Forest biomass retrieval approaches from earth observation in different biomes

Rodríguez-Veiga, Pedro; Quegan, S.; Carreiras, João M. B.; Persson, Henrik J.; Fransson, Johan E. S.; Hościło, Agata; Ziółkowski, Dariusz; Stereńczak, Krzysztof; Lohberger, Sandra; Stängel, Matthias; Berninger, Anna; Siegert, Florian; Avitabile, Valerio; Herold, Martin; Mermoz, Stéphane; Bouvet, Alexandre; Toan, Thuy Le; Carvalhais, Nuno; Santoro, Maurizio; Cartus, Oliver; Rauste, Yrjö; Mathieu, Renaud; Asner, Gregory P.; Thiel, Christian; Pathe, Carsten; Schmullius, C.; Seifert, Frank Martin; Tansey, Kevin; Balzter, Heiko

doi:10.1016/j.jag.2018.12.008

Cited by 75 publications

(95 citation statements)

References 94 publications

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“…Various techniques have been developed for observing the biomass and productivity of forest ecosystems scattered throughout the world [1,2]. Yet the measurements of large-scale forest biomass represent the population especially in a large-scale estimation.…”

Section: Forest Biomass Estimationmentioning

confidence: 99%

“…To sort through and clarify thoughts, we divided possible uncertainty into two parts corresponding to two stages in forest biomass estimation: (1) volume prediction, in which the sampling error occurs due to the sampling design that affects the sample representative in NFI, and the non-sampling error may exist as the model error in the volume calculation using tree and stand-level data. (2) Biomass conversion, in which the non-sampling error primarily includes the measurement and biomass model errors. The former could affect model parameterization, and the latter may be caused by the structure of the model itself.…”

Section: Forest Biomass Estimationmentioning

confidence: 99%

“…Any observed data lying in the restricted zone strongly suggests that there may be problems in field measurement or counting. After fixing these problems by setting up restrictions on regression parameters, the reliability of these two PEs will be improved and Equations (1) and (2) can then be rewritten as a conventional single equation by canceling B s :…”

Section: Parametric Equationsmentioning

confidence: 99%

“…Outliers make wood density estimates (ρ) largely inconsistent, which changes the curvature (β) of Equation (3). In short, (1), and bottom right parts do this for Equation (2). A restricted zone is designed ranging from the lower-bound of B t (shows a proportion as 80% stem and 20% other parts of the tree) to upper-bound of B s (shows maximum wood density of 0.7).…”

Section: Parameter Improvementmentioning

confidence: 99%

“…§ The ρ denotes improved wood density. (1), and bottom right parts do this for Equation (2). CD means coefficient of determination; n is the numbers of plots.…”

Section: Comparison Of Two Relationshipsmentioning

confidence: 99%

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Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

Liu

Zhou

Lei

et al. 2019

Forests

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The method of forest biomass estimation based on a relationship between the volume and biomass has been applied conventionally for estimating stand above-and below-ground biomass (SABB, t ha −1 ) from mean growing stock volume (m 3 ha −1 ). However, few studies have reported on the diagnosis of the volume-SABB equations fitted using field data. This paper addresses how to (i) check parameters of the volume-SABB equations, and (ii) reduce the bias while building these equations. In our analysis, all equations were applied based on the measurements of plots (biomass or volume per hectare) rather than individual trees. The volume-SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions. Stem biomass is an intermediate variable (parametric variable) in the PEs, of which one is established by regressing the relationship between stem biomass and volume, and the other is created by regressing the allometric relationship of stem biomass and SABB. A graphical analysis of the PEs proposes a concept of "restricted zone," which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data. The sampling simulations were performed using pseudo data (artificially generated in order to test a model) for the model test. Both analyses of the regression and simulation demonstrate that the wood density impacts the parameters more than the allometric relationship does. This paper presents an applicable method for testing the field data using reasonable wood densities, restricting the error in field data processing based on limited field plots, and achieving a better understanding of the uncertainty in building those equations.cannot be conducted directly [3,4] due to restrictions like heavy load of fieldwork [5,6], non-destructive measurement requirements [7], and difficulty of belowground biomass (BGB) measurement [8][9][10]. Indirect methods have been applied in estimating forest biomass through the amount of growing volume [11]. These indirect methods can be classified [12][13][14][15] as three conceptually different types: (i) the empirical statistical approach, (ii) the biogeochemical-mechanistic simulation approach, and (iii) the remote sensing approach. The first type is conventional. It usually estimates the biomass using a biomass expansion factor (BEF) or biomass conversion and expansion factor (BCEF) [11], and biomass allometric equations [14,16]. The factors are helpful for converting the biomass conveniently, and can be improved by addressing the variation of BEFs over time [17,18]. For more accurate estimation, the volume-based biomass equations were also frequently employed based on detailed data of plots on each stratum. These allometric models are constructed based on measurements from field samples. Depending on the sample size, a number of tree-level and stand-level models have been developed for applications corresponding to different data sources. Recently, Di Cosmo et al. [16] have deeply discussed the characters, features, and uses of different...

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Section: Forest Biomass Estimationmentioning

confidence: 99%

Section: Forest Biomass Estimationmentioning

confidence: 99%

Section: Parametric Equationsmentioning

confidence: 99%

Section: Parameter Improvementmentioning

confidence: 99%

“…§ The ρ denotes improved wood density. (1), and bottom right parts do this for Equation (2). CD means coefficient of determination; n is the numbers of plots.…”

Section: Comparison Of Two Relationshipsmentioning

confidence: 99%

See 3 more Smart Citations

Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

Liu

Zhou

Lei

et al. 2019

Forests

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A Circumpolar Perspective on the Contribution of Trees to the Boreal Forest Carbon Balance

Pappas

Babst

Fatichi

et al. 2023

Advances in Global Change Research

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Partitioned estimates of the boreal forest carbon (C) sink components are crucial for understanding processes and developing science-driven adaptation and mitigation strategies under climate change. Here, we provide a concise tree-centered overview of the boreal forest C balance and offer a circumpolar perspective on the contribution of trees to boreal forest C dynamics. We combine an ant’s-eye view, based on quantitative in situ observations of C balance, with a bird’s-eye perspective on C dynamics across the circumboreal region using large-scale data sets. We conclude with an outlook addressing the trajectories of the circumboreal C dynamics in response to projected environmental changes.

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Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Erasmi

Klinge

Dulamsuren

et al. 2021

Environ Monit Assess

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The monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

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Forest biomass retrieval approaches from earth observation in different biomes

Cited by 75 publications

References 94 publications

Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

A Circumpolar Perspective on the Contribution of Trees to the Boreal Forest Carbon Balance

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

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