Ground Data are Essential for Biomass Remote Sensing Missions

Chave, Jérôme; Davies, Stuart J.; Phillips, Oliver L.; Lewis, Simon L.; Sist, Plínio; Schepaschenko, Dmitry; Armston, John; Baker, Timothy R.; Coomes, David A.; Disney, Mathias; Duncanson, Laura; Hérault, Bruno; Labrière, Nicolas; Meyer, Victoria; Réjou‐Méchain, Maxime; Scipal, Klaus; Saatchi, Sassan

doi:10.1007/s10712-019-09528-w

Cited by 120 publications

(107 citation statements)

References 51 publications

(49 reference statements)

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“…We selected all permanent plots (Table , SUPMAT 1, 2, 3) with high‐level of botanical determination that respect the international standards (Chave et al. ), most of them belonging also to the Guyadiv network ( http://atdnmorphospecies.myspecies.info/node/781). All individual trees (≥10 cm in diameter at breast height) present in the plots were marked, mapped, and measured.…”

Section: Methodsmentioning

confidence: 99%

Long‐term influence of early human occupations on current forests of the Guiana Shield

Odonne

Bel

Burst

et al. 2019

Ecology

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To decipher the long-term influences of pre-Columbian land occupations on contemporary forest structure, diversity, and functioning in Amazonia, most of the previous research focused on the alluvial plains of the major rivers of the Amazon basin. Terra firme, that is, nonflooded forests, particularly from the Guiana Shield, are yet to be explored. In this study, we aim to give new insights into the subtle traces of pre-Columbian influences on present-day forests given the archaeological context of terra firme forests of the Guiana Shield. Following archaeological prospects on 13 sites in French Guiana, we carried out forest inventories inside and outside archaeological sites and assessed the potential pre-Columbian use of the sampled tree species using an original ethnobotanical database of the Guiana Shield region. Aboveground biomass (320 and 380 T/ha, respectively), basal area (25-30 and 30-35 m 2 /ha, respectively), and tree density (550 and 700 stem/ha, respectively) were all significantly lower on anthropized plots (As) than on nonanthropized plots (NAs). Ancient human presence shaped the species composition of the sampled forests with Arecaceae, Burseraceae, and Lauraceae significantly more frequent in As and Annonaceae and Lecythidaceae more frequent in NAs. Although alpha diversity was not different between As and NAs, the presence of pre-Columbian sites enhances significantly the forest beta diversity at the landscape level. Finally, trees with edible fruits are positively associated with pre-Columbian sites, whereas trees used for construction or for their bark are negatively associated with pre-Columbian sites. Half a millennium after their abandonment, former occupied places from the inner Guiana Shield still bear noticeable differences with nonanthropized places. Considering the lack of data concerning archaeology of terra firme Amazonian forests, our results suggest that pre-Columbian influences on the structure (lower current biomass), diversity (higher beta diversity), and composition (linked to the past human tree uses) of current Amazonian forests might be more important than previously thought.

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Section: Methodsmentioning

confidence: 99%

Long‐term influence of early human occupations on current forests of the Guiana Shield

Odonne

Bel

Burst

et al. 2019

Ecology

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“…Currently, researchers of several international networks such as RAINFOR (the Amazon Forest Inventory Network, http://www.rainfor.org/), AfriTRON (African Tropical Rainforest Observation Network, www.afritron.org), and ForestGEO (Global Earth Observatory, https://forestgeo.si.edu/) network, have engaged in utilizing long-term permanent sample plots to monitor forest biomass and dynamics [116]. The Forest Observation System (FOS, https://forest-observation-system.net/) which includes, but is not limited to data records from these networks, is tasked to coordinate in situ activities in relation to the BIOMASS mission [117]. The biomass plot data will be in unified format, and processed with standardized procedures.…”

Section: Compilation Of Field Biomass Datamentioning

confidence: 99%

A Review of Regional and Global Gridded Forest Biomass Datasets

2019

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Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.

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“…Chave et al (2004), using an OLS model constructed from a compilation of pan-tropical calibration data, found relative uncertainty to approximate 5-10 % at the 1 ha stand-scale (Chave et al, 2014), when accounting for source R2 using the standard error of the regression, and R3 using a Taylor series expansion. Réjou-Méchain et al (2017), using the same model and calibration data, but perturbing the parameters of the model via a Bayesian framework to simulate further error arising from R2, found relative uncertainty to approximate 10 % at the 1 ha stand-scale (Chave et al, 2019). Picard et al (2015a) considered R2 still further, and cognisant of the multiple, nominally suitable allometric models available, estimated their aggregate variance FIGURE 1 | Definition of the terms used in this paper to describe the concept of error in out-of-sample pan-tropical allometric AGB predictions (ISO 5727 and BIPM definitions) (ISO-5725-1:1994(ISO-5725-1: (en), 1994JCGM-200:2012JCGM-200: , 2012.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Bias in Pan-Tropical Biomass Predictions

Burt¹,

Calders²,

Cuní-Sanchez³

et al. 2020

Front. For. Glob. Change

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Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree-and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in-and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree-and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.

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Ground Data are Essential for Biomass Remote Sensing Missions

Cited by 120 publications

References 51 publications

Long‐term influence of early human occupations on current forests of the Guiana Shield

Long‐term influence of early human occupations on current forests of the Guiana Shield

A Review of Regional and Global Gridded Forest Biomass Datasets

Assessment of Bias in Pan-Tropical Biomass Predictions

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