2015
DOI: 10.1016/j.jag.2015.04.003
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Abstract: In the present study, we aimed to map canopy heights in the Brazilian Amazon mainly on the basis of spaceborne LiDAR and cloud-free MODIS imagery with a new method (the Self-Organizing Relationships method) for spatial modeling of the LiDAR footprint. To evaluate the general versatility, we compared the created canopy height map with two different canopy height estimates on the basis of our original field study plots (799 plots located in eight study sites) and a previously developed canopy height map. The com… Show more

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Cited by 14 publications
(7 citation statements)
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“…As observed in D. Magnabosco Marra et al: Predicting biomass of central Amazonian forests our (Table 2) and other data sets (Sileshi, 2014), the high collinearity between DBH and H can distort coefficient values, inflate standard errors and lead to unreliable estimates. The increased availability of new tools such as Lidar can improve the resolution of data on tree height and thus biomass (Marvin et al, 2014;Sawada et al, 2015), but currently the areas where such data are available are limited. The calibration of remote-sensing-based biomass models for diverse tropical forest still relies on the degree of uncertainty associated to plot-level AGB estimates (Chen et al, 2015).…”
Section: Suitability Of the Chosen Predictors For Practical Applicationmentioning
confidence: 99%
“…As observed in D. Magnabosco Marra et al: Predicting biomass of central Amazonian forests our (Table 2) and other data sets (Sileshi, 2014), the high collinearity between DBH and H can distort coefficient values, inflate standard errors and lead to unreliable estimates. The increased availability of new tools such as Lidar can improve the resolution of data on tree height and thus biomass (Marvin et al, 2014;Sawada et al, 2015), but currently the areas where such data are available are limited. The calibration of remote-sensing-based biomass models for diverse tropical forest still relies on the degree of uncertainty associated to plot-level AGB estimates (Chen et al, 2015).…”
Section: Suitability Of the Chosen Predictors For Practical Applicationmentioning
confidence: 99%
“…Empirical correlations between leaf functional traits (Wright et al 2004) Feldpausch et al 2011, Banin et al 2012, Girardin et al 2013, Malhi et al 2006, Baker et al 2004 Mg ha -1 250-350 Malhi et al 2006, Feldpausch et al 2012, Baker et al 2004Canopy height m 27-38 Feldpausch et al 2011, Asner et al 2013, Girardin et al 2013 m 0.18-0.26 Lieberman et al 1996, Banin et al 2014, Sawada et al 2015 Leaf area index m m -2 3.5-5.5 Myneni et al 2007, Doughty and Goulden 2008, Caldararu, Palmer and Purves 2012 Total net primary production Mg ha -1 a -1 10-20 Malhi, Doughty and Galbraith 2011, Aragão et al 2009, Malhi et al 2013…”
Section: Discussionmentioning
confidence: 99%
“…Forest structure is also the topic in Sawada et al (2015) who deals with the retrieval of forest canopy height from spaceborne LiDAR. An extension of the SOM machine learning method is adopted for building a rule-based system of input/output relationships linking a suite of explanatory MODIS dataset components with observed canopy height for continuous spatial modeling of canopy height in Amazon forest at 500 m resolution.…”
Section: The Special Issuementioning
confidence: 99%