Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004

Powell, Scott; Cohen, Warren B.; Kennedy, Robert E.; Healey, Sean P.; Huang, Chengquan

doi:10.1007/s10021-013-9713-9

Cited by 26 publications

(23 citation statements)

References 44 publications

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“…Forest land remaining forest land lost 184 ± 10 Tg C year −1 to disturbance (13% from natural disturbance, 87% from harvest); these were compensated by net carbon gains of 452 ± 48 Tg C year −1 , 75% of which occurred within timberland areas (Table 4). C losses from natural and human induced disturbances reduced the potential net C sink in US forests by 42% compared to the potential sink estimated without disturbance effects included, an estimate that is similar to other studies [28, 34]. …”

Section: Resultssupporting

confidence: 84%

“…Zhou et al [36] estimate total C emissions from wood harvest in 35 eastern US states as 168 Tg C year −1 between 2002 and 2010, while our estimate for the same geographic extent is 132 ± 8 Tg C year −1 between 2006 and 2010. Other national scale estimates of emissions from wood harvest are lower, such as that of Williams et al [37] (107 Tg year −1 in 2005) and Powell et al [34] (74 Tg C year −1 between 1986 and 2004). Hicke and Zeppel [38] estimated that bark beetles and fire together resulted in gross emissions of 32 Tg C year −1 in the western US between 1997 and 2010.…”

Section: Discussionmentioning

confidence: 99%

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Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Harris

Hagen

Saatchi

et al. 2016

Carbon Balance Manage

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BackgroundLocating terrestrial sources and sinks of carbon (C) will be critical to developing strategies that contribute to the climate change mitigation goals of the Paris Agreement. Here we present spatially resolved estimates of net C change across United States (US) forest lands between 2006 and 2010 and attribute them to natural and anthropogenic processes.ResultsForests in the conterminous US sequestered −460 ± 48 Tg C year−1, while C losses from disturbance averaged 191 ± 10 Tg C year−1. Combining estimates of net C losses and gains results in net carbon change of −269 ± 49 Tg C year−1. New forests gained −8 ± 1 Tg C year−1, while deforestation resulted in losses of 6 ± 1 Tg C year−1. Forest land remaining forest land lost 185 ± 10 Tg C year−1 to various disturbances; these losses were compensated by net carbon gains of −452 ± 48 Tg C year−1. C loss in the southern US was highest (105 ± 6 Tg C year−1) with the highest fractional contributions from harvest (92%) and wind (5%). C loss in the western US (44 ± 3 Tg C year−1) was due predominantly to harvest (66%), fire (15%), and insect damage (13%). The northern US had the lowest C loss (41 ± 2 Tg C year−1) with the most significant proportional contributions from harvest (86%), insect damage (9%), and conversion (3%). Taken together, these disturbances reduced the estimated potential C sink of US forests by 42%.ConclusionThe framework presented here allows for the integration of ground and space observations to more fully inform US forest C policy and monitoring efforts.

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Section: Resultssupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Harris

Hagen

Saatchi

et al. 2016

Carbon Balance Manage

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“…The problems of estimating change from RS data are well-known in the tropical forest literature (Brown, 2002, Mitchard et al 2014. An approach examining transitions among land cover categories can be effectively used to identify sites and volume of losses-as has been done recently with Landfire specifically (Zheng et al 2011, Powell et al 2014-but is less appropriate as an estimator of net change in forest carbon. For estimating net change on forested land, an inventory-based approach such as the FIA, when the data is available, is likely to be more reliable than an approach relying on category changes such as one based on Landfire.…”

Section: Improving Landscape-scale Carbon Flux Estimates For Californmentioning

confidence: 99%

Source or Sink? A comparison of Landfire- and FIA-based estimates of change in aboveground live tree carbon in California’s forests

Holland

Stewart

Potts

2019

Environ. Res. Lett.

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Forests play a central role in addressing climate change, and accurate estimates of forest carbon are critical for the development of actions that reduce emissions from forests and that maximize sequestration by forests. Methodological challenges persist regarding how best to estimate forest carbon stocks and flux at regulatory-relevant scales. Using California, USA as a case study, we compare two approaches to stock-difference forest carbon estimation for aboveground live trees: one based on ground inventories and one on land cover classification of remotely-sensed data. Previous work using ground inventory data from the Forest Inventory and Analysis Program (FIA) showed net aboveground carbon (AGC) sequestration by live trees in California forests, while estimates using land cover classification from the Landscape Fire and Resource Management Planning Tools (Landfire) showed net reductions in live tree AGC over a similar time period. We examined the discrepancy by re-analyzing the FIA inventory data through the lens of a category-change analysis based on Landfire. This analysis showed more than 50% of the live tree AGC in fewer than 4% of Landfire-equivalent categories and that the overwhelming majority (>80%) of forest area did not change height category between measurement periods. Despite the lack of categorical change, the majority of FIA plots increased in both 95th percentile tree height and in live tree AGC. These findings suggest that an approach based on observing categorical changes risks undercounting AGC sequestration resulting from growth and thus overstating the relative importance of AGC reductions that result from disturbances. This would bias AGC flux estimates downward, leading us to validate the conclusion that live trees in California were a net sink of aboveground carbon in the decade ending in 2016. Our findings suggest an inventory-based or hybrid approach is preferable to methods that depend on categorical bins for estimating AGC in disturbance-prone forest ecosystems.

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“…Wall-to-wall lidar coverage for large areas is still costly and logistically prohibitive for many regional and NFI programs; however, 500 m resolution MODIS-based biomass linkage to climate and forest disturbance has demonstrated that forest development and climate controls were important contributors to the yearly AGB increase from 2001 to 2010 [43]. The Landsat time series (LTS) change detection methods provide pixel-level characterization of forest disturbance and recovery [44][45][46][47] and thus LTS change metrics can improve and overcome some limitations in forest structure [38] and facilitate predictions of forest AGB dynamics over time [14,48,49]. Forest disturbance data based on Vegetation Change Tracker (VCT) data can be used to integrate with AGB.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Shen

Huang

et al. 2016

Remote Sensing

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Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007)(2008)(2009)(2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R 2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.

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Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004

Cited by 26 publications

References 44 publications

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Source or Sink? A comparison of Landfire- and FIA-based estimates of change in aboveground live tree carbon in California’s forests

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

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