Representative regional models of post-disturbance forest carbon accumulation: Integrating inventory data and a growth and yield model

Raymond, Crystal L.; Healey, Sean P.; Peduzzi, Alicia; Patterson, Paul L.

doi:10.1016/j.foreco.2014.09.038

Cited by 27 publications

(27 citation statements)

References 57 publications

(76 reference statements)

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“…Ecosystem recovery following disturbances usually absorbs C from the atmosphere to partially compensate the C losses caused by disturbance. It is documented that ecosystem recovery from disturbance contributes largely to the increasing C sink in forest ecosystems (Caspersen et al 2000, McMahon et al 2010, Raymond et al 2015 and the enlarging seasonal CO 2 amplitude in northern hemisphere (Graven et al 2013, Kasischke et al 2010, Zimov et al 1999. During recovery, ecosystem biomass usually accumulates in three stages: slow stage, followed by a fast stage and another slow stage, during which it reaches the maximum value (Pare andBergeron 1995, Preger et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Recovery time and state change of terrestrial carbon cycle after disturbance

Hararuk

et al. 2017

Environ. Res. Lett.

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Ecosystems usually recover from disturbance until a stable state, during which carbon (C) is accumulated to compensate for the C loss associated with disturbance events. However, it is not well understood how likely it is for an ecosystem to recover to an alternative state and how long it takes to recover toward a stable state. Here, we synthesized the results from 77 peer-reviewed case studies that examined ecosystem recovery following disturbances to quantify state change (relative changes between pre-disturbance and fully recovered states) and recovery times for various C cycle variables and disturbance types. We found that most ecosystem C pools and fluxes fully recovered to a stable state that was not significantly different from the pre-disturbance state, except for leaf area index and net primary productivity, which were 10% and 35% higher than the pre-disturbance value, respectively, in forest ecosystem. Recovery times varied largely among variables and disturbance types in the forest, with the longest recovery time required for total biomass (104 ± 33 years) and the shortest time required for C fluxes (23 ± 5 years). The longest and shortest recovery times for different disturbance types are deforestation (101 ± 28 years) and drought (10 ± 1 years), respectively. The recovery time was related to disturbance severity with severer disturbances requiring longer recovery times. However, in the long term, recovery had a strong tendency to drive ecosystem C accumulation towards an equilibrium state. Although we assumed disturbances are static, the recovery-related estimates and relationships revealed in this study are crucial for improving the estimates of disturbance impacts and long-term C balance in terrestrial ecosystems within a disturbance-recovery cycle.

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Section: Introductionmentioning

confidence: 99%

Recovery time and state change of terrestrial carbon cycle after disturbance

Hararuk

et al. 2017

Environ. Res. Lett.

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“…codes 0 '***', 0.001 '**', 0.01 '*', 0.05 '. ', 0.1 '', 1 recovery rates between 16 and 30 years were described (Mazzei et al 2010;West et al 2014;Poorter et al 2016;Raymond et al 2015). Under scenario LDE 1 , we found an average recovery time of 26 years, which is shorter than the cutting cycle of 35 years mandatory in Indonesia.…”

Section: Recovery Timementioning

confidence: 67%

Carbon recovery following selective logging in tropical rainforests in Kalimantan, Indonesia

Butarbutar¹,

Soedirman

Neupane

et al. 2019

For. Ecosyst.

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Background: The knowledge gap regarding post-logging carbon recovery by increased growth is becoming more crucial to understand the significant contribution of forest to climate change mitigation. We assessed the ability of tropical forests in Indonesia to recover carbon following conventional logging. We evaluated carbon re-growth of 10,415 trees in permanent sample plots (PSPs) in East Kalimantan. Four different post-harvesting silvicultural treatments including liberation, refining, thinning, and control were applied in the PSPs. We estimated the carbon recovery period using three different scenarios of total carbon losses due to logging. In the first scenario, we used an existing factor of logging damage and increased it for assuming the range of carbon losses due to different logging practices.Results: Under the existing conventional logging practice, the concession annually emits 51.18 tC•ha − 1 , of which 16.8% are extracted from the forest as raw timber, 38% are logging losses, and 45.2% are emissions due to infrastructure development for logging operation. Increasing the logging damage factor two and three times led to an increase in carbon emission to 70.76 and 90.34 tC•ha − 1 , respectively. The recovery time of the aboveground carbon is 26 years in Scenario 1, 36 years in Scenario 2, and 46 years in Scenario 3. We found no significant effect of the silvicultural treatment type on carbon recovery, but significant effect of the sites was observed. Conclusions: We found that the time taken to restore the carbon to the level found in undisturbed forests is considerably longer than the current intervention cycles. The time needed to recover biomass and carbon-stock noticeably depends on the intensity of logging interventions, demonstrating the benefits of using improved harvesting e.g., reduced impact logging to reduce emissions. The study found that site variability has a significant effect on the carbon recovery time. Different silvicultural treatments, on the other hand, have no effect on the recovery time. The study suggests that it is not appropriate to establish an intervention cycle based on arbitrary choice; the time between interventions must be based on logging losses and site specific growth potential to ensure sustainable management of forests.

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“…The stand dynamics implied by these layers are linked to empirically calibrated carbon storage functions (Raymond et al, 2015) to enable modeling of storage under historical and hypothetical disturbance regimes. Monte Carlo simulation of error in each of the remote sensing products driving ForCaMF is calibrated directly from inventory data, providing integrated uncertainty estimates (Healey et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

The role of remote sensing in process-scaling studies of managed forest ecosystems

Masek

Hayes

Hughes

et al. 2015

Forest Ecology and Management

119

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a b s t r a c tSustaining forest resources requires a better understanding of forest ecosystem processes, and how management decisions and climate change may affect these processes in the future. While plot and inventory data provide our most detailed information on forest carbon, energy, and water cycling, applying this understanding to broader spatial and temporal domains requires scaling approaches. Remote sensing provides a powerful resource for ''upscaling'' process understanding to regional and continental domains. The increased range of available remote sensing modalities, including interferometric radar, lidar, and hyperspectral imagery, allows the retrieval of a broad range of forest attributes. This paper reviews the application of remote sensing for upscaling forest attributes from the plot scale to regional domains, with particular emphasis on how remote sensing products can support parameterization and validation of ecosystem process models. We focus on four key ecological attributes of forests: composition, structure, productivity and evapotranspiration, and disturbance dynamics. For each attribute, we discuss relevant remote sensing technologies, provide examples of their application, and critically evaluate both strengths and challenges associated with their use.Published by Elsevier B.V.

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Representative regional models of post-disturbance forest carbon accumulation: Integrating inventory data and a growth and yield model

Cited by 27 publications

References 57 publications

Recovery time and state change of terrestrial carbon cycle after disturbance

Recovery time and state change of terrestrial carbon cycle after disturbance

Carbon recovery following selective logging in tropical rainforests in Kalimantan, Indonesia

The role of remote sensing in process-scaling studies of managed forest ecosystems

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