Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring.

Vista sencilla de ítem
dc.contributorDANIEL 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.
dc.creatorPAPA, D. de A.
dc.creatorALMEIDA, D. R. A. de
dc.creatorSILVA, C. A.
dc.creatorFIGUEIREDO, E. O.
dc.creatorSTARK, S. C.
dc.creatorVALBUENA, R.
dc.creatorRODRIGUEZ, L. C. E.
dc.creatorOLIVEIRA, M. V. N. d'
dc.date2019-11-26T18:10:07Z
dc.date2019-11-26T18:10:07Z
dc.date2019-11-26
dc.date2020
dc.date2020-04-20T11:11:11Z
dc.date.accessioned2026-06-30T22:52:03Z
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.
dc.identifierForest Ecology and Management, v. 457, 1176342019, Feb. 2020.
dc.identifier0378-1127
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1115167
dc.identifier10.1016/j.foreco.2019.117634
dc.identifier.urihttp://hdl.handle.net/123456789/369839
dc.languageeng
dc.rightsopenAccess
dc.subjectManejo florestal
dc.subjectField forest inventory
dc.subjectFiled sampling
dc.subjectAmostragem de campo
dc.subjectCaracterísticas de plantas
dc.subjectCubierta forestal
dc.subjectEspacios vacíos en el dosel
dc.subjectÍndice de área foliar
dc.subjectAnálisis de conglomerados
dc.subjectAnálisis estadístico
dc.subjectEmbrapa Acre
dc.subjectRio Branco (AC)
dc.subjectAcre
dc.subjectAmazônia Ocidental
dc.subjectWestern Amazon
dc.subjectAmazonia Occidental
dc.subjectAdministração Florestal
dc.subjectFloresta Tropical
dc.subjectInventário Florestal
dc.subjectAmostragem
dc.subjectPopulação de Planta
dc.subjectSensoriamento Remoto
dc.subjectRaio Laser
dc.subjectEstrutura Vegetal
dc.subjectCampo Experimental
dc.subjectAnálise Estatística
dc.subjectTropical forests
dc.subjectForest management
dc.subjectPlant characteristics
dc.subjectRemote sensing
dc.subjectLidar
dc.subjectForest canopy
dc.subjectCanopy gaps
dc.subjectLeaf area index
dc.subjectCluster analysis
dc.subjectStatistical analysis
dc.titleEvaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring.
dc.typeArtigo de periódico

Archivos

Colecciones

Embrapa Acre (CPAF-AC)