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| 1. |  | ROCHA, A. L. F.; NUNES, R. Z. de A.; MATOS, R. S.; FONSECA FILHO, H. D. da; BEZERRA, J. de A.; LIMA, A. R.; GUIMARÃES, F. E. G.; PAMPLONA, A. M. S. R.; MAJOLO, C.; SOUZA, M. G. de; CAMPELO, P. H.; TÃLU, s.; BAGNATO, V. S.; INADA, N. M.; SANCHES, E. A. Alternative controlling agent of Theobroma grandiflorum pests: nanoscale surface and fractal analysis of gelatin/PCL loaded particles containing Lippia origanoides essential oil. Nanomaterials, v. 12, n. 15, art. 2712, 2022.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Acre. Para informações adicionais entre em contato com cpafac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
27/07/2021 |
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Data da última atualização: |
19/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALMEIDA, D. R. A. de; BROADBENT, E. N.; FERREIRA, M. P.; MELI, P.; ZAMBRANO, A. M. A.; GORGENS, E. B.; RESENDE, A. F.; ALMEIDA, C. T. de; AMARAL, C. R. do; CORTE, A. P. D.; SILVA, C. A.; ROMANELLI, J. P.; PRATA, G. A.; PAPA, D. de A.; STARK, S. C.; VALBUENA, R.; NELSON, B. W.; GUILLEMOT, J.; FÉRET, J. B.; CHAZDON, R.; BRANCALION, P. H. S. |
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Afiliação: |
DANILO ROBERTI ALVES DE ALMEIDA, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ) / University of Florida, Gainesville, FL, USA; EBEN NORTH BROADBENT, University of Florida, Gainesville, FL, USA; MATHEUS PINHEIRO FERREIRA, Military Institute of Engineering (IME); PAULA MELI, Universidad de La Frontera, Temuco, Chile; ANGELICA MARIA ALMEYDA ZAMBRANO, University of Florida, Gainesville, FL, USA; ERIC BASTOS GORGENS, Federal University of Jequitinhonha e Mucuri Valleys (UFVJM), Diamantina, Minas Gerais; ANGELICA FARIA RESENDE, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ); CATHERINE TORRES DE ALMEIDA, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ); CIBELE HUMMEL DO AMARAL, Federal University of Viçosa; ANA PAULA DALLA CORTE, Federal University of Parana; CARLOS ALBERTO SILVA, University of Florida / University of Maryland; JOÃO P. ROMANELLI, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ); GABRIEL ATTICCIATI PRATA, University of Florida, Gainesville, FL, USA; DANIEL DE ALMEIDA PAPA, CPAF-AC; SCOTT C. STARK, Michigan State University, East Lansing, MI, USA; RUBEN VALBUENA, Bangor University, Bangor, UK; BRUCE WALKER NELSON, National Institute for Amazon Research (INPA); JOANNES GUILLEMOT, "Luiz de Queiroz" College of Agriculture, University of Sao ˜ Paulo (USP/ESALQ) /; JEAN-BAPTISTE FÉRET, Université Montpellier, Montpellier, France; ROBIN CHAZDON, University of the Sunshine Coast, Australia; PEDRO H. S. BRANCALION, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ). |
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Título: |
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Remote Sensing of Environment, v. 264, 112582, Oct. 2021. |
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ISSN: |
0034-4257 |
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DOI: |
https://doi.org/10.1016/j.rse.2021.112582 |
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Idioma: |
Inglês |
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Conteúdo: |
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. MenosRemote 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 ... Mostrar Tudo |
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Palavras-Chave: |
Aeronave remotamente pilotada; Bosques tropicales; Drone; Hyperspectral remote sensing; Índice de vegetación; Leaf area density; Manejo florestal; Manejo forestal; Mata Atlântica; Monitoreo ambiental; Restauração florestal; Restauración de bosques; Vehículos aéreos no tripulados. |
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Thesagro: |
Área Foliar; Floresta Tropical; Raio Laser; Sensoriamento Remoto; Vegetação. |
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Thesaurus NAL: |
Environmental monitoring; Forest management; Forest restoration; Lidar; Tropical forests; Unmanned aerial vehicles; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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001 2133117
005 2021-11-19
008 2021 bl uuuu u00u1 u #d
022 $a0034-4257
024 7 $ahttps://doi.org/10.1016/j.rse.2021.112582$2DOI
100 1 $aALMEIDA, D. R. A. de
245 $aMonitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion.$h[electronic resource]
260 $c2021
520 $aRemote 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.
650 $aEnvironmental monitoring
650 $aForest management
650 $aForest restoration
650 $aLidar
650 $aTropical forests
650 $aUnmanned aerial vehicles
650 $aVegetation index
650 $aÁrea Foliar
650 $aFloresta Tropical
650 $aRaio Laser
650 $aSensoriamento Remoto
650 $aVegetação
653 $aAeronave remotamente pilotada
653 $aBosques tropicales
653 $aDrone
653 $aHyperspectral remote sensing
653 $aÍndice de vegetación
653 $aLeaf area density
653 $aManejo florestal
653 $aManejo forestal
653 $aMata Atlântica
653 $aMonitoreo ambiental
653 $aRestauração florestal
653 $aRestauración de bosques
653 $aVehículos aéreos no tripulados
700 1 $aBROADBENT, E. N.
700 1 $aFERREIRA, M. P.
700 1 $aMELI, P.
700 1 $aZAMBRANO, A. M. A.
700 1 $aGORGENS, E. B.
700 1 $aRESENDE, A. F.
700 1 $aALMEIDA, C. T. de
700 1 $aAMARAL, C. R. do
700 1 $aCORTE, A. P. D.
700 1 $aSILVA, C. A.
700 1 $aROMANELLI, J. P.
700 1 $aPRATA, G. A.
700 1 $aPAPA, D. de A.
700 1 $aSTARK, S. C.
700 1 $aVALBUENA, R.
700 1 $aNELSON, B. W.
700 1 $aGUILLEMOT, J.
700 1 $aFÉRET, J. B.
700 1 $aCHAZDON, R.
700 1 $aBRANCALION, P. H. S.
773 $tRemote Sensing of Environment$gv. 264, 112582, Oct. 2021.
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