Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115167
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dc.contributor.authorPAPA, D. de A.eng
dc.contributor.authorALMEIDA, D. R. A. deeng
dc.contributor.authorSILVA, C. A.eng
dc.contributor.authorFIGUEIREDO, E. O.eng
dc.contributor.authorSTARK, S. C.eng
dc.contributor.authorVALBUENA, R.eng
dc.contributor.authorRODRIGUEZ, L. C. E.eng
dc.contributor.authorOLIVEIRA, M. V. N. d'eng
dc.date.accessioned2019-11-26T18:10:07Z-
dc.date.available2019-11-26T18:10:07Z-
dc.date.created2019-11-26
dc.date.issued2020
dc.identifier.citationForest Ecology and Management, v. 457, 1176342019, Feb. 2020.eng
dc.identifier.issn0378-1127eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1115167-
dc.descriptionIn 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.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectManejo florestaleng
dc.subjectField forest inventoryeng
dc.subjectFiled samplingeng
dc.subjectAmostragem de campoeng
dc.subjectCaracterísticas de plantaseng
dc.subjectCubierta forestaleng
dc.subjectEspacios vacíos en el doseleng
dc.subjectÍndice de área foliareng
dc.subjectAnálisis de conglomeradoseng
dc.subjectAnálisis estadísticoeng
dc.subjectEmbrapa Acreeng
dc.subjectRio Branco (AC)eng
dc.subjectAcreeng
dc.subjectAmazônia Ocidentaleng
dc.subjectWestern Amazoneng
dc.subjectAmazonia Occidentaleng
dc.titleEvaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring.eng
dc.typeArtigo de periódicoeng
dc.date.updated2020-04-20T11:11:11Z
dc.subject.thesagroAdministração Florestaleng
dc.subject.thesagroFloresta Tropicaleng
dc.subject.thesagroInventário Florestaleng
dc.subject.thesagroAmostragemeng
dc.subject.thesagroPopulação de Plantaeng
dc.subject.thesagroSensoriamento Remotoeng
dc.subject.thesagroRaio Lasereng
dc.subject.thesagroEstrutura Vegetaleng
dc.subject.thesagroCampo Experimentaleng
dc.subject.thesagroAnálise Estatísticaeng
dc.subject.nalthesaurusTropical forestseng
dc.subject.nalthesaurusForest managementeng
dc.subject.nalthesaurusPlant characteristicseng
dc.subject.nalthesaurusRemote sensingeng
dc.subject.nalthesaurusLidareng
dc.subject.nalthesaurusForest canopyeng
dc.subject.nalthesaurusCanopy gapseng
dc.subject.nalthesaurusLeaf area indexeng
dc.subject.nalthesaurusCluster analysiseng
dc.subject.nalthesaurusStatistical analysiseng
riaa.ainfo.id1115167eng
riaa.ainfo.lastupdate2020-04-20 -03:00:00
dc.identifier.doi10.1016/j.foreco.2019.117634eng
dc.contributor.institutionDANIEL 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.eng
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