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Registro Completo |
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
26/11/2019 |
Data da última atualização: |
02/07/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
PAPA, D. de A.; ALMEIDA, D. R. A. de; SILVA, C. A.; FIGUEIREDO, E. O.; STARK, S. C.; VALBUENA, R.; RODRIGUEZ, L. C. E.; OLIVEIRA, M. V. N. d'. |
Afiliação: |
DANIEL DE ALMEIDA PAPA, CPAF-AC; Danilo Roberti Alves de Almeida, ESALQ/USP; Carlos Alberto Silva, University of Maryland, Geographical Sciences Department, USA; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; Scott C. Stark, Michigan State University, East Lansing, MI, USA; Ruben Valbuena, Bangor University, School of Natural Sciences, United Kingdom; Luiz Carlos Estraviz Rodriguez, ESALQ/USP; MARCUS VINICIO NEVES D OLIVEIRA, CPAF-AC. |
Título: |
Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Forest Ecology and Management, v. 457, 1176342019, Feb. 2020. |
ISSN: |
0378-1127 |
DOI: |
10.1016/j.foreco.2019.117634 |
Idioma: |
Inglês |
Conteúdo: |
In high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility. Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to what extent forest plot sampling effort could be reduced, while accurately estimating mean vegetation characteristics in the landscape, by stratifying with ALS structural properties, relative to a random, uniformed conventional approach. The study was developed in an 800-ha area of wet Amazonian forest (Acre, Brazil), including portions of palms, bamboo and dense forest. We estimated relevant structural attributes from ALS: canopy height, openness, rugosity and fractions of leaf area index (LAI) along the vertical profile. We clustered vegetation to define heterogeneity into structural types, employing the Ward method and Euclidean distance. Also, principal component analysis was employed to characterize the groups using field and ALS-derived structural attributes. We simulated sampling intensities to estimate the gain in reducing the field efforts based on pre-stratified and non-stratified forest inventory scenarios. The resulting stratification clearly distinguished the forest?s structural variation gradient and the vegetation density profile. For a fixed uncertainty of 10% in basal area estimation, the ALS-aided stratified inventory reduced the necessary number of field plots by 41%, relative to simple random sampling. The resulting reduction in sampling effort can offset the cost of ALS data collection, significantly enhancing its financial feasibility. In addition, ALS provides broad-coverage quantifications of basal area (or aboveground carbon stock), canopy structure, and accurate terrain characterization, which have an added value for forest management. MenosIn high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility. Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to what extent forest plot sampling effort could be reduced, while accurately estimating mean vegetation characteristics in the landscape, by stratifying with ALS structural properties, relative to a random, uniformed conventional approach. The study was developed in an 800-ha area of wet Amazonian forest (Acre, Brazil), including portions of palms, bamboo and dense forest. We estimated relevant structural attributes from ALS: canopy height, openness, rugosity and fractions of leaf area index (LAI) along the vertical profile. We clustered vegetation to define heterogeneity into structural types, employing the Ward method and Euclidean distance. Also, principal component analysis was employed to characterize the groups using field... Mostrar Tudo |
Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Amostragem de campo; Análisis de conglomerados; Análisis estadístico; Características de plantas; Cubierta forestal; Embrapa Acre; Espacios vacíos en el dosel; Field forest inventory; Filed sampling; Índice de área foliar; Manejo florestal; Rio Branco (AC); Western Amazon. |
Thesagro: |
Administração Florestal; Amostragem; Análise Estatística; Campo Experimental; Estrutura Vegetal; Floresta Tropical; Inventário Florestal; População de Planta; Raio Laser; Sensoriamento Remoto. |
Thesaurus Nal: |
Canopy gaps; Cluster analysis; Forest canopy; Forest management; Leaf area index; Lidar; Plant characteristics; Remote sensing; Statistical analysis; Tropical forests. |
Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205509/1/26910.pdf
|
Marc: |
LEADER 04340naa a2200661 a 4500 001 2115167 005 2021-07-02 008 2020 bl uuuu u00u1 u #d 022 $a0378-1127 024 7 $a10.1016/j.foreco.2019.117634$2DOI 100 1 $aPAPA, D. de A. 245 $aEvaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring.$h[electronic resource] 260 $c2020 520 $aIn high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility. Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to what extent forest plot sampling effort could be reduced, while accurately estimating mean vegetation characteristics in the landscape, by stratifying with ALS structural properties, relative to a random, uniformed conventional approach. The study was developed in an 800-ha area of wet Amazonian forest (Acre, Brazil), including portions of palms, bamboo and dense forest. We estimated relevant structural attributes from ALS: canopy height, openness, rugosity and fractions of leaf area index (LAI) along the vertical profile. We clustered vegetation to define heterogeneity into structural types, employing the Ward method and Euclidean distance. Also, principal component analysis was employed to characterize the groups using field and ALS-derived structural attributes. We simulated sampling intensities to estimate the gain in reducing the field efforts based on pre-stratified and non-stratified forest inventory scenarios. The resulting stratification clearly distinguished the forest?s structural variation gradient and the vegetation density profile. For a fixed uncertainty of 10% in basal area estimation, the ALS-aided stratified inventory reduced the necessary number of field plots by 41%, relative to simple random sampling. The resulting reduction in sampling effort can offset the cost of ALS data collection, significantly enhancing its financial feasibility. In addition, ALS provides broad-coverage quantifications of basal area (or aboveground carbon stock), canopy structure, and accurate terrain characterization, which have an added value for forest management. 650 $aCanopy gaps 650 $aCluster analysis 650 $aForest canopy 650 $aForest management 650 $aLeaf area index 650 $aLidar 650 $aPlant characteristics 650 $aRemote sensing 650 $aStatistical analysis 650 $aTropical forests 650 $aAdministração Florestal 650 $aAmostragem 650 $aAnálise Estatística 650 $aCampo Experimental 650 $aEstrutura Vegetal 650 $aFloresta Tropical 650 $aInventário Florestal 650 $aPopulação de Planta 650 $aRaio Laser 650 $aSensoriamento Remoto 653 $aAcre 653 $aAmazonia Occidental 653 $aAmazônia Ocidental 653 $aAmostragem de campo 653 $aAnálisis de conglomerados 653 $aAnálisis estadístico 653 $aCaracterísticas de plantas 653 $aCubierta forestal 653 $aEmbrapa Acre 653 $aEspacios vacíos en el dosel 653 $aField forest inventory 653 $aFiled sampling 653 $aÍndice de área foliar 653 $aManejo florestal 653 $aRio Branco (AC) 653 $aWestern Amazon 700 1 $aALMEIDA, D. R. A. de 700 1 $aSILVA, C. A. 700 1 $aFIGUEIREDO, E. O. 700 1 $aSTARK, S. C. 700 1 $aVALBUENA, R. 700 1 $aRODRIGUEZ, L. C. E. 700 1 $aOLIVEIRA, M. V. N. d' 773 $tForest Ecology and Management$gv. 457, 1176342019, Feb. 2020.
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Embrapa Acre (CPAF-AC) |
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110. | | COSTA, F. de S.; SALES, M. F. L.; VALENTIM, J. F.; BARDALES, N. G.; AMARAL, E. F. do; COSTA, C. R. da; CATANI, V. Soil carbon sequestration in grass and grass-legume pastures in the western Brazilian Amazon. In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande. Proceedings... Brasília, DF: Embrapa, 2016. p. 353-355. (Embrapa Gado de Corte. Documentos, 216). Organizadores: Roberto Giolo de Almeida (Coordenador), Patrícia Perondi Anchão Oliveira, Maurício Saito, Cleber Oliveira Soares, Lucas Galvan, Lucimara Chiari, Fabiana Villa Alves, Davi José Bungenstab.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Acre. |
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115. | | PAPA, D. de A.; ALMEIDA, D. R. A. de; SILVA, C. A.; FIGUEIREDO, E. O.; STARK, S. C.; VALBUENA, R.; RODRIGUEZ, L. C. E.; OLIVEIRA, M. V. N. d'. Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring. Forest Ecology and Management, v. 457, 1176342019, Feb. 2020.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Acre. |
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117. | | MATTAR, E. P. L.; OLIVEIRA, E. de; JESUS, J. C. da S. de; ARAÚJO, M. L. de; SIVIERO, A.; SANTOS JUNIOR, H. C. dos. Creolo beans production systems in Juruá valley, Acre, Brazilian Amazon. Indian Journal of Traditional Knowledge, New Delhi, v. 15, n. 4, p. 619-624, Oct. 2016.Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
Biblioteca(s): Embrapa Acre. |
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118. | | FARIAS, S. M. de A. P.; SIVIERO, A.; PEREIRA, K. R. M.; MACEDO, P. E. F. de; SANTOS, R. S.; PASSOS, J. R. de S.; FURTADO, E. L. Degradação biológica e a durabilidade natural de espécies florestais da Amazônia em campo de apodrecimento no Acre. Research, Society and Development, v. 12, n. 4, e10112440990, 2023.Tipo: Artigo em Periódico Indexado | Circulação/Nível: C - 0 |
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