Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

Author(s):Show less -

Leite, Rodrigo Vieira ^[1] ; Silva, Carlos Alberto ^[2] ; Broadbent, Eben North ^[3] ; do Amaral, Cibele Hummel ^[1] ; Liesenberg, Veraldo ^[4] ; Alves de Almeida, Danilo Roberti ^[5] ; Mohan, Midhun ^[6] ; Godinho, Sergio ^{[7, 8]} ; Cardil, Adrian ^{[9, 10, 11]} ; Hamamura, Caio ^[12] ; de Faria, Bruno Lopes ^[13] ; Brancalion, Pedro H. S. ^[5] ; Hirsch, Andre ^[14] ; Marcatti, Gustavo Eduardo ^[14] ; Dalla Corte, Ana Paula ^[15] ; Almeyda Zambrano, Angelica Maria ^[16] ; Teixeira da Costa, Maira Beatriz ^[17] ; Trondoli Matricardi, Eraldo Aparecido ^[17] ; da Silva, Anne Laura ^[14] ; Goya, Lucas Ruggeri Re Y. ^[14] ; Valbuena, Ruben ^[18] ; Furtado de Mendonca, Bruno Araujo ^[19] ; Silva Junior, Celso H. L. ^{[20, 21]} ; Aragao, Luiz E. O. C. ^{[22, 21]} ; Garcia, Mariano ^[23] ; Liang, Jingjing ^[24] ; Merrick, Trina ^{[25, 26]} ; Hudak, Andrew T. ^[27] ; Xiao, Jingfeng ^[28] ; Hancock, Steven ^[29] ; Duncason, Laura ^[30] ; Ferreira, Matheus Pinheiro ^[31] ; Valle, Denis ^[32] ; Saatchi, Sassan ^[33] ; Klauberg, Carine ^[14]

Affiliation:Show less -

^[1] Fed Univ Vicosa UFV, Dept Forest Engn, Av Peter Henry Rolfs, BR-36570900 Vicosa, MG - Brazil

^[2] Univ Florida, Sch Forest Fisheries & Geomat Sci, Forest Biometr & Remote Sensing Lab Silva Lab, POB 110410, Gainesville, FL 32611 - USA

^[3] Univ Florida, Sch Forest Fisheries & Geomat Sci, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL 32611 - USA

^[4] Santa Catarina State Univ UDESC, Coll Agr & Vet, Dept Forest Engn, Lages, SC - Brazil

^[5] Univ Sao Paulo USP ESALQ, Luiz de Queiroz Coll Agr, Dept Forest Sci, Piracicaba, SP - Brazil

^[6] Univ Calif Berkeley, Dept Geog, Berkeley, CA 94709 - USA

^[7] Univ Evora, EaRSLab Earth Remote Sensing Lab, P-7000671 Evora - Portugal

^[8] Univ Evora, Inst Earth Sci ICT, Rua Romao Ramalho 59, P-7002554 Evora - Portugal

^[9] Technosylva Inc, La Jolla, CA - USA

^[10] Univ Lleida, Dept Crop & Forest Sci, Lleida - Spain

^[11] Joint Res Unit CTFC AGROTECNIO, Solsona - Spain

^[12] Fed Inst Educ Sci & Technol Sao Paulo, BR-11533160 Sao Paulo, SP - Brazil

^[13] Fed Univ Vales Jequitinhonha & Mucuri UFVJM, Dept Forest Sci, Campus JK, Diamantina, MG - Brazil

^[14] Fed Univ Sao Joao Del Rei UFSJ, BR-35701970 Sete Lagoas, MG - Brazil

^[15] Fed Univ Parana UFPR, Dept Forest Engn, BR-80210130 Curitiba, Parana - Brazil

^[16] Univ Florida, Ctr Latin Amer Studies, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL 32611 - USA

^[17] Univ Brasilia, Dept Forestry, Campus Darcy Ribeiro, BR-70910900 Brasilia, DF - Brazil

^[18] Bangor Univ, Sch Nat Sci, Bangor LL57 2W, Gwynedd - Wales

^[19] Univ Fed Rural Rio de Janeiro, Silviculture Dept, Rua Floresta, BR-23897005 Seropedica, RJ - Brazil

^[20] Univ Estadual Maranhao UEMA, Dept Engn Agr, BR-65055310 Sao Luis, MA - Brazil

^[21] Natl Inst Space Res, Earth Observat & Geoinformat Div, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[22] Univ Exeter, Coll Life & Environm Sci, Exeter, Devon - England

^[23] Univ Alcala De Henares, Dept Geol Geog & Environm, Environm Remote Sensing Res Grp, Calle Colegios 2, Alcala De Henares 28801 - Spain

^[24] Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 - USA

^[25] Vanderbilt Univ, Dept Earth & Environm Sci, Nashville, TN 37240 - USA

^[26] Florida State Univ, Dept Geog, Tallahassee, FL 32306 - USA

^[27] US Forest Serv, USDA, Rocky Mt Res Stn, 1221 South Main St, Moscow, ID 83843 - USA

^[28] Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03820 - USA

^[29] Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian - Scotland

^[30] Univ Maryland, Dept Geog Sci, College Pk, MD 20740 - USA

^[31] Mil Inst Engn IME, Cartog Engn Sect, Praca Gen Tiburcio 80, BR-22290270 Rio De Janeiro, RJ - Brazil

^[32] Univ Florida, Sch Forest Fisheries & Geomat Sci, POB 110410, 136 Newins Ziegler Hall, Gainesville, FL 32611 - USA

^[33] CALTECH, NASA Jet Prop Lab, Pasadena, CA 91109 - USA

• Document type: Journal article
• Source: REMOTE SENSING OF ENVIRONMENT; v. 268, JAN 2022.
• Web of Science Citations: 0

Abstract

Quantifying fuel load over large areas is essential to support integrated fire management initiatives in fire-prone regions to preserve carbon stock, biodiversity and ecosystem functioning. It also allows a better understanding of global climate regulation as a potential carbon sink or source. Large area assessments usually require data from spaceborne remote sensors, but most of them cannot measure the vertical variability of vegetation structure, which is required for accurately measuring fuel loads and defining management interventions. The recently launched NASA's Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor holds potential to meet this demand. However, its capability for estimating fuel load has yet not been evaluated. In this study, we developed a novel framework and tested machine learning models for predicting multi-layer fuel load in the Brazilian tropical savanna (i.e., Cerrado biome) using GEDI data. First, lidar data were collected using an unnamed aerial vehicle (UAV). The flights were conducted over selected sample plots in distinct Cerrado vegetation formations (i.e., grassland, savanna, forest) where field measurements were conducted to determine the load of surface, herbaceous, shrubs and small trees, woody fuels and the total fuel load. Subsequently, GEDI-like full-waveforms were simulated from the high-density UAV-lidar 3-D point clouds from which vegetation structure metrics were calculated and correlated to field-derived fuel load components using Random Forest models. From these models, we generate fuel load maps for the entire Cerrado using all on-orbit available GEDI data. Overall, the models had better performance for woody fuels and total fuel loads (R-2 = 0.88 and 0.71, respectively). For components at the lower stratum, models had moderate to low performance (R-2 between 0.15 and 0.46) but still showed reliable results. The presented framework can be extended to other fire-prone regions where accurate measurements of fuel components are needed. We hope this study will contribute to the expansion of spaceborne lidar applications for integrated fire management activities and supporting carbon monitoring initiatives in tropical savannas worldwide. (AU)

• FAPESP's process: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral