Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Chen, Qi; Laurin, Gaia Vaglio; Валентини, Р.

doi:10.1016/j.rse.2015.01.009

Cited by 119 publications

(107 citation statements)

References 42 publications

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“…Chen et al [46] found that the impact of allometric error contributed about 11% to the total relative prediction error. Mean biomass estimates of the ANR from both ALS and InSAR were lower than the mean estimate from the model with TE (Table 4).…”

Section: Resultsmentioning

confidence: 99%

“…The Pólya-urn resampling scheme generates a design-consistent posterior predictive distribution of the property in interest, given that the sample is reasonably large and representative of the population ( [43], pp. [44][45][46]. We consider our field sample of u=30 observations as representative of the population, and the Pólya-urn resampling generated posterior predictive distributions of biomass for U = 60, 120, and 180 observations based on the sample.…”

Section: Relative Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

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Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m 2 , sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m 2 to achieve the same precision as an inventory supported by ALS data. OPEN ACCESSRemote Sens. 2015, 7 9866

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Section: Resultsmentioning

confidence: 99%

Section: Relative Efficiencymentioning

confidence: 99%

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

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“…In this research, each sample plot was first examined to make sure each plot was located within the forest sites and had good representation of the surveyed forest stand. In addition to the plot size and geolocation error, another critical factor is the use of allometric models for AGB calculation for each plot based on field measurement [80]. Improper selection of the allometric models for specific tree species may produce high uncertainty of AGB calculation at the plot level, thus affecting the AGB estimation performance using the remote sensing data.…”

Section: Uncertainties Due To Sample Plotsmentioning

confidence: 99%

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

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“…This effect is not consistent with the general assumption that individual errors should be compensated for at the plot level. Although the dependence of error propagation on tree size distribution has already been raised (Magnabosco Marra et al, 2015;Mascaro et al, 2011), it is generally omitted from error propagation procedures (e.g., Picard et al, 2014;Moundounga Mavouroulou et al, 2014;Chen et al, 2015). When propagating local bias to 130 1 ha plots in central Africa, the reference pantropical model led to plot-level errors ranging from −15 to +8 %.…”

Section: Model Error Propagation Depends On Targeted Plot Structurementioning

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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Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Cited by 119 publications

References 42 publications

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

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

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