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Airborne laser scanning for terrain modeling in the Amazon forest Acta Amazonica
ANDRADE,Mariana Silva; GORGENS,Eric Bastos; REIS,Cristiano Rodrigues; CANTINHO,Roberta Zecchini; ASSIS,Mauro; SATO,Luciane; OMETTO,Jean Pierre Henry Balbaud.
ABSTRACT Very few studies have been devoted to understanding the digital terrain model (DTM) creation for Amazon forests. DTM has a special and important role when airborne laser scanning is used to estimate vegetation biomass. We examined the influence of pulse density, spatial resolution, filter algorithms, vegetation density and slope on the DTM quality. Three Amazonian forested areas were surveyed with airborne laser scanning, and each original point cloud was reduced targeting to 20, 15, 10, 8, 6, 4, 2, 1, 0.75, 0.5 and 0.25 pulses per square meter based on a random resampling process. The DTM from resampled clouds was compared with the reference DTM produced from the original LiDAR data by calculating the deviation pixel by pixel and summarizing it...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Ground filter; Airborne LiDAR; Digital terrain model; DTM; ALS.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0044-59672018000400271
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Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+. Repositório Alice
LEITOLD, V.; KELLER, M.; MORTON, D. C.; COOK, B. D.; SHIMABUKURO, Y. E..
Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing.
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Tropical montane forest; Airbone lidar; Digital terrain model; Elevation accuracy; Data thinning; Canopy height; Biomass estimation; REDD+.
Ano: 2015 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1019401
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