Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest

Drake, Jason B.; Dubayah, Ralph; Knox, Robert G.; Clark, David B.; Blair, J. B.

doi:10.1016/s0034-4257(02)00013-5

Cited by 266 publications

(183 citation statements)

References 32 publications

Supporting

Mentioning

168

Contrasting

Unclassified

Order By: Relevance

“…Simple linear models using maximum canopy height, median canopy height, mean canopy height and QMCH were reported to account for 80%, 70%, 73% and 80% of variation in the above ground biomass observations, respectively. Above ground biomass estimation studies using the Laser Vegetation Imaging Sensor (LVIS; Blair et al 1999) have been reported by Drake et al (2002aDrake et al ( , 2002bDrake et al ( , 2003. These studies have primarily focused on the tropical forests of Costa Rica and Panama.…”

Section: Previous Researchmentioning

confidence: 99%

“…Unlike the other laser height metrics explored, the predictive capabilities of the HOME metric were attributed to its sensitivity to the vertical organization and density of canopy structural elements (Drake et al 2002a ). The subsequent studies by Drake et al (2002bDrake et al ( , 2003 have not focused on above ground biomass estimation, but rather on exploring the relationship between vertical canopy profiles derived from field data and laser-based canopy height profiles. Here, the authors address the reasons as to why laser height metrics are correlated with above ground biomass (Drake et al 2002b ), and whether the relationship between laser height metrics (i.e.…”

Section: Previous Researchmentioning

confidence: 99%

“…The subsequent studies by Drake et al (2002bDrake et al ( , 2003 have not focused on above ground biomass estimation, but rather on exploring the relationship between vertical canopy profiles derived from field data and laser-based canopy height profiles. Here, the authors address the reasons as to why laser height metrics are correlated with above ground biomass (Drake et al 2002b ), and whether the relationship between laser height metrics (i.e. HOME) and above ground biomass could be generalized to other tropical forest regions and types (Drake et al 2003).…”

Section: Previous Researchmentioning

confidence: 99%

“…(1) full waveform laser scanning (Means et al 1999, Drake et al 2002a, 2002b; (2) discrete return laser profiling (Nelson et al 1988a(Nelson et al , 1988b(Nelson et al , 2004; and (3) discrete return laser scanning (Lim et al 2003a ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Lim

Treitz

2004

Scandinavian Journal of Forest Research

185

127

View full text Add to dashboard Cite

). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Received Nov. 11, 2003. Accepted Aug. 10, 2004. Scand. J. For. Res. 19: 558 Á/570, 2004 A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in rootmean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha(1 , respectively.

show abstract

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Lim

Treitz

2004

Scandinavian Journal of Forest Research

185

127

View full text Add to dashboard Cite

show abstract

“…Segundo Parresol (1999), uma característica desejável das equações de regressão para estimativa da biomassa dos componentes das árvores é que a soma da biomassa estimada para cada componente da árvore seja igual àquela estimada para toda a árvore. Foram utilizados dados de sensoriamento remoto para estimativa do volume e da biomassa florestal (MONTES et al, 2000;DRAKE et al, 2002), porém essa estimativa depende da prévia medição das dimensões de árvores no local a ser avaliado.…”

Section: Introductionunclassified

Equações para estimar a biomassa da parte aérea e do sistema radicular em povoamentos de Eucalyptus grandis em sítios com produtividades distintas

Mello

Gonçalves

2008

Rev. Árvore

View full text Add to dashboard Cite

RESUMO -O objetivo principal deste trabalho foi ajustar equações para a estimativa da biomassa da parte aérea e sistema radicular de povoamentos de Eucalyptus grandis aos 11 anos de idade, crescendo em dois sítios com produtividades distintas. O solo argiloso (sítio mais produtivo) foi classificado como Latossolo Vermelho Distrófico (LVd) e o solo arenoso (sítio menos produtivo), como Neossolo Quartzarênico (RQ). Foi realizado um inventário de biomassa de 10 árvores-amostra em cada sítio, incluindo os componentes da parte aérea (fuste, casca, folhas e galhos) e do sistema radicular (raízes finas e grossas). Ajustaram-se dois modelos logarítmicos para estimar a biomassa das árvores, nos quais se utilizaram como variáveis independentes o diâmetro à altura do peito (DAP), a altura total das árvores (H) ou a variável combinada DAP 2 H. Os modelos testados foram significativos no nível de 95% de probabilidade, pelo teste t, de Student. O melhor ajuste das equações foi obtido com a variável DAP 2 H. A validação de equações comuns aos dois sítios foi baseada na análise conjunta do coeficiente de determinação (R²), erro-padrão da estimativa (S y.x ) e análise gráfica dos resíduos porcentuais. As equações ajustadas tiveram maior precisão na estimativa da biomassa do fuste (R 2 = 0,99; S y.x = 0,12) e da casca (R 2 = 0,97; S y.x = 0,24). Apesar dos R 2 > 0,70, as equações para a estimativa da biomassa de folhas e galhos apresentaram baixa capacidade preditiva, caso em que se recomenda a adição de outras variáveis associadas ao tamanho da copa das árvores. Equações específicas para cada local, visando estimar a biomassa de raízes finas [LVd (R 2 = 0,97; S y.x = 0,30); RQ (R 2 = 0,96; S y.x = 0,15)] e grossas [LVd (R 2 = 0,98; S y.x = 0,19); RQ (R 2 = 0,99; S y.x = 0,15)], mostraram uma precisão melhor que as equações comuns, uma vez que houve maior alocação relativa de biomassa radicular no solo mais arenoso (RQ).Palavras-chave: Eucalipto, qualidade do sítio e solo. EQUATIONS TO ESTIMATE SHOOT AND ROOT SYSTEM BIOMASS IN Eucalyptus grandis STANDS AT SITES WITH DIFFERENT PRODUCTIVITIES

show abstract

Characterizing Forest Canopy Structure and Ground Topography Using Lidar

Dubayah

Peterson

Rhoads

et al. 2005

Encyclopedia of Hydrological Sciences

View full text Add to dashboard Cite

Remote sensing observations increasingly are used to obtain the detailed information needed about land surface state required for hydrological analyses. However, many of the surface state parameters required for such analyses are related to the vertical structures of surface vegetation and topography, that are often difficult to measure using passive optical and radar remote sensing technologies. A new technology, lidar (light detection and ranging) remote sensing, has proven highly effective for characterizing land surface structure in great detail, including subcanopy topography, canopy height, foliar profile and biomass, among others. In this article, we review the theory and application of lidar remote sensing for characterizing land surface states for hydrological analysis. First we present a brief overview of lidar, and discuss similarities and distinctions between the small‐footprint systems commonly used in commercial applications and large‐footprint approaches used in research and space‐based systems. We next describe common land surface variables that are often used in hydrological analysis and how these are related to vertical structures observable from lidar. Lastly, we present how lidar may be used to recover or parameterize these surface variables; in particular we focus on the observation of forest and topographic structures.

show abstract

Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest

Cited by 266 publications

References 32 publications

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

Equações para estimar a biomassa da parte aérea e do sistema radicular em povoamentos de Eucalyptus grandis em sítios com produtividades distintas

Characterizing Forest Canopy Structure and Ground Topography Using Lidar

Contact Info

Product

Resources

About