A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Araza, Arnan; Bruin, S. de; Herold, Martin; Quegan, S.; Labrière, Nicolas; Rodríguez-Veiga, Pedro; Avitabile, Valerio; Santoro, Maurizio; Mitchard, Edward T. A.; Ryan, Casey M.; Phillips, Oliver L.; Willcock, Simon; Verbeeck, Hans; Carreiras, João M. B.; Hein, Lars; Schelhaas, Mart-Jan; Pacheco-Pascagaza, Ana María; Bispo, Polyanna da Conceição; Laurin, Gaia Vaglio; Vieilledent, Ghislain; Slik, Ferry; Wijaya, Arief; Lewis, Simon L.; Morel, Alexandra; Liang, Jingjing; Sukhdeo, Hansrajie; Schepaschenko, Dmitry; Čavlović, Juro; Gilani, Hammad; Lucas, Richard

doi:10.1016/j.rse.2022.112917

Cited by 63 publications

(79 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2022) will provide new input for AGB analysis. Recent studies have evidenced that novel approaches could significantly improve the quality of AGB estimations, which could be affected by uncertainties derived even from plot data (Araza et al., 2022; Duncanson et al., 2021). Here, we presented an approach based on previously validated and published maps, which have implemented a series of methods to deal with uncertainty and error (e.g., propagation of errors).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019

et al. 2022

View full text Add to dashboard Cite

Here we present a global time‐series of global forest above ground biomass from 2000 to 2019, analyzing spatiotemporal patterns of carbon balance, accounting for losses and gains. We generated a global Above‐Ground Biomass (AGB) map for the year 2000 and assessed its correlation with different satellite products. We generated a multi‐year analysis of AGB changes at the pixel level was generated, estimating carbon (C) loss and gain. Finally, we estimated the C losses due to forest clearing and wildfires analyzing their trends across biomes and countries. Our results show that the global mean annual loss was 2.88 ± 0.33 PgC yr−1, while global mean C gain was 2.95 ± 0.43 PgC yr−1, resulting in a neutral to sink behavior of −0.06 ± 0.58 PgC yr−1. The mean annual C loss by forest clearing was 1.04 ± 0.03 PgC yr−1, with an increasing trend of +0.03 ± 0.01 PgC yr−1. Eight biomes and 54 countries showed a significant increasing trend of C loss by forest clearing. Wildfires C losses reached 0.351 ± 0.02 Pg C yr−1, representing the 33.71% of forest clearing C losses. Boreal forest presented the highest C losses from wildfires, while significant increasing trends were evidenced in five biomes. We also find increasing trends of wildfire C loss in 20 countries while decreasing trends were identified in 10 countries. Our findings highlight the importance of designing strong policies to halt deforestation as agreed in the recent COP26 and provide information to identify priority areas to start implementing these policies in the short term.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Additionally, the selection of the best‐fitted pixel as approach could be also complemented with weighted averaging techniques as been stated by Araza et al. (2022) and Avitabile et al. (2016).…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The height, cover, PAI and AGBD products used in this study are from the rst 18 months of mission data, between April 2019 and September 2020, and represent a total of > 400 million 3D structure samples. GEDI presents the rst ever global-scale satellite dataset designed speci cally for measuring forest structure, and overcomes limitations for forest height, cover, and AGB mapping in dense forests where previous maps have little sensitivity to AGB 20 .…”

Section: Gedi Mission Overview and Datasetsmentioning

confidence: 99%

“…Intact forests, especially tropical forests, can sequester twice as much carbon than more human-impacted forests and planted monocultures 17,19 . Protected forest areas are thought to contribute a large fraction (~27%) of the net global GHG sink 4 but large uncertainties remain in the magnitudes of AGB stocks and uxes in terrestrial ecosystems 20,21 . As a result, the degree to which protected status contributes to avoided carbon emissions or enhanced sequestration at a global scale remains highly uncertain.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Global Protected Areas for Climate Change Mitigation

Duncanson

Liang

Leitold

et al. 2022

Preprint

View full text Add to dashboard Cite

Forests play a critical role in stabilizing Earth’s climate. Establishing Protected Areas (PAs) represents one approach to forest conservation, but PAs were rarely created to mitigate climate change. The global impact of PAs on the carbon cycle, through avoided emissions and/or enhanced growth, has not previously been quantified due to a lack of accurate global carbon stock maps. Here we used ~412 million lidar samples from NASA’s GEDI mission to estimate a total of 19.7 +/- 1.8 Gt of additional Aboveground Biomass (AGB) associated with PA status. These higher C stocks are primarily attributed to avoided emissions, and are roughly equivalent to annual global fossil fuel emissions. The total measured PA AGB was 125.3 Gt (+/- 0.63), 26% of all mapped terrestrial woody AGB. These results underscore the importance of conservation of high integrity1, high biomass forests for avoiding carbon emissions and preserving future sequestration.

show abstract

“…The AGB means the total mass of organic matter per unit area of the maize at a certain time, which is an important indicator of productivity in the ecosystem. The most accurate approach to measuring the authentic values of AGB is to obtain the weight by destructive harvesting [39]. The FAGB was obtained by weighing each part separately before measuring the physical parameters of the maize, where the wilted parts of the maize leaves were removed in the late growing season.…”

Section: Measurements Of Maize Canopy Parametersmentioning

confidence: 99%

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

Chen

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient ( mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error () of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error ()). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.

show abstract

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Cited by 63 publications

References 66 publications

Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019

Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019

The Effectiveness of Global Protected Areas for Climate Change Mitigation

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

Contact Info

Product

Resources

About