Long-term carbon loss and recovery following selective logging in Amazon forests

Maoyi Huang, Gregory P. Asner

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Amazon deforestation contributes significantly to global carbon (C) emissions. In comparison, the contribution from selective logging to atmospheric CO2 emissions, and its impact on regional C dynamics, is highly uncertain. Using a new geographically based modeling approach in combination with high resolution remote sensing data from 1999 to 2002, we estimate that C emissions were 0.04-0.05 Pg C yr-1 due to selective logging from a ∼2,664,960 km2 region of the Brazilian Amazon. Selective logging was responsible for 15-19% higher carbon emissions than reported from deforestation (clear-cutting) alone. Our simulations indicated that forest carbon lost via selective logging lasts two to three decades following harvest, and that the original live biomass takes up to a century to recover, if the forests are not subsequently cleared. The two-to three-decade loss of carbon results from the biomass damaged by logging activities, including leaves, wood, and roots, estimated to be 89.1 Tg C yr-1 from 1999 to 2002 over the study region, leaving 70.0 Tg C yr-1 and 7.9 Tg C yr-1 to accumulate as coarse woody debris and soil C, respectively. While avoided deforestation is central to crediting rain forest nations for reduced carbon emissions, the extent and intensity of selective logging are also critical to determining carbon emissions in the context of Reduced Emissions from Deforestation and Forest Degradation (REDD). We show that a combination of automated high-resolution satellite monitoring and detailed forest C modeling can yield spatially explicit estimates of harvest-related C losses and subsequent recovery in support of REDD and other international carbon market mechanisms.

Original languageEnglish (US)
Article numberGB3028
JournalGlobal Biogeochemical Cycles
Volume24
Issue number3
DOIs
StatePublished - Oct 13 2010
Externally publishedYes

Fingerprint

selective logging
Carbon
Deforestation
deforestation
Recovery
carbon emission
carbon
coarse woody debris
Biomass
biomass
clearcutting
modeling
loss
Debris
remote sensing
Rain
degradation
Remote sensing
market
Wood

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Environmental Science(all)
  • Atmospheric Science

Cite this

Long-term carbon loss and recovery following selective logging in Amazon forests. / Huang, Maoyi; Asner, Gregory P.

In: Global Biogeochemical Cycles, Vol. 24, No. 3, GB3028, 13.10.2010.

Research output: Contribution to journalArticle

@article{5a251833ff4b44f8a8b123d49c7b005d,
title = "Long-term carbon loss and recovery following selective logging in Amazon forests",
abstract = "Amazon deforestation contributes significantly to global carbon (C) emissions. In comparison, the contribution from selective logging to atmospheric CO2 emissions, and its impact on regional C dynamics, is highly uncertain. Using a new geographically based modeling approach in combination with high resolution remote sensing data from 1999 to 2002, we estimate that C emissions were 0.04-0.05 Pg C yr-1 due to selective logging from a ∼2,664,960 km2 region of the Brazilian Amazon. Selective logging was responsible for 15-19{\%} higher carbon emissions than reported from deforestation (clear-cutting) alone. Our simulations indicated that forest carbon lost via selective logging lasts two to three decades following harvest, and that the original live biomass takes up to a century to recover, if the forests are not subsequently cleared. The two-to three-decade loss of carbon results from the biomass damaged by logging activities, including leaves, wood, and roots, estimated to be 89.1 Tg C yr-1 from 1999 to 2002 over the study region, leaving 70.0 Tg C yr-1 and 7.9 Tg C yr-1 to accumulate as coarse woody debris and soil C, respectively. While avoided deforestation is central to crediting rain forest nations for reduced carbon emissions, the extent and intensity of selective logging are also critical to determining carbon emissions in the context of Reduced Emissions from Deforestation and Forest Degradation (REDD). We show that a combination of automated high-resolution satellite monitoring and detailed forest C modeling can yield spatially explicit estimates of harvest-related C losses and subsequent recovery in support of REDD and other international carbon market mechanisms.",
author = "Maoyi Huang and Asner, {Gregory P.}",
year = "2010",
month = "10",
day = "13",
doi = "10.1029/2009GB003727",
language = "English (US)",
volume = "24",
journal = "Global Biogeochemical Cycles",
issn = "0886-6236",
publisher = "American Geophysical Union",
number = "3",

}

TY - JOUR

T1 - Long-term carbon loss and recovery following selective logging in Amazon forests

AU - Huang, Maoyi

AU - Asner, Gregory P.

PY - 2010/10/13

Y1 - 2010/10/13

N2 - Amazon deforestation contributes significantly to global carbon (C) emissions. In comparison, the contribution from selective logging to atmospheric CO2 emissions, and its impact on regional C dynamics, is highly uncertain. Using a new geographically based modeling approach in combination with high resolution remote sensing data from 1999 to 2002, we estimate that C emissions were 0.04-0.05 Pg C yr-1 due to selective logging from a ∼2,664,960 km2 region of the Brazilian Amazon. Selective logging was responsible for 15-19% higher carbon emissions than reported from deforestation (clear-cutting) alone. Our simulations indicated that forest carbon lost via selective logging lasts two to three decades following harvest, and that the original live biomass takes up to a century to recover, if the forests are not subsequently cleared. The two-to three-decade loss of carbon results from the biomass damaged by logging activities, including leaves, wood, and roots, estimated to be 89.1 Tg C yr-1 from 1999 to 2002 over the study region, leaving 70.0 Tg C yr-1 and 7.9 Tg C yr-1 to accumulate as coarse woody debris and soil C, respectively. While avoided deforestation is central to crediting rain forest nations for reduced carbon emissions, the extent and intensity of selective logging are also critical to determining carbon emissions in the context of Reduced Emissions from Deforestation and Forest Degradation (REDD). We show that a combination of automated high-resolution satellite monitoring and detailed forest C modeling can yield spatially explicit estimates of harvest-related C losses and subsequent recovery in support of REDD and other international carbon market mechanisms.

AB - Amazon deforestation contributes significantly to global carbon (C) emissions. In comparison, the contribution from selective logging to atmospheric CO2 emissions, and its impact on regional C dynamics, is highly uncertain. Using a new geographically based modeling approach in combination with high resolution remote sensing data from 1999 to 2002, we estimate that C emissions were 0.04-0.05 Pg C yr-1 due to selective logging from a ∼2,664,960 km2 region of the Brazilian Amazon. Selective logging was responsible for 15-19% higher carbon emissions than reported from deforestation (clear-cutting) alone. Our simulations indicated that forest carbon lost via selective logging lasts two to three decades following harvest, and that the original live biomass takes up to a century to recover, if the forests are not subsequently cleared. The two-to three-decade loss of carbon results from the biomass damaged by logging activities, including leaves, wood, and roots, estimated to be 89.1 Tg C yr-1 from 1999 to 2002 over the study region, leaving 70.0 Tg C yr-1 and 7.9 Tg C yr-1 to accumulate as coarse woody debris and soil C, respectively. While avoided deforestation is central to crediting rain forest nations for reduced carbon emissions, the extent and intensity of selective logging are also critical to determining carbon emissions in the context of Reduced Emissions from Deforestation and Forest Degradation (REDD). We show that a combination of automated high-resolution satellite monitoring and detailed forest C modeling can yield spatially explicit estimates of harvest-related C losses and subsequent recovery in support of REDD and other international carbon market mechanisms.

UR - http://www.scopus.com/inward/record.url?scp=77957699265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77957699265&partnerID=8YFLogxK

U2 - 10.1029/2009GB003727

DO - 10.1029/2009GB003727

M3 - Article

AN - SCOPUS:77957699265

VL - 24

JO - Global Biogeochemical Cycles

JF - Global Biogeochemical Cycles

SN - 0886-6236

IS - 3

M1 - GB3028

ER -