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10. | | LEAL, M. S.; DIAS, H. C. T.; MINGOTI, R.; FERREIRA, M. P.; TONELLO, K. C. Definition of priority areas for soil and water conservation in the Itanguá watershed, Capão Bonito, SP state, Brazil. Ciência Florestal, Santa Maria, v. 29, n. 2, p. 531-544, abr./jun. 2019. Biblioteca(s): Embrapa Territorial. |
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14. | | ANDRADE, I. B. de; LINS, L. F.; FERREIRA, M. P. de B.; SILVEIRA, A. V. M.; RABELO, M. C. Cuidados higiênicos com o leite de cabra: um breve levantamento amostral de pequenos produtores rurais assistidos em projeto de extensão no estado de Pernambuco, Brasil. Higiene Alimentar, v. 31, n. 266/267, p. 2025-2029, mar./abr. 2017. Edição dos trabalhos apresentados no VIII Congresso Latino-Americano e XIV Congresso Brasileiro de Higienistas de Alimentos, IV Encontro Nacional de Vigilância de Zoonoses, VI Encontro do Sistema Brasileiro de Inspeção de Produtos de... Biblioteca(s): Embrapa Caprinos e Ovinos. |
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18. | | FERREIRA, M. P. de B.; CARVALHO, F. F. R. de; BARROS, N. N.; MELLO, A. de A. Relationship between body measurements and live weight in Saanen and Nubian goats breeds. In: INTERNATIONAL CONFERENCE ON GOATS, 7., 2000, Tours, Poitiers, France. Proceedings... Paris: INRA: IGA: Institut de Elevage, 2000. v. 1, p. 223. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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20. | | FERREIRA, M. P.; OLIVEIRA, M. C. N. de; MANDARINO, J. M. G.; SILVA, J. B. da; IDA, E. I.; CARRÃO-PANIZZI, M. C. Changes in the isoflavone profile and in the chemical composition of tempeh during processing and refrigeration. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 46, n. 11, p. 1555-1561, nov. 2011. Biblioteca(s): Embrapa Soja; Embrapa Trigo; Embrapa Unidades Centrais. |
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Registros recuperados : 26 | |
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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
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
27/07/2021 |
Data da última atualização: |
19/11/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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). |
Título: |
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Remote Sensing of Environment, v. 264, 112582, Oct. 2021. |
ISSN: |
0034-4257 |
DOI: |
https://doi.org/10.1016/j.rse.2021.112582 |
Idioma: |
Inglês |
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 |
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. |
Thesagro: |
Área Foliar; Floresta Tropical; Raio Laser; Sensoriamento Remoto; Vegetação. |
Thesaurus NAL: |
Environmental monitoring; Forest management; Forest restoration; Lidar; Tropical forests; Unmanned aerial vehicles; Vegetation index. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 04321naa a2200685 a 4500 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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