Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Réjou‐Méchain, Maxime; Muller‐Landau, Helene C.; Detto, Matteo; Thomas, Sean C.; Toan, Thuy Le; Saatchi, Sassan; Barreto-Silva, J. S.; Bourg, Norman A.; Bunyavejchewin, Sarayudh; Butt, Nathalie; Brockelman, Warren Y.; Cao, Min; Cárdenas, Dairon; Chiang, Jyh‐Min; Chuyong, George B.; Clay, Keith; Condit, Richard; Dattaraja, H. S.; Davies, Stuart J.; Duque, Álvaro; Esufali, Shameema; Ewango, Corneille E. N.; Fernando, Ruchira; Fletcher, Christine; Gunatilleke, I. A. U. N.; Hao, Zhanqing; Harms, Kyle E.; Hart, Térese B.; Hérault, Bruno; Howe, Robert W.; Hubbell, Stephen P.; Johnson, Daniel J.; Kenfack, David; Larson, Andrew J.; Lin, Luxiang; Lin, Yiching; Lutz, James A.; Makana, Jean‐Remy; Malhi, Yadvinder; Marthews, Toby R.; McEwan, Ryan W.; McMahon, Sean M.; McShea, William J.; Muscarella, Robert; Nathalang, Anuttara; Noor, Nur Supardi Md.; Nytch, Christopher J.; Oliveira, Alexandre Adalardo de; Phillips, Richard P.; Pongpattananurak, Nantachai; Punchi‐Manage, Ruwan; Salim, Roshan Jahn Mohd; Schurman, Jon; Sukumar, Raman; Suresh, H. S.; Suwanvecho, U.; Thomas, Duncan W.; Thompson, Jill; Uriarte, María; Valencia, Renato; Vicentini, Alberto; Wolf, Amy; Yap, Sandra L.; Yuan, Zuoqiang; Zartman, Charles E.; Zimmerman, Jess K.; Chave, Jérôme

doi:10.5194/bg-11-6827-2014

Cited by 101 publications

(74 citation statements)

References 52 publications

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“…Thus, the largest expected AGB change between the field surveys and the radar data acquisition was 68 t.ha − 1 . However, note that the oldest inventories, which were conducted in IFB, were located on poor sandy soils characterized by a pool of species with higher wood density and lower growth rates (Fayolle et al, 2012;Réjou-Méchain et al, 2014). Thus, the largest AGB change of 68 t.ha −1 is unlikely to occur in that area.…”

Section: Reduction Of the Uncertaintiesmentioning

confidence: 95%

“…We discarded the upscaled plots that contained less than two 0.5-ha field plots. The remaining upscaled plots contained a mean field plot size of 2.1 ± 0.6 ha and were all ≥ 1 ha, which may correspond to an AGB estimation sampling error less than 17% according to Réjou-Méchain et al (2014). Then, we attempted to optimize the representativeness of the ground information in the whole 1-km pixels and selected the pixels that were likely to contain homogeneous forests.…”

Section: Reduction Of the Uncertaintiesmentioning

confidence: 99%

See 1 more Smart Citation

Decrease of L-band SAR backscatter with biomass of dense forests

Mermoz

Réjou‐Méchain

Villard

et al. 2015

Remote Sensing of Environment

119

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Section: Reduction Of the Uncertaintiesmentioning

confidence: 95%

Section: Reduction Of the Uncertaintiesmentioning

confidence: 99%

Decrease of L-band SAR backscatter with biomass of dense forests

Mermoz

Réjou‐Méchain

Villard

et al. 2015

Remote Sensing of Environment

119

View full text Add to dashboard Cite

“…Combining forest structure parameters from stand level estimates with extensive forest cover maps derived from remote sensing have been shown to have the potential for providing reasonable biomass estimates at local, regional and global level [13,14]. However, from stand level AGB estimates to wall-to-wall extrapolation using different satellite data, the assessment of carbon stocks or AGB for tropical forests is still spoilt by uncertainty [15] thus weakening the results at regional scales. One of the major reasons for uncertainty in AGB estimates from most medium to high spatial resolution space-borne optical and radar platforms is the saturation of remote sensing signals at fairly low levels of biomass (150-200 Mg·ha −1 ), far below the upper range of values found in tropical forests (>500 Mg·ha −1 ) [16].…”

Section: Introductionmentioning

confidence: 99%

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

et al. 2017

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Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha −1 . The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture-AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R 2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture-AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km 2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.

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“…However, local forest biomass estimations commonly represent the foundation for the calibration and validation of remote sensing models. As a consequence, uncertainties and errors in local biomass estimations may propagate dramatically to broad-scale forest carbon stock assessment (Avitabile et al, 2011;Pelletier et al, 2011;Réjou-Méchain et al, 2014). Aboveground biomass (AGB) is the major pool of biomass in tropical forests (Eggleston et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Ploton¹,

Barbier²,

Takoudjou³

et al. 2016

Biogeosciences

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Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that Published by Copernicus Publications on behalf of the European Geosciences Union. P. Ploton et al.: Closing a gap in tropical forest biomass estimationthe proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

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Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Cited by 101 publications

References 52 publications

Decrease of L-band SAR backscatter with biomass of dense forests

Decrease of L-band SAR backscatter with biomass of dense forests

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

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