, CAMILA V. J.; BARLOW, JOS; RINCON, LORENA M.; CAMPANHARO, WESLEY A.; NUNES, CASSIO A.; DE ALMEIDA, CATHERINE T.; SILVA JUNIOR, CELSO H. L.; CASSOL, HENRIQUE L. G.; DALAGNOL, RICARDO; STARK, SCOTT C.; GRACA, PAULO M. L. A.; ARAGAO, LUIZ E. O. C. Drought-driven wildfire impacts on structure and dynamics in a wet Central Amazonian forest. PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, v. 288, n. 1951 MAY 26 2021. Web of Science Citations: 0.
HEINRICH, VIOLA H. A.; DALAGNOL, RICARDO; CASSOL, HENRIQUE L. G.; ROSAN, THAIS M.; DE ALMEIDA, CATHERINE TORRES; SILVA JUNIOR, CELSO H. L.; CAMPANHARO, WESLEY A.; HOUSE, JOANNA I.; SITCH, STEPHEN; HALES, TRISTRAM C.; ADAMI, MARCOS; ANDERSON, LIANA O.; ARAGAO, LUIZ E. O. C. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. NATURE COMMUNICATIONS, v. 12, n. 1 MAR 19 2021. Web of Science Citations: 5.
DALAGNOL, RICARDO; WAGNER, FABIEN H.; GALVAO, LENIO S.; STREHER, ANNIA S.; PHILLIPS, OLIVER L.; GLOOR, EMANUEL; PUGH, THOMAS A. M.; OMETTO, JEAN P. H. B.; ARAGAO, LUIZ E. O. C. Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. SCIENTIFIC REPORTS, v. 11, n. 1 JAN 14 2021. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.