Review of ground-based methods to measure the distribution of biomass in forest canopies

Seidel, Dominik; Fleck, Stefan; Leuschner, Christoph; Hammett, Tom

doi:10.1007/s13595-011-0040-z

Cited by 71 publications

(49 citation statements)

References 128 publications

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“…Terrestrial LIDAR as a measurement method for trees developed in the past 10 years (Simonse et al 2003;Aschoff and Spiecker 2004;Thies and Spiecker 2004;Gorte and Pfeifer 2004;Pfeifer and Winterhalder 2004;Danson et al 2007;Fleck et al 2007;Maas et al 2007;Henning and Radtke 2006;Bucksch and Lindenbergh 2008;Bucksch and Fleck 2009;Seidel et al 2011). The basic measurement principle of terrestrial LIDAR is to measure distance and angles to all objects visible from one viewpoint with a 3D-laserscanner and to combine these 3D-coordinates with those measured from other viewpoints to build up a complete 3D-representation.…”

Section: Terrestrial Lidar Measurementsmentioning

confidence: 99%

Comparison of conventional eight-point crown projections with LIDAR-based virtual crown projections in a temperate old-growth forest

Fleck

Mölder

Jacob

et al. 2011

Annals of Forest Science

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& Introduction Sight-based field measurements of tree crown projection area and canopy height are common praxis in forest science but difficult to validate. We quantified their measurement errors based on the virtual representation of an 11-species old-growth forest provided by high-resolution terrestrial LIDAR (light detection and ranging) measurements. & Objectives Based on the expectations (a) that violations of the triangulation theory are the main error source of height measurements, and (b) that approximations of tree crowns with fixed angles are not flexible enough for irregular crown shapes in natural stands, we investigated the relative accuracies of triangulation measurements of height of crown base (B T ) vs. tree height (H T ) and of different crown projection methods. B T (±0.52 m) showed lower measurement errors than H T (±2.4 m). & Results and conclusions Larger deviations between fieldmeasured and virtually executed crown projections could partly be attributed to structural differences of the crowns that were two-dimensionally quantified as space capture index (SCI). The largest deviations between both methods occurred on suppressed tree crowns and tall Quercus robur trees in the stand. Because of the method-inherent underestimation of crown projections with fixed angular grid, we propose the use of flexible angles by trained operators.

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Section: Terrestrial Lidar Measurementsmentioning

confidence: 99%

Comparison of conventional eight-point crown projections with LIDAR-based virtual crown projections in a temperate old-growth forest

Fleck

Mölder

Jacob

et al. 2011

Annals of Forest Science

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“…Substantial work has been undertaken using lidar to map vegetation structure and biomass distributions (see reviews by Seidel et al, 2011 andWulder et al, 2012). These include the estimation of leaf area index (LAI; Riaño et al, 2004;Richardson et al, 2009;Hopkinson et al, 2013), vegetation roughness (Streuker and Antonarakis et al, 2010), alpine tree lines , and total carbon storage and sequestration rates in forest, grassland, savannahs, and/or shrubland communities (Asner et al, 2012a;Baccini et al, 2012;Mascaro et al, 2011;Simard et al, 2011;Antonarakis et al, 2014).…”

Section: Advances In Ecology Using Lidarmentioning

confidence: 99%

Laser vision: lidar as a transformative tool to advance critical zone science

Harpold

Marshall

Lyon

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demon- Published by Copernicus Publications on behalf of the European Geosciences Union. 2882 A. A. Harpold et al.: Laser vision: lidar as a transformative toolstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.

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“…To achieve high accuracy, such detailed information has been mainly collected in visual field surveys by experts (Hyyppä, 2001). However, considering the acquisition of large-area forest inventories, these field measurement activities require a large amount of time, money, and human resources (Segura et al, 2005;Seidel et al, 2011;Wang et al, 2011). In contrast, since remote sensing data, which is obtained by wide-area observation at a single time, has become popular in the last few decades (Rosenqvist et al, 2003;Masek et al 2008), estimating forest biomass from remote sensing is expected to contribute the monitoring activity.…”

Section: Introductionmentioning

confidence: 99%

Optimal Wavelength Selection on Hyperspectral Data With Fused Lasso for Biomass Estimation of Tropical Rain Forest

Takayama¹,

Iwasaki²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/7KEY WORDS: Biomass, Hyperspectral, Forest Management, Fused Lasso, GLCM ABSTRACT:Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous largearea forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the crossvalidation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

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Review of ground-based methods to measure the distribution of biomass in forest canopies

Cited by 71 publications

References 128 publications

Comparison of conventional eight-point crown projections with LIDAR-based virtual crown projections in a temperate old-growth forest

Comparison of conventional eight-point crown projections with LIDAR-based virtual crown projections in a temperate old-growth forest

Laser vision: lidar as a transformative tool to advance critical zone science

Optimal Wavelength Selection on Hyperspectral Data With Fused Lasso for Biomass Estimation of Tropical Rain Forest

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