Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Gonçalves, F. G.; Treuhaft, R. N.; Law, B. E.; Almeida, André Quintão de; Walker, Wayne; Baccini, Alessandro; Santos, João Roberto dos; Graça, Paulo Maurı́cio Lima de Alencastro

doi:10.3390/rs9010047

Cited by 27 publications

(34 citation statements)

References 60 publications

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“…Our limited error analysis indicated that random variations in tree diameter and stand density caused the largest uncertainty with our estimates of Δ C w (Table ). Overall, mean (+ SD ) uncertainty associated with the measurement of tree dimeter and stand density was approximately 15 + 3 and 24 + 5%, respectively, which is comparable to that estimated in other studies (Gonçalves et al, ), and the relative uncertainty associated with estimating stand density increased somewhat as mean stand density increased.…”

Section: Discussionsupporting

confidence: 79%

“…There are many possible sources of uncertainty with estimating of C w , including measurements of D BH , wood density (ρ), and stand tree density (D s ), and estimating tree height (h) from D BH (Chave et al, 2005;Clark, Brown, Kicklighter, Chambers, Thomlinson, Ni, & Holland, 2001;Gonçalves et al, 2017;Marthews et al, 2012). We used bootstrapping to quantify the random uncertainty associated with estimating D BH , ρ, D s , and h and the cumulative effect of this uncertainty on calculating AGC and C w .…”

Section: Discussionmentioning

confidence: 99%

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Spatial and Temporal Variations in Aboveground Woody Carbon Storage for Cerrado Forests and Woodlands of Mato Grosso, Brazil

et al. 2019

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Tropical forests and savanna account for nearly 65% of the total global terrestrial net primary production (NPP); however, there are still large uncertainties in tropical forest NPP because of limited field measurements, especially in the structurally diverse Brazilian savanna (cerrado). To address this uncertainty, we measured patterns of aboveground wood C stocks (C w ) and rates of wood C storage (ΔC w ) over a 7-year period for cerrado forests and woodlands of southern Mato Grosso, Brazil, arrayed across hydrological and soil fertility gradients. We focused on ΔC w because it is an important component of NPP, and wood is a stable, long-term, C storage reservoir. Annual rates of ΔC w were significantly affected by estimates of P and cation (K and Ca) availability, and analysis of covariance indicated that relationships between ΔC w and nutrient availability were independent of stand hydrology. Both upland and hyperseasonal stands exhibited a decline in ΔC w during the 2015-16 El Niño event, which was exceptionally warm and dry. A limited analysis of the uncertainty associated with the field measurements ranged from 7% for wood density to 24% for tree density, while the uncertainty associated with derived quantities ranged from 10% for tree height to 41% for C w . Overall, these results suggest that soil fertility and annual precipitation are important drivers of ΔC w and that warming and drying associated with climate change will cause a decline in aboveground woody C storage for these, and similar, tropical forests and woodlands.Plain Language Summary Tropical forests and savannas are important reservoirs for global carbon (C) storage, but there are still uncertainties in how much C is stored in these ecosystems, especially for savanna. We measured the amount and rate of C stored in aboveground wood for forests and woodlands of the Brazilian savanna in the southern part of the Amazon Basin. Rates of wood C storage increased as nutrient availability increased, and C storage declined during drought years for both seasonally flooded and non-flooded forests and woodlands. These results have implications for how climate and land-use change alter wood C storage in these globally important ecosystems.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variations in Aboveground Woody Carbon Storage for Cerrado Forests and Woodlands of Mato Grosso, Brazil

et al. 2019

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“…The scatter of the red points about the dashed line of Figure 9 is 0.3 Mg-ha −1 -m −1 , which, when multiplied by the average phase-height rate (from Table 1) of 0.5 m-yr −1 yields a 0.15 Mg-ha −1 -yr −1 systematic effect. This scatter is in part due to the error in measured AGB of 25% [24]. In looking up a plot's AGB/h φ , a 25% error will be made in the AGB argument of (13).…”

Section: The Performance Of Phase-height and Agb Rate And Future Enhamentioning

confidence: 99%

“…The diameter, height, and wood density were used with allometric equations [23] to estimate AGB. The accuracy of the AGB estimates was 25% [24]. Plots were geolocated with sub-meter accuracy using differential GPS and a total station.…”

Section: Tapajós National Forest and Field Measurementsmentioning

confidence: 99%

Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry

Treuhaft

Yang

Gonçalves

et al. 2017

Forests

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Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO 2 , which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes in vertical structure and AGB, with sub-hectare and monthly spatial and temporal resolution, respectively. The phase-height observation is described, with a focus on how it is related to vegetation-density, radar-power vertical profiles, and mean canopy heights, which are, in turn, related to AGB. The study site covers 18 × 60 km in the Tapajós National Forest in the Brazilian Amazon. Phase-heights over Tapajós were measured by DLR's TanDEM-X radar interferometer 32 times in a 3.2 year period from 2011-2014. Fieldwork was done on 78 secondary and primary forest plots. In the absence of disturbance, rates of change of phase-height for the 78 plots were estimated by fitting the phase-heights to time with a linear model. Phase-height time series for the disturbed plots were fit to the logistic function to track jumps in phase-height. The epochs of clearing for the disturbed plots were identified with ≈1-month accuracy. The size of the phase-height change due to disturbance was estimated with ≈2-m accuracy. The monthly time resolution will facilitate REDD+ monitoring. Phase-height rates of change were shown to correlate with LiDAR RH90 height rates taken over a subset of the TanDEM-X data's time span (2012)(2013). The average rate of change of phase-height across all 78 plots was 0.5 m-yr −1 with a standard deviation of 0.6 m-yr −1 . For 42 secondary forest plots, the average rate of change of phase-height was 0.8 m-yr −1 with a standard deviation of 0.6 m-yr −1 . For 36 primary forest plots, the average phase-height rate was 0.1 m-yr −1 with a standard deviation of 0.5 m-yr −1 . A method for converting phase-height rates to AGB-rates of change was developed using previously measured phase-heights and field-estimated AGB. For all 78 plots, the average AGB-rate was 1.7 Mg-ha −1 -yr −1 with a standard deviation of 4.0 Mg-ha −1 -yr −1 . The secondary-plot average AGB-rate was 2.1 Mg-ha −1 -yr −1 , with a standard deviation of 2.4 Mg-ha −1 -yr −1 . For primary plots, the AGB average rate was 1.1 Mg-ha −1 -yr −1 with a standard deviation of 5.2 Mg-ha −1 -yr −1 . Given the standard deviations and the number of plots in each category, rates in secondary forests and all forests were significantly different from zero; rates in primary forests were consistent with zero. AGB-rates were compared to change models for Tapajós and to LiDAR-based change measurements in other tropical forests. Strategies for improving AGB dynamical monitoring with X-band interferometry are discussed.

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“…Hence, the objective of this paper was to determine the economic value of carbon stock and carbon sequestration in a dipterocarp tree dominated forest. The methods to determine carbon stock include forest inventories with allometric tree biomass regression models (Foody et al 2001;Gonçalves et al 2017;Lu 2005). Hence, such data is important for managing forested areas for reducing and mitigating CO 2 emission (Van Breugel et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Carbon Stock and Sequestration Valuation in a Mixed Dipterocarp Forest of Malaysia

Matthew¹,

Shuib²,

Muhammad³

et al. 2018

JSM

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Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Cited by 27 publications

References 60 publications

Spatial and Temporal Variations in Aboveground Woody Carbon Storage for Cerrado Forests and Woodlands of Mato Grosso, Brazil

Spatial and Temporal Variations in Aboveground Woody Carbon Storage for Cerrado Forests and Woodlands of Mato Grosso, Brazil

Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry

Carbon Stock and Sequestration Valuation in a Mixed Dipterocarp Forest of Malaysia

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