Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion

Autor(es):Mostrar menos -

de Almeida, Danilo Roberti Alves ^{[1, 2]} ; Broadbent, Eben North ^[2] ; Ferreira, Matheus Pinheiro ^[3] ; Meli, Paula ^[4] ; Zambrano, Angelica Maria Almeyda ^[5] ; Gorgens, Eric Bastos ^[6] ; Resende, Angelica Faria ^[1] ; de Almeida, Catherine Torres ^[1] ; do Amaral, Cibele Hummel ^[7] ; Corte, Ana Paula Dalla ^[8] ; Silva, Carlos Alberto ^{[9, 10]} ; Romanelli, Joao P. ^[1] ; Prata, Gabriel Atticciati ^[2] ; Papa, Daniel de Almeida ^[11] ; Stark, Scott C. ^[12] ; Valbuena, Ruben ^[13] ; Nelsonn, Bruce Walker ^[14] ; Guillemot, Joannes ^{[1, 15, 16]} ; Feret, Jean-Baptiste ^[17] ; Chazdon, Robin ^[18] ; Brancalion, Pedro H. S. ^[1]

Afiliação do(s) autor(es):Mostrar menos -

^[1] Univ Sao Paulo USP ESALQ, Luiz de Queiroz Coll Agr, Dept Forest Sci, Piracicaba, SP - Brazil

^[2] Univ Florida, Sch Forest Resources & Conservat, Spatial Ecol & Conservat Lab, Gainesville, FL 32611 - USA

^[3] Mil Inst Engn IME, Cartog Engn Dept, Rio De Janeiro, RJ - Brazil

^[4] Univ La Frontera, Landscape Ecol & Conservat Lab LEPCON, Temuco - Chile

^[5] Univ Florida, Ctr Latin Amer Studies, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL - USA

^[6] Fed Univ Jequitinhonha & Mucuri Valleys UFVJM, Dept Forestry, Diamantina, MG - Brazil

^[7] Univ Fed Vicosa, Dept Forest Engn, Vicosa, MG - Brazil

^[8] Univ Fed Parana, Dept Forest Engn, Curitiba, Parana - Brazil

^[9] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 - USA

^[10] Univ Florida, Sch Forest Fisheries & Geomat Sci, Gainesville, FL - USA

^[11] Embrapa Acre, Rio Branco, Acre - Brazil

^[12] Michigan State Univ, Dept Forestry, E Lansing, MI 48824 - USA

^[13] Bangor Univ, Sch Nat Sci, Bangor, Gwynedd - Wales

^[14] Natl Inst Amazon Res INPA, Manaus, Amazonas - Brazil

^[15] CIRAD, UMR ECO & SOLS, F-34398 Montpellier - France

^[16] Univ Montpellier, Inst Agro, CIRAD, INRAE, Eco & Sols, IRD, Montpellier - France

^[17] Univ Montpellier, CNRS, AgroParisTech, CIRAD, INRAE, TETIS, Montpellier - France

^[18] Univ Sunshine Coast, Trop Forests & People Res Ctr, Sippy Downs, Qld 4556 - Australia

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING OF ENVIRONMENT; v. 264, OCT 2021.
• Citações Web of Science: 2

Resumo

Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate highresolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data-canopy height, leaf area index (LAI), and understory LAI-and eighteen variables derived from hyperspectral data-15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale. (AU)

• Processo FAPESP: 19/24049-5 - Monitoramento das florestas em restauração do Estado do São Paulo: aplicação de novas ferramentas de sensoriamento remoto e subsídios para políticas públicas
• Beneficiário: Angelica Faria de Resende
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 19/08533-4 - Compreendendo os aspectos ecológicos e sociais das ações de restauração em regiões tropicais através de revisões sistemáticas e metanálises
• Beneficiário: João Paulo Romanelli
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 18/21338-3 - Monitoramento da restauração de paisagens florestais usando veículo aéreo não tripulado com sensoriamento remoto Lidar e hiperespectral
• Beneficiário: Danilo Roberti Alves de Almeida
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 18/18416-2 - Compreendendo florestas restauradas para o benefício das pessoas e da natureza - NewFor
• Beneficiário: Pedro Henrique Santin Brancalion
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOTA - Temático
• Processo FAPESP: 19/14697-0 - Monitoramento da demografia e diversidade de florestas em processo de restauração usando um sistema drone-lidar-hiperespectral
• Beneficiário: Danilo Roberti Alves de Almeida
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado