Detection of large above ground biomass variability in lowland forest ecosystems by airborne LiDAR

Jubanski, Juilson; Ballhorn, Uwe; Kronseder, Karin; Franke, Jonas; Siegert, Florian

doi:10.5194/bgd-9-11815-2012

Cited by 14 publications

(21 citation statements)

References 34 publications

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“…Lidar scanning technologies send beams through the canopy and measure the forest structure (e.g., leaf and branch density as the emitted beams interact with them) (32). Hereby, the density of signals returned at a specific height may be comparable to the observed pattern of leaf area density across height (31). Knowledge on the heterogeneous crown structure of forests derived from this study can potentially enhance the interpretation of such remote sensing measurements.…”

Section: Discussionmentioning

confidence: 94%

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The structure of tropical forests and sphere packings

Taubert

Jahn

Dobner

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The search for simple principles underlying the complex architecture of ecological communities such as forests still challenges ecological theorists. We use tree diameter distributions-fundamental for deriving other forest attributes-to describe the structure of tropical forests. Here we argue that tree diameter distributions of natural tropical forests can be explained by stochastic packing of tree crowns representing a forest crown packing system: a method usually used in physics or chemistry. We demonstrate that tree diameter distributions emerge accurately from a surprisingly simple set of principles that include site-specific tree allometries, random placement of trees, competition for space, and mortality. The simple static model also successfully predicted the canopy structure, revealing that most trees in our two studied forests grow up to 30-50 m in height and that the highest packing density of about 60% is reached between the 25-and 40-m height layer. Our approach is an important step toward identifying a minimal set of processes responsible for generating the spatial structure of tropical forests.tropical forest | forest size structure | stochastic geometry | tree crown packing | leaf area F orests are one of the world's best investigated ecosystems (1-4). However, despite all of the studies devoted to forests, mechanistic connections among important features of forest physiognomy are not fully understood. Of crucial importance for this are tree diameter distributions, which have been used for decades in ecology and forestry to characterize the state of forests. Tree diameter distributions are available for many forests of the world and allow together with tree allometries prediction of other important forest attributes like leaf area (5), basal area (6), above-ground biomass (7), tree density, and the presence or absence of disturbances (8). Tropical forests usually include many small trees and far fewer large ones (1, 9). Diameter distributions can also be predicted from dynamic forest models (10-13) in which the forest structure emerges from the interplay between the dynamic processes of mortality, regeneration, competition, and growth.In this study, we argue that tree diameter distributions of natural tropical forests can be predicted by stochastic packing theory-a method usually used in physics or chemistry-together with sitespecific tree allometries. Packing systems have a long history of use across a wide range of disciplines, going back as far as Kepler's analysis of regular packing of spheres (i.e., with a maximum of 74% filled volume). Irregular stochastic packing of spheres has been analyzed in the last decades and shows lower maximal packing densities (e.g., jammed sphere packing systems in physics, with 55-64% filled volume) (14, 15). We show here that simple principles of stochastic packing theory together with tree allometries and other simple structural rules allow for an accurate prediction of observed tree diameter distributions and related structural attributes for two tropical for...

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Section: Discussionmentioning

confidence: 94%

“…Our findings provide important insights into the heterogeneous structure of forests that might be useful also for remote sensing of forests (28,31). Lidar scanning technologies send beams through the canopy and measure the forest structure (e.g., leaf and branch density as the emitted beams interact with them) (32).…”

Section: Discussionmentioning

confidence: 99%

The structure of tropical forests and sphere packings

Taubert

Jahn

Dobner

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Similarly, Hudak et al [45] used LiDAR measurements with significant differences in point densities and found out that different point densities do not affect AGB estimations if LiDAR measurements were independently analyzed. Previous studies revealed that the centroid height is an appropriate height parameter of the LiDAR point cloud to estimate AGB in tropical forests taking also the point distribution over the different vegetation layers into account [27][28][29]. It was therefore used in this study to estimate AGB and its changes.…”

Section: Biomass Estimationmentioning

confidence: 99%

“…Different point cloud statistics of the vegetation height and canopy cover were tested for their performance to predict AGB in peat swamp forests using linear, multiple linear and power functions. Appropriate parameters to estimate AGB were found to be relative height quartiles (r 2 = 0.71, RMSE = 115.2 t/ha or r 2 = 0.7, RMSE = 28.6 t/ha) [27,31] the AGB regression models can be improved by using the point density as input [29]. Multi-temporal LiDAR measurements offer tremendous potential for REDD+ requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have demonstrated the great potential of airborne LiDAR to precisely predict AGB in tropical forests [24][25][26] whereby only few studies have been conducted in the special ecosystem of peat swamp forests [27][28][29][30][31]. Different point cloud statistics of the vegetation height and canopy cover were tested for their performance to predict AGB in peat swamp forests using linear, multiple linear and power functions.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

2013

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Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height

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