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Registro Completo |
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
26/11/2019 |
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Data da última atualização: |
02/07/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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'. |
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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. |
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Título: |
Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Forest Ecology and Management, v. 457, 1176342019, Feb. 2020. |
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ISSN: |
0378-1127 |
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DOI: |
10.1016/j.foreco.2019.117634 |
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Idioma: |
Inglês |
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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 |
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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. |
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Thesagro: |
Administração Florestal; Amostragem; Análise Estatística; Campo Experimental; Estrutura Vegetal; Floresta Tropical; Inventário Florestal; População de Planta; Raio Laser; Sensoriamento Remoto. |
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Thesaurus Nal: |
Canopy gaps; Cluster analysis; Forest canopy; Forest management; Leaf area index; Lidar; Plant characteristics; Remote sensing; Statistical analysis; Tropical forests. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205509/1/26910.pdf
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Marc: |
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001 2115167
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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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Registro original: |
Embrapa Acre (CPAF-AC) |
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| Registros recuperados : 49 | |
| 1. |  | PAPA, D. de A. Impacto do manejo de precisão em florestas tropicais. 2018. 112 f. Dissertação (Mestrado em Ciências: Silvicultura e Manejo Florestal) - Programa de Pós-graduação Recursos Florestais, Escola Superior de Agricultura "Luiz de Queiroz", Universidade de São Paulo, Piracicaba. Orientador: Luiz Carlos Estraviz...| Biblioteca(s): Embrapa Acre. |
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| Registros recuperados : 49 | |
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