Detecting and projecting changes in forest biomass from plot data

Muller‐Landau, Helene C.; Detto, Matteo; Chisholm, Ryan A.; Hubbell, Stephen P.; Condit, Richard

doi:10.1017/cbo9781107323506.018

Cited by 31 publications

(55 citation statements)

References 68 publications

(102 reference statements)

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“…In this case, multiple plots (≤1 ha) are recommended rather than one large plot to avoid spatial autocorrelation effects (25). However, average CV values calculated in this study were greater than 20% for most variables (Table 2 and Tables S1 and S3), indicating high spatial sampling error regardless of where plots are placed within the landscape (23).…”

Section: Discussionmentioning

confidence: 71%

“…1 ha scale) are an unbiased sample of the landscape (ca. 10 2 -to 10 4 -ha scale) (21)(22)(23). In the case of Amazonia, fewer than 500 field inventory plots are often used to represent more than 10 9 ha of forest (9).…”

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confidence: 99%

“…Many studies have quantified biases and errors related to the number and size of field plots (26)(27)(28)(29), within-plot sampling (30), or a combination of both (21)(22)(23)31). Others have used models…”

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confidence: 99%

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Amazonian landscapes and the bias in field studies of forest structure and biomass

Marvin

Asner

Knapp

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

110

123

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Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.canopy structure | field inventory plots | forest carbon | Peru tropical forest | LiDAR

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Section: Discussionmentioning

confidence: 71%

mentioning

confidence: 99%

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Amazonian landscapes and the bias in field studies of forest structure and biomass

Marvin

Asner

Knapp

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

110

123

View full text Add to dashboard Cite

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“…1 ha scale) are an unbiased sample of the landscape (ca. 10 2 -to 10 4 -ha scale) [28,55], and previous findings of diminishing uncertainties between field-based and LiDAR-based estimates at this resolution [20]. The 1-ha grid scheme and the horizontal precision of the 50-ha plot corners (0.050 m) helped to reduce co-registration errors related to misalignment between field subplots and LiDAR data, as well as plot-edge effects.…”

Section: Discussionmentioning

confidence: 64%

Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

et al. 2015

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Mapping aboveground carbon density in tropical forests can support CO 2 emission monitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accuracy and generality of calibrations of LiDAR-based aboveground carbon density (ACD) predictions with those obtained from field inventory techniques should be intensified in order to advance tropical forest carbon mapping. Here we present results from the application of a general ACD estimation model applied with small-footprint LiDAR data and field-based estimates of a 50-ha forest plot in Ecuador's Yasuní National Park. Subplots used for calibration and validation of the general LiDAR equation were selected based on analysis of topographic position and spatial distribution of aboveground carbon stocks. The results showed that stratification of plot locations based on topography can improve the calibration and application of ACD estimation using airborne LiDAR (R 2 = 0.94, RMSE = 5.81 Mg¨C¨ha´1, BIAS = 0.59). These results strongly suggest that a general LiDAR-based approach can be used for mapping aboveground carbon stocks in western lowland Amazonian forests.

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“…If the purpose of an analysis is to assess and compare trends in growth through time then any change in the protocol impacts results. In many well-measured long-term plots POM criteria have become stricter over time (e.g., Budongo Forest in Uganda (Sheil, 1995), Barro Colorado in Panama (Muller-Landau et al, 2014)), so applying the suggested method would produce a biased estimate of growth trends (an underestimate). Similarly, for assessing changes in tree size-class or forest stand structure through time, this metric is biased.…”

Section: Discussionmentioning

confidence: 99%

Methods to estimate aboveground wood productivity from long-term forest inventory plots

Talbot

Lewis

Lopez‐Gonzalez

et al. 2014

Forest Ecology and Management

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a b s t r a c tForest inventory plots are widely used to estimate biomass carbon storage and its change over time. While there has been much debate and exploration of the analytical methods for calculating biomass, the methods used to determine rates of wood production have not been evaluated to the same degree. This affects assessment of ecosystem fluxes and may have wider implications if inventory data are used to parameterise biospheric models, or scaled to large areas in assessments of carbon sequestration. Here we use a dataset of 35 long-term Amazonian forest inventory plots to test different methods of calculating wood production rates. These address potential biases associated with three issues that routinely impact the interpretation of tree measurement data: (1) changes in the point of measurement (POM) of stem diameter as trees grow over time; (2) unequal length of time between censuses; and (3) the treatment of trees that pass the minimum diameter threshold (''recruits''). We derive corrections that control for changing POM height, that account for the unobserved growth of trees that die within census intervals, and that explore different assumptions regarding the growth of recruits during the previous census interval. For our dataset we find that annual aboveground coarse wood production (AGWP; in Mg ha À1 year À1 of dry matter) is underestimated on average by 9.2% if corrections are not made to control for changes in POM height. Failure to control for the length of sampling intervals results in a mean underestimation of 2.7% in annual AGWP in our plots for a mean interval length of 3.6 years. Different methods for treating recruits result in mean differences of up to 8.1% in AGWP. In general, the greater the length of time a plot is sampled for and the greater the time elapsed between censuses, the greater the tendency to underestimate wood production. We recommend that POM changes, census interval length, and the http://dx

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Detecting and projecting changes in forest biomass from plot data

Cited by 31 publications

References 68 publications

Amazonian landscapes and the bias in field studies of forest structure and biomass

Amazonian landscapes and the bias in field studies of forest structure and biomass

Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

Methods to estimate aboveground wood productivity from long-term forest inventory plots

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